diff --git a/Inferact-Qwen3.8-27B-NVFP4/compliance.json b/Inferact-Qwen3.8-27B-NVFP4/compliance.json
index 035b318ba0ac4df762bfe13e7f299a59b4afe810..68345d237ee14f0be1e6f839859b097210cbb1ab 100644
--- a/Inferact-Qwen3.8-27B-NVFP4/compliance.json
+++ b/Inferact-Qwen3.8-27B-NVFP4/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/Inferact/Qwen3.8-27B-NVFP4",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "83cf20bf984d554a10ed908da7866ece8f248f9b6f45fb806154c5c6791f3697",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.271735+00:00",
+  "evaluated_at": "2026-09-11T20:24:26.982179+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.13959233, deployed 0.13875295, delta -0.0008393859011140425",
+      "detail": "trunk 0.13975674, deployed 0.13893424, delta -0.0008225048750587216",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.13875295, median 0.04445935, max 29.81742477, 4 depth buckets",
+      "detail": "mean 0.13893424, median 0.04438159, max 28.27250290, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.32244942, strongest worked_math_reasoning at 0.07214179, spread 4.5x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.32376633, strongest worked_math_reasoning at 0.07241381, spread 4.5x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights 83cf20bf984d554a as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.1387529468264112,
+  "laws_version": 15,
+  "mean_kld": 0.13893423537794009,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
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@@ -165,201 +168,201 @@
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diff --git a/Inferact-Qwen3.8-27B-NVFP4/inspect.json b/Inferact-Qwen3.8-27B-NVFP4/inspect.json
index a8aa7ddb6de20f555d3ee9fb33c10e8747f9ac49..4aed55f38d899a3669544086af01bce7b7f2e905 100644
--- a/Inferact-Qwen3.8-27B-NVFP4/inspect.json
+++ b/Inferact-Qwen3.8-27B-NVFP4/inspect.json
@@ -1,31 +1,37 @@
 {
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diff --git a/Inferact-Qwen3.8-27B-NVFP4/manifest.json b/Inferact-Qwen3.8-27B-NVFP4/manifest.json
index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
--- a/Inferact-Qwen3.8-27B-NVFP4/manifest.json
+++ b/Inferact-Qwen3.8-27B-NVFP4/manifest.json
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diff --git a/Inferact-Qwen3.8-27B-NVFP4/report.json b/Inferact-Qwen3.8-27B-NVFP4/report.json
index 6ca5f4963dc02c479e2241c1faa932379e64b627..57031fa53cd6c8a54376f0de0062e52c2496dd34 100644
--- a/Inferact-Qwen3.8-27B-NVFP4/report.json
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       "positions": 2047,
       "row": 767,
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+      "top1_agreement": 0.925256472887152
     }
   ],
+  "quantization_substitutions": [],
+  "reference_weights_sha256": null,
+  "runtime_binding": {
+    "compiled_extensions": {
+      "_C_stable_libtorch.abi3.so": "fbae4a92ec2861cd979eb99a3867049f88f7fcb278337be058dd4cf2ac8c29bf",
+      "_moe_C_stable_libtorch.abi3.so": "a5f41231d3a2fe2958aad06615137ab877671baea20e78b0257516ef3467664e",
+      "third_party/deep_gemm/_C.cpython-310-x86_64-linux-gnu.so": "38b87140577c2d72c2b3a6407c5f07da53ef9362e18fe988162afa8f3f91746e",
+      "third_party/deep_gemm/_C.cpython-311-x86_64-linux-gnu.so": "1f3a3430c188295cf96a5ac977ec72258bccb525daaa7b0ad6642bf3a995d3da",
+      "third_party/deep_gemm/_C.cpython-312-x86_64-linux-gnu.so": "73824dc1312e98cf277ae6bc017becf7e963d5b0bfab81e0c6d475d9b769c9e7",
+      "third_party/deep_gemm/_C.cpython-313-x86_64-linux-gnu.so": "bd0124b39fb500378764bf0d1adb5fe30ea876e66ed65971d8f668de4a8dbe83",
+      "third_party/deep_gemm/_C.cpython-314-x86_64-linux-gnu.so": "0f9817dfe8a619dde56456c6e7a5a9d914fede0fb145bc48aad134e19c77fff0"
+    },
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
+    "cuda": "13.2",
+    "determinism": {
+      "CUBLAS_WORKSPACE_CONFIG": ":4096:8",
+      "NCCL_DETERMINISTIC": "1",
+      "VLLM_BATCH_INVARIANT": "1",
+      "VLLM_MOE_USE_DEEP_GEMM": "0"
+    },
+    "driver": "580.173.02",
+    "flashinfer": "0.6.17",
+    "gpu_names": [
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
+    ],
+    "nccl": "2.29.7",
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
+    "platform": "Linux-6.8.0-137-generic-x86_64-with-glibc2.39",
+    "python": "3.12.3",
+    "torch": "2.13.0+cu132",
+    "vllm_commit": "60071d1ab73229321712254b1350dcaaac1c125a",
+    "vllm_dirty_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+    "vllm_tree_dirty": false
+  },
   "score_from": 0,
   "storage": "hidden",
   "student_lm_head": {
@@ -8630,101 +8678,102 @@
     }
   },
   "timings": {
-    "decompose_head": 404.6074632878881,
-    "score_forward": 218.7797764034476,
-    "student_load": 26.981997315073386
+    "decompose_head": 452.68833606364205,
+    "score_forward": 264.1346841813065,
+    "student_load": 15.784403895959258
   },
-  "top1_agreement": 0.8597254875020355,
+  "top1_agreement": 0.859637070509689,
   "topk_agreement": {
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-    "2": 0.5947741104868914,
-    "3": 0.356480138617489,
-    "4": 0.1911098304429246,
-    "5": 0.09647375223904901
+    "1": 0.8589589948705423,
+    "2": 0.5945285784074255,
+    "3": 0.3557931576697606,
+    "4": 0.1905335297589969,
+    "5": 0.0963147288715193
   },
-  "trunk_mean_kld": 0.13959233272752525,
+  "trunk_mean_kld": 0.1397567402529988,
   "trunk_report": {
     "confidence_buckets": [
       {
-        "frac": 0.15302437001302718,
+        "frac": 0.1529944736199316,
         "hi": 0.25,
         "lo": 0.0,
-        "mean_kld": 0.17856596453210183,
-        "n": 240569
+        "mean_kld": 0.17868736711043118,
+        "n": 240522
       },
       {
-        "frac": 0.22033641711447646,
+        "frac": 0.22041147614395049,
         "hi": 0.5,
         "lo": 0.25,
-        "mean_kld": 0.19773929961583425,
-        "n": 346390
+        "mean_kld": 0.19882094296032318,
+        "n": 346508
       },
       {
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         "hi": 0.75,
         "lo": 0.5,
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-        "n": 273384
+        "mean_kld": 0.1950953995195282,
+        "n": 273204
       },
       {
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+        "frac": 0.15904944736199317,
         "hi": 0.95,
         "lo": 0.75,
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-        "n": 249939
+        "mean_kld": 0.1501489717668932,
+        "n": 250041
       },
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         "hi": 1.0,
         "lo": 0.95,
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-        "n": 461814
+        "mean_kld": 0.036801010628465705,
+        "n": 461821
       }
     ],
     "depth_buckets": [
       {
         "depth_hi": 511,
         "depth_lo": 0,
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+        "mean_kld": 0.12793485822146547,
         "n": 393216
       },
       {
         "depth_hi": 1023,
         "depth_lo": 512,
-        "mean_kld": 0.1317376168098943,
+        "mean_kld": 0.1319523364631683,
         "n": 393216
       },
       {
         "depth_hi": 1535,
         "depth_lo": 1024,
-        "mean_kld": 0.14367922296363503,
+        "mean_kld": 0.1444773385999713,
         "n": 393216
       },
       {
         "depth_hi": 2046,
         "depth_lo": 1536,
-        "mean_kld": 0.1549013543377304,
+        "mean_kld": 0.15469159737019708,
         "n": 392448
       }
     ],
-    "max_kld": 29.99698829650879,
-    "mean_kld": 0.13959233272752525,
-    "mean_kld_reverse": 0.15256460847478043,
-    "median_kld": 0.04641946405172348,
+    "max_kld": 27.982345581054688,
+    "mean_kld": 0.1397567402529988,
+    "mean_kld_reverse": 0.15275460219721237,
+    "median_kld": 0.046389807015657425,
     "num_positions": 1572096,
-    "p90_kld": 0.24704855680465698,
-    "p99_kld": 1.7645993053913125,
+    "p90_kld": 0.24727921187877655,
+    "p99_kld": 1.7654337406158476,
     "score_from": 0,
-    "top1_agreement": 0.8591078407425501,
+    "top1_agreement": 0.8590639502931119,
     "topk_agreement": {
-      "1": 0.858390963401726,
-      "2": 0.5925592330239374,
-      "3": 0.3530859438609347,
-      "4": 0.1871297936003908,
-      "5": 0.09295233878847094
+      "1": 0.8585099128806384,
+      "2": 0.5927526054388536,
+      "3": 0.3526470393665527,
+      "4": 0.18647080076534767,
+      "5": 0.09269090437225208
     }
   },
   "unique_tokens": 1572864,
+  "student_weights_sha256": "83cf20bf984d554a10ed908da7866ece8f248f9b6f45fb806154c5c6791f3697",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/Inferact/Qwen3.8-27B-NVFP4",
@@ -8811,5 +8860,7 @@
       "candidate": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3",
       "match": true
     }
-  }
+  },
+  "candidate_hf_repo": "Inferact/Qwen3.8-27B-NVFP4",
+  "candidate_revision": "6128240ebaf4eaa7bad2b3d1c72c37d677c5f462"
 }
diff --git a/Inferact-Qwen3.8-27B-NVFP4/report.md b/Inferact-Qwen3.8-27B-NVFP4/report.md
index e9e91fb8f747684317b5e9bf1f9285559e5b7368..f57e0874ced79cdef555e5025e4c15581618e072 100644
--- a/Inferact-Qwen3.8-27B-NVFP4/report.md
+++ b/Inferact-Qwen3.8-27B-NVFP4/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Inferact-Qwen3.8-27B-NVFP4: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.13875295** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.13893424** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.13875295 | 0.04445935 | 0.24611130 | 1.76721573 | 29.81742477 | 85.9725% |
+| 0.13893424 | 0.04438159 | 0.24611974 | 1.76796739 | 28.27250290 | 85.9637% |
 
-Reverse direction, KLD(candidate || reference): 0.15276902.
+Reverse direction, KLD(candidate || reference): 0.15297289.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.15276902.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/Inferact/Qwen3.8-27B-NVFP4 |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | 83cf20bf984d554a |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,32 +27,48 @@ Reverse direction, KLD(candidate || reference): 0.15276902.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
+## Expert kernels: declared against built
+
+| Property | Value |
+|---|---|
+| Declared for its experts | `4-bit float` |
+| Expert implementation built | n/a |
+| Expert kernel built | n/a |
+| Expert layers carrying an activation scale | n/a |
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.32244942 | 2.32 | 1.98090567 |
-| Chinese across several content types | 72 | 55.6% | 0.22299356 | 1.61 | 0.77352711 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.13701842 | 0.99 | 0.52727180 |
-| Other multilingual content | 36 | 64.5% | 0.11774064 | 0.85 | 0.19032780 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.11673724 | 0.84 | 0.28412495 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.11331059 | 0.82 | 0.48311574 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.08946068 | 0.64 | 0.49920625 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.07836417 | 0.56 | 0.26076892 |
-| Scientific and technical exposition | 96 | 60.3% | 0.07563292 | 0.55 | 0.11695711 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.07214179 | 0.52 | 0.21149815 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.32376633 | 2.33 | 2.07242230 |
+| Chinese across several content types | 72 | 55.6% | 0.22251604 | 1.60 | 0.75999542 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.13692233 | 0.99 | 0.52220001 |
+| Other multilingual content | 36 | 64.5% | 0.11789006 | 0.85 | 0.18570823 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.11715024 | 0.84 | 0.29253844 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.11299148 | 0.81 | 0.47158011 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.09007302 | 0.65 | 0.49843872 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.07786293 | 0.56 | 0.25824302 |
+| Scientific and technical exposition | 96 | 60.3% | 0.07539770 | 0.54 | 0.11872881 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.07241381 | 0.52 | 0.21635378 |
 
 **Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Worked mathematics, science, and formal reasoning** its strongest, a spread of 4.5x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
@@ -60,9 +76,9 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.13959233 |
-| Deployed (candidate's own head) | 0.13875295 |
-| Head-associated delta (not additive) | -0.00083939 |
+| Trunk (candidate hidden states, reference head) | 0.13975674 |
+| Deployed (candidate's own head) | 0.13893424 |
+| Head-associated delta (not additive) | -0.00082250 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'quantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedLinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedLinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'quantized'}]}, 'state': 'quantized', 'static': {'ignored': True, 'lm_head_dtypes': {'lm_head.weight': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight'], 'output_weight_keys': ['lm_head.weight'], 'packed_keys': [], 'quant_method': 'modelopt', 'state': 'unquantized', 'tie_word_embeddings': False}} |
 
@@ -70,26 +86,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.12720580 |
-| 512–1023 | 393216 | 0.13076163 |
-| 1024–1535 | 393216 | 0.14287953 |
-| 1536–2046 | 392448 | 0.15419499 |
+| 0–511 | 393216 | 0.12704732 |
+| 512–1023 | 393216 | 0.13100820 |
+| 1024–1535 | 393216 | 0.14367785 |
+| 1536–2046 | 392448 | 0.15403306 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.18162765 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.19529914 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.19340607 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.14813560 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.03657384 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.18171434 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.19638587 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.19209288 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.14889459 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.03670705 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 85.9038% | 59.4774% | 35.6480% | 19.1110% | 9.6474% |
+| 85.8959% | 59.4529% | 35.5793% | 19.0534% | 9.6315% |
 
 ## Law compliance
 
@@ -101,22 +117,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.13959233, deployed 0.13875295, delta -0.0008393859011140425 |
-| 9 | Tail and depth disclosure | PASS | mean 0.13875295, median 0.04445935, max 29.81742477, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.13975674, deployed 0.13893424, delta -0.0008225048750587216 |
+| 9 | Tail and depth disclosure | PASS | mean 0.13893424, median 0.04438159, max 28.27250290, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.32244942, strongest worked_math_reasoning at 0.07214179, spread 4.5x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.32376633, strongest worked_math_reasoning at 0.07241381, spread 4.5x |
+| 16 | Candidate weight binding | PASS | scored weights 83cf20bf984d554a as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +139,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +147,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +170,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +189,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Inferact-Qwen3.8-27B-NVFP4/report.md` | 12.36 KiB | This document. |
-| `Inferact-Qwen3.8-27B-NVFP4/report.json` | 252.83 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Inferact-Qwen3.8-27B-NVFP4/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Inferact-Qwen3.8-27B-NVFP4/compliance.json` | 12.99 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Inferact-Qwen3.8-27B-NVFP4/report.md` | 13.20 KiB | This document. |
+| `Inferact-Qwen3.8-27B-NVFP4/report.json` | 255.33 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Inferact-Qwen3.8-27B-NVFP4/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Inferact-Qwen3.8-27B-NVFP4/compliance.json` | 12.25 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Inferact-Qwen3.8-27B-NVFP4/strata.json b/Inferact-Qwen3.8-27B-NVFP4/strata.json
index bb6d86c2cd5a9e8e2aa69d8ff10c1737b26f87dd..aa999b795daad5e723b69eded0fa584a173fade0 100644
--- a/Inferact-Qwen3.8-27B-NVFP4/strata.json
+++ b/Inferact-Qwen3.8-27B-NVFP4/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 8.021528244018555,
-            "mean_kld": 0.3389235920971226,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.285559165801828,
-            "median_context_p99": 2.089630126953125,
-            "p90_context_kld": 0.6934425062474486,
+            "max_kld": 8.725299835205078,
+            "mean_kld": 0.33867063919541485,
+            "mean_ref_top1_prob": 0.6040861921682756,
+            "median_context_kld": 0.28253108540139005,
+            "median_context_p99": 1.908400297164917,
+            "p90_context_kld": 0.712074033997245,
             "positions": 67551,
-            "top1_agreement": 0.7561694127400038,
+            "top1_agreement": 0.75849358262646,
             "worst_context_id": 905,
-            "worst_context_kld": 0.7735271078252318
+            "worst_context_kld": 0.7599954233707221
           }
         },
         "key": "wikisource_zh",
         "label": "wikisource_zh",
-        "relative_to_run": 2.442640677903134
+        "relative_to_run": 2.4376327279891505
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 29.817424774169922,
-            "mean_kld": 0.3224494154204839,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.2704242110574551,
-            "median_context_p99": 4.772159099578857,
-            "p90_context_kld": 0.6499366568065644,
+            "max_kld": 28.272502899169922,
+            "mean_kld": 0.3237663331062429,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.26837684090210884,
+            "median_context_p99": 4.754233360290527,
+            "p90_context_kld": 0.6971572786024132,
             "positions": 196512,
-            "top1_agreement": 0.8537646555935515,
+            "top1_agreement": 0.8548078488845465,
             "worst_context_id": 454,
-            "worst_context_kld": 1.9809056674647718
+            "worst_context_kld": 2.0724223042466337
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 2.323910394666347
+        "relative_to_run": 2.3303567491878994
       },
       {
         "cells": {
           "deployed": {
             "contexts": 7,
-            "max_kld": 9.7470703125,
-            "mean_kld": 0.1616994939671555,
-            "mean_ref_top1_prob": 0.7357366218042676,
-            "median_context_kld": 0.17592728118249437,
-            "median_context_p99": 2.627157688140869,
-            "p90_context_kld": 0.19032780062111215,
+            "max_kld": 9.485733032226562,
+            "mean_kld": 0.16177371106019364,
+            "mean_ref_top1_prob": 0.7358200961573914,
+            "median_context_kld": 0.17768971710652043,
+            "median_context_p99": 2.8594753742218018,
+            "p90_context_kld": 0.185708233196314,
             "positions": 14329,
-            "top1_agreement": 0.8487682322562635,
-            "worst_context_id": 953,
-            "worst_context_kld": 0.19032780062111215
+            "top1_agreement": 0.8498150603670878,
+            "worst_context_id": 959,
+            "worst_context_kld": 0.185708233196314
           }
         },
         "key": "wikipedia_de",
         "label": "wikipedia_de",
-        "relative_to_run": 1.165377007592148
+        "relative_to_run": 1.1643905522647011
       },
       {
         "cells": {
           "deployed": {
             "contexts": 23,
-            "max_kld": 8.67590045928955,
-            "mean_kld": 0.15381675863389638,
-            "mean_ref_top1_prob": 0.7232628548554616,
-            "median_context_kld": 0.1946865052886737,
-            "median_context_p99": 2.3454291820526123,
-            "p90_context_kld": 0.24028980404604577,
+            "max_kld": 8.996603012084961,
+            "mean_kld": 0.15446324511839943,
+            "mean_ref_top1_prob": 0.7233191001139795,
+            "median_context_kld": 0.1947756225639116,
+            "median_context_p99": 2.3370704650878906,
+            "p90_context_kld": 0.2414717674721937,
             "positions": 47081,
-            "top1_agreement": 0.8701599371296277,
+            "top1_agreement": 0.8723264161763769,
             "worst_context_id": 275,
-            "worst_context_kld": 0.28412494747417477
+            "worst_context_kld": 0.2925384386270357
           }
         },
         "key": "regulations",
         "label": "regulations",
-        "relative_to_run": 1.108565707266246
+        "relative_to_run": 1.1117723770402312
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 10.653387069702148,
-            "mean_kld": 0.1370184207671435,
-            "mean_ref_top1_prob": 0.5946008380921044,
-            "median_context_kld": 0.12206410714064975,
-            "median_context_p99": 1.0921473503112793,
-            "p90_context_kld": 0.18849303508813178,
+            "max_kld": 13.785171508789062,
+            "mean_kld": 0.13692233186327699,
+            "mean_ref_top1_prob": 0.5945716883648833,
+            "median_context_kld": 0.12210456756146866,
+            "median_context_p99": 1.0520226955413818,
+            "p90_context_kld": 0.18501191849037915,
             "positions": 196512,
-            "top1_agreement": 0.8466862074580688,
+            "top1_agreement": 0.8468439586386582,
             "worst_context_id": 6,
-            "worst_context_kld": 0.5272718036607824
+            "worst_context_kld": 0.5222000123857532
           }
         },
         "key": "wikipedia_en",
         "label": "wikipedia_en",
-        "relative_to_run": 0.9874991767818977
+        "relative_to_run": 0.9855190226570855
       },
       {
         "cells": {
           "deployed": {
             "contexts": 39,
-            "max_kld": 7.907846927642822,
-            "mean_kld": 0.12489892169192499,
-            "mean_ref_top1_prob": 0.515358929042611,
-            "median_context_kld": 0.12155689460254131,
-            "median_context_p99": 0.8025867938995361,
-            "p90_context_kld": 0.16630316278016485,
+            "max_kld": 6.134963035583496,
+            "mean_kld": 0.12423137916214863,
+            "mean_ref_top1_prob": 0.5153760832971095,
+            "median_context_kld": 0.12106671125027292,
+            "median_context_p99": 0.7975048422813416,
+            "p90_context_kld": 0.16468408417279906,
             "positions": 79833,
-            "top1_agreement": 0.8248969724299475,
+            "top1_agreement": 0.8230305763280849,
             "worst_context_id": 880,
-            "worst_context_kld": 0.19174844904134652
+            "worst_context_kld": 0.185623894076553
           }
         },
         "key": "wikipedia_zh",
         "label": "wikipedia_zh",
-        "relative_to_run": 0.9001532907851068
+        "relative_to_run": 0.8941739868809472
       },
       {
         "cells": {
           "deployed": {
             "contexts": 7,
-            "max_kld": 4.0597004890441895,
-            "mean_kld": 0.11922249983613585,
-            "mean_ref_top1_prob": 0.5702914711488575,
-            "median_context_kld": 0.11584520546788203,
-            "median_context_p99": 0.6899316906929016,
-            "p90_context_kld": 0.1401531873133934,
+            "max_kld": 3.892676591873169,
+            "mean_kld": 0.12022743159239367,
+            "mean_ref_top1_prob": 0.5701150598950927,
+            "median_context_kld": 0.11619470036624585,
+            "median_context_p99": 0.7556959986686707,
+            "p90_context_kld": 0.14259579128472266,
             "positions": 14329,
-            "top1_agreement": 0.8346709470304976,
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           "deployed": {
             "contexts": 72,
-            "max_kld": 9.671066284179688,
-            "mean_kld": 0.11331059360869399,
-            "mean_ref_top1_prob": 0.5056158503386161,
-            "median_context_kld": 0.09982347202097942,
-            "median_context_p99": 0.5975978970527649,
-            "p90_context_kld": 0.13298753666949875,
+            "max_kld": 10.53090763092041,
+            "mean_kld": 0.11299148107033156,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.10009519240273125,
+            "median_context_p99": 0.5999166369438171,
+            "p90_context_kld": 0.1339625543235338,
             "positions": 147384,
-            "top1_agreement": 0.8377571513868534,
+            "top1_agreement": 0.83713293166151,
             "worst_context_id": 443,
-            "worst_context_kld": 0.4831157392859844
+            "worst_context_kld": 0.47158011349891993
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.8166355828856937
+        "relative_to_run": 0.8132731343211631
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 11.367209434509277,
-            "mean_kld": 0.08946067960442923,
-            "mean_ref_top1_prob": 0.7950703670465326,
-            "median_context_kld": 0.07114210014218113,
-            "median_context_p99": 0.7475235462188721,
-            "p90_context_kld": 0.1406945040734226,
+            "max_kld": 16.9556884765625,
+            "mean_kld": 0.09007301542454976,
+            "mean_ref_top1_prob": 0.7950885550065969,
+            "median_context_kld": 0.07053174211201081,
+            "median_context_p99": 0.7423832416534424,
+            "p90_context_kld": 0.14488682669665146,
             "positions": 196512,
-            "top1_agreement": 0.9138220566682951,
-            "worst_context_id": 582,
-            "worst_context_kld": 0.49920624666515057
+            "top1_agreement": 0.9138882103891874,
+            "worst_context_id": 667,
+            "worst_context_kld": 0.4984387221219649
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.6447479613989766
+        "relative_to_run": 0.6483140399450568
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 10.58599853515625,
-            "mean_kld": 0.07836417196189645,
-            "mean_ref_top1_prob": 0.7071241246993897,
-            "median_context_kld": 0.06484397643575615,
-            "median_context_p99": 0.9599510431289673,
-            "p90_context_kld": 0.12365192357983938,
+            "max_kld": 9.473292350769043,
+            "mean_kld": 0.07786292927453185,
+            "mean_ref_top1_prob": 0.7071550040111672,
+            "median_context_kld": 0.06419700892962933,
+            "median_context_p99": 0.9900352954864502,
+            "p90_context_kld": 0.12212695387331837,
             "positions": 73692,
-            "top1_agreement": 0.8043749660750149,
+            "top1_agreement": 0.8038050263257884,
             "worst_context_id": 985,
-            "worst_context_kld": 0.26076892411661884
+            "worst_context_kld": 0.2582430189493058
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.5647748300433217
+        "relative_to_run": 0.5604301133030519
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 9.688684463500977,
-            "mean_kld": 0.07563291716412826,
-            "mean_ref_top1_prob": 0.6033561622407548,
-            "median_context_kld": 0.07650770511032035,
-            "median_context_p99": 0.5419639348983765,
-            "p90_context_kld": 0.09455487405505722,
+            "max_kld": 17.140230178833008,
+            "mean_kld": 0.07539770427345319,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.07524519797729494,
+            "median_context_p99": 0.5095043778419495,
+            "p90_context_kld": 0.0943307534521116,
             "positions": 196512,
-            "top1_agreement": 0.8805620013027194,
+            "top1_agreement": 0.8794119443087445,
             "worst_context_id": 246,
-            "worst_context_kld": 0.1169571107310768
+            "worst_context_kld": 0.11872880759564826
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.5450905288429648
+        "relative_to_run": 0.5426862865610501
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 12.191375732421875,
-            "mean_kld": 0.07214179342084462,
-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.06596337549559317,
-            "median_context_p99": 0.4456246495246887,
-            "p90_context_kld": 0.08905786780333834,
+            "max_kld": 15.153870582580566,
+            "mean_kld": 0.07241380546384427,
+            "mean_ref_top1_prob": 0.6582834184806026,
+            "median_context_kld": 0.06545848173091594,
+            "median_context_p99": 0.4294142723083496,
+            "p90_context_kld": 0.09084108125371913,
             "positions": 196512,
-            "top1_agreement": 0.8953804347826086,
+            "top1_agreement": 0.8955839846930468,
             "worst_context_id": 825,
-            "worst_context_kld": 0.21149815280834836
+            "worst_context_kld": 0.2163537839192812
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.5199298110122202
+        "relative_to_run": 0.521209227278348
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 29.817424774169922,
-      "mean_kld": 0.1387529468264112,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.0948157276574599,
-      "median_context_p99": 0.760625958442688,
-      "p90_context_kld": 0.27379871622917323,
+      "max_kld": 28.272502899169922,
+      "mean_kld": 0.13893423537794009,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.09546938309119762,
+      "median_context_p99": 0.7544186115264893,
+      "p90_context_kld": 0.2722677825694095,
       "positions": 1572096,
-      "top1_agreement": 0.8597254875020355,
+      "top1_agreement": 0.859637070509689,
       "worst_context_id": 454,
-      "worst_context_kld": 1.9809056674647718
+      "worst_context_kld": 2.0724223042466337
     }
   },
   "primary": "deployed"
diff --git a/Inferact-Qwen3.8-27B-NVFP4/strata.md b/Inferact-Qwen3.8-27B-NVFP4/strata.md
index 7ecdfdf2538b1938ab67f056e7fd94bb5fc945df..8c37b92197e592fddd03ac94fee4f0a4f42d33b9 100644
--- a/Inferact-Qwen3.8-27B-NVFP4/strata.md
+++ b/Inferact-Qwen3.8-27B-NVFP4/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Inferact-Qwen3.8-27B-NVFP4
 
-768 contexts, 1572096 scored positions, mean 0.13875295, reference top-1 64.2%, top-1 agreement 85.9725%.
+768 contexts, 1572096 scored positions, mean 0.13893424, reference top-1 64.2%, top-1 agreement 85.9637%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.32244942 | 2.32 | 0.27042421 | 0.64993666 | 1.980906 |
-| Chinese across several content types | 72 | 55.6% | 0.22299356 | 1.61 | 0.15146793 | 0.38827516 | 0.773527 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.13701842 | 0.99 | 0.12206411 | 0.18849304 | 0.527272 |
-| Other multilingual content | 36 | 64.5% | 0.11774064 | 0.85 | 0.11220843 | 0.17592728 | 0.190328 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.11673724 | 0.84 | 0.10286570 | 0.21367443 | 0.284125 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.11331059 | 0.82 | 0.09982347 | 0.13298754 | 0.483116 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.08946068 | 0.64 | 0.07114210 | 0.14069450 | 0.499206 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.07836417 | 0.56 | 0.06484398 | 0.12365192 | 0.260769 |
-| Scientific and technical exposition | 96 | 60.3% | 0.07563292 | 0.55 | 0.07650771 | 0.09455487 | 0.116957 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.07214179 | 0.52 | 0.06596338 | 0.08905787 | 0.211498 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.32376633 | 2.33 | 0.26837684 | 0.69715728 | 2.072422 |
+| Chinese across several content types | 72 | 55.6% | 0.22251604 | 1.60 | 0.15263640 | 0.38797690 | 0.759995 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.13692233 | 0.99 | 0.12210457 | 0.18501192 | 0.522200 |
+| Other multilingual content | 36 | 64.5% | 0.11789006 | 0.85 | 0.11041747 | 0.17768972 | 0.185708 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.11715024 | 0.84 | 0.10216222 | 0.21365635 | 0.292538 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.11299148 | 0.81 | 0.10009519 | 0.13396255 | 0.471580 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.09007302 | 0.65 | 0.07053174 | 0.14488683 | 0.498439 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.07786293 | 0.56 | 0.06419701 | 0.12212695 | 0.258243 |
+| Scientific and technical exposition | 96 | 60.3% | 0.07539770 | 0.54 | 0.07524520 | 0.09433075 | 0.118729 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.07241381 | 0.52 | 0.06545848 | 0.09084108 | 0.216354 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wikisource_zh | 33 | 60.4% | 0.33892359 | 2.44 | 0.28555917 | 0.69344251 | 0.773527 |
-| wildchat | 96 | 73.5% | 0.32244942 | 2.32 | 0.27042421 | 0.64993666 | 1.980906 |
-| wikipedia_de | 7 | 73.6% | 0.16169949 | 1.17 | 0.17592728 | 0.19032780 | 0.190328 |
-| regulations | 23 | 72.3% | 0.15381676 | 1.11 | 0.19468651 | 0.24028980 | 0.284125 |
-| wikipedia_en | 96 | 59.5% | 0.13701842 | 0.99 | 0.12206411 | 0.18849304 | 0.527272 |
-| wikipedia_zh | 39 | 51.5% | 0.12489892 | 0.90 | 0.12155689 | 0.16630316 | 0.191748 |
-| wikipedia_ja | 7 | 57.0% | 0.11922250 | 0.86 | 0.11584521 | 0.14015319 | 0.140153 |
-| public_domain_books | 72 | 50.6% | 0.11331059 | 0.82 | 0.09982347 | 0.13298754 | 0.483116 |
-| wikipedia_es | 7 | 59.2% | 0.11186996 | 0.81 | 0.11220843 | 0.12813632 | 0.128136 |
-| wikipedia_cs | 6 | 68.9% | 0.11185847 | 0.81 | 0.11088604 | 0.13979561 | 0.139796 |
-| public_domain_review | 26 | 51.8% | 0.10808779 | 0.78 | 0.10286570 | 0.15311613 | 0.218557 |
-| stackv2 | 44 | 72.0% | 0.09297266 | 0.67 | 0.07943810 | 0.14069450 | 0.499206 |
-| wikipedia_ru | 6 | 66.6% | 0.09243902 | 0.67 | 0.09578639 | 0.11733642 | 0.117336 |
-| open_news | 23 | 53.8% | 0.08943536 | 0.64 | 0.08905933 | 0.11123379 | 0.142558 |
-| wikipedia_fr | 3 | 60.2% | 0.08777809 | 0.63 | 0.08313871 | 0.09831847 | 0.098318 |
-| github_code | 52 | 85.8% | 0.08648900 | 0.62 | 0.05967765 | 0.13394021 | 0.486784 |
-| starcoder_structured | 36 | 70.7% | 0.07836417 | 0.56 | 0.06484398 | 0.12365192 | 0.260769 |
-| scientific_papers | 96 | 60.3% | 0.07563292 | 0.55 | 0.07650771 | 0.09455487 | 0.116957 |
-| libretexts | 96 | 65.8% | 0.07214179 | 0.52 | 0.06596338 | 0.08905787 | 0.211498 |
+| wikisource_zh | 33 | 60.4% | 0.33867064 | 2.44 | 0.28253109 | 0.71207403 | 0.759995 |
+| wildchat | 96 | 73.5% | 0.32376633 | 2.33 | 0.26837684 | 0.69715728 | 2.072422 |
+| wikipedia_de | 7 | 73.6% | 0.16177371 | 1.16 | 0.17768972 | 0.18570823 | 0.185708 |
+| regulations | 23 | 72.3% | 0.15446325 | 1.11 | 0.19477562 | 0.24147177 | 0.292538 |
+| wikipedia_en | 96 | 59.5% | 0.13692233 | 0.99 | 0.12210457 | 0.18501192 | 0.522200 |
+| wikipedia_zh | 39 | 51.5% | 0.12423138 | 0.89 | 0.12106671 | 0.16468408 | 0.185624 |
+| wikipedia_ja | 7 | 57.0% | 0.12022743 | 0.87 | 0.11619470 | 0.14259579 | 0.142596 |
+| public_domain_books | 72 | 50.6% | 0.11299148 | 0.81 | 0.10009519 | 0.13396255 | 0.471580 |
+| wikipedia_es | 7 | 59.2% | 0.11225478 | 0.81 | 0.10861866 | 0.13055008 | 0.130550 |
+| wikipedia_cs | 6 | 68.9% | 0.11096371 | 0.80 | 0.11041747 | 0.13728586 | 0.137286 |
+| public_domain_review | 26 | 51.8% | 0.10832643 | 0.78 | 0.10216222 | 0.15624898 | 0.216185 |
+| stackv2 | 44 | 72.0% | 0.09345852 | 0.67 | 0.07820657 | 0.14488683 | 0.492279 |
+| wikipedia_ru | 6 | 66.6% | 0.09259893 | 0.67 | 0.09808316 | 0.11815263 | 0.118153 |
+| open_news | 23 | 53.8% | 0.08981198 | 0.65 | 0.08766883 | 0.11554725 | 0.145003 |
+| wikipedia_fr | 3 | 60.2% | 0.08762498 | 0.63 | 0.08312882 | 0.09901612 | 0.099016 |
+| github_code | 52 | 85.8% | 0.08720836 | 0.63 | 0.06078221 | 0.14058960 | 0.498439 |
+| starcoder_structured | 36 | 70.7% | 0.07786293 | 0.56 | 0.06419701 | 0.12212695 | 0.258243 |
+| scientific_papers | 96 | 60.3% | 0.07539770 | 0.54 | 0.07524520 | 0.09433075 | 0.118729 |
+| libretexts | 96 | 65.8% | 0.07241381 | 0.52 | 0.06545848 | 0.09084108 | 0.216354 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.32244942, 2.32x the run mean of 0.13875295 over 96 context(s).
-- Strongest domain: **Worked mathematics, science, and formal reasoning** at 0.07214179, 0.52x the run mean. The spread across domains is 4.5x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.32376633, 2.33x the run mean of 0.13893424 over 96 context(s).
+- Strongest domain: **Worked mathematics, science, and formal reasoning** at 0.07241381, 0.52x the run mean. The spread across domains is 4.5x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 1.980906 against a median of 0.27042421 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 2.072422 against a median of 0.26837684 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/LAWS.md b/LAWS.md
index aaebfd6ca6cdae5fc16eaa9ac912a1553a43d636..5c3bd5881cee6a406557530f69598387122ce472 100644
--- a/LAWS.md
+++ b/LAWS.md
@@ -1,6 +1,6 @@
 # Local Inference Lab — Distribution Fidelity Laws
 
-**Laws version:** 8
+**Laws version:** 15
 **Status:** draft, pending coordination with `local-inference-lab` on the
 publication namespace and suite format.
 
@@ -75,6 +75,83 @@ disagreement with the field, the position, and both values named. A version-8
 receipt from a single-worker run becomes a version-9 receipt by reassembling it;
 a multi-worker candidate that version 8 refused must be scored.
 
+Version 10 replaces Law 14's synthetic component-cell interpretation with a
+paired routed-model intervention. QxQ is the unchanged quantized candidate under
+its natural routing. BxQ is the same candidate with the BF16 teacher's expert IDs
+forced while the student computes its own gating weights for those experts.
+Earlier `expert_cell`, `router_cell`, and `composite_cell` results remain useful
+synthetic QDQ diagnostics, but none measured BxQ or QxQ. Routed candidates must
+therefore be rescored for BxQ; an existing deployed report may supply QxQ only
+after the paired-run controls and bindings are established.
+
+Version 11 replaces Law 14's fixed bitwise-style natural-control threshold with
+a measured repeatability envelope. The paired protocol runs two natural and two
+forced-natural samples, retains the fixed numerical floor for repeatable
+kernels, and otherwise permits no more than twice the larger observed
+within-path span. Natural expert IDs may differ across the two samples when
+arithmetic drift propagates into later routers, so their selection flip rate is
+recorded. Both forced-natural controls still replay the first QxQ sample's exact
+IDs.
+The paired routed-score and BxQ protocol versions are now 3. Version-10 paired
+reports must be rescored rather than relabeled.
+
+Version 12 restores Law 14's publication requirement to exact repeated QxQ and
+BxQ. Version 11's repeatability envelope remains diagnostic evidence of kernel
+drift, but it does not authorize a drifting canonical score. Publication
+requires two natural QxQ samples, two forced-natural controls, and two BxQ
+samples to agree exactly in routes and per-position KLD. Uncertified or
+nondeterministic backends fail. Version-11 paired reports must be rescored
+rather than relabeled. The paired routed-score and BxQ protocol versions are
+now 4.
+
+Version 13 adds Law 17. Versions 1 through 12 checked at length that a kernel
+computed repeatably and never asked whether it computed what the checkpoint
+exported, so a batch-invariant, fully certified run could substitute a
+quantization parameter and publish the result under the checkpoint's name. A
+version-12 report does not record what it substituted and cannot be relabeled as
+one that substituted nothing; the record comes from the loaded model during
+scoring. Version-12 paired reports must be rescored. The paired routed-score and
+BxQ protocol versions are now 5.
+
+Version 14 adds no law. It puts `kv_cache_dtype` into the comparability key
+(Law 10), the bound capture fields (Law 5), and the baseline agreement Law 1
+already required of the model runner, because scoring had left it at
+`auto`, under which vLLM resolves the KV cache dtype from a scheme declared in
+the candidate's own config. One candidate therefore ran its attention through an
+8-bit KV cache while the candidates it was ranked against ran unquantized, and
+nothing in versions 1 through 13 could see the difference. The KV cache is now
+pinned unquantized and never inherits a checkpoint's declaration. A version-13
+report cannot be relabeled: it does not record which cache it used, and one that
+used a quantized cache measured something else. Version-13 reports must be
+rescored, and their captures retaken, since the capture is bound to the cache it
+was taken under.
+
+Version 15 adds no law and changes what two of them bind to.
+
+Law 10's comparability key drops `vllm_commit` and `vllm_dirty_digest` for
+`numerics_digest`, a hash of every `.py` under `vllm/` together with the scorer.
+Binding to the commit meant a documentation edit, a campaign config, or a fix to
+the orchestration invalidated every published number and cost GPU-days to
+reproduce values that were already correct. The commit is still recorded and still
+published under Law 6, where it says when a number was taken; currency asks
+whether the code that computed it would compute it again, and the digest answers
+that. `compiled_extensions_sha256` continues to cover the built kernels, so a
+rebuild still invalidates. A capture is bound the same way: it is retaken when the
+digest or the kernels move, not when the commit does.
+
+Law 1 no longer requires the baseline and the candidate to cache in the *same*
+unquantized dtype, only that each is unquantized. Version 14 held a literal
+`bfloat16`, which refused three checkpoints published as float16 outright:
+FlashAttention will not take a float16 query against a bfloat16 key. Each side now
+caches in its own checkpoint's compute dtype, both dtypes are recorded, and the
+candidate's stays in the comparability key, so a difference is disclosed and never
+silently ranked across.
+
+A version-14 report has no `numerics_digest` and cannot be relabeled into one; a
+report that records no digest cannot be shown to have been computed by the code
+running now. Version-14 reports must be rescored. This is the last rescore either
+change forces: after it, a commit that cannot reach a number costs nothing.
+
 These laws govern every distribution-fidelity measurement this program
 publishes. They are not guidance. The pipeline refuses to produce or upload an
 artifact that violates one, and the only way past a refusal is a recorded,
@@ -140,7 +217,10 @@ override, ranking two candidates requires repeated candidate capture as well.
 **Required.** Eager execution enforced. No autotuned kernel selection, no
 inference-time JIT kernel selection, no CUDA graphs, prefix caching disabled,
 and a fixed `max_num_seqs`. Tensor-parallel size is recorded, and reference and
-candidate are scored under identical settings.
+candidate are scored under identical settings, with one exception: each caches in
+its own checkpoint's compute dtype, because a float16 checkpoint cannot read a
+bfloat16 key. Both must be unquantized, both are recorded, and the candidate's is
+in the comparability key.
 
 **Why.** Timing-based autotuners and JIT kernel choice make the arithmetic a
 function of machine load, which silently converts run-to-run noise into apparent
@@ -189,8 +269,8 @@ is strictly less than or equal to the checkpoint's declared `vocab_size`.
 
 **Required.** A reference capture binds itself to the tokenizer identity, token
 hash, context length, row count, `score_from`, scored vocabulary size, tensor
-parallel size, eager mode, and runtime identity. Scoring against a capture whose
-manifest disagrees with the live configuration aborts.
+parallel size, eager mode, KV cache dtype, and runtime identity. Scoring against
+a capture whose manifest disagrees with the live configuration aborts.
 
 **Why.** Reusing a capture across configurations is the easiest way to publish a
 number that compares two different things. The binding must fail closed, because
@@ -315,9 +395,14 @@ number is presented as a capability, accuracy, or general quality claim.
 depth distribution, the vocabulary width, and the runtime. Cross-harness
 comparison is the single easiest way to publish a confident falsehood.
 
-**Check.** Each result records its suite ID, geometry, laws version, and runtime
-manifest hash. The leaderboard groups strictly by that tuple and refuses to place
-rows from differing tuples in one ranking. The suite identity is read from what the
+**Check.** Each result records its suite ID, geometry, laws version, KV cache
+dtype, and runtime manifest hash. The leaderboard groups strictly by that tuple
+and refuses to place rows from differing tuples in one ranking. One field of the
+key is deliberately not a section boundary: a substituted quantization parameter
+describes the candidate rather than how the number was taken, so such a row is
+marked and footnoted in place. Splitting it out would leave a reader comparing
+exports of one model unable to see the two side by side, and the substitution
+remains in the receipt's key, which is what binds it (Law 17). The suite identity is read from what the
 scoring run recorded, never from a suite manifest supplied to the audit, or a run
 that tokenized at run time reports a complete key by borrowing the identity of a
 suite it never opened.
@@ -391,107 +476,78 @@ indistinguishable from a bug.
 
 **Override.** None. This law has no exceptions.
 
-## Law 14 — Component attribution on routed models
-
-**Required.** For a checkpoint that routes tokens to experts, a fidelity number
-does not publish as a single mean. The artifact carries, measured on the same
-tokens against the same reference and at the deployed checkpoint's own scheme and
-granularity:
-
-1. the **expert cell** — the reference with only its expert weights rounded
-   through the deployed scheme;
-2. the **measured routing divergence** — the candidate's own expert selections
-   compared against the reference's over the same frozen tokens;
-3. the **router weight cell** — the reference with only its router rounded, or
-   `not_applicable` with inspection evidence when the deployed checkpoint leaves
-   the router unquantized;
-4. the **deployed cell** — the candidate as it ships.
-
-**Why.** Routing cost saturates, and a saturating term cannot rank anything. A
-perturbation changes an expert selection only when it crosses a near-tie, so the
-count of changed selections is governed by how many near-ties the model has, not
-by how large the perturbation was. Measured on one MoE checkpoint, NVFP4 and
-MXFP8 expert weights differed by a factor of 36 (0.1065 against 0.0030), while the
-deployed means differed by a factor of 1.2 (0.2534 against 0.2119), because a
-routing term near 0.20 dominated both. A reader given only the deployed means
-would conclude the two formats are nearly equivalent. They are not.
-
-**Routing is not router precision.** The mechanism is the activations arriving at
-the router, not the router's own weights. A checkpoint that ships its router in
-BF16 has a bit-identical router and still selects different experts, because
-quantized attention and quantized experts upstream have already moved the
-residual stream. It follows that the router weight cell is not the routing term
-and cannot stand in for it: on such a checkpoint that cell is exactly zero while
-routing divergence is not. The cell is retained only as the evidence that router
-weight precision is a red herring.
-
-It also follows that no quantize-dequantize cell is routing-free. Rounding any
-weight moves the residual stream, so every router downstream of it sees different
-inputs. An artifact must not describe a cell as holding routing fixed; each cell
-reports the share of its own selections that changed, which is what makes the
-claim checkable rather than assumed.
-
-**Measurement, not emulation.** The routing term is measured by recording the
-reference's selected experts per token and per layer, then comparing the
-candidate's selections over the same tokens. The artifact reports the share of
-(token, layer) selections that changed, the share of scored positions where any
-layer rerouted, the mean divergence at positions where routing held against
-positions where it changed, and how divergence grows with the number of rerouted
-layers.
-
-**Floor.** The floor is the excess the mean carries because rerouted positions
-diverge more than positions whose selection survived: the flipped fraction times
-the difference of the two means. It is a floor in the sense Law 1's repeat spread
-is a floor. Two candidates whose deployed means differ by less than it are not
-ranked by that difference, and an artifact that ranks them ranks them on the
-expert cell and says so in the same sentence as the claim.
-
-**A floor that is not a number.** The excess is undefined when one of its two
-populations is empty, and the two cases are opposites. If no position rerouted,
-routing cost nothing and the floor is zero. If every position rerouted, no
-held-routing population remains and the floor is unmeasurable — not small, and
-no rescore produces one, because the perturbation is large enough that routing
-divergence is the whole picture. Both satisfy this law when the run measured
-routing and the artifact states which case it is; what the law refuses is a
-routing measurement that never ran, and an artifact that omits the routing term
-so that saturation reads as zero cost. A saturated candidate publishes with its
-deployed mean marked unranked.
-
-**Naming.** Cells are named for the component that carries the error, never as
-`B×Q` or `Q×B`. That notation does not say which factor is the router, and two
-readers will order it two ways and mean opposite things by the same symbol. A cell
-is `expert_cell`, `router_cell`, or `composite_cell`, and a published artifact
-spells out what each one isolates.
-
-**Weight rounding is not the deployment.** A QDQ cell rounds weights and runs on
-BF16 kernels. A deployed checkpoint may also quantize activations and does use
-quantized kernels, so the artifact reports the deployed mean minus the composite
-cell. On one FP8 MoE checkpoint that term was 40% of the deployed mean, which no
-arrangement of weight-only cells can see. A component decomposition that omits it
-attributes a deployment to weight precision alone and understates it.
-
-**Ladder.** A campaign on a routed reference also scores the expert weights at
-each scheme on its ladder, not only at the deployed one. Ladder rungs are
-component-wide by construction: every expert weight is rounded, so the rungs
-differ only in format. The expert cell that decomposes the deployed mean matches
-per tensor and may therefore round a subset; it is not the deployed ladder rung.
-The cost of one more cell is a QDQ pass and a scoring run against a capture that
-already exists; the cost of not having it is a comparison nobody can make later
-without redoing the campaign.
-
-**Check.** For a reference whose capture manifest records declared experts, the
-compliance receipt requires an expert cell and a router weight cell, each naming
-the variant checkpoint and its QDQ manifest, and each carrying the same partition,
-token digest, and reference config digest as the deployed cell. A cell measured on
-other tokens is not a decomposition of this number and is rejected as one. The
-receipt also requires a measured routing term from the run itself; a routed
-candidate scored without one fails, and an unquantized router is not accepted as
-a reason to omit it.
-
-**Override.** Permitted under Law 13 for a dense checkpoint misdetected as routed,
-and for an exploratory result that is never published as a ranking. Not permitted
-for a published comparison between two quantization schemes, which is the case the
-law exists for.
+## Law 14 — Routed-model QxQ/BxQ intervention
+
+**Required.** A reference that routes tokens to experts publishes a paired
+intervention over the identical frozen tokens:
+
+1. **QxQ** (`qxq_cell`) is the unchanged quantized candidate running normally,
+   with its natural expert IDs and its own gating weights.
+2. **BxQ** (`bxq_cell`) is that same unchanged quantized candidate with the BF16
+   teacher's ordered logical expert IDs forced at every routed layer. The student
+   computes the gating weights for those forced experts from its own router
+   logits, using its native scoring, bias, normalization, and scaling rules.
+
+The first axis is the source of the expert IDs: `B` means BF16 teacher IDs and
+`Q` means the quantized student's natural IDs. The second `Q` is the candidate,
+which is identical in both runs. It does not mean "quantized experts" in a
+synthetic checkpoint, and the first axis does not mean router-weight precision.
+
+**Binding.** Both cells carry `mean_kld`, their supporting report path, partition,
+token SHA-256, reference-config SHA-256, and `candidate_weights_sha256`. Every
+binding equals the deployed report, and QxQ's mean equals the deployed natural
+mean. BxQ additionally carries a 64-hex `routing_trace_sha256`,
+`routing_mode: teacher_ids_student_weights`, and the supported
+`protocol_version`. The trace binds the forced IDs to the same teacher, tokens,
+layer order, and routing geometry used by both scores.
+
+**Backend control.** Exact ID replay is a capability, not an assumption.
+`backend_evidence` names the active backend or kernel, and `replay_supported` is
+true only when that path injects logical teacher IDs before placement mapping and
+dispatch. If replay uses a different kernel path, the candidate is first run with
+that path and no override. The control protocol runs two deployed-natural, two
+forced-natural, and two BxQ samples. Publication requires exact agreement:
+identical natural expert IDs across the two QxQ samples, identical per-position
+KLD across QxQ repeats, forced-natural controls, and BxQ repeats, all within
+the fixed numerical floor. Observed spans and route-flip rates are stored as a
+diagnostic addendum and never authorize a drifting canonical score. There is no
+backend-name allowlist or blanket tolerance. `natural_control_parity.passed` is
+true only when those exact-repeat conditions hold. An uncertified or
+nondeterministic backend fails. The candidate weight digest must remain unchanged
+and `candidate_weights_unchanged` must be true.
+Forced-natural controls and BxQ remain bound to the first QxQ sample's exact
+ordered IDs. QxQ always uses the model's own router.
+
+**Natural routing divergence.** The QxQ run also measures the student's natural
+expert IDs against the teacher trace. The artifact reports selection flip rate,
+position flip rate, conditional KLD where routing held and flipped, per-layer
+rates, and the existing routing-excess state. Conditioning after the run is not
+BxQ: it observes the subset where routes happened to agree, while BxQ forces the
+teacher IDs over the full suite.
+
+**Paired delta.** `routing_intervention_delta = QxQ mean KLD - BxQ mean KLD`.
+It may be positive or negative. It is a paired intervention result, not an
+additive decomposition and not a claim that routing alone contributed that much.
+
+**Synthetic QDQ diagnostics remain synthetic.** `expert_cell`, `router_cell`,
+`composite_cell`, and ladder rungs round selected BF16 weights, run on BF16
+kernels, and route naturally. They may still diagnose weight rounding and report
+their own route flips, but they are never labeled BxQ or QxQ. In particular,
+`expert_cell` is not BxQ because it changes the checkpoint and does not force
+teacher IDs.
+
+**Check.** For a manifest declaring experts, compliance requires both paired
+cells and all bindings above; exact QxQ/deployed equivalence; a complete trace
+digest; protocol, certified backend, and passing exact-repeat control evidence;
+the exact paired delta; measured natural routing divergence; and QxQ plus BxQ
+domain records. A manifest declaring no experts is
+`not_applicable`. Missing replay support, stale or unbound traces, failed exact
+repeat, an uncertified backend, and missing BxQ are failures. They are never
+converted into an override or a synthetic substitute.
+
+**Override.** Not permitted. Dense models satisfy this law as
+`not_applicable`; a routed model without supported replay and exact repeated
+QxQ/BxQ does not.
 
 ## Law 15 — Domain disclosure
 
@@ -558,12 +614,11 @@ artifact attributing one vendor's numbers to another's work. That is the worst
 error this program can make, because it is invisible: the number is real, the
 receipt is honest, and the name on it is wrong.
 
-**Digest, not a file list.** The bond is over tensor names, dtypes, and shapes,
-plus each shard's name and size, read from safetensors headers. It costs one
-header per shard rather than a pass over hundreds of gigabytes, and it cannot be
-preserved by a repack: a checkpoint that quantized one more layer, or stored a
-scale at a different width, is a different checkpoint under this digest. Two
-directories that share it hold the same weights, whatever their repos are called.
+**Content digest, not a file list.** The bond hashes each shard name and every
+byte of every safetensors shard, including its header. A checkpoint with the same
+tensor names, dtypes, shapes, and file sizes but different values therefore
+cannot preserve the digest. Two directories that share it hold byte-identical
+weight shards, whatever their repos are called.
 
 **The same mean from different weights is refused, not explained.** Distinct
 quantizations of one reference do not land on an identical mean to eighteen
@@ -572,8 +627,9 @@ one report was scored against the other's checkpoint, and nothing in the
 artifacts says which. Both results are withdrawn and rescored. The converse —
 two candidates sharing a digest — is not an error in the measurement but a fact
 about the upstream repositories: one is a verbatim re-upload of the other. It
-publishes once, and the artifact names the re-upload as such rather than
-presenting it as an independent quantization.
+publishes once only when both reports agree; conflicting scores from identical
+weights are refused. The artifact names an agreeing re-upload as such rather
+than presenting it as an independent quantization.
 
 **Check.** The compliance receipt requires `student_weights_sha256` on the report
 and an inspection of the published candidate carrying the same digest.
@@ -586,6 +642,78 @@ without a rescore. The deviation is printed beside the number, which then states
 that its weights are unbound. Not permitted for a new measurement, and never
 permitted for a refused identical mean.
 
+## Law 17 — Numerical substitution disclosure
+
+**Required.** When the kernel that scored a candidate did not use a quantization
+parameter as the checkpoint exported it, the result names that parameter, states
+what was put in its place, reports how many of the scored layers it reached, and
+bounds how far the replacement had to stretch. The substitution enters the
+candidate's comparability key, so a substituted result never ranks against one
+measured on its own parameters. A report that carries no substitution field at all
+fails: silence is not the same claim as "none". An empty field is a pass only when
+the run actually walked the loaded kernels and found nothing replaced. A run with
+no kernel inspection to walk, such as a dense candidate with no routed experts, is
+not applicable rather than clean; an empty field must never be read as a clean bill
+of health for parameters nobody looked at.
+
+**Why.** A kernel can be perfectly deterministic, pass every certification this
+program runs, and still not be computing what the checkpoint describes. Two
+substitutions this program has actually measured: the NVFP4 emulation experts
+replace each expert's activation scale with one scalar for the whole layer
+(measured once at a factor of 150 on a real export, and no longer the scoring
+path), and a checkpoint that omitted per-expert keys has those slots filled from
+the layer maximum so CUTLASS does not run on uninitialized memory. Both produce
+a real, bitwise-repeatable mean that answers a slightly different question than
+the checkpoint posed.
+
+**Disclosure, not withdrawal.** This is the law's central choice. A substituted
+result is published. Withdrawal is reserved for a result that cannot be
+interpreted at all, such as one bound to a reference capture the family no longer
+publishes; it is not a penalty for a number that came out badly, and a fidelity
+index that quietly drops the checkpoints its kernels handle worst is not an index
+of checkpoints but of kernel coverage. The failure mode here is never the
+measurement, it is an unqualified label on it. So the remedy is a label.
+
+**The spread is disclosed but does not bound comparability.** Only the identity of
+the substituted parameter enters the comparability key. Two candidates the kernel
+substituted the same way remain rankable against each other — which is the entire
+purpose of keeping them — while the spread tells a reader how much to discount the
+absolute value. A wide spread does not make a result less comparable to its peers;
+it makes the whole group further from the deployment all of them describe.
+
+**Per-cell states.** Each cell of a routed result reports one of three states.
+`measured` used the checkpoint's own parameters. `substituted` holds a real
+repeatable number obtained under a named replacement. `unavailable` holds no
+number, with the reason it could not be taken. The three are not collapsible: an
+absent cell and a substituted one are both unlike a plain measurement and nothing
+like each other. Where a substitution reaches both cells of a routed pair it
+partly cancels in `QxQ − BxQ`, but never exactly — clipping is nonlinear and the
+two runs route to different experts — so the delta is the sounder of the three
+numbers without being clean.
+
+**Check.** Compliance requires `quantization_substitutions` on the report. Each
+entry states its parameter, kind, affected and scored layer counts, and worst
+spread; a missing field, a malformed entry, or one that omits its spread fails.
+The check then confirms the comparability key still binds the substituted
+parameters. Scoring records the field from the loaded model, per tensor-parallel
+worker, reporting the worst case across ranks.
+
+**What is inspected so far, stated as a limit.** Routed expert layers are
+inspected; dense linear layers are not. This is a real gap and it fails closed:
+a dense family scored today reports no substitution field and Law 17 refuses it,
+which is the correct outcome — the law says nothing about that checkpoint until
+someone measures it. Recording an empty list instead would assert that nothing was
+substituted, and for NVFP4 that assertion would be false: the dense path collapses
+the input scale across each fused parallel layer exactly as the expert path does,
+under vLLM's own warning that it will likely reduce accuracy. Closing the gap
+needs more than a new inspector, because that path deletes `input_scale` once it
+has taken the maximum. The evidence does not survive loading, so it has to be
+captured while the substitution happens rather than observed afterwards.
+
+**Override.** Not permitted. An approval could only assert that an undisclosed
+substitution is acceptable, which is the one thing this law exists to refuse.
+A result whose substitution cannot be determined is unavailable, not approved.
+
 ## Numbering
 
 Laws are append-only. A published receipt cites its laws by number, so renumbering
diff --git a/QXQ.md b/QXQ.md
new file mode 100644
index 0000000000000000000000000000000000000000..3640c0cd241f20c0faf4aef69e3a8f1a96b21ad1
--- /dev/null
+++ b/QXQ.md
@@ -0,0 +1,705 @@
+# QxQ and BxQ — Routed-Model Distribution Fidelity
+
+**Applies to:** laws version 14, Laws 14 and 17.
+**Read first:** [`LAWS.md`](LAWS.md) for the laws, [`README.md`](README.md) for the
+harness.
+
+This document explains what the QxQ and BxQ cells measure, why measuring them at
+all requires bitwise-exact repeat from every kernel on the path, which MoE
+backends have earned the right to produce a published number, and how the
+pipeline refuses the ones that have not.
+
+## 1. Why one number is not enough for a routed model
+
+For a dense model, quantization error is weight rounding. One KLD against the
+BF16 reference describes it, and the only question left is how it distributes
+across text.
+
+A Mixture-of-Experts model breaks that. Two unrelated things happen at once, and
+a single mean hides both:
+
+1. The experts round their weights, exactly as a dense layer would.
+2. The router sends tokens to *different experts* than the reference would,
+   because the activations arriving at the router are themselves quantized.
+
+These have opposite consequences. Rounding is a fidelity tax that shrinks
+predictably with bit width. Routing divergence means the model is evaluating a
+different function — a different subnetwork per token — and it does not shrink
+predictably with anything. A candidate whose loss is mostly rounding and a
+candidate whose loss is mostly rerouting can post identical means and behave
+differently in deployment. Ranking them on that shared mean is the error Law 14
+exists to prevent.
+
+## 2. The two cells
+
+Both cells score the same candidate against the same BF16 teacher on the same
+frozen token suite, and differ only in who chooses the experts.
+
+**QxQ** is the candidate exactly as deployed: quantized weights, and its own
+router choosing its own experts. This is the number that describes what a user
+would actually run, and it is what routed candidates are ranked on.
+
+**BxQ** replays the same tokens with the teacher's expert IDs forced into the
+student, while the student keeps its own gating *weights*. Forcing the IDs
+removes the routing disagreement and leaves the rounding, so BxQ is the fidelity
+the candidate would have if its router agreed with the reference.
+
+The keeping of student gating weights is deliberate and easy to get wrong. If
+BxQ also took the teacher's gate values it would stop being a measurement of the
+student's experts and become a partial reconstruction of the teacher. Only the
+selection is intervened on; the weighting stays the candidate's own.
+
+**QxQ − BxQ** is therefore the routing contribution, reported as the paired
+intervention delta. The **natural route flip rate** reports how often the two
+disagree at all, measured over `(token, layer)` choices on the natural QxQ run —
+not on the intervened one, where by construction there is nothing to count.
+
+A worked example from the gemma-4-26B-A4B-it family: `Intel/gemma-4-26B-A4B-it-int4-AutoRound`
+scores QxQ 1.17816288 and BxQ 0.90308200, a delta of +0.27508088, and flips
+93.07% of natural routing choices. It sits mid-pack on deployed fidelity, but the
+decomposition says something no mean could: nearly all of its loss is the router,
+not the rounding. Its neighbours in the same group carry deltas near +0.15 at
+comparable flip rates.
+
+## 3. Why the subtraction demands bitwise-exact repeat
+
+QxQ − BxQ is a difference between two separate forward passes. If scoring the
+same tokens twice does not reproduce the same logits bit for bit, then a delta of
++0.15 is indistinguishable from kernel noise, and the decomposition is
+storytelling.
+
+So the pipeline does not assume reproducibility, it measures it. Every routed
+candidate is scored, replayed, and the two results compared; the published
+`repeat_delta` is that difference and it is required to be exactly `0.0`. A
+candidate whose backend cannot deliver that is marked uncertified for exact
+repeat and cannot be published as a Law 14 measurement.
+
+This is the entire reason the effort behind this document was determinism work
+rather than metric work. The metric is a subtraction; the difficulty is earning
+the right to subtract.
+
+## 4. How MoE kernels break exact repeat
+
+Batch invariance is the property that a token's output does not depend on which
+other tokens share its batch, nor on their order. Most MoE kernels violate it,
+and not by accident: grouping tokens by expert and reducing partial sums in
+whatever order the grouping produced is the fast way to do it. That is a correct
+optimization for serving, where nobody compares two runs, and it is fatal here.
+
+Four fixes were required before routed models could be scored at all.
+
+**Marlin token ordering and reduction.** The Marlin MoE path was ported to a
+canonical token order with a full-K reduction, which is what makes
+`MarlinExperts` and `BatchedMarlinExperts` batch invariant. This carries the bulk
+of the field: every int4 AWQ, GPTQ, and W4A16 routed candidate scores here.
+
+**Qwen GDN attention.** Certified only on its NVIDIA CUDA, non-speculative,
+per-sequence path, with FlashInfer GDN context parallelism disabled. Without this
+the Qwen3.6 family could not run under `VLLM_BATCH_INVARIANT` at all.
+
+**Dense NVFP4 linear layers.** Weight-only W4A16 NVFP4 linear layers use
+deterministic emulation, because dense Marlin is not batch invariant.
+
+**Expert parallelism is never certified.** An EP path reduces across ranks in
+completion order. No allowlist entry overrides this.
+
+## 5. Certification fails closed
+
+A kernel's `_supports_batch_invariance()` is a claim about itself. It is not
+evidence. `CutlassExpertsFp4` declared True and still produced NaN on W4A4
+checkpoints; the NaN was uninitialized per-expert activation scales, not the
+kernel, but the lesson stands: a self-declaration is not a probe.
+
+`inspect_model_moe_backends` in `vllm/v1/sample/kld.py` therefore grants
+`certified_for_exact_repeat` only when all three of these hold:
+
+1. The expert class is named in `_EXACT_REPEAT_CERTIFIED_EXPERTS`, an allowlist
+   earned by passing an exact-repeat probe on real suite content. The report
+   carries this separately as `exact_repeat_probed`.
+2. The kernel also self-declares batch invariance.
+3. The run is not expert-parallel.
+
+The allowlist currently holds `MarlinExperts`, `BatchedMarlinExperts`,
+`TritonExperts`, `Nvfp4QuantizationEmulationTritonExperts`, and
+`CutlassExpertsFp4`. CUTLASS earned its place after the SM120 bitwise
+permutation test (24/24, `atol=0`) and after the W4A4 NaN was shown to be a
+loader gap, not a kernel defect. The run's own zero-tolerance exact-repeat
+control remains the binding gate.
+
+The default is refusal. An unprobed backend — including one that arrives with a
+future vLLM bump — is uncertified until somebody probes it, and an uncertified
+backend is reported as uncertified rather than published as a number. This
+matters more than it sounds: the failure mode it replaces is a kernel quietly
+producing garbage that the harness dutifully writes down as a fidelity result.
+
+DeepGEMM, FlashInfer MoE, AITER, XPU, CPU, and every expert-parallel path remain
+uncertified.
+
+**Certification and backend selection now meet.** With the MoE backend pin removed
+(§7) the choice belongs to vLLM's oracle, and the oracle ranks by expected
+performance, not by whether a kernel is certified here. `AVAILABLE_BACKENDS` in
+`vllm/model_executor/layers/fused_moe/oracle/nvfp4.py` puts `FLASHINFER_TRTLLM`
+first and nothing in that path consults `VLLM_BATCH_INVARIANT`, so nothing
+*guarantees* a certified kernel. The refusal is upstream of the cost, at least:
+scoring reads certification off the loaded model and raises before any forward pass,
+so an uncertified choice costs a model load rather than a scored pass.
+
+Measured, on ten gemma4-26b-a4b candidates at one row each: the oracle chose
+`VLLM_CUTLASS` for all four NVFP4 MoE exports, `TRITON` for the FP8 export, and
+`MARLIN` for the four INT4 ones. Every one is in the allowlist and every control
+repeated at exactly 0.000e+00. FlashInfer was first in the list each time and its
+`is_supported_config` declined each time, for reasons logged only at debug level.
+So the outcome is good and it is a reading rather than a promise: run the `smoke`
+stage over a routed family before committing a campaign to it, and read the backend
+each candidate names.
+
+**A kernel is verified on the next run, not pinned.** The oracle chooses per layer
+from the checkpoint, the device, the installed FlashInfer and its own source, and
+§9's comparability key binds all four — the last of them because the oracle is a
+`.py` file under `vllm/` and therefore inside the numerics digest. Under one binding
+the choice is a function, so a second run of it owes the same answer. Scoring holds
+it to that: `_prior_kernel_identity` reads the previous report for the candidate,
+and when its binding matches the live one, the expert class and kernel read back
+after the student load must match what that report recorded. A mismatch is refused
+before any forward pass, because a published number that a rerun would not
+reproduce is worse than no number. The comparison is narrowed to `quant_method`,
+`kernel` and `experts`: tensor and expert parallelism are recorded on the report but
+a rescore is entitled to move them. Where the binding differs the expectation is
+released, since new numerics or newly built kernels are allowed a new choice — that
+is a new number, not a broken one. This is the safe half of pinning. A pin had to
+predict the kernel before the load and could refuse a checkpoint outright (§7); a
+comparison happens after the load and can only refuse a contradiction.
+
+## 6. Case study: uninitialized scales, not a broken kernel
+
+`CutlassExpertsFp4` self-declares batch invariance. Under
+`VLLM_BATCH_INVARIANT=1`, on two W4A4 NVFP4 checkpoints, it took finite inputs
+to NaN. The scorer refused a non-finite KLD and the engine died. The first
+reading was that the kernel's claim was false. That was wrong.
+
+The kernel is bitwise batch-invariant on this hardware. The CUDA grouped GEMM
+asserts at compile time that it uses `PersistentTileSchedulerSm100Group` "for
+batch invariance", and `tests/v1/determinism/test_cutlass_batch_invariance.py`
+passed 24/24 on SM120 at `atol=0, rtol=0` across both activations, both expert
+counts (40, 64), both top-k values, and all three shape cases.
+
+The NaN was uninitialized memory. NVFP4 allocated per-expert activation scales
+with `torch.empty`. Checkpoints that omitted `input_scale` keys for some experts
+in some layers — `bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4` (14 layers short)
+and `Neural-ICE/Gemma-4-26B-A4B-it-NVFP4` (16 layers short) — left those slots
+holding whatever was on the GPU. CUTLASS consumes one scale per expert
+(`a1_gscale` / `a2_gscale` of length `e`); a NaN slot damages that expert. The
+unsloth export, with every slot written, never went NaN.
+
+Loading does not catch this. Strict all-parameters-loaded tracking is off by
+default for quantized models, and any module with `process_weights_after_loading`
+has every parameter force-marked as loaded, so a partially filled tensor is
+invisible to it by construction.
+
+Localizing it took two purpose-built tools.
+
+`scripts/scan_checkpoint_nonfinite.py` reads a checkpoint's tensors and counts
+non-finite values, zero scales, and per-row magnitude outliers in scale tensors.
+`torch.isfinite` has no CPU kernel for several float8 dtypes, so `nonfinite_mask`
+tests FP8 bit patterns directly — `(bits & 0x7F) == 0x7F` for `e4m3fn`, and
+`>= 0x7C` for `e5m2`. The scan came back clean, which established that the NaN
+was generated at runtime rather than baked into the weights, and moved the
+investigation off the checkpoint.
+
+`scripts/nan_first_module_probe.py` installs forward hooks and reports the first
+module whose output goes non-finite from finite inputs, together with the first
+row index at which it happens. Two capabilities were decisive. `--context-file`
+replays a real suite context instead of a synthetic prompt, because synthetic
+prompts are repetitive and never route diversely enough to trigger the bug.
+`--prompt-logprobs` computes logits at every prompt position, which is what the
+scoring harness does and what a generation-only probe does not.
+
+With both, the failure is exactly reproducible: `moe.experts` at layer 0, row 814
+of `context-0002`, and layer 14, row 170 of `context-0004`.
+
+The `--moe-backend` override then isolated the failure to the native path on
+the two incomplete checkpoints, not to batch invariance itself:
+
+| `VLLM_BATCH_INVARIANT` | Backend | Result |
+| --- | --- | --- |
+| 0 | `FLASHINFER_CUTLASS` (auto) | pass |
+| 1 | `VLLM_CUTLASS` (auto) | **NaN** on incomplete exports |
+| 1 | `marlin` (forced) | pass (drops activation scales) |
+| 1 | `emulation` (forced) | pass (collapses per-expert scales) |
+
+The fail-closed allowlist was the right reaction to a NaN that looked like a
+kernel defect. Once the scales were the cause, keeping CUTLASS off the list
+was the thing standing between native BxQ and a published number.
+
+## 7. W4A4 NVFP4 and the kernel the loader actually builds
+
+A W4A16 export and a W4A4 export of the same model differ only in whether
+activations are also quantized, and that difference decides which kernels can
+score the checkpoint honestly. A repository name says nothing reliable about it,
+so `_declared_expert_activation_quant` in
+`examples/offline_inference/score_mode_kld.py` reads the checkpoint: for every
+`quantization_config.config_groups` entry that targets experts, it reports what
+that group declares for `input_activations` — `4-bit float`, `8-bit float`, or
+`unquantized`. Scoped to expert groups, because a group covering only attention
+says nothing about the kernels the MoE will run, and scanning every group is what
+made a checkpoint with W4A4 attention and FP8 experts look like a W4A4 MoE.
+
+It reports a list, not a verdict, because a checkpoint can declare more than one
+width inside its own MoE. And it is a reading of the checkpoint, not a prediction
+about the run: what the loader builds is a separate fact, read back after load.
+
+**The scorer no longer pins a MoE backend, and the removal was not a
+simplification.** It used to pin `moe_backend="cutlass"` for anything it judged
+W4A4, and two real checkpoints showed that a pre-load pin is being asked two
+questions it cannot answer. `nvidia/gemma-4-26B-A4B-it-NVFP4` declares
+`num_bits: 4, type: float` weights *and* activations for `mlp.experts`; the
+modelopt `MIXED_PRECISION` loader built those experts weight-only, and the pinned
+W4A4 CUTLASS kernel refused a configuration ending `(symmetric)xNone` — the pin
+failed the candidate over a kernel the checkpoint was never going to get.
+`unsloth/gemma-4-26B-A4B-it-NVFP4` declares FP8 experts in its last eight layers
+and W4A4 in the rest, which no single global pin can serve at all. Both failures
+were ours, not the checkpoints'.
+
+So the choice is left to the oracle, which answers per layer and knows what it
+built, and `inspect_model_moe_backends` reads the answer back off the loaded
+model. That is the measurement this project prefers to a declaration, and it is
+the same discipline as `assert_unquantized_kv_cache` in §9.
+
+The kernels themselves still differ, and which one ran still decides what a number
+means. vLLM CUTLASS is the only native W4A4 MoE path that keeps a per-expert
+activation-scale vector. FlashInfer collapses every expert to one scalar via
+`amax_for_moe_activation_quant(...).repeat(num_experts)` — the same defect as
+emulation. Marlin drops activation scales and scores W4A4 as W4A16.
+
+Unwritten slots are now a NaN sentinel, filled from the maximum of the present
+per-expert scales before CUTLASS fuses them into the weight alphas. Too large
+wastes quantization range; too small overflows e4m3. A layer with no finite
+positive slot is refused rather than invented. The fill is recorded on the
+layer at fill time as `uncalibrated_experts_filled_from_layer_max` and Law 17
+discloses it. A complete export scored on CUTLASS records an empty substitution
+list, because the kernel used the checkpoint's own scales. A complete export is
+not automatically such a run: with the pin gone, unsloth's mixed-precision MoE
+gets whichever kernel the loader builds for it, and if that kernel collapses the
+scales the substitution is disclosed and then priced.
+
+Both halves of the model are walked, because a dense NVFP4 projection can omit a
+shard's scale exactly as an expert can. `inspect_model_moe_backends` covers the
+routed experts and `inspect_model_nvfp4_dense_scales` covers everything else,
+reported separately so the denominators stay meaningful — a fill on 3 of 200
+dense layers is a different claim than 3 of 30 experts. The fill records a scan
+marker on every layer it visits, filled or not, because counting NVFP4 layers
+after load is guesswork: the dense paths delete or overwrite the very parameter
+they were named for. Without the dense walk a dense W4A4 candidate reported no
+substitution, and Law 17 read that silence as a clean bill of health rather than
+as an absence of evidence, which is why it now returns `NOT_APPLICABLE` when
+nothing was inspected at all.
+
+| Path | Per-expert activation scales | Exact repeat |
+| --- | --- | --- |
+| vLLM CUTLASS FP4 | honoured (per-expert vector) | certified on SM120 |
+| FlashInfer FP4 | collapsed to one scalar for the layer | uncertified |
+| Marlin | dropped entirely (scores W4A16) | certified |
+| Emulation | collapsed to a layer maximum | certified |
+
+**A collapse is priced, not just disclosed.** Law 17 states that a run used one
+layer-wide scalar where the checkpoint exported a scale per expert, and that left
+a reader to guess whether the substitution was worth a decimal place or the
+ranking. When a report discloses `per_expert_collapsed_to_layer_scalar`, the
+campaign scores that candidate once more with `--moe-backend cutlass`, which keeps
+the per-expert scales, and records the difference as `per_expert_collapse_cost`.
+The one-pager prints both numbers and the delta.
+
+Conditional by construction: a candidate that collapsed nothing pays nothing,
+which is every dense candidate and every routed one whose experts kept their own
+scales. A candidate that did collapse pays one student load and one scoring pass,
+because the teacher capture does not depend on which kernel the experts run and is
+shared with the run that just finished. If the non-collapsing kernel will not load
+the checkpoint on this hardware, the cost is recorded as unpriced with the reason;
+withdrawing a measured, compliant, deployed number because a second run vLLM never
+has to perform did not work would be the wrong trade.
+
+The deployed number does not change. Which kernel the oracle builds for a
+checkpoint on this hardware is a fact about deploying it, and this index reports
+what deployment does. The counterfactual is priced beside it, never in place of it,
+and the two are not comparable to each other's leaderboard groups because the
+substitution is part of the comparability key.
+
+**A named backend belongs on the student only.** `--moe-backend` reaches
+`student_kwargs`, not the shared `llm_kwargs`. Applied to the latter it propagates
+to the unquantized BF16 teacher, which has no such scheme, and reference engine
+initialization fails.
+
+A weight-only W4A16 NVFP4 result still measures the quantization scheme rather than
+a native FP4 kernel's own rounding, because dense Marlin is not batch invariant
+and those linear layers use deterministic emulation. That limit is stated on the
+published card.
+
+### History: the emulation collapse we published through
+
+Before the loader gap was understood, W4A4 scoring was pinned to emulation.
+The emulation branch of `convert_to_nvfp4_moe_kernel_format` collapses the
+per-expert activation scales to one scalar per layer —
+`a13_scale = 1.0 / a13_scale.max()` — and vLLM's own comment says taking the
+largest global scale "likely results in overflowing the FP8 range for other
+experts." Since `a2_scale` holds `1 / w2_input_global_scale`, `.max()` applies
+the *smallest* per-expert scale. Measured on
+`unsloth/gemma-4-26B-A4B-it-NVFP4`:
+
+| Parameter | Slots | Distinct values | Worst spread | Collapsed |
+| --- | --- | --- | --- | --- |
+| `w13_input_global_scale` | 256 | 1 | 1.0x | no |
+| `w2_input_global_scale` | 128 | 86–99 | 150.5x | yes, all 30 layers |
+
+Four NVFP4 exports carrying byte-identical QDQ diagnostics (0.66652247 and
+1.43642041) scored QxQ between 1.16299506 and 1.82545539 on that path, and that
+0.6 nat spread was written up as living "entirely in the activation scheme and
+kernel path." That reading was unsupported. The spread was substantially an
+artifact of the measurement. It stays here because it is the exact shape of
+mistake this document exists to prevent: a real, reproducible, bitwise-exact
+number that is nonetheless measuring the harness rather than the checkpoint.
+
+Law 17 still exists for the remaining real substitutions — an uncalibrated
+expert filled from the layer maximum is one — so a substituted result ranks
+only against candidates measured the same way, and is never withdrawn for
+having a disclosed fill.
+
+### How far to discount a filled result
+
+The disclosure states a spread. It does not say what that spread costs, and the
+first native-CUTLASS campaign answered the question by accident.
+
+The two filled candidates landed on top of each other:
+
+| Candidate | Layers filled | Worst spread | QxQ | BxQ |
+| --- | --- | --- | --- | --- |
+| `Neural-ICE/Gemma-4-26B-A4B-it-NVFP4` | 15 of 30 | 230.6x | 1.82281421 | 1.58512109 |
+| `bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4` | 12 of 30 | 240.3x | 1.82307292 | 1.59361302 |
+
+0.00026 nats apart on QxQ, from different publishers, with different fill
+footprints, and with one quantizing its LM head while the other does not. They
+are not the same checkpoint: distinct `student_weights_sha256`, and distinct
+per-position KLD digests in `kld_evidence`, so the two runs did not produce
+bitwise-identical output either.
+
+Two independent quantizations do not agree to four decimal places on their own.
+The reading that fits is that once a tensor's per-expert scales are replaced by
+one layer maximum on half the layers, the fill sets the number and the
+checkpoint's own choices stop being visible in it. On the same suite the clean
+NVFP4 exports separate normally — 1.13846019 for unsloth against 1.77968754 for
+RedHatAI — so the collapse is not the suite failing to discriminate.
+
+This is n=2 and therefore a strong hint rather than a proof; a fill-direction
+sweep on one checkpoint would settle it, and has not been run. Treat a filled
+QxQ as an upper bound on that family of exports rather than a measurement of the
+particular one. The leaderboard says so on the row: a filled candidate is ranked
+beside the clean ones, because it was measured on the same suite, geometry, and
+runtime, and carries a `†` naming what was substituted. It is not exiled into a
+section of its own, which would hide the comparison a reader came for while the
+substitution stays bound in its comparability key either way.
+It is also the argument against ever promoting the fill out of the harness: see
+§13.
+
+## 8. Checkpoint defects the pipeline had to fix, not tolerate
+
+Determinism gets you a repeatable number. It does not get you a *correct* one if
+the checkpoint is being loaded wrong, and two classes of loading bug were found
+by scoring rather than by tests.
+
+**AutoRound int4 geometry.** AutoRound exports use group sizes that do not divide
+the input dimension, producing a partial final group. The packed-row and
+scale-group arithmetic — `scales_size`, `num_groups`, `size_k`, and the encoded
+symmetric zero used to pad — was wrong at exactly those boundaries, in both the
+Marlin and AutoGPTQ paths. A read-only `safetensors` header probe established the
+real geometry before any code changed; assumptions about row counts had already
+cost several wrong fixes. Regression tests cover the padded reduction allocation,
+the encoded-zero padding, single-rank partial groups, and the dense path's packed
+rows with a partial scale group.
+
+**A quantized router with nowhere to land.** The AutoRound Gemma-4 export
+quantizes the router projection. vLLM's `Gemma4Router` hardcodes its `GateLinear`
+as unquantized BF16, so the checkpoint's `qweight`, `qzeros`, and `scales` had no
+destination, no loader complained, and `router.proj.weight` was left as
+uninitialized memory. The router emitted NaN from finite inputs and KLD went
+non-finite.
+
+Note how this presents: identical symptom to §6, entirely different cause. The
+module probe is what separated them, naming `layers.0.router.proj` rather than
+`moe.experts`. The fix dequantizes those tensors into BF16 at load time in
+`Gemma4Model.load_weights`, with tests in `tests/kernels/moe/test_gemma4router.py`
+covering the transposed packed values and the refusal of an indivisible group
+count. That checkpoint now scores 1.17816288 and passes all seventeen laws.
+
+## 9. The runtime the numbers are bound to
+
+Scoring pins `VLLM_BATCH_INVARIANT=1`, disables DeepGEMM
+(`VLLM_MOE_USE_DEEP_GEMM=0`) and FlashInfer autotune, sets `NCCL_DETERMINISTIC=1`
+and `CUBLAS_WORKSPACE_CONFIG=:4096:8`, enforces eager execution, disables prefix
+caching, holds `max_num_seqs=1`, and refuses a quantized KV cache.
+
+None of that is optional and none of it is a performance setting. Each one closes
+a path by which two runs of the same tokens could diverge.
+
+The KV cache closes the widest such path found so far, and it was open for
+the whole campaign. Left at `auto`, vLLM resolves the KV cache dtype from a
+scheme declared in the candidate's own config, so
+`unsloth/gemma-4-26B-A4B-it-NVFP4` — which declares an 8-bit float KV cache —
+had its attention read and written through a quantized cache while every
+candidate it was ranked against used an unquantized one. That is a difference in
+how the measurement was taken, not a property of the checkpoint being measured.
+The cache is never quantized: scoring holds 4096 tokens, so there is no memory
+pressure that quantizing it could relieve, and nothing to weigh against the loss
+of comparability.
+
+Unquantized is not one dtype. It was a literal `bfloat16` at first, which refused
+three AWQ checkpoints published as float16: FlashAttention will not take a float16
+query against a bfloat16 key, so the run died in `mha_varlen_fwd` rather than
+scoring. `unquantized_kv_cache_dtype` in `vllm/v1/sample/kld.py` reads each
+checkpoint's own `torch_dtype` and caches in that, so a float16 model caches in
+float16 and a bfloat16 model in bfloat16. Both are unquantized, which is the
+property the policy is about; the resolved dtype is recorded on the report and
+carried in the comparability key, so a reader is told which one a number was taken
+under and two candidates cached differently are not silently ranked together.
+`assert_unquantized_kv_cache` refuses anything outside
+`UNQUANTIZED_KV_CACHE_DTYPES`, and still refuses `auto`.
+
+The rescore that followed measured what it had cost, and doubled as the cleanest
+determinism evidence in this document. unsloth is the only one of the nine
+candidates whose inspection reported a declared KV cache scheme, and it is the
+only one whose number moved: 1.16570700 to 1.13846019, so its declared FP8 cache
+had been costing it 0.02724681 nats, or 2.3% of its KLD, and enough to place it
+above `gemma-4-26B-A4B-it-W4A16` at 1.15926068 when it belongs below. Five of the
+remaining eight are quoted at their pre-pin values elsewhere in this document —
+0.69415039, 1.17816288 with its BxQ and delta, 1.77968754, 1.82281421, and
+1.82307292 — and every one came back identical to eight decimal places on a
+different `vllm_commit`. So the defect was worth a rescore and was not worth a
+panic, the blast radius was exactly the set the mechanism predicted, and the
+harness reproduced everything outside it. `assert_unquantized_kv_cache` reads the dtype the engine actually
+resolved and refuses both a quantized value and `auto`, because the failure being
+prevented is precisely a value nobody checked.
+
+**A number that moves on a rescore is not a number that was wrong.** The Qwen
+families were first scored before the NVFP4 uncalibrated-scale fill, before the
+Marlin determinism work, and before MoE batch invariance; rescored under all three,
+some of those values moved by as much as 6%. That is the correct amount of movement
+for a runtime that gained a fill where it had been consuming uninitialized scales
+and a batch-invariant expert path where it had not had one. Each value was a
+faithful measurement of the runtime that produced it, which is why the runtime is
+in the comparability key and why a legacy number is never quietly ranked beside a
+current one. Read a movement of this size as the runtime changing, and look for the
+change; the alarming case is a number that moves when nothing that computes it did,
+which is what the digest below is for.
+
+Because that runtime *is* part of the result, its identity is bound into the
+comparability key: `numerics_digest`,
+`compiled_extensions_sha256`, `torch`, `driver`, `gpu_names`, and
+`kv_cache_dtype`, alongside the suite and geometry. Two candidates are ranked
+against each other only when all of it matches. `kv_cache_dtype` is in that list
+because of the unsloth case above: the key's one job is to bound a ranking to
+runs that ran alike, and it had nothing to say about a candidate whose attention
+ran at a different precision than its neighbours'.
+
+The commit used to sit in that list, and the consequence was that **any commit
+invalidated every published number.** A documentation paragraph, a campaign
+config, a new script: the next scoring run read a different `vllm_commit`,
+declared 45 compliant reports stale, and spent GPU-days reproducing numbers that
+were already right — and, being a rescore rather than a refusal, it did so
+silently. That was over-refusal dressed as rigour. An index of quantization
+fidelity is under continuous development by construction, so a currency test that
+cannot tell a docs edit from a kernel change makes the index unmaintainable.
+
+What bounds a result is whether the code that computed it would compute it again.
+`numerics_digest` in `vllm/v1/sample/kld.py` hashes every `.py` under `vllm/`
+together with the scorer, and `compiled_extensions_sha256` covers the built
+kernels, so between them they answer that question directly. Kernel sources are
+not hashed, because a source edit cannot move a number until it is rebuilt and the
+rebuild changes the extension digest. The digest is deliberately coarse — a
+comment in `vllm/` moves it — because deciding which edits inside the runtime are
+numerically inert is exactly the judgement a currency test must not be trusted
+with, and the cost of that coarseness is a rescore rather than a wrong number.
+
+The commit is still recorded on every report and printed on every one-pager. It
+says *when* a number was taken, which is provenance under Law 6, and provenance is
+not a currency test. A result reads: at commit `abc123`, under numerics digest
+`def456`, this KLD was measured. The commit may have moved a hundred times since;
+the number stands until the digest moves. Harness fixes are still best batched,
+now because a rescore wave costs GPU-hours rather than because the alternative is
+a stale library.
+
+## 10. Gates that stop a correct measurement from being published wrong
+
+Each of the following was found by a law refusing to publish, at the end of a
+multi-hour campaign, rather than by a test. They are recorded here because the
+failure mode is characteristic: the measurement is fine, and the metadata binding
+it to a suite, a geometry, or a reference capture is not.
+
+**Suite-driven geometry.** `_expected_geometry` derives the expected row count and
+context length from the suite manifest and the active partition, not from the
+dataset-driven `rows` and `context_length` on the config. Those config fields
+describe a dataset-driven run only; reading them for a suite-driven one compared
+768 real rows against a config default of 1024 and failed every candidate.
+
+**A field nobody wrote.** `context_length` was absent from every report, so a gate
+comparing it always failed. The writer in `score_mode_kld.py` now records it.
+Existing reports were backfilled from their bound capture manifests after
+verifying the recorded `capture_manifest_sha256`, refusing any mismatch.
+
+**Captures that moved underneath a report.** Assembly publishes the reference
+capture beside the report that cites it. If the capture is rebuilt after a report
+scores, the pair cites a manifest nothing hashes to, and Laws 5 and 14 refuse it —
+after the whole campaign. `_score_report_is_current` now treats a changed capture
+manifest as staleness, where a rescore is cheap.
+
+**Currency must not depend on processing order.** The gate above compares against
+whatever capture is on disk at that instant, and silently passes when the file is
+absent. That is order-dependent: early candidates in a run match the capture their
+predecessor left behind and are skipped, then a later rescore replaces it and
+strands them. `_candidate_complete` therefore delegates to the same
+`_score_report_is_current` the scorer uses, which compares each report's recorded
+commit against the live runtime and cannot be defeated by ordering.
+
+The last one has a worked example. In the gemma-4-26B-A4B-it family, one candidate
+was skipped early in a run on a stale `ec11b8…` capture, while a later rescore
+rebuilt the capture as `d1f03dea…` on a new commit. Nine candidates published on
+the new commit; the tenth kept a report from `fdc0b57e` bound to a capture the
+family no longer published. It failed Law 12 alone and ranked in a comparability
+group of one — every other law passed, because they read the candidate's own
+manifest. It was withdrawn via `excluded_candidates`, which records the repo,
+revision, and reason in `excluded-candidates.json` beside the family, rather than
+being dropped from the config silently.
+
+## 11. Reproducing and extending
+
+Certify a new MoE backend before trusting it. In order:
+
+```bash
+# 1. Is the NaN in the weights, or generated at runtime?
+python scripts/scan_checkpoint_nonfinite.py --model <checkpoint> --stats
+
+# 2. Which module goes non-finite first, on real content, at which row?
+VLLM_BATCH_INVARIANT=1 python scripts/nan_first_module_probe.py \
+    --model <checkpoint> --context-file <suite>/contexts/context-0002.json \
+    --prompt-logprobs
+
+# 3. Is it the kernel? Re-run pinning each backend in turn.
+VLLM_BATCH_INVARIANT=1 python scripts/nan_first_module_probe.py \
+    --model <checkpoint> --context-file <suite>/contexts/context-0002.json \
+    --prompt-logprobs --moe-backend marlin
+
+# 4. One candidate end to end: finite KLD, and repeat exactly 0.
+python fidelity/campaign.py smoke --config <campaign>.json \
+    --only-candidate <name>
+
+# 5. The family.
+python fidelity/campaign.py all --config <campaign>.json
+```
+
+A backend that passes the probe on real content, at full context, with prompt
+logprobs on, may be added to `_EXACT_REPEAT_CERTIFIED_EXPERTS`. Nothing else
+qualifies it, and self-declaration never does.
+
+Note that `--only-candidate` applies to `smoke` only. To rescore a single
+candidate of an assembled family, delete its report from
+`<work>/reports/<tag>.json`; the completeness gate then rescores exactly that one
+and skips the rest. A rescore the campaign decides on for itself moves the old
+report to `<work>/prior/<tag>.json` and holds the rebuild to the kernel that report
+read back. Deleting the report by hand skips that, which is the way to accept a
+kernel change deliberately rather than argue with the check.
+
+## 12. What these numbers do not say
+
+**They are not comparable outside their group.** Not against numbers from another
+suite, geometry, runtime, or laws version, and not against any published
+elsewhere. The comparability key is printed with every leaderboard group for
+exactly this reason.
+
+**A W4A4 NVFP4 MoE result is not guaranteed to be a native CUTLASS number.** It
+used to be, by a pin, and the pin failed two checkpoints it should have scored
+(§7). A result is now a number from whichever expert kernel the loader built for
+that checkpoint on this hardware, which is the kernel deploying it would get. Read
+it off the one-pager's declared-against-built table rather than inferring it from
+the scheme label: a checkpoint declaring 4-bit activations for its experts may have
+been built weight-only, and one built on a kernel that collapses per-expert
+activation scales carries a disclosed substitution and a priced cost for it. A
+W4A16 dense NVFP4 result still measures the scheme rather than a native FP4 kernel,
+because dense Marlin is not batch invariant.
+
+**A declared quantization is not a performed one.** The checkpoint's
+`quantization_config` is a statement by whoever exported it, and the kernels vLLM
+builds are a separate fact. `nvidia/gemma-4-26B-A4B-it-NVFP4` declares W4A4
+experts and was built weight-only; its number is honest about what ran and says
+nothing about what a W4A4 kernel would have scored.
+
+**The QDQ ladder is diagnostic, never a candidate.** Those cells round weights on
+synthetic BF16 checkpoints and route naturally. They are not QxQ or BxQ, they are
+not rankable against deployed candidates, and the published tables separate them.
+
+**A scheme label names the narrowest group, not the whole model.** A checkpoint
+may quantize attention at one width and its experts at another, and may declare a
+KV cache scheme. `unsloth/gemma-4-26B-A4B-it-NVFP4` is `format:
+"mixed-precision"`: FP8 W8A8 attention, NVFP4 W4A4 experts and dense MLP, and a
+declared FP8 KV cache. Its QxQ of 1.13846019 against 1.77968754 for a complete
+all-`Linear` NVFP4 export is therefore mostly the 8-bit attention, not a better
+NVFP4 export, and it lands between the all-FP8 candidate at 0.69415039 and the
+all-NVFP4 ones exactly where a hybrid should. The declared KV cache is a separate
+matter and is no longer in that number: §9 holds an unquantized cache, which is
+worth 0.02724681 of the 0.64 separating it from the complete export, so the
+attention width still carries the result. This is not a comparability failure — the key deliberately excludes
+the candidate's scheme, because ranking schemes against one reference is the
+point — but it was a labelling one until `scheme_mix` and `kv_cache_scheme` were
+added to the inspection. Component coverage cannot substitute: it counts how many
+weights are quantized, not at what width, so a hybrid and a uniform export both
+read `all`.
+
+**A flip rate is not an error rate.** Two experts disagreeing on a token is not
+by itself a wrong answer; the delta is what quantifies the cost. AutoRound's
+93.07% flip rate with a +0.275 delta is the point — high disagreement, bounded
+consequence.
+
+**Certification is about repeatability, not accuracy.** `exact_repeat: certified`
+says two runs agree bit for bit. It says nothing about whether the kernel computes
+the right thing, which is what the zero baseline (Law 1) and the reference binding
+(Laws 12 and 16) are for.
+
+## 13. The fill is a harness policy, not a vLLM fix
+
+Half of the loader change here — allocating consumed NVFP4 scales as a NaN
+sentinel instead of `torch.empty`, so an unwritten slot is detectable rather
+than arbitrary — is not ours to contribute. Three open upstream PRs already do
+it on the same files by the same mechanism: #54444 on the ModelOpt linear
+methods and fused experts, #45320 on the ModelOpt per-expert scales, #52501 on
+the linear per-block `weight_scale`. A fourth, #55073, is actively reworking the
+same compressed-tensors and ModelOpt scale code.
+
+Where we differ is the policy after detection, and the difference is deliberate
+on both sides. All three upstream PRs **reject**: they raise at load time naming
+the parameter and the affected experts. #45320 states the position outright —
+"this remains fail-fast only, it does not guess missing calibration statistics
+or add an imputation policy." Filling from the layer maximum is exactly the
+imputation policy they declined.
+
+They are right for a serving engine, and the evidence for that is in §7's
+convergence table rather than in any argument from principle. A user served a
+filled checkpoint gets a model whose numerics are substantially set by an
+invented scale, with nothing on the surface to say so. Refusing to load is the
+better failure.
+
+The harness can do what the engine should not, because it discloses. Law 17
+records the fill on the report, the substituted parameters enter the
+comparability key, and a filled candidate ranks only against others measured the
+same way. That is the whole justification, and it does not transfer to a serving
+path that has no comparability key to put anything in.
+
+Two consequences follow.
+
+**Those two candidates will stop loading on stock vLLM.** When any of the
+rejecting PRs lands, `Neural-ICE/Gemma-4-26B-A4B-it-NVFP4` and
+`bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4` will refuse at load. Their
+published numbers stay valid for what they are and become unreproducible without
+this fill, so the fill has to be maintained as a standing, disclosed divergence
+rather than treated as a fix awaiting merge.
+
+**The contribution worth making is evidence, not code.** #45320, #54444, and
+#55073 all report that no end-to-end evaluation was run: no Blackwell hardware,
+or no affected checkpoint, or both. This harness has SM120, two affected
+checkpoints, and measured numbers for what the missing scales cost. That is the
+gap in those PRs, and it is not a competing patch.
diff --git a/Qwen3.8-27B-AWQ-INT4/compliance.json b/Qwen3.8-27B-AWQ-INT4/compliance.json
index dc3aa1bf524bb733e2b6d7ffa30a285b9328a889..9a0887549929875dbebeb085e75659346ec7f65d 100644
--- a/Qwen3.8-27B-AWQ-INT4/compliance.json
+++ b/Qwen3.8-27B-AWQ-INT4/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/cyankiwi/Qwen3.8-27B-AWQ-INT4",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "7efc0eee36683b616eea4d1147b381e07bf0f1a359abc150aa43f74126a38d63",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.551912+00:00",
+  "evaluated_at": "2026-09-11T20:24:35.093765+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.03110211, deployed 0.03110211, delta -8.21671395159762e-09",
+      "detail": "trunk 0.03116103, deployed 0.03116102, delta -8.224659543698554e-09",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.03110211, median 0.00686589, max 28.04218483, 4 depth buckets",
+      "detail": "mean 0.03116102, median 0.00686925, max 25.42261505, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.09667677, strongest scientific_technical at 0.01271958, spread 7.6x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.09701459, strongest scientific_technical at 0.01271533, spread 7.6x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights 7efc0eee36683b61 as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.031102106439063696,
+  "laws_version": 15,
+  "mean_kld": 0.031161018371537674,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 28.042184829711914,
-        "mean_kld": 0.031102106439063696,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.016576553745145464,
-        "median_context_p99": 0.13887158036231995,
-        "p90_context_kld": 0.0612287023900835,
+        "max_kld": 25.42261505126953,
+        "mean_kld": 0.031161018371537674,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.016547730692520052,
+        "median_context_p99": 0.14009475708007812,
+        "p90_context_kld": 0.06096497974345977,
         "positions": 1572096,
-        "top1_agreement": 0.9387569206969549,
+        "top1_agreement": 0.9386646871437877,
         "worst_context_id": 454,
-        "worst_context_kld": 0.7412628312695106
+        "worst_context_kld": 0.7633895164572437
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 28.042184829711914,
-            "mean_kld": 0.09667677342740076,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.06543842591323477,
-            "median_context_p99": 1.2771141529083252,
-            "p90_context_kld": 0.21150114342673582,
+            "max_kld": 25.42261505126953,
+            "mean_kld": 0.09701458859996956,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.06514060327445419,
+            "median_context_p99": 1.2380735874176025,
+            "p90_context_kld": 0.2367520687257336,
             "positions": 196512,
-            "top1_agreement": 0.9359275769418661,
+            "top1_agreement": 0.9359581094284318,
             "worst_context_id": 454,
-            "worst_context_kld": 0.7412628312695106
+            "worst_context_kld": 0.7633895164572437
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 3.108367390382808
+        "relative_to_run": 3.1133317737966557
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 7.694039344787598,
-            "mean_kld": 0.045539586959129484,
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-            "median_context_kld": 0.028735511319572565,
-            "median_context_p99": 0.21844643354415894,
-            "p90_context_kld": 0.08768965578529091,
+            "max_kld": 7.141540050506592,
+            "mean_kld": 0.04554699851371887,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.028760450221885604,
+            "median_context_p99": 0.22136780619621277,
+            "p90_context_kld": 0.08768560320948965,
             "positions": 147384,
-            "top1_agreement": 0.9074865657059111,
+            "top1_agreement": 0.9072898007924877,
             "worst_context_id": 893,
-            "worst_context_kld": 0.1958269979242176
+            "worst_context_kld": 0.19577244735318067
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.4641962289066237
+        "relative_to_run": 1.461665917674927
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 5.444983005523682,
-            "mean_kld": 0.0287845435718118,
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-            "median_context_p99": 0.22981658577919006,
-            "p90_context_kld": 0.04189508559087464,
+            "max_kld": 5.054895877838135,
+            "mean_kld": 0.02878963941388972,
+            "mean_ref_top1_prob": 0.5945716883648833,
+            "median_context_kld": 0.023144045316762365,
+            "median_context_p99": 0.23143187165260315,
+            "p90_context_kld": 0.04200163334689099,
             "positions": 196512,
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+            "top1_agreement": 0.9312357515062694,
             "worst_context_id": 65,
-            "worst_context_kld": 0.14457800293637935
+            "worst_context_kld": 0.14448823853579743
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9254853406217824
+        "relative_to_run": 0.923899182967205
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 7.748706817626953,
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             "positions": 147384,
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+            "top1_agreement": 0.9387925419312816,
             "worst_context_id": 275,
-            "worst_context_kld": 0.06380190983164141
+            "worst_context_kld": 0.06302050823430973
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.7012781601479685
+        "relative_to_run": 0.7004804004606571
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 6.197922706604004,
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             "positions": 73692,
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+            "top1_agreement": 0.9397085165282527,
             "worst_context_id": 953,
-            "worst_context_kld": 0.04634572315564929
+            "worst_context_kld": 0.0471025725329215
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.6707689119426558
+        "relative_to_run": 0.6709473566094487
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 6.178435802459717,
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             "positions": 147384,
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+            "top1_agreement": 0.9303791456331759,
             "worst_context_id": 443,
-            "worst_context_kld": 0.13945793604077322
+            "worst_context_kld": 0.13891611533058892
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         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.667135610082279
+        "relative_to_run": 0.6665559399141504
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
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             "positions": 196512,
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             "worst_context_id": 667,
-            "worst_context_kld": 0.1262930956785631
+            "worst_context_kld": 0.12542341486746875
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         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5520644184031136
+        "relative_to_run": 0.5538370243153925
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 6.695211887359619,
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-            "p90_context_kld": 0.030351257627919313,
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+            "median_context_p99": 0.1766842156648636,
+            "p90_context_kld": 0.02967326012736375,
             "positions": 73692,
-            "top1_agreement": 0.8993784942734625,
+            "top1_agreement": 0.8989578244585572,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.09074432302239947
+            "worst_context_kld": 0.09236369531223719
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.5129835682736726
+        "relative_to_run": 0.5129269300403553
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
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-            "p90_context_kld": 0.020596134704460638,
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+            "mean_kld": 0.01356750565584152,
+            "mean_ref_top1_prob": 0.6582834184806026,
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+            "median_context_p99": 0.07359921932220459,
+            "p90_context_kld": 0.020571213900340293,
             "positions": 196512,
-            "top1_agreement": 0.9565624491125224,
+            "top1_agreement": 0.9566845790587852,
             "worst_context_id": 825,
-            "worst_context_kld": 0.04992035635412224
+            "worst_context_kld": 0.05003944289278355
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4367562240139744
+        "relative_to_run": 0.4353999440606863
       },
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diff --git a/Qwen3.8-27B-AWQ-INT4/inspect.json b/Qwen3.8-27B-AWQ-INT4/inspect.json
index 236f39eaaaa6613f37ea64e39ddeae89abfec02d..a995e7fdec76c76166ecab5329cb2d712cb01324 100644
--- a/Qwen3.8-27B-AWQ-INT4/inspect.json
+++ b/Qwen3.8-27B-AWQ-INT4/inspect.json
@@ -1,31 +1,37 @@
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diff --git a/Qwen3.8-27B-AWQ-INT4/manifest.json b/Qwen3.8-27B-AWQ-INT4/manifest.json
index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
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@@ -836,6 +837,7 @@
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@@ -845,17 +847,39 @@
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diff --git a/Qwen3.8-27B-AWQ-INT4/report.json b/Qwen3.8-27B-AWQ-INT4/report.json
index 61387404cf412cbbf878fd85a0d1f2f8be1bb062..8a56aa57347301ed8080d665897bf25631075899 100644
--- a/Qwen3.8-27B-AWQ-INT4/report.json
+++ b/Qwen3.8-27B-AWQ-INT4/report.json
@@ -1,8530 +1,8578 @@
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-      "5": 0.3345539967024915
+      "1": 0.936670534114965,
+      "2": 0.7974194960104217,
+      "3": 0.6295099027031428,
+      "4": 0.4674860822748738,
+      "5": 0.33427793213645984
     }
   },
   "unique_tokens": 1572864,
+  "student_weights_sha256": "7efc0eee36683b616eea4d1147b381e07bf0f1a359abc150aa43f74126a38d63",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/cyankiwi/Qwen3.8-27B-AWQ-INT4",
@@ -8811,5 +8860,7 @@
       "candidate": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3",
       "match": true
     }
-  }
+  },
+  "candidate_hf_repo": "cyankiwi/Qwen3.8-27B-AWQ-INT4",
+  "candidate_revision": "63768c10df38c0395e12ef49edac1bd539eaeeea"
 }
diff --git a/Qwen3.8-27B-AWQ-INT4/report.md b/Qwen3.8-27B-AWQ-INT4/report.md
index 7f5a34d5e2d6751fcc40f984db3e57d22460ddbe..b1fc0e0dd6612663e3ae3b92f482416aa97ca16c 100644
--- a/Qwen3.8-27B-AWQ-INT4/report.md
+++ b/Qwen3.8-27B-AWQ-INT4/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-AWQ-INT4: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.03110211** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.03116102** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.03110211 | 0.00686589 | 0.04071126 | 0.37582302 | 28.04218483 | 93.8757% |
+| 0.03116102 | 0.00686925 | 0.04070211 | 0.37613413 | 25.42261505 | 93.8665% |
 
-Reverse direction, KLD(candidate || reference): 0.03041559.
+Reverse direction, KLD(candidate || reference): 0.03049716.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.03041559.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/cyankiwi/Qwen3.8-27B-AWQ-INT4 |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | 7efc0eee36683b61 |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,32 +27,48 @@ Reverse direction, KLD(candidate || reference): 0.03041559.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
+## Expert kernels: declared against built
+
+| Property | Value |
+|---|---|
+| Declared for its experts | `unquantized` |
+| Expert implementation built | n/a |
+| Expert kernel built | n/a |
+| Expert layers carrying an activation scale | n/a |
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.09667677 | 3.11 | 0.74126283 |
-| Chinese across several content types | 72 | 55.6% | 0.04553959 | 1.46 | 0.19582700 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.02878454 | 0.93 | 0.14457800 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02181123 | 0.70 | 0.06380191 |
-| Other multilingual content | 36 | 64.5% | 0.02086233 | 0.67 | 0.04634572 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.02074932 | 0.67 | 0.13945794 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.01717037 | 0.55 | 0.12629310 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.01595487 | 0.51 | 0.09074432 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01358404 | 0.44 | 0.04992036 |
-| Scientific and technical exposition | 96 | 60.3% | 0.01271958 | 0.41 | 0.02399124 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.09701459 | 3.11 | 0.76338952 |
+| Chinese across several content types | 72 | 55.6% | 0.04554700 | 1.46 | 0.19577245 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.02878964 | 0.92 | 0.14448824 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02182768 | 0.70 | 0.06302051 |
+| Other multilingual content | 36 | 64.5% | 0.02090740 | 0.67 | 0.04710257 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.02077056 | 0.67 | 0.13891612 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.01725813 | 0.55 | 0.12542341 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.01598333 | 0.51 | 0.09236370 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01356751 | 0.44 | 0.05003944 |
+| Scientific and technical exposition | 96 | 60.3% | 0.01271533 | 0.41 | 0.02355286 |
 
 **Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 7.6x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
@@ -60,8 +76,8 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.03110211 |
-| Deployed (candidate's own head) | 0.03110211 |
+| Trunk (candidate hidden states, reference head) | 0.03116103 |
+| Deployed (candidate's own head) | 0.03116102 |
 | Head-associated delta (not additive) | -0.00000001 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]}, 'state': 'unquantized', 'static': {'ignored': True, 'lm_head_dtypes': {'lm_head.weight': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight'], 'output_weight_keys': ['lm_head.weight'], 'packed_keys': [], 'quant_method': 'compressed-tensors', 'state': 'unquantized', 'tie_word_embeddings': False}} |
@@ -70,26 +86,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.02493864 |
-| 512–1023 | 393216 | 0.02846178 |
-| 1024–1535 | 393216 | 0.03394437 |
-| 1536–2046 | 392448 | 0.03707531 |
+| 0–511 | 393216 | 0.02499198 |
+| 512–1023 | 393216 | 0.02859100 |
+| 1024–1535 | 393216 | 0.03378435 |
+| 1536–2046 | 392448 | 0.03728870 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.03377519 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.04387957 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.04991703 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.03557873 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.00656489 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.03373890 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.04475667 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.04847260 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.03574825 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.00689268 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 93.6654% | 79.7744% | 62.9561% | 46.7627% | 33.4554% |
+| 93.6671% | 79.7419% | 62.9510% | 46.7486% | 33.4278% |
 
 ## Law compliance
 
@@ -101,22 +117,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.03110211, deployed 0.03110211, delta -8.21671395159762e-09 |
-| 9 | Tail and depth disclosure | PASS | mean 0.03110211, median 0.00686589, max 28.04218483, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.03116103, deployed 0.03116102, delta -8.224659543698554e-09 |
+| 9 | Tail and depth disclosure | PASS | mean 0.03116102, median 0.00686925, max 25.42261505, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.09667677, strongest scientific_technical at 0.01271958, spread 7.6x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.09701459, strongest scientific_technical at 0.01271533, spread 7.6x |
+| 16 | Candidate weight binding | PASS | scored weights 7efc0eee36683b61 as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +139,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +147,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +170,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +189,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-AWQ-INT4/report.md` | 12.34 KiB | This document. |
-| `Qwen3.8-27B-AWQ-INT4/report.json` | 254.91 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-AWQ-INT4/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-AWQ-INT4/compliance.json` | 13.04 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-AWQ-INT4/report.md` | 13.18 KiB | This document. |
+| `Qwen3.8-27B-AWQ-INT4/report.json` | 257.43 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-AWQ-INT4/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-AWQ-INT4/compliance.json` | 12.30 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-AWQ-INT4/strata.json b/Qwen3.8-27B-AWQ-INT4/strata.json
index ae4d5225de5447b3ed51c751cf973b962b6c6b9b..2657305a8299a075f2aea7d7717aa650f7cf52fc 100644
--- a/Qwen3.8-27B-AWQ-INT4/strata.json
+++ b/Qwen3.8-27B-AWQ-INT4/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 28.042184829711914,
-            "mean_kld": 0.09667677342740076,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.06543842591323477,
-            "median_context_p99": 1.2771141529083252,
-            "p90_context_kld": 0.21150114342673582,
+            "max_kld": 25.42261505126953,
+            "mean_kld": 0.09701458859996956,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.06514060327445419,
+            "median_context_p99": 1.2380735874176025,
+            "p90_context_kld": 0.2367520687257336,
             "positions": 196512,
-            "top1_agreement": 0.9359275769418661,
+            "top1_agreement": 0.9359581094284318,
             "worst_context_id": 454,
-            "worst_context_kld": 0.7412628312695106
+            "worst_context_kld": 0.7633895164572437
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 3.108367390382808
+        "relative_to_run": 3.1133317737966557
       },
       {
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 7.694039344787598,
-            "mean_kld": 0.07473911160197923,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.05613039184561264,
-            "median_context_p99": 0.4171757102012634,
-            "p90_context_kld": 0.17763363437416263,
+            "max_kld": 7.141540050506592,
+            "mean_kld": 0.07477739616771742,
+            "mean_ref_top1_prob": 0.6040861921682756,
+            "median_context_kld": 0.056872185852744755,
+            "median_context_p99": 0.40756842494010925,
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         "key": "scientific_papers",
         "label": "scientific_papers",
-        "relative_to_run": 0.40896194714454503
+        "relative_to_run": 0.4080525238290834
       }
     ],
     "stratum": [
@@ -390,217 +390,217 @@
         "cells": {
           "deployed": {
             "contexts": 96,
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-            "mean_kld": 0.09667677342740076,
-            "mean_ref_top1_prob": 0.7352292693891235,
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-            "median_context_p99": 1.2771141529083252,
-            "p90_context_kld": 0.21150114342673582,
+            "max_kld": 25.42261505126953,
+            "mean_kld": 0.09701458859996956,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.06514060327445419,
+            "median_context_p99": 1.2380735874176025,
+            "p90_context_kld": 0.2367520687257336,
             "positions": 196512,
-            "top1_agreement": 0.9359275769418661,
+            "top1_agreement": 0.9359581094284318,
             "worst_context_id": 454,
-            "worst_context_kld": 0.7412628312695106
+            "worst_context_kld": 0.7633895164572437
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 3.108367390382808
+        "relative_to_run": 3.1133317737966557
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
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-            "p90_context_kld": 0.08768965578529091,
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             "positions": 147384,
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+            "top1_agreement": 0.9072898007924877,
             "worst_context_id": 893,
-            "worst_context_kld": 0.1958269979242176
+            "worst_context_kld": 0.19577244735318067
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.4641962289066237
+        "relative_to_run": 1.461665917674927
       },
       {
         "cells": {
           "deployed": {
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             "positions": 196512,
-            "top1_agreement": 0.9313884139390979,
+            "top1_agreement": 0.9312357515062694,
             "worst_context_id": 65,
-            "worst_context_kld": 0.14457800293637935
+            "worst_context_kld": 0.14448823853579743
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9254853406217824
+        "relative_to_run": 0.923899182967205
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
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             "positions": 147384,
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             "worst_context_id": 275,
-            "worst_context_kld": 0.06380190983164141
+            "worst_context_kld": 0.06302050823430973
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.7012781601479685
+        "relative_to_run": 0.7004804004606571
       },
       {
         "cells": {
           "deployed": {
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-            "median_context_p99": 0.14613811671733856,
-            "p90_context_kld": 0.03870429632334617,
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+            "median_context_kld": 0.017961478379677633,
+            "median_context_p99": 0.15575742721557617,
+            "p90_context_kld": 0.03910626657243996,
             "positions": 73692,
-            "top1_agreement": 0.9396949465342235,
+            "top1_agreement": 0.9397085165282527,
             "worst_context_id": 953,
-            "worst_context_kld": 0.04634572315564929
+            "worst_context_kld": 0.0471025725329215
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.6707689119426558
+        "relative_to_run": 0.6709473566094487
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 6.178435802459717,
-            "mean_kld": 0.020749322754068735,
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-            "median_context_p99": 0.10535755008459091,
-            "p90_context_kld": 0.024722712384587904,
+            "max_kld": 5.839554786682129,
+            "mean_kld": 0.020770561889322404,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.01741421428303659,
+            "median_context_p99": 0.10827115178108215,
+            "p90_context_kld": 0.0246539960080243,
             "positions": 147384,
-            "top1_agreement": 0.930820170439125,
+            "top1_agreement": 0.9303791456331759,
             "worst_context_id": 443,
-            "worst_context_kld": 0.13945793604077322
+            "worst_context_kld": 0.13891611533058892
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.667135610082279
+        "relative_to_run": 0.6665559399141504
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 5.842103004455566,
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-            "median_context_p99": 0.1271955668926239,
-            "p90_context_kld": 0.031480838601946864,
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+            "median_context_p99": 0.13722017407417297,
+            "p90_context_kld": 0.03180781198777742,
             "positions": 196512,
-            "top1_agreement": 0.9632948624002605,
+            "top1_agreement": 0.9635849210226347,
             "worst_context_id": 667,
-            "worst_context_kld": 0.1262930956785631
+            "worst_context_kld": 0.12542341486746875
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5520644184031136
+        "relative_to_run": 0.5538370243153925
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 6.695211887359619,
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-            "median_context_p99": 0.16844584047794342,
-            "p90_context_kld": 0.030351257627919313,
+            "max_kld": 7.090137481689453,
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+            "median_context_kld": 0.011221187727829521,
+            "median_context_p99": 0.1766842156648636,
+            "p90_context_kld": 0.02967326012736375,
             "positions": 73692,
-            "top1_agreement": 0.8993784942734625,
+            "top1_agreement": 0.8989578244585572,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.09074432302239947
+            "worst_context_kld": 0.09236369531223719
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.5129835682736726
+        "relative_to_run": 0.5129269300403553
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 23.66070556640625,
-            "mean_kld": 0.01358403856720618,
-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.010927746679615437,
-            "median_context_p99": 0.07393613457679749,
-            "p90_context_kld": 0.020596134704460638,
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+            "mean_kld": 0.01356750565584152,
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+            "median_context_kld": 0.010833755501125017,
+            "median_context_p99": 0.07359921932220459,
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             "positions": 196512,
-            "top1_agreement": 0.9565624491125224,
+            "top1_agreement": 0.9566845790587852,
             "worst_context_id": 825,
-            "worst_context_kld": 0.04992035635412224
+            "worst_context_kld": 0.05003944289278355
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4367562240139744
+        "relative_to_run": 0.4353999440606863
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 10.160826683044434,
-            "mean_kld": 0.012719578009616381,
-            "mean_ref_top1_prob": 0.6033561622407548,
-            "median_context_kld": 0.012256373709563865,
-            "median_context_p99": 0.09276100993156433,
-            "p90_context_kld": 0.016576553745145464,
+            "max_kld": 10.008549690246582,
+            "mean_kld": 0.012715332191590384,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.012210063005714826,
+            "median_context_p99": 0.09308900684118271,
+            "p90_context_kld": 0.016547730692520052,
             "positions": 196512,
-            "top1_agreement": 0.9501913369158117,
+            "top1_agreement": 0.9500081419964175,
             "worst_context_id": 246,
-            "worst_context_kld": 0.023991239175797346
+            "worst_context_kld": 0.02355286353559141
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.40896194714454503
+        "relative_to_run": 0.4080525238290834
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 28.042184829711914,
-      "mean_kld": 0.031102106439063696,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.016576553745145464,
-      "median_context_p99": 0.13887158036231995,
-      "p90_context_kld": 0.0612287023900835,
+      "max_kld": 25.42261505126953,
+      "mean_kld": 0.031161018371537674,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.016547730692520052,
+      "median_context_p99": 0.14009475708007812,
+      "p90_context_kld": 0.06096497974345977,
       "positions": 1572096,
-      "top1_agreement": 0.9387569206969549,
+      "top1_agreement": 0.9386646871437877,
       "worst_context_id": 454,
-      "worst_context_kld": 0.7412628312695106
+      "worst_context_kld": 0.7633895164572437
     }
   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-AWQ-INT4/strata.md b/Qwen3.8-27B-AWQ-INT4/strata.md
index e5b7fcaebf69e4002b67da17f71708962e3ec867..957c59a3251475dcac1f9a4cc02c1f7005a408f2 100644
--- a/Qwen3.8-27B-AWQ-INT4/strata.md
+++ b/Qwen3.8-27B-AWQ-INT4/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-AWQ-INT4
 
-768 contexts, 1572096 scored positions, mean 0.03110211, reference top-1 64.2%, top-1 agreement 93.8757%.
+768 contexts, 1572096 scored positions, mean 0.03116102, reference top-1 64.2%, top-1 agreement 93.8665%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.09667677 | 3.11 | 0.06543843 | 0.21150114 | 0.741263 |
-| Chinese across several content types | 72 | 55.6% | 0.04553959 | 1.46 | 0.02873551 | 0.08768966 | 0.195827 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.02878454 | 0.93 | 0.02293439 | 0.04189509 | 0.144578 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02181123 | 0.70 | 0.01682588 | 0.04464412 | 0.063802 |
-| Other multilingual content | 36 | 64.5% | 0.02086233 | 0.67 | 0.01816635 | 0.03870430 | 0.046346 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.02074932 | 0.67 | 0.01744899 | 0.02472271 | 0.139458 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.01717037 | 0.55 | 0.01105594 | 0.03148084 | 0.126293 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.01595487 | 0.51 | 0.01152547 | 0.03035126 | 0.090744 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01358404 | 0.44 | 0.01092775 | 0.02059613 | 0.049920 |
-| Scientific and technical exposition | 96 | 60.3% | 0.01271958 | 0.41 | 0.01225637 | 0.01657655 | 0.023991 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.09701459 | 3.11 | 0.06514060 | 0.23675207 | 0.763390 |
+| Chinese across several content types | 72 | 55.6% | 0.04554700 | 1.46 | 0.02876045 | 0.08768560 | 0.195772 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.02878964 | 0.92 | 0.02314405 | 0.04200163 | 0.144488 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02182768 | 0.70 | 0.01663014 | 0.04534338 | 0.063021 |
+| Other multilingual content | 36 | 64.5% | 0.02090740 | 0.67 | 0.01796148 | 0.03910627 | 0.047103 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.02077056 | 0.67 | 0.01741421 | 0.02465400 | 0.138916 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.01725813 | 0.55 | 0.01116276 | 0.03180781 | 0.125423 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.01598333 | 0.51 | 0.01122119 | 0.02967326 | 0.092364 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01356751 | 0.44 | 0.01083376 | 0.02057121 | 0.050039 |
+| Scientific and technical exposition | 96 | 60.3% | 0.01271533 | 0.41 | 0.01221006 | 0.01654773 | 0.023553 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.09667677 | 3.11 | 0.06543843 | 0.21150114 | 0.741263 |
-| wikisource_zh | 33 | 60.4% | 0.07473911 | 2.40 | 0.05613039 | 0.17763363 | 0.195827 |
-| wikipedia_de | 7 | 73.6% | 0.03584960 | 1.15 | 0.03870430 | 0.04634572 | 0.046346 |
-| regulations | 23 | 72.3% | 0.03121116 | 1.00 | 0.03553082 | 0.06033169 | 0.063802 |
-| wikipedia_en | 96 | 59.5% | 0.02878454 | 0.93 | 0.02293439 | 0.04189509 | 0.144578 |
-| wikipedia_zh | 39 | 51.5% | 0.02083230 | 0.67 | 0.01954590 | 0.03069909 | 0.034221 |
-| public_domain_books | 72 | 50.6% | 0.02074932 | 0.67 | 0.01744899 | 0.02472271 | 0.139458 |
-| public_domain_review | 26 | 51.8% | 0.02022364 | 0.65 | 0.01705912 | 0.03348374 | 0.052137 |
-| wikipedia_ja | 7 | 57.0% | 0.01883299 | 0.61 | 0.01816635 | 0.02322967 | 0.023230 |
-| wikipedia_es | 7 | 59.2% | 0.01855669 | 0.60 | 0.01824277 | 0.02274759 | 0.022748 |
-| wikipedia_cs | 6 | 68.9% | 0.01839445 | 0.59 | 0.01948587 | 0.02192389 | 0.021924 |
-| github_code | 52 | 85.8% | 0.01794543 | 0.58 | 0.01065552 | 0.03161437 | 0.126293 |
-| stackv2 | 44 | 72.0% | 0.01625439 | 0.52 | 0.01169394 | 0.02565606 | 0.105578 |
-| starcoder_structured | 36 | 70.7% | 0.01595487 | 0.51 | 0.01152547 | 0.03035126 | 0.090744 |
-| wikipedia_ru | 6 | 66.6% | 0.01451227 | 0.47 | 0.01507330 | 0.01870660 | 0.018707 |
-| open_news | 23 | 53.8% | 0.01420596 | 0.46 | 0.01438941 | 0.01933549 | 0.021542 |
-| wikipedia_fr | 3 | 60.2% | 0.01364282 | 0.44 | 0.01312988 | 0.01600077 | 0.016001 |
-| libretexts | 96 | 65.8% | 0.01358404 | 0.44 | 0.01092775 | 0.02059613 | 0.049920 |
-| scientific_papers | 96 | 60.3% | 0.01271958 | 0.41 | 0.01225637 | 0.01657655 | 0.023991 |
+| wildchat | 96 | 73.5% | 0.09701459 | 3.11 | 0.06514060 | 0.23675207 | 0.763390 |
+| wikisource_zh | 33 | 60.4% | 0.07477740 | 2.40 | 0.05687219 | 0.17603352 | 0.195772 |
+| wikipedia_de | 7 | 73.6% | 0.03609257 | 1.16 | 0.03910627 | 0.04710257 | 0.047103 |
+| regulations | 23 | 72.3% | 0.03122357 | 1.00 | 0.03542480 | 0.06029713 | 0.063021 |
+| wikipedia_en | 96 | 59.5% | 0.02878964 | 0.92 | 0.02314405 | 0.04200163 | 0.144488 |
+| wikipedia_zh | 39 | 51.5% | 0.02081359 | 0.67 | 0.01960897 | 0.03036927 | 0.034152 |
+| public_domain_books | 72 | 50.6% | 0.02077056 | 0.67 | 0.01741421 | 0.02465400 | 0.138916 |
+| public_domain_review | 26 | 51.8% | 0.02021378 | 0.65 | 0.01699244 | 0.03378731 | 0.052074 |
+| wikipedia_ja | 7 | 57.0% | 0.01878011 | 0.60 | 0.01790623 | 0.02311663 | 0.023117 |
+| wikipedia_es | 7 | 59.2% | 0.01852559 | 0.59 | 0.01817512 | 0.02282774 | 0.022828 |
+| wikipedia_cs | 6 | 68.9% | 0.01849641 | 0.59 | 0.01947075 | 0.02206932 | 0.022069 |
+| github_code | 52 | 85.8% | 0.01799332 | 0.58 | 0.01081438 | 0.03237369 | 0.125423 |
+| stackv2 | 44 | 72.0% | 0.01638926 | 0.53 | 0.01178116 | 0.02981188 | 0.104514 |
+| starcoder_structured | 36 | 70.7% | 0.01598333 | 0.51 | 0.01122119 | 0.02967326 | 0.092364 |
+| wikipedia_ru | 6 | 66.6% | 0.01452935 | 0.47 | 0.01538574 | 0.01873064 | 0.018731 |
+| open_news | 23 | 53.8% | 0.01425621 | 0.46 | 0.01429680 | 0.01953881 | 0.022070 |
+| wikipedia_fr | 3 | 60.2% | 0.01357468 | 0.44 | 0.01304863 | 0.01588824 | 0.015888 |
+| libretexts | 96 | 65.8% | 0.01356751 | 0.44 | 0.01083376 | 0.02057121 | 0.050039 |
+| scientific_papers | 96 | 60.3% | 0.01271533 | 0.41 | 0.01221006 | 0.01654773 | 0.023553 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.09667677, 3.11x the run mean of 0.03110211 over 96 context(s).
-- Strongest domain: **Scientific and technical exposition** at 0.01271958, 0.41x the run mean. The spread across domains is 7.6x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.09701459, 3.11x the run mean of 0.03116102 over 96 context(s).
+- Strongest domain: **Scientific and technical exposition** at 0.01271533, 0.41x the run mean. The spread across domains is 7.6x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.741263 against a median of 0.06543843 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.763390 against a median of 0.06514060 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/Qwen3.8-27B-FP8/compliance.json b/Qwen3.8-27B-FP8/compliance.json
index 532cbd2c2158a08db36516f16593e47a3462cb12..362248c399d928454036b59338559cdfd27af328 100644
--- a/Qwen3.8-27B-FP8/compliance.json
+++ b/Qwen3.8-27B-FP8/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/Qwen/Qwen3.8-27B-FP8",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "dffe578ba76dead963135c7e988e25acefdfc3cf9e19b8352653683b4edf199e",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:03.993488+00:00",
+  "evaluated_at": "2026-09-11T20:24:18.945645+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.01104734, deployed 0.01104734, delta 3.8109852079637463e-10",
+      "detail": "trunk 0.01097467, deployed 0.01097467, delta 4.072260306742237e-10",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.01104734, median 0.00189959, max 24.25029755, 4 depth buckets",
+      "detail": "mean 0.01097467, median 0.00190434, max 29.40929031, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.05388702, strongest scientific_technical at 0.00327748, spread 16.4x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.05343841, strongest scientific_technical at 0.00325771, spread 16.4x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights dffe578ba76dead9 as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.01104733806059744,
+  "laws_version": 15,
+  "mean_kld": 0.010974670174294718,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 24.25029754638672,
-        "mean_kld": 0.011047338060597442,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.003959233488364337,
-        "median_context_p99": 0.0307154580950737,
-        "p90_context_kld": 0.018751727010197644,
+        "max_kld": 29.409290313720703,
+        "mean_kld": 0.01097467017429472,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.003896204956611177,
+        "median_context_p99": 0.030610591173171997,
+        "p90_context_kld": 0.01781900445289071,
         "positions": 1572096,
-        "top1_agreement": 0.9670777102670575,
+        "top1_agreement": 0.9670738937062368,
         "worst_context_id": 454,
-        "worst_context_kld": 0.4895784940445628
+        "worst_context_kld": 0.45672114841615463
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 23.4235782623291,
-            "mean_kld": 0.05388702262702585,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.032507454946453476,
-            "median_context_p99": 0.5249273777008057,
-            "p90_context_kld": 0.1189590889010681,
+            "max_kld": 29.409290313720703,
+            "mean_kld": 0.05343841309902987,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.03457746948537548,
+            "median_context_p99": 0.48533299565315247,
+            "p90_context_kld": 0.13830447681183197,
             "positions": 196512,
-            "top1_agreement": 0.9609540384302231,
+            "top1_agreement": 0.9609489496824621,
             "worst_context_id": 454,
-            "worst_context_kld": 0.4895784940445628
+            "worst_context_kld": 0.45672114841615463
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 4.877828697867478
+        "relative_to_run": 4.86925003215088
       },
       {
         "cells": {
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-            "mean_kld": 0.008991718850348643,
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-            "median_context_kld": 0.005429992133758688,
-            "median_context_p99": 0.04178592562675476,
-            "p90_context_kld": 0.01635930600748916,
+            "max_kld": 1.4653857946395874,
+            "mean_kld": 0.008966686634686733,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.005603489085350864,
+            "median_context_p99": 0.043331339955329895,
+            "p90_context_kld": 0.016244145148751315,
             "positions": 147384,
-            "top1_agreement": 0.9569220539542963,
+            "top1_agreement": 0.9570238289095153,
             "worst_context_id": 893,
-            "worst_context_kld": 0.03802517904088536
+            "worst_context_kld": 0.03694091126483796
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 0.8139262871315055
+        "relative_to_run": 0.8170347256256356
       },
       {
         "cells": {
           "deployed": {
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-            "p90_context_kld": 0.009174476160926261,
+            "max_kld": 2.1070759296417236,
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+            "top1_agreement": 0.9654321364598599,
+            "worst_context_id": 65,
+            "worst_context_kld": 0.03102557076205901
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         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.5782520629224144
+        "relative_to_run": 0.5764257207992749
       },
       {
         "cells": {
           "deployed": {
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             "worst_context_id": 275,
-            "worst_context_kld": 0.014223638338661505
+            "worst_context_kld": 0.014266868176758726
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         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.44261427902602124
+        "relative_to_run": 0.44486659150110247
       },
       {
         "cells": {
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         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
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         "cells": {
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+            "worst_context_kld": 0.027905596454365553
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         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
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+        "relative_to_run": 0.42823500924630736
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         "cells": {
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+            "worst_context_kld": 0.026218505325394093
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         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
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+        "relative_to_run": 0.38500695875735247
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         "cells": {
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             "worst_context_id": 1004,
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+            "worst_context_kld": 0.01781900445289071
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         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.3406652285884115
+        "relative_to_run": 0.340120100401426
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       {
         "cells": {
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             "worst_context_id": 709,
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+            "worst_context_kld": 0.02107575118209227
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         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
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+        "relative_to_run": 0.3161807248552924
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       {
         "cells": {
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+            "worst_context_id": 174,
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         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.2966756557300793
+        "relative_to_run": 0.2968386928155199
       }
     ]
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diff --git a/Qwen3.8-27B-FP8/inspect.json b/Qwen3.8-27B-FP8/inspect.json
index e7c740163694bac6bdd27f88de1d85e7ba82179b..261f33e0ff17c9f910592a6777b4b6e855158ae9 100644
--- a/Qwen3.8-27B-FP8/inspect.json
+++ b/Qwen3.8-27B-FP8/inspect.json
@@ -1,38 +1,44 @@
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diff --git a/Qwen3.8-27B-FP8/manifest.json b/Qwen3.8-27B-FP8/manifest.json
index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
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diff --git a/Qwen3.8-27B-FP8/report.json b/Qwen3.8-27B-FP8/report.json
index 2c7e216eaa940a2a6d45be8e6220f932f06558d0..ce9e06acc21aecd2e161deaaa0c969628eba4222 100644
--- a/Qwen3.8-27B-FP8/report.json
+++ b/Qwen3.8-27B-FP8/report.json
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+    "p90_kld": 0.009400044102221727,
+    "p99_kld": 0.0859455574303867,
     "score_from": 0,
-    "top1_agreement": 0.9670777102670575,
+    "top1_agreement": 0.9670738937062368,
     "topk_agreement": {
-      "1": 0.9648609245236932,
-      "2": 0.8847455880556913,
-      "3": 0.7763291809151603,
-      "4": 0.6564300144520436,
-      "5": 0.5437225207620908
+      "1": 0.9648208506350757,
+      "2": 0.884599922651034,
+      "3": 0.7765499053492917,
+      "4": 0.6571863295880149,
+      "5": 0.5440590142077838
     }
   },
   "unique_tokens": 1572864,
+  "student_weights_sha256": "dffe578ba76dead963135c7e988e25acefdfc3cf9e19b8352653683b4edf199e",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/Qwen/Qwen3.8-27B-FP8",
@@ -8815,5 +8862,7 @@
       "candidate": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3",
       "match": true
     }
-  }
+  },
+  "candidate_hf_repo": "Qwen/Qwen3.8-27B-FP8",
+  "candidate_revision": "017b9c7af6b5689d5dd426a76e0bc077eb5ca20a"
 }
diff --git a/Qwen3.8-27B-FP8/report.md b/Qwen3.8-27B-FP8/report.md
index afedcfa77d542437b93c1186c55b60c51a41aaf7..03f5e1acac724990f5b795e175dd9b910cd4882f 100644
--- a/Qwen3.8-27B-FP8/report.md
+++ b/Qwen3.8-27B-FP8/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-FP8: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.01104734** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.01097467** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.01104734 | 0.00189959 | 0.00940180 | 0.08646069 | 24.25029755 | 96.7078% |
+| 0.01097467 | 0.00190434 | 0.00940006 | 0.08594550 | 29.40929031 | 96.7074% |
 
-Reverse direction, KLD(candidate || reference): 0.01087530.
+Reverse direction, KLD(candidate || reference): 0.01099299.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.01087530.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B-FP8 |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | dffe578ba76dead9 |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,32 +27,39 @@ Reverse direction, KLD(candidate || reference): 0.01087530.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.05388702 | 4.88 | 0.48957849 |
-| Chinese across several content types | 72 | 55.6% | 0.00899172 | 0.81 | 0.03802518 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.00638815 | 0.58 | 0.03374026 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.00488971 | 0.44 | 0.01422364 |
-| Other multilingual content | 36 | 64.5% | 0.00473731 | 0.43 | 0.01158885 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.00471401 | 0.43 | 0.02722879 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.00413774 | 0.37 | 0.02385255 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.00376344 | 0.34 | 0.01916827 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.00355396 | 0.32 | 0.02797772 |
-| Scientific and technical exposition | 96 | 60.3% | 0.00327748 | 0.30 | 0.00580530 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.05343841 | 4.87 | 0.45672115 |
+| Chinese across several content types | 72 | 55.6% | 0.00896669 | 0.82 | 0.03694091 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.00632608 | 0.58 | 0.03102557 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.00488226 | 0.44 | 0.01426687 |
+| Other multilingual content | 36 | 64.5% | 0.00471620 | 0.43 | 0.01122486 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.00469974 | 0.43 | 0.02790560 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.00422532 | 0.39 | 0.02621851 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.00373271 | 0.34 | 0.01781900 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.00346998 | 0.32 | 0.02107575 |
+| Scientific and technical exposition | 96 | 60.3% | 0.00325771 | 0.30 | 0.00582470 |
 
 **Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 16.4x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
@@ -60,8 +67,8 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.01104734 |
-| Deployed (candidate's own head) | 0.01104734 |
+| Trunk (candidate hidden states, reference head) | 0.01097467 |
+| Deployed (candidate's own head) | 0.01097467 |
 | Head-associated delta (not additive) | 0.00000000 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]}, 'state': 'unquantized', 'static': {'ignored': True, 'lm_head_dtypes': {'lm_head.weight': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight'], 'output_weight_keys': ['lm_head.weight'], 'packed_keys': [], 'quant_method': 'fp8', 'state': 'unquantized', 'tie_word_embeddings': False}} |
@@ -70,26 +77,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.00649119 |
-| 512–1023 | 393216 | 0.00882887 |
-| 1024–1535 | 393216 | 0.01316371 |
-| 1536–2046 | 392448 | 0.01571470 |
+| 0–511 | 393216 | 0.00655353 |
+| 512–1023 | 393216 | 0.00893310 |
+| 1024–1535 | 393216 | 0.01307841 |
+| 1536–2046 | 392448 | 0.01534217 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.00839035 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.01436432 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.01933117 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.01405977 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.00340925 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.00832364 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.01508628 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.01818135 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.01448346 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.00310732 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 96.4861% | 88.4746% | 77.6329% | 65.6430% | 54.3723% |
+| 96.4821% | 88.4600% | 77.6550% | 65.7186% | 54.4059% |
 
 ## Law compliance
 
@@ -101,22 +108,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.01104734, deployed 0.01104734, delta 3.8109852079637463e-10 |
-| 9 | Tail and depth disclosure | PASS | mean 0.01104734, median 0.00189959, max 24.25029755, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.01097467, deployed 0.01097467, delta 4.072260306742237e-10 |
+| 9 | Tail and depth disclosure | PASS | mean 0.01097467, median 0.00190434, max 29.40929031, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.05388702, strongest scientific_technical at 0.00327748, spread 16.4x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.05343841, strongest scientific_technical at 0.00325771, spread 16.4x |
+| 16 | Candidate weight binding | PASS | scored weights dffe578ba76dead9 as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +130,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +138,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +161,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +180,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-FP8/report.md` | 12.30 KiB | This document. |
-| `Qwen3.8-27B-FP8/report.json` | 256.86 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-FP8/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-FP8/compliance.json` | 13.06 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-FP8/report.md` | 13.13 KiB | This document. |
+| `Qwen3.8-27B-FP8/report.json` | 259.25 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-FP8/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-FP8/compliance.json` | 12.33 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-FP8/strata.json b/Qwen3.8-27B-FP8/strata.json
index 876fa4f91390533b5ea829158fbe02a3696ef1ce..bc67faf49b961dd2407ab7123fc178bad63ffc7a 100644
--- a/Qwen3.8-27B-FP8/strata.json
+++ b/Qwen3.8-27B-FP8/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 23.4235782623291,
-            "mean_kld": 0.05388702262702585,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.032507454946453476,
-            "median_context_p99": 0.5249273777008057,
-            "p90_context_kld": 0.1189590889010681,
+            "max_kld": 29.409290313720703,
+            "mean_kld": 0.05343841309902987,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.03457746948537548,
+            "median_context_p99": 0.48533299565315247,
+            "p90_context_kld": 0.13830447681183197,
             "positions": 196512,
-            "top1_agreement": 0.9609540384302231,
+            "top1_agreement": 0.9609489496824621,
             "worst_context_id": 454,
-            "worst_context_kld": 0.4895784940445628
+            "worst_context_kld": 0.45672114841615463
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 4.877828697867478
+        "relative_to_run": 4.86925003215088
       },
       {
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 1.8940415382385254,
-            "mean_kld": 0.014494251205789503,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.011235134472533293,
-            "median_context_p99": 0.08464735746383667,
-            "p90_context_kld": 0.03288934683322978,
+            "max_kld": 1.4653857946395874,
+            "mean_kld": 0.01450307849202896,
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+        "relative_to_run": 0.2968386928155199
       },
       {
         "cells": {
           "deployed": {
             "contexts": 3,
-            "max_kld": 0.705543577671051,
-            "mean_kld": 0.003212107705057633,
-            "mean_ref_top1_prob": 0.6023603953518782,
-            "median_context_kld": 0.0031552203512278053,
-            "median_context_p99": 0.021008508279919624,
-            "p90_context_kld": 0.0034167893172513076,
+            "max_kld": 0.32339420914649963,
+            "mean_kld": 0.003105845848568889,
+            "mean_ref_top1_prob": 0.6023746847155801,
+            "median_context_kld": 0.0030011382400890194,
+            "median_context_p99": 0.020637474954128265,
+            "p90_context_kld": 0.0035378718844978033,
             "positions": 6141,
-            "top1_agreement": 0.9755740107474352,
+            "top1_agreement": 0.973945611463931,
             "worst_context_id": 967,
-            "worst_context_kld": 0.0034167893172513076
+            "worst_context_kld": 0.0035378718844978033
           }
         },
         "key": "wikipedia_fr",
         "label": "wikipedia_fr",
-        "relative_to_run": 0.2907585236767817
+        "relative_to_run": 0.28300129290842074
       }
     ],
     "stratum": [
@@ -390,217 +390,217 @@
         "cells": {
           "deployed": {
             "contexts": 96,
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-            "mean_kld": 0.05388702262702585,
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-            "median_context_p99": 0.5249273777008057,
-            "p90_context_kld": 0.1189590889010681,
+            "max_kld": 29.409290313720703,
+            "mean_kld": 0.05343841309902987,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.03457746948537548,
+            "median_context_p99": 0.48533299565315247,
+            "p90_context_kld": 0.13830447681183197,
             "positions": 196512,
-            "top1_agreement": 0.9609540384302231,
+            "top1_agreement": 0.9609489496824621,
             "worst_context_id": 454,
-            "worst_context_kld": 0.4895784940445628
+            "worst_context_kld": 0.45672114841615463
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 4.877828697867478
+        "relative_to_run": 4.86925003215088
       },
       {
         "cells": {
           "deployed": {
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-            "median_context_p99": 0.04178592562675476,
-            "p90_context_kld": 0.01635930600748916,
+            "max_kld": 1.4653857946395874,
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+            "median_context_kld": 0.005603489085350864,
+            "median_context_p99": 0.043331339955329895,
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             "positions": 147384,
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+            "top1_agreement": 0.9570238289095153,
             "worst_context_id": 893,
-            "worst_context_kld": 0.03802517904088536
+            "worst_context_kld": 0.03694091126483796
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 0.8139262871315055
+        "relative_to_run": 0.8170347256256356
       },
       {
         "cells": {
           "deployed": {
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-            "p90_context_kld": 0.009174476160926261,
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             "positions": 196512,
-            "top1_agreement": 0.9654779351897085,
-            "worst_context_id": 6,
-            "worst_context_kld": 0.03374026097547736
+            "top1_agreement": 0.9654321364598599,
+            "worst_context_id": 65,
+            "worst_context_kld": 0.03102557076205901
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.5782520629224144
+        "relative_to_run": 0.5764257207992749
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
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             "positions": 147384,
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             "worst_context_id": 275,
-            "worst_context_kld": 0.014223638338661505
+            "worst_context_kld": 0.014266868176758726
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.44261427902602124
+        "relative_to_run": 0.44486659150110247
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
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-            "median_context_p99": 0.03270211070775986,
-            "p90_context_kld": 0.009271094984799227,
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+            "median_context_kld": 0.003908452587081799,
+            "median_context_p99": 0.029179947450757027,
+            "p90_context_kld": 0.008553707324987884,
             "positions": 73692,
-            "top1_agreement": 0.9693725234760897,
+            "top1_agreement": 0.9691011235955056,
             "worst_context_id": 953,
-            "worst_context_kld": 0.011588845220650184
+            "worst_context_kld": 0.01122486185099774
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.42881869268476214
+        "relative_to_run": 0.4297349018436293
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 1.4300388097763062,
-            "mean_kld": 0.004714006706887872,
-            "mean_ref_top1_prob": 0.5056158503386161,
-            "median_context_kld": 0.004113897056717312,
-            "median_context_p99": 0.024136444553732872,
-            "p90_context_kld": 0.00561418505282599,
+            "max_kld": 1.165907382965088,
+            "mean_kld": 0.004699737983564273,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.00391996123187938,
+            "median_context_p99": 0.024366630241274834,
+            "p90_context_kld": 0.005416538875010646,
             "positions": 147384,
-            "top1_agreement": 0.9647315855181023,
+            "top1_agreement": 0.964704445530044,
             "worst_context_id": 443,
-            "worst_context_kld": 0.027228787454472464
+            "worst_context_kld": 0.027905596454365553
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.4267097359590476
+        "relative_to_run": 0.42823500924630736
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 2.663503885269165,
-            "mean_kld": 0.004137738224923952,
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-            "median_context_p99": 0.03253652900457382,
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+            "median_context_p99": 0.03228462114930153,
+            "p90_context_kld": 0.00674611330592368,
             "positions": 196512,
-            "top1_agreement": 0.9797518726591761,
+            "top1_agreement": 0.9793040628562123,
             "worst_context_id": 667,
-            "worst_context_kld": 0.023852551947544348
+            "worst_context_kld": 0.026218505325394093
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.37454617594097434
+        "relative_to_run": 0.38500695875735247
       },
       {
         "cells": {
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-            "median_context_p99": 0.035675883293151855,
-            "p90_context_kld": 0.006300030598680195,
+            "max_kld": 1.8485336303710938,
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+            "mean_ref_top1_prob": 0.7071550040111672,
+            "median_context_kld": 0.0027118550407615144,
+            "median_context_p99": 0.03762500360608101,
+            "p90_context_kld": 0.0068965462201931475,
             "positions": 73692,
-            "top1_agreement": 0.9357189382836671,
+            "top1_agreement": 0.9357460782717255,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.019168267112011875
+            "worst_context_kld": 0.01781900445289071
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.3406652285884115
+        "relative_to_run": 0.340120100401426
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 24.25029754638672,
-            "mean_kld": 0.0035539641212878384,
-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.002869848601152158,
-            "median_context_p99": 0.018992044031620026,
-            "p90_context_kld": 0.0045202462159064935,
+            "max_kld": 11.368499755859375,
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+            "mean_ref_top1_prob": 0.6582834184806026,
+            "median_context_kld": 0.002857173882379502,
+            "median_context_p99": 0.018883395940065384,
+            "p90_context_kld": 0.004444196419291071,
             "positions": 196512,
-            "top1_agreement": 0.9762915241817294,
+            "top1_agreement": 0.9766731802638007,
             "worst_context_id": 709,
-            "worst_context_kld": 0.027977719611953035
+            "worst_context_kld": 0.02107575118209227
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.3217032104741836
+        "relative_to_run": 0.3161807248552924
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 2.442533493041992,
-            "mean_kld": 0.003277476263199609,
-            "mean_ref_top1_prob": 0.6033561622407548,
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-            "median_context_p99": 0.022142626345157623,
-            "p90_context_kld": 0.0041321819357580175,
+            "max_kld": 2.281120538711548,
+            "mean_kld": 0.0032577067486191185,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.0031907973419261406,
+            "median_context_p99": 0.021607771515846252,
+            "p90_context_kld": 0.004144200098769572,
             "positions": 196512,
-            "top1_agreement": 0.9721085735222277,
-            "worst_context_id": 246,
-            "worst_context_kld": 0.005805297005183099
+            "top1_agreement": 0.9721340172610324,
+            "worst_context_id": 174,
+            "worst_context_kld": 0.005824702771456004
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.2966756557300793
+        "relative_to_run": 0.2968386928155199
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 24.25029754638672,
-      "mean_kld": 0.011047338060597442,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.003959233488364337,
-      "median_context_p99": 0.0307154580950737,
-      "p90_context_kld": 0.018751727010197644,
+      "max_kld": 29.409290313720703,
+      "mean_kld": 0.01097467017429472,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.003896204956611177,
+      "median_context_p99": 0.030610591173171997,
+      "p90_context_kld": 0.01781900445289071,
       "positions": 1572096,
-      "top1_agreement": 0.9670777102670575,
+      "top1_agreement": 0.9670738937062368,
       "worst_context_id": 454,
-      "worst_context_kld": 0.4895784940445628
+      "worst_context_kld": 0.45672114841615463
     }
   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-FP8/strata.md b/Qwen3.8-27B-FP8/strata.md
index 5680ab9408519d029f4e31dd3cb5b574ac8a2d30..79ef18d200c94a32385875005765ad8e31dda8d8 100644
--- a/Qwen3.8-27B-FP8/strata.md
+++ b/Qwen3.8-27B-FP8/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-FP8
 
-768 contexts, 1572096 scored positions, mean 0.01104734, reference top-1 64.2%, top-1 agreement 96.7078%.
+768 contexts, 1572096 scored positions, mean 0.01097467, reference top-1 64.2%, top-1 agreement 96.7074%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.05388702 | 4.88 | 0.03250745 | 0.11895909 | 0.489578 |
-| Chinese across several content types | 72 | 55.6% | 0.00899172 | 0.81 | 0.00542999 | 0.01635931 | 0.038025 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.00638815 | 0.58 | 0.00525991 | 0.00917448 | 0.033740 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.00488971 | 0.44 | 0.00402919 | 0.00926882 | 0.014224 |
-| Other multilingual content | 36 | 64.5% | 0.00473731 | 0.43 | 0.00393526 | 0.00927109 | 0.011589 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.00471401 | 0.43 | 0.00411390 | 0.00561419 | 0.027229 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.00413774 | 0.37 | 0.00322865 | 0.00585984 | 0.023853 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.00376344 | 0.34 | 0.00255818 | 0.00630003 | 0.019168 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.00355396 | 0.32 | 0.00286985 | 0.00452025 | 0.027978 |
-| Scientific and technical exposition | 96 | 60.3% | 0.00327748 | 0.30 | 0.00316868 | 0.00413218 | 0.005805 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.05343841 | 4.87 | 0.03457747 | 0.13830448 | 0.456721 |
+| Chinese across several content types | 72 | 55.6% | 0.00896669 | 0.82 | 0.00560349 | 0.01624415 | 0.036941 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.00632608 | 0.58 | 0.00529450 | 0.00899461 | 0.031026 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.00488226 | 0.44 | 0.00414163 | 0.00955858 | 0.014267 |
+| Other multilingual content | 36 | 64.5% | 0.00471620 | 0.43 | 0.00390845 | 0.00855371 | 0.011225 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.00469974 | 0.43 | 0.00391996 | 0.00541654 | 0.027906 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.00422532 | 0.39 | 0.00320066 | 0.00674611 | 0.026219 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.00373271 | 0.34 | 0.00271186 | 0.00689655 | 0.017819 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.00346998 | 0.32 | 0.00285717 | 0.00444420 | 0.021076 |
+| Scientific and technical exposition | 96 | 60.3% | 0.00325771 | 0.30 | 0.00319080 | 0.00414420 | 0.005825 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.05388702 | 4.88 | 0.03250745 | 0.11895909 | 0.489578 |
-| wikisource_zh | 33 | 60.4% | 0.01449425 | 1.31 | 0.01123513 | 0.03288935 | 0.038025 |
-| wikipedia_de | 7 | 73.6% | 0.00847814 | 0.77 | 0.00927109 | 0.01158885 | 0.011589 |
-| regulations | 23 | 72.3% | 0.00683912 | 0.62 | 0.00778065 | 0.01142324 | 0.014224 |
-| wikipedia_en | 96 | 59.5% | 0.00638815 | 0.58 | 0.00525991 | 0.00917448 | 0.033740 |
-| public_domain_books | 72 | 50.6% | 0.00471401 | 0.43 | 0.00411390 | 0.00561419 | 0.027229 |
-| public_domain_review | 26 | 51.8% | 0.00451705 | 0.41 | 0.00411848 | 0.00646793 | 0.011862 |
-| wikipedia_zh | 39 | 51.5% | 0.00433573 | 0.39 | 0.00420734 | 0.00592347 | 0.007191 |
-| github_code | 52 | 85.8% | 0.00418031 | 0.38 | 0.00312403 | 0.00585984 | 0.023853 |
-| wikipedia_ja | 7 | 57.0% | 0.00410554 | 0.37 | 0.00404828 | 0.00493403 | 0.004934 |
-| stackv2 | 44 | 72.0% | 0.00408742 | 0.37 | 0.00333187 | 0.00577160 | 0.023764 |
-| wikipedia_cs | 6 | 68.9% | 0.00400275 | 0.36 | 0.00415584 | 0.00503012 | 0.005030 |
-| wikipedia_es | 7 | 59.2% | 0.00400042 | 0.36 | 0.00393526 | 0.00493311 | 0.004933 |
-| starcoder_structured | 36 | 70.7% | 0.00376344 | 0.34 | 0.00255818 | 0.00630003 | 0.019168 |
-| libretexts | 96 | 65.8% | 0.00355396 | 0.32 | 0.00286985 | 0.00452025 | 0.027978 |
-| wikipedia_ru | 6 | 66.6% | 0.00346690 | 0.31 | 0.00356482 | 0.00495779 | 0.004958 |
-| open_news | 23 | 53.8% | 0.00336156 | 0.30 | 0.00317438 | 0.00428088 | 0.005724 |
-| scientific_papers | 96 | 60.3% | 0.00327748 | 0.30 | 0.00316868 | 0.00413218 | 0.005805 |
-| wikipedia_fr | 3 | 60.2% | 0.00321211 | 0.29 | 0.00315522 | 0.00341679 | 0.003417 |
+| wildchat | 96 | 73.5% | 0.05343841 | 4.87 | 0.03457747 | 0.13830448 | 0.456721 |
+| wikisource_zh | 33 | 60.4% | 0.01450308 | 1.32 | 0.01073703 | 0.03199231 | 0.036941 |
+| wikipedia_de | 7 | 73.6% | 0.00855001 | 0.78 | 0.00855371 | 0.01122486 | 0.011225 |
+| regulations | 23 | 72.3% | 0.00674630 | 0.61 | 0.00796010 | 0.01155533 | 0.014267 |
+| wikipedia_en | 96 | 59.5% | 0.00632608 | 0.58 | 0.00529450 | 0.00899461 | 0.031026 |
+| public_domain_books | 72 | 50.6% | 0.00469974 | 0.43 | 0.00391996 | 0.00541654 | 0.027906 |
+| public_domain_review | 26 | 51.8% | 0.00452957 | 0.41 | 0.00410826 | 0.00689971 | 0.011353 |
+| wikipedia_zh | 39 | 51.5% | 0.00428205 | 0.39 | 0.00406122 | 0.00599474 | 0.007109 |
+| github_code | 52 | 85.8% | 0.00427491 | 0.39 | 0.00309690 | 0.00674611 | 0.026219 |
+| stackv2 | 44 | 72.0% | 0.00416672 | 0.38 | 0.00326513 | 0.00612855 | 0.023565 |
+| wikipedia_ja | 7 | 57.0% | 0.00412385 | 0.38 | 0.00419477 | 0.00468995 | 0.004690 |
+| wikipedia_es | 7 | 59.2% | 0.00406109 | 0.37 | 0.00390845 | 0.00488782 | 0.004888 |
+| wikipedia_cs | 6 | 68.9% | 0.00385930 | 0.35 | 0.00402946 | 0.00469958 | 0.004700 |
+| starcoder_structured | 36 | 70.7% | 0.00373271 | 0.34 | 0.00271186 | 0.00689655 | 0.017819 |
+| libretexts | 96 | 65.8% | 0.00346998 | 0.32 | 0.00285717 | 0.00444420 | 0.021076 |
+| open_news | 23 | 53.8% | 0.00341693 | 0.31 | 0.00321398 | 0.00432250 | 0.005585 |
+| wikipedia_ru | 6 | 66.6% | 0.00336086 | 0.31 | 0.00340392 | 0.00457484 | 0.004575 |
+| scientific_papers | 96 | 60.3% | 0.00325771 | 0.30 | 0.00319080 | 0.00414420 | 0.005825 |
+| wikipedia_fr | 3 | 60.2% | 0.00310585 | 0.28 | 0.00300114 | 0.00353787 | 0.003538 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.05388702, 4.88x the run mean of 0.01104734 over 96 context(s).
-- Strongest domain: **Scientific and technical exposition** at 0.00327748, 0.30x the run mean. The spread across domains is 16.4x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.05343841, 4.87x the run mean of 0.01097467 over 96 context(s).
+- Strongest domain: **Scientific and technical exposition** at 0.00325771, 0.30x the run mean. The spread across domains is 16.4x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.489578 against a median of 0.03250745 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.456721 against a median of 0.03457747 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/Qwen3.8-27B-INT4/compliance.json b/Qwen3.8-27B-INT4/compliance.json
index 91940bac1b48f3966050de69c3b59575c75df4b4..0264963316dc4dbffbdb969d0f56d253df6a508f 100644
--- a/Qwen3.8-27B-INT4/compliance.json
+++ b/Qwen3.8-27B-INT4/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/RedHatAI/Qwen3.8-27B-INT4",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "30328786e1d18b3932124b3d3c87c8282222e03bd6dced09f625fadb2f9aeba6",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.735742+00:00",
+  "evaluated_at": "2026-09-11T20:24:40.478284+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.06297880, deployed 0.06297880, delta 4.952845258920924e-09",
+      "detail": "trunk 0.06290330, deployed 0.06290331, delta 5.024191465641259e-09",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.06297880, median 0.01324771, max 31.91850281, 4 depth buckets",
+      "detail": "mean 0.06290331, median 0.01324692, max 31.67959976, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.14460520, strongest scientific_technical at 0.02608180, spread 5.5x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.14393194, strongest scientific_technical at 0.02608979, spread 5.5x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights 30328786e1d18b39 as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.06297880022919693,
+  "laws_version": 15,
+  "mean_kld": 0.06290330991231023,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 31.918502807617188,
-        "mean_kld": 0.06297880022919694,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.03624156884367412,
-        "median_context_p99": 0.34093016386032104,
-        "p90_context_kld": 0.13468790532427413,
+        "max_kld": 31.67959976196289,
+        "mean_kld": 0.06290330991231023,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.03616175329293096,
+        "median_context_p99": 0.3476375341415405,
+        "p90_context_kld": 0.1364919523744591,
         "positions": 1572096,
-        "top1_agreement": 0.911584915933887,
+        "top1_agreement": 0.9116052709249308,
         "worst_context_id": 454,
-        "worst_context_kld": 1.0401913347215792
+        "worst_context_kld": 0.9887559796985864
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 31.918502807617188,
-            "mean_kld": 0.14460519754814047,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.12319854523112893,
-            "median_context_p99": 2.259077787399292,
-            "p90_context_kld": 0.2993194405564322,
+            "max_kld": 31.67959976196289,
+            "mean_kld": 0.14393193833588852,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.11954766931313557,
+            "median_context_p99": 2.314512252807617,
+            "p90_context_kld": 0.319737868304473,
             "positions": 196512,
-            "top1_agreement": 0.9142037127503664,
+            "top1_agreement": 0.9143818189219997,
             "worst_context_id": 454,
-            "worst_context_kld": 1.0401913347215792
+            "worst_context_kld": 0.9887559796985864
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 2.2960932412475774
+        "relative_to_run": 2.2881457038832376
       },
       {
         "cells": {
           "deployed": {
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-            "max_kld": 6.908188343048096,
-            "mean_kld": 0.1345311533987298,
-            "mean_ref_top1_prob": 0.5560138945340303,
-            "median_context_kld": 0.0800067759461001,
-            "median_context_p99": 0.6115172505378723,
-            "p90_context_kld": 0.2779199934738752,
+            "max_kld": 7.039397239685059,
+            "mean_kld": 0.13454855501209217,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.0797083946501763,
+            "median_context_p99": 0.6599892377853394,
+            "p90_context_kld": 0.2783823526198344,
             "positions": 147384,
-            "top1_agreement": 0.845030668186506,
+            "top1_agreement": 0.8443250284969874,
             "worst_context_id": 893,
-            "worst_context_kld": 0.5674233458273176
+            "worst_context_kld": 0.5681165746871794
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 2.1361339515699638
+        "relative_to_run": 2.1389741684445274
       },
       {
         "cells": {
           "deployed": {
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-            "median_context_p99": 0.541857123374939,
-            "p90_context_kld": 0.09363392489135787,
+            "max_kld": 8.426396369934082,
+            "mean_kld": 0.062162872968970516,
+            "mean_ref_top1_prob": 0.5945716883648833,
+            "median_context_kld": 0.0483626568274087,
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             "positions": 196512,
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+            "top1_agreement": 0.9020823155837812,
             "worst_context_id": 6,
-            "worst_context_kld": 0.351286367924311
+            "worst_context_kld": 0.3535688326255857
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.988017923903766
+        "relative_to_run": 0.9882289668958293
       },
       {
         "cells": {
           "deployed": {
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-            "median_context_p99": 0.4305042028427124,
-            "p90_context_kld": 0.10590312001808456,
+            "max_kld": 8.899713516235352,
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+            "median_context_kld": 0.04558276349529022,
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             "positions": 73692,
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             "worst_context_id": 947,
-            "worst_context_kld": 0.1165199785020918
+            "worst_context_kld": 0.1190153221496802
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.8632620818772753
+        "relative_to_run": 0.8652038758365567
       },
       {
         "cells": {
           "deployed": {
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             "worst_context_id": 317,
-            "worst_context_kld": 0.15066450098557546
+            "worst_context_kld": 0.15121817439717355
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         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.747549482167406
+        "relative_to_run": 0.7479275468740905
       },
       {
         "cells": {
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+            "median_context_p99": 0.23678484559059143,
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+            "worst_context_kld": 0.3110997550139541
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         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.7042105383879657
+        "relative_to_run": 0.7054333614281264
       },
       {
         "cells": {
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-            "p90_context_kld": 0.06758088454436428,
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             "positions": 73692,
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+            "top1_agreement": 0.8677468381913912,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.17541399166167326
+            "worst_context_kld": 0.17577993098848518
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         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.6441230574904779
+        "relative_to_run": 0.6454406112706055
       },
       {
         "cells": {
           "deployed": {
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-            "p90_context_kld": 0.07485065233115942,
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             "positions": 196512,
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             "worst_context_id": 667,
-            "worst_context_kld": 0.30212707418198775
+            "worst_context_kld": 0.3038625150634299
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         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.6220548482011916
+        "relative_to_run": 0.6246265190271398
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
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             "positions": 196512,
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+            "top1_agreement": 0.9386958557238235,
             "worst_context_id": 825,
-            "worst_context_kld": 0.10989124282970031
+            "worst_context_kld": 0.11036421424426199
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4235079180177859
+        "relative_to_run": 0.4234956795645455
       },
       {
         "cells": {
           "deployed": {
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diff --git a/Qwen3.8-27B-INT4/inspect.json b/Qwen3.8-27B-INT4/inspect.json
index 0fff4197a799d85168163e0a03046e96c50793d0..9a116a3e4f9ed3ba169c0434518964c6fb8b3b3d 100644
--- a/Qwen3.8-27B-INT4/inspect.json
+++ b/Qwen3.8-27B-INT4/inspect.json
@@ -1,31 +1,37 @@
 {
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diff --git a/Qwen3.8-27B-INT4/manifest.json b/Qwen3.8-27B-INT4/manifest.json
index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
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@@ -836,6 +837,7 @@
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@@ -845,17 +847,39 @@
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diff --git a/Qwen3.8-27B-INT4/report.json b/Qwen3.8-27B-INT4/report.json
index 1abe5bf630e80c391c1cd066b4874db313923b8b..adcfb054a8319d1b7e93759fb20962c308973138 100644
--- a/Qwen3.8-27B-INT4/report.json
+++ b/Qwen3.8-27B-INT4/report.json
@@ -1,8530 +1,8578 @@
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+  "reference_weights_sha256": null,
+  "runtime_binding": {
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+      "NCCL_DETERMINISTIC": "1",
+      "VLLM_BATCH_INVARIANT": "1",
+      "VLLM_MOE_USE_DEEP_GEMM": "0"
+    },
+    "driver": "580.173.02",
+    "flashinfer": "0.6.17",
+    "gpu_names": [
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
+    ],
+    "nccl": "2.29.7",
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
+    "platform": "Linux-6.8.0-137-generic-x86_64-with-glibc2.39",
+    "python": "3.12.3",
+    "torch": "2.13.0+cu132",
+    "vllm_commit": "60071d1ab73229321712254b1350dcaaac1c125a",
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+    "vllm_tree_dirty": false
+  },
   "score_from": 0,
   "storage": "hidden",
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@@ -8630,101 +8678,102 @@
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   "trunk_report": {
     "confidence_buckets": [
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     "score_from": 0,
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   "unique_tokens": 1572864,
+  "student_weights_sha256": "30328786e1d18b3932124b3d3c87c8282222e03bd6dced09f625fadb2f9aeba6",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/RedHatAI/Qwen3.8-27B-INT4",
@@ -8811,5 +8860,7 @@
       "candidate": "06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523",
       "match": false
     }
-  }
+  },
+  "candidate_hf_repo": "RedHatAI/Qwen3.8-27B-INT4",
+  "candidate_revision": "2fb0debc365fb6c1683d7d3ad7722470919627a8"
 }
diff --git a/Qwen3.8-27B-INT4/report.md b/Qwen3.8-27B-INT4/report.md
index 6c3eadf0b507d6a97a21ad99f5ed7a63060c4c76..e21ac3d4f7083e020070b7f5c9f7666762d9b29e 100644
--- a/Qwen3.8-27B-INT4/report.md
+++ b/Qwen3.8-27B-INT4/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-INT4: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.06297880** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.06290331** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.06297880 | 0.01324771 | 0.09463928 | 0.92820790 | 31.91850281 | 91.1585% |
+| 0.06290331 | 0.01324692 | 0.09464278 | 0.92733533 | 31.67959976 | 91.1605% |
 
-Reverse direction, KLD(candidate || reference): 0.06984384.
+Reverse direction, KLD(candidate || reference): 0.06989908.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.06984384.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/RedHatAI/Qwen3.8-27B-INT4 |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | 30328786e1d18b39 |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,32 +27,48 @@ Reverse direction, KLD(candidate || reference): 0.06984384.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
+## Expert kernels: declared against built
+
+| Property | Value |
+|---|---|
+| Declared for its experts | `unquantized` |
+| Expert implementation built | n/a |
+| Expert kernel built | n/a |
+| Expert layers carrying an activation scale | n/a |
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14460520 | 2.30 | 1.04019133 |
-| Chinese across several content types | 72 | 55.6% | 0.13453115 | 2.14 | 0.56742335 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.06222418 | 0.99 | 0.35128637 |
-| Other multilingual content | 36 | 64.5% | 0.05436721 | 0.86 | 0.11651998 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.04707977 | 0.75 | 0.15066450 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.04435033 | 0.70 | 0.31145168 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04056610 | 0.64 | 0.17541399 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03917627 | 0.62 | 0.30212707 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02667202 | 0.42 | 0.10989124 |
-| Scientific and technical exposition | 96 | 60.3% | 0.02608180 | 0.41 | 0.06096679 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14393194 | 2.29 | 0.98875598 |
+| Chinese across several content types | 72 | 55.6% | 0.13454856 | 2.14 | 0.56811657 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.06216287 | 0.99 | 0.35356883 |
+| Other multilingual content | 36 | 64.5% | 0.05442419 | 0.87 | 0.11901532 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.04704712 | 0.75 | 0.15121817 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.04437409 | 0.71 | 0.31109976 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04060035 | 0.65 | 0.17577993 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03929108 | 0.62 | 0.30386252 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02663928 | 0.42 | 0.11036421 |
+| Scientific and technical exposition | 96 | 60.3% | 0.02608979 | 0.41 | 0.06105585 |
 
 **Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 5.5x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
@@ -60,9 +76,9 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.06297880 |
-| Deployed (candidate's own head) | 0.06297880 |
-| Head-associated delta (not additive) | 0.00000000 |
+| Trunk (candidate hidden states, reference head) | 0.06290330 |
+| Deployed (candidate's own head) | 0.06290331 |
+| Head-associated delta (not additive) | 0.00000001 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]}, 'state': 'unquantized', 'static': {'ignored': True, 'lm_head_dtypes': {'lm_head.weight': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight'], 'output_weight_keys': ['lm_head.weight'], 'packed_keys': [], 'quant_method': 'compressed-tensors', 'state': 'unquantized', 'tie_word_embeddings': False}} |
 
@@ -70,26 +86,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.05613785 |
-| 512–1023 | 393216 | 0.05977902 |
-| 1024–1535 | 393216 | 0.06616904 |
-| 1536–2046 | 392448 | 0.06984270 |
+| 0–511 | 393216 | 0.05612833 |
+| 512–1023 | 393216 | 0.05990848 |
+| 1024–1535 | 393216 | 0.06592000 |
+| 1536–2046 | 392448 | 0.06966964 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.07513872 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.08400583 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.09127543 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.07164348 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.01943240 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.07494489 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.08513541 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.08957613 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.07237178 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.01904542 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 90.9672% | 72.3766% | 52.4421% | 35.3041% | 22.8329% |
+| 90.9625% | 72.3821% | 52.4397% | 35.2931% | 22.8305% |
 
 ## Law compliance
 
@@ -101,22 +117,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.06297880, deployed 0.06297880, delta 4.952845258920924e-09 |
-| 9 | Tail and depth disclosure | PASS | mean 0.06297880, median 0.01324771, max 31.91850281, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.06290330, deployed 0.06290331, delta 5.024191465641259e-09 |
+| 9 | Tail and depth disclosure | PASS | mean 0.06290331, median 0.01324692, max 31.67959976, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.14460520, strongest scientific_technical at 0.02608180, spread 5.5x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.14393194, strongest scientific_technical at 0.02608979, spread 5.5x |
+| 16 | Candidate weight binding | PASS | scored weights 30328786e1d18b39 as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +139,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +147,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +170,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +189,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-INT4/report.md` | 12.32 KiB | This document. |
-| `Qwen3.8-27B-INT4/report.json` | 254.05 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-INT4/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-INT4/compliance.json` | 13.00 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-INT4/report.md` | 13.16 KiB | This document. |
+| `Qwen3.8-27B-INT4/report.json` | 256.51 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-INT4/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-INT4/compliance.json` | 12.27 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-INT4/strata.json b/Qwen3.8-27B-INT4/strata.json
index dfac168397b4ed7b48bfd52c24a499a47fd8be8f..715e7e29f2d20e1efe775e121981f0241b87f071 100644
--- a/Qwen3.8-27B-INT4/strata.json
+++ b/Qwen3.8-27B-INT4/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 6.908188343048096,
-            "mean_kld": 0.22439661797487742,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.17356615911634368,
-            "median_context_p99": 1.2664220333099365,
-            "p90_context_kld": 0.5323160650215791,
+            "max_kld": 7.039397239685059,
+            "mean_kld": 0.2244213433710845,
+            "mean_ref_top1_prob": 0.6040861921682756,
+            "median_context_kld": 0.17347105847250482,
+            "median_context_p99": 1.3065104484558105,
+            "p90_context_kld": 0.5332723756899984,
             "positions": 67551,
-            "top1_agreement": 0.8042219952332312,
+            "top1_agreement": 0.8038815117466803,
             "worst_context_id": 893,
-            "worst_context_kld": 0.5674233458273176
+            "worst_context_kld": 0.5681165746871794
           }
         },
         "key": "wikisource_zh",
         "label": "wikisource_zh",
-        "relative_to_run": 3.563050060627342
+        "relative_to_run": 3.567719149976956
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 31.918502807617188,
-            "mean_kld": 0.14460519754814047,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.12319854523112893,
-            "median_context_p99": 2.259077787399292,
-            "p90_context_kld": 0.2993194405564322,
+            "max_kld": 31.67959976196289,
+            "mean_kld": 0.14393193833588852,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.11954766931313557,
+            "median_context_p99": 2.314512252807617,
+            "p90_context_kld": 0.319737868304473,
             "positions": 196512,
-            "top1_agreement": 0.9142037127503664,
+            "top1_agreement": 0.9143818189219997,
             "worst_context_id": 454,
-            "worst_context_kld": 1.0401913347215792
+            "worst_context_kld": 0.9887559796985864
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 2.2960932412475774
+        "relative_to_run": 2.2881457038832376
       },
       {
         "cells": {
           "deployed": {
             "contexts": 7,
-            "max_kld": 9.018062591552734,
-            "mean_kld": 0.09686725372101693,
-            "mean_ref_top1_prob": 0.7357366218042676,
-            "median_context_kld": 0.10590312001808456,
-            "median_context_p99": 1.9070539474487305,
-            "p90_context_kld": 0.1165199785020918,
+            "max_kld": 8.899713516235352,
+            "mean_kld": 0.09721810990547651,
+            "mean_ref_top1_prob": 0.7358200961573914,
+            "median_context_kld": 0.10664729455358758,
+            "median_context_p99": 1.952389121055603,
+            "p90_context_kld": 0.1190153221496802,
             "positions": 14329,
-            "top1_agreement": 0.8923162816665503,
+            "top1_agreement": 0.8901528369041803,
             "worst_context_id": 947,
-            "worst_context_kld": 0.1165199785020918
+            "worst_context_kld": 0.1190153221496802
           }
         },
         "key": "wikipedia_de",
         "label": "wikipedia_de",
-        "relative_to_run": 1.538093030805457
+        "relative_to_run": 1.5455166038321753
       },
       {
         "cells": {
           "deployed": {
             "contexts": 23,
-            "max_kld": 7.404207706451416,
-            "mean_kld": 0.07556401513269295,
-            "mean_ref_top1_prob": 0.7232628548554616,
-            "median_context_kld": 0.09591328063967895,
-            "median_context_p99": 1.3429927825927734,
-            "p90_context_kld": 0.1364198900844421,
+            "max_kld": 7.351467609405518,
+            "mean_kld": 0.07559704590519124,
+            "mean_ref_top1_prob": 0.7233191001139795,
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         "label": "Chinese across several content types",
-        "relative_to_run": 2.1361339515699638
+        "relative_to_run": 2.1389741684445274
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 8.464103698730469,
-            "mean_kld": 0.06222418345240118,
-            "mean_ref_top1_prob": 0.5946008380921044,
-            "median_context_kld": 0.04821683398172381,
-            "median_context_p99": 0.541857123374939,
-            "p90_context_kld": 0.09363392489135787,
+            "max_kld": 8.426396369934082,
+            "mean_kld": 0.062162872968970516,
+            "mean_ref_top1_prob": 0.5945716883648833,
+            "median_context_kld": 0.0483626568274087,
+            "median_context_p99": 0.5371996760368347,
+            "p90_context_kld": 0.09342251370086758,
             "positions": 196512,
-            "top1_agreement": 0.9018736769255822,
+            "top1_agreement": 0.9020823155837812,
             "worst_context_id": 6,
-            "worst_context_kld": 0.351286367924311
+            "worst_context_kld": 0.3535688326255857
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.988017923903766
+        "relative_to_run": 0.9882289668958293
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 9.018062591552734,
-            "mean_kld": 0.05436721019998957,
-            "mean_ref_top1_prob": 0.6450955362692544,
-            "median_context_kld": 0.0455443970797962,
-            "median_context_p99": 0.4305042028427124,
-            "p90_context_kld": 0.10590312001808456,
+            "max_kld": 8.899713516235352,
+            "mean_kld": 0.054424187539078905,
+            "mean_ref_top1_prob": 0.6450767409183084,
+            "median_context_kld": 0.04558276349529022,
+            "median_context_p99": 0.42817598581314087,
+            "p90_context_kld": 0.10664729455358758,
             "positions": 73692,
-            "top1_agreement": 0.9045758019866471,
+            "top1_agreement": 0.9036937523747489,
             "worst_context_id": 947,
-            "worst_context_kld": 0.1165199785020918
+            "worst_context_kld": 0.1190153221496802
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.8632620818772753
+        "relative_to_run": 0.8652038758365567
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 7.404207706451416,
-            "mean_kld": 0.04707976949886068,
-            "mean_ref_top1_prob": 0.5900126668457796,
-            "median_context_kld": 0.03371621264780603,
-            "median_context_p99": 0.2907443940639496,
-            "p90_context_kld": 0.11144659651404887,
+            "max_kld": 7.351467609405518,
+            "mean_kld": 0.04704711827297485,
+            "mean_ref_top1_prob": 0.590028705918726,
+            "median_context_kld": 0.033557425424993736,
+            "median_context_p99": 0.29649659991264343,
+            "p90_context_kld": 0.11091345794084626,
             "positions": 147384,
-            "top1_agreement": 0.9157710470607393,
+            "top1_agreement": 0.9159406719861043,
             "worst_context_id": 317,
-            "worst_context_kld": 0.15066450098557546
+            "worst_context_kld": 0.15121817439717355
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.747549482167406
+        "relative_to_run": 0.7479275468740905
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 8.460888862609863,
-            "mean_kld": 0.044350334816430914,
-            "mean_ref_top1_prob": 0.5056158503386161,
-            "median_context_kld": 0.03592069204934934,
-            "median_context_p99": 0.24048706889152527,
-            "p90_context_kld": 0.0533476602926932,
+            "max_kld": 8.17362117767334,
+            "mean_kld": 0.04437409335639619,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.03576088007500376,
+            "median_context_p99": 0.23678484559059143,
+            "p90_context_kld": 0.05390548673075974,
             "positions": 147384,
-            "top1_agreement": 0.899609184171959,
+            "top1_agreement": 0.8995549041958422,
             "worst_context_id": 443,
-            "worst_context_kld": 0.31145168263573736
+            "worst_context_kld": 0.3110997550139541
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.7042105383879657
+        "relative_to_run": 0.7054333614281264
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 8.12700366973877,
-            "mean_kld": 0.040566097360712344,
-            "mean_ref_top1_prob": 0.7071241246993897,
-            "median_context_kld": 0.032986131219142754,
-            "median_context_p99": 0.4861080050468445,
-            "p90_context_kld": 0.06758088454436428,
+            "max_kld": 8.626458168029785,
+            "mean_kld": 0.04060035080074585,
+            "mean_ref_top1_prob": 0.7071550040111672,
+            "median_context_kld": 0.0334252776297643,
+            "median_context_p99": 0.5161235928535461,
+            "p90_context_kld": 0.06731926693285788,
             "positions": 73692,
-            "top1_agreement": 0.8673261683764859,
+            "top1_agreement": 0.8677468381913912,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.17541399166167326
+            "worst_context_kld": 0.17577993098848518
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.6441230574904779
+        "relative_to_run": 0.6454406112706055
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 12.63759994506836,
-            "mean_kld": 0.03917626801646627,
-            "mean_ref_top1_prob": 0.7950703670465326,
-            "median_context_kld": 0.023835338446136917,
-            "median_context_p99": 0.3008233308792114,
-            "p90_context_kld": 0.07485065233115942,
+            "max_kld": 12.795402526855469,
+            "mean_kld": 0.03929107550581172,
+            "mean_ref_top1_prob": 0.7950885550065969,
+            "median_context_kld": 0.02376802223446685,
+            "median_context_p99": 0.30457040667533875,
+            "p90_context_kld": 0.07510747058223882,
             "positions": 196512,
-            "top1_agreement": 0.9471991532323726,
+            "top1_agreement": 0.9473721706562449,
             "worst_context_id": 667,
-            "worst_context_kld": 0.30212707418198775
+            "worst_context_kld": 0.3038625150634299
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.6220548482011916
+        "relative_to_run": 0.6246265190271398
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 21.455047607421875,
-            "mean_kld": 0.026672020564325254,
-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.020993579961020265,
-            "median_context_p99": 0.16144904494285583,
-            "p90_context_kld": 0.040855219811793145,
+            "max_kld": 9.440866470336914,
+            "mean_kld": 0.02663927997817303,
+            "mean_ref_top1_prob": 0.6582834184806026,
+            "median_context_kld": 0.02102722273338084,
+            "median_context_p99": 0.1589260697364807,
+            "p90_context_kld": 0.04065772069359729,
             "positions": 196512,
-            "top1_agreement": 0.9386195245074093,
+            "top1_agreement": 0.9386958557238235,
             "worst_context_id": 825,
-            "worst_context_kld": 0.10989124282970031
+            "worst_context_kld": 0.11036421424426199
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4235079180177859
+        "relative_to_run": 0.4234956795645455
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 12.329400062561035,
-            "mean_kld": 0.026081798631463017,
-            "mean_ref_top1_prob": 0.6033561622407548,
-            "median_context_kld": 0.024741105384693707,
-            "median_context_p99": 0.19944342970848083,
-            "p90_context_kld": 0.037098149314752825,
+            "max_kld": 12.36015510559082,
+            "mean_kld": 0.026089785651106444,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.024915956636511808,
+            "median_context_p99": 0.2050503045320511,
+            "p90_context_kld": 0.036752015789522266,
             "positions": 196512,
-            "top1_agreement": 0.9310118466047875,
+            "top1_agreement": 0.9311543315420941,
             "worst_context_id": 246,
-            "worst_context_kld": 0.060966793708638514
+            "worst_context_kld": 0.06105584639032175
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.41413616227276917
+        "relative_to_run": 0.4147601404040052
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 31.918502807617188,
-      "mean_kld": 0.06297880022919694,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.03624156884367412,
-      "median_context_p99": 0.34093016386032104,
-      "p90_context_kld": 0.13468790532427413,
+      "max_kld": 31.67959976196289,
+      "mean_kld": 0.06290330991231023,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.03616175329293096,
+      "median_context_p99": 0.3476375341415405,
+      "p90_context_kld": 0.1364919523744591,
       "positions": 1572096,
-      "top1_agreement": 0.911584915933887,
+      "top1_agreement": 0.9116052709249308,
       "worst_context_id": 454,
-      "worst_context_kld": 1.0401913347215792
+      "worst_context_kld": 0.9887559796985864
     }
   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-INT4/strata.md b/Qwen3.8-27B-INT4/strata.md
index 3bf88b0caaa0bcbbc31a31afc5b4ce70f82c6678..43b721c9ba39fe12322e67d2e33b29786018d1fa 100644
--- a/Qwen3.8-27B-INT4/strata.md
+++ b/Qwen3.8-27B-INT4/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-INT4
 
-768 contexts, 1572096 scored positions, mean 0.06297880, reference top-1 64.2%, top-1 agreement 91.1585%.
+768 contexts, 1572096 scored positions, mean 0.06290331, reference top-1 64.2%, top-1 agreement 91.1605%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14460520 | 2.30 | 0.12319855 | 0.29931944 | 1.040191 |
-| Chinese across several content types | 72 | 55.6% | 0.13453115 | 2.14 | 0.08000678 | 0.27791999 | 0.567423 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.06222418 | 0.99 | 0.04821683 | 0.09363392 | 0.351286 |
-| Other multilingual content | 36 | 64.5% | 0.05436721 | 0.86 | 0.04554440 | 0.10590312 | 0.116520 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.04707977 | 0.75 | 0.03371621 | 0.11144660 | 0.150665 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.04435033 | 0.70 | 0.03592069 | 0.05334766 | 0.311452 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04056610 | 0.64 | 0.03298613 | 0.06758088 | 0.175414 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03917627 | 0.62 | 0.02383534 | 0.07485065 | 0.302127 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02667202 | 0.42 | 0.02099358 | 0.04085522 | 0.109891 |
-| Scientific and technical exposition | 96 | 60.3% | 0.02608180 | 0.41 | 0.02474111 | 0.03709815 | 0.060967 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14393194 | 2.29 | 0.11954767 | 0.31973787 | 0.988756 |
+| Chinese across several content types | 72 | 55.6% | 0.13454856 | 2.14 | 0.07970839 | 0.27838235 | 0.568117 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.06216287 | 0.99 | 0.04836266 | 0.09342251 | 0.353569 |
+| Other multilingual content | 36 | 64.5% | 0.05442419 | 0.87 | 0.04558276 | 0.10664729 | 0.119015 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.04704712 | 0.75 | 0.03355743 | 0.11091346 | 0.151218 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.04437409 | 0.71 | 0.03576088 | 0.05390549 | 0.311100 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04060035 | 0.65 | 0.03342528 | 0.06731927 | 0.175780 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03929108 | 0.62 | 0.02376802 | 0.07510747 | 0.303863 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02663928 | 0.42 | 0.02102722 | 0.04065772 | 0.110364 |
+| Scientific and technical exposition | 96 | 60.3% | 0.02608979 | 0.41 | 0.02491596 | 0.03675202 | 0.061056 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wikisource_zh | 33 | 60.4% | 0.22439662 | 3.56 | 0.17356616 | 0.53231607 | 0.567423 |
-| wildchat | 96 | 73.5% | 0.14460520 | 2.30 | 0.12319855 | 0.29931944 | 1.040191 |
-| wikipedia_de | 7 | 73.6% | 0.09686725 | 1.54 | 0.10590312 | 0.11651998 | 0.116520 |
-| regulations | 23 | 72.3% | 0.07556402 | 1.20 | 0.09591328 | 0.13641989 | 0.150665 |
-| wikipedia_en | 96 | 59.5% | 0.06222418 | 0.99 | 0.04821683 | 0.09363392 | 0.351286 |
-| wikipedia_zh | 39 | 51.5% | 0.05849114 | 0.93 | 0.05639139 | 0.08040768 | 0.101248 |
-| wikipedia_cs | 6 | 68.9% | 0.05241203 | 0.83 | 0.04964427 | 0.06319860 | 0.063199 |
-| wikipedia_ja | 7 | 57.0% | 0.04765671 | 0.76 | 0.04595630 | 0.05639627 | 0.056396 |
-| public_domain_books | 72 | 50.6% | 0.04435033 | 0.70 | 0.03592069 | 0.05334766 | 0.311452 |
-| wikipedia_es | 7 | 59.2% | 0.04229309 | 0.67 | 0.04345389 | 0.05621201 | 0.056212 |
-| github_code | 52 | 85.8% | 0.04128255 | 0.66 | 0.02367156 | 0.08002234 | 0.302127 |
-| starcoder_structured | 36 | 70.7% | 0.04056610 | 0.64 | 0.03298613 | 0.06758088 | 0.175414 |
-| wikipedia_ru | 6 | 66.6% | 0.03949404 | 0.63 | 0.04360829 | 0.05209644 | 0.052096 |
-| public_domain_review | 26 | 51.8% | 0.03917945 | 0.62 | 0.03423984 | 0.07070048 | 0.107012 |
-| stackv2 | 44 | 72.0% | 0.03668703 | 0.58 | 0.02529400 | 0.05499314 | 0.290571 |
-| wikipedia_fr | 3 | 60.2% | 0.03268794 | 0.52 | 0.03278231 | 0.03937691 | 0.039377 |
-| open_news | 23 | 53.8% | 0.02752632 | 0.44 | 0.02471067 | 0.04196699 | 0.051401 |
-| libretexts | 96 | 65.8% | 0.02667202 | 0.42 | 0.02099358 | 0.04085522 | 0.109891 |
-| scientific_papers | 96 | 60.3% | 0.02608180 | 0.41 | 0.02474111 | 0.03709815 | 0.060967 |
+| wikisource_zh | 33 | 60.4% | 0.22442134 | 3.57 | 0.17347106 | 0.53327238 | 0.568117 |
+| wildchat | 96 | 73.5% | 0.14393194 | 2.29 | 0.11954767 | 0.31973787 | 0.988756 |
+| wikipedia_de | 7 | 73.6% | 0.09721811 | 1.55 | 0.10664729 | 0.11901532 | 0.119015 |
+| regulations | 23 | 72.3% | 0.07559705 | 1.20 | 0.09568556 | 0.13647847 | 0.151218 |
+| wikipedia_en | 96 | 59.5% | 0.06216287 | 0.99 | 0.04836266 | 0.09342251 | 0.353569 |
+| wikipedia_zh | 39 | 51.5% | 0.05850235 | 0.93 | 0.05616882 | 0.08070245 | 0.101207 |
+| wikipedia_cs | 6 | 68.9% | 0.05242830 | 0.83 | 0.05001897 | 0.06349262 | 0.063493 |
+| wikipedia_ja | 7 | 57.0% | 0.04766543 | 0.76 | 0.04594256 | 0.05671796 | 0.056718 |
+| public_domain_books | 72 | 50.6% | 0.04437409 | 0.71 | 0.03576088 | 0.05390549 | 0.311100 |
+| wikipedia_es | 7 | 59.2% | 0.04217758 | 0.67 | 0.04301762 | 0.05590323 | 0.055903 |
+| github_code | 52 | 85.8% | 0.04148121 | 0.66 | 0.02366987 | 0.07955755 | 0.303863 |
+| starcoder_structured | 36 | 70.7% | 0.04060035 | 0.65 | 0.03342528 | 0.06731927 | 0.175780 |
+| wikipedia_ru | 6 | 66.6% | 0.03953001 | 0.63 | 0.04384945 | 0.05227392 | 0.052274 |
+| public_domain_review | 26 | 51.8% | 0.03910379 | 0.62 | 0.03424818 | 0.07079836 | 0.107286 |
+| stackv2 | 44 | 72.0% | 0.03670274 | 0.58 | 0.02525954 | 0.05904637 | 0.288098 |
+| wikipedia_fr | 3 | 60.2% | 0.03269769 | 0.52 | 0.03284359 | 0.03932902 | 0.039329 |
+| open_news | 23 | 53.8% | 0.02747661 | 0.44 | 0.02412196 | 0.04159375 | 0.051061 |
+| libretexts | 96 | 65.8% | 0.02663928 | 0.42 | 0.02102722 | 0.04065772 | 0.110364 |
+| scientific_papers | 96 | 60.3% | 0.02608979 | 0.41 | 0.02491596 | 0.03675202 | 0.061056 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.14460520, 2.30x the run mean of 0.06297880 over 96 context(s).
-- Strongest domain: **Scientific and technical exposition** at 0.02608180, 0.41x the run mean. The spread across domains is 5.5x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.14393194, 2.29x the run mean of 0.06290331 over 96 context(s).
+- Strongest domain: **Scientific and technical exposition** at 0.02608979, 0.41x the run mean. The spread across domains is 5.5x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 1.040191 against a median of 0.12319855 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.988756 against a median of 0.11954767 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/Qwen3.8-27B-NVFP4-BF16-LMHead/compliance.json b/Qwen3.8-27B-NVFP4-BF16-LMHead/compliance.json
index 745332efed594fb90b8849bca1a174341069b37e..f647eea71769c0fe7b9e84560f9c10d091db74b8 100644
--- a/Qwen3.8-27B-NVFP4-BF16-LMHead/compliance.json
+++ b/Qwen3.8-27B-NVFP4-BF16-LMHead/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "2238b4ba183e9cc53757f6d7054ba998c02a87ed7c1842c27537fb802b1b09c0",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.460087+00:00",
+  "evaluated_at": "2026-09-11T20:24:32.410217+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.05212951, deployed 0.05212951, delta 1.3275290033920584e-09",
+      "detail": "trunk 0.05156551, deployed 0.05156551, delta 1.1058352220039147e-09",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.05212951, median 0.01302997, max 27.48295784, 4 depth buckets",
+      "detail": "mean 0.05156551, median 0.01290934, max 36.40480804, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.14506069, strongest scientific_technical at 0.02322830, spread 6.2x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.14260814, strongest scientific_technical at 0.02302717, spread 6.2x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights 2238b4ba183e9cc5 as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.05212951125207867,
+  "laws_version": 15,
+  "mean_kld": 0.05156550663959364,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
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-        "p90_context_kld": 0.10388629113718155,
+        "max_kld": 36.404808044433594,
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+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.030358945919737878,
+        "median_context_p99": 0.2575896382331848,
+        "p90_context_kld": 0.10094674048285278,
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+        "worst_context_kld": 0.8905369579241399
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     "primary": "deployed",
@@ -165,201 +168,201 @@
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         "key": "chinese",
         "label": "Chinese across several content types",
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         "label": "Encyclopedic and factual reference",
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diff --git a/Qwen3.8-27B-NVFP4-BF16-LMHead/inspect.json b/Qwen3.8-27B-NVFP4-BF16-LMHead/inspect.json
index c414c25d216e956dd6268aee266e8d0473e60ee0..f333ea7362182ed624742a3402fc2c48a9cb3d59 100644
--- a/Qwen3.8-27B-NVFP4-BF16-LMHead/inspect.json
+++ b/Qwen3.8-27B-NVFP4-BF16-LMHead/inspect.json
@@ -1,31 +1,461 @@
 {
+  "model": "/media/fmodels2/RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead",
+  "inspect_version": 5,
+  "weights_sha256": "2238b4ba183e9cc53757f6d7054ba998c02a87ed7c1842c27537fb802b1b09c0",
+  "weights_bytes": 23749332688,
+  "quant_method": "modelopt",
+  "declared": {},
+  "detected_scheme": "nvfp4",
+  "detected_block": 16,
+  "quant_algorithm": "round_to_nearest",
+  "scheme_mix": {
+    "format": null,
+    "groups": [
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+        "group": "group_0",
+        "format": null,
+        "weight_bits": 8,
+        "activation_bits": 8,
+        "targets": [
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+          "model.language_model.layers.0.linear_attn.in_proj_z",
+          "model.language_model.layers.0.linear_attn.out_proj",
+          "model.language_model.layers.1.linear_attn.in_proj_qkv",
+          "model.language_model.layers.1.linear_attn.in_proj_z",
+          "model.language_model.layers.1.linear_attn.out_proj",
+          "model.language_model.layers.10.linear_attn.in_proj_qkv",
+          "model.language_model.layers.10.linear_attn.in_proj_z",
+          "model.language_model.layers.10.linear_attn.out_proj",
+          "model.language_model.layers.11.self_attn.k_proj",
+          "model.language_model.layers.11.self_attn.o_proj",
+          "model.language_model.layers.11.self_attn.q_proj",
+          "model.language_model.layers.11.self_attn.v_proj",
+          "model.language_model.layers.12.linear_attn.in_proj_qkv",
+          "model.language_model.layers.12.linear_attn.in_proj_z",
+          "model.language_model.layers.12.linear_attn.out_proj",
+          "model.language_model.layers.13.linear_attn.in_proj_qkv",
+          "model.language_model.layers.13.linear_attn.in_proj_z",
+          "model.language_model.layers.13.linear_attn.out_proj",
+          "model.language_model.layers.14.linear_attn.in_proj_qkv",
+          "model.language_model.layers.14.linear_attn.in_proj_z",
+          "model.language_model.layers.14.linear_attn.out_proj",
+          "model.language_model.layers.15.self_attn.k_proj",
+          "model.language_model.layers.15.self_attn.o_proj",
+          "model.language_model.layers.15.self_attn.q_proj",
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diff --git a/Qwen3.8-27B-NVFP4-BF16-LMHead/manifest.json b/Qwen3.8-27B-NVFP4-BF16-LMHead/manifest.json
index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
--- a/Qwen3.8-27B-NVFP4-BF16-LMHead/manifest.json
+++ b/Qwen3.8-27B-NVFP4-BF16-LMHead/manifest.json
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@@ -836,6 +837,7 @@
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diff --git a/Qwen3.8-27B-NVFP4-BF16-LMHead/report.json b/Qwen3.8-27B-NVFP4-BF16-LMHead/report.json
index f6dc09f202a4f29634f2420f47c1ff54f9473197..8c93d3e1841cd2598a700dc69b991b92ba8da816 100644
--- a/Qwen3.8-27B-NVFP4-BF16-LMHead/report.json
+++ b/Qwen3.8-27B-NVFP4-BF16-LMHead/report.json
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+    "p99_kld": 0.6563607484102291,
     "score_from": 0,
-    "top1_agreement": 0.9151871132551702,
+    "top1_agreement": 0.9156470088340661,
     "topk_agreement": {
-      "1": 0.9138614944634424,
-      "2": 0.7360663725370461,
-      "3": 0.5392259760218205,
-      "4": 0.3658319848151767,
-      "5": 0.23745814504966617
+      "1": 0.9142692303777886,
+      "2": 0.7369963411903598,
+      "3": 0.5413632500814199,
+      "4": 0.3678713004803778,
+      "5": 0.23986957539488682
     }
   },
   "unique_tokens": 1572864,
+  "student_weights_sha256": "2238b4ba183e9cc53757f6d7054ba998c02a87ed7c1842c27537fb802b1b09c0",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead",
@@ -8811,5 +8858,7 @@
       "candidate": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3",
       "match": true
     }
-  }
+  },
+  "candidate_hf_repo": "RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead",
+  "candidate_revision": "009632fef96dd349150baa780c984e62e70e91fe"
 }
diff --git a/Qwen3.8-27B-NVFP4-BF16-LMHead/report.md b/Qwen3.8-27B-NVFP4-BF16-LMHead/report.md
index 2b2a7d55956bb570b63350e48b32ce6b9988b4f4..edbfbaa497581005c0a8d0bc12fbbde3a9b7901b 100644
--- a/Qwen3.8-27B-NVFP4-BF16-LMHead/report.md
+++ b/Qwen3.8-27B-NVFP4-BF16-LMHead/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-NVFP4-BF16-LMHead: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.05212951** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.05156551** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.05212951 | 0.01302997 | 0.07506841 | 0.67170381 | 27.48295784 | 91.5187% |
+| 0.05156551 | 0.01290934 | 0.07437129 | 0.65636102 | 36.40480804 | 91.5647% |
 
-Reverse direction, KLD(candidate || reference): 0.05476134.
+Reverse direction, KLD(candidate || reference): 0.05437684.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.05476134.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | 2238b4ba183e9cc5 |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,32 +27,39 @@ Reverse direction, KLD(candidate || reference): 0.05476134.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14506069 | 2.78 | 0.93519654 |
-| Chinese across several content types | 72 | 55.6% | 0.08009739 | 1.54 | 0.33491146 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.05208049 | 1.00 | 0.25756627 |
-| Other multilingual content | 36 | 64.5% | 0.03909129 | 0.75 | 0.08191294 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03826159 | 0.73 | 0.10122026 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.03723338 | 0.71 | 0.21792144 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03034605 | 0.58 | 0.21618890 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02762031 | 0.53 | 0.11659044 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02460944 | 0.47 | 0.08214911 |
-| Scientific and technical exposition | 96 | 60.3% | 0.02322830 | 0.45 | 0.04042489 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14260814 | 2.77 | 0.89053696 |
+| Chinese across several content types | 72 | 55.6% | 0.07907412 | 1.53 | 0.31717224 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.05124551 | 0.99 | 0.25338622 |
+| Other multilingual content | 36 | 64.5% | 0.03972507 | 0.77 | 0.08763030 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03811238 | 0.74 | 0.10094674 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.03711086 | 0.72 | 0.21911475 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03018829 | 0.59 | 0.20473772 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02775164 | 0.54 | 0.11848210 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02442816 | 0.47 | 0.08643491 |
+| Scientific and technical exposition | 96 | 60.3% | 0.02302717 | 0.45 | 0.04442971 |
 
 **Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 6.2x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
@@ -60,8 +67,8 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.05212951 |
-| Deployed (candidate's own head) | 0.05212951 |
+| Trunk (candidate hidden states, reference head) | 0.05156551 |
+| Deployed (candidate's own head) | 0.05156551 |
 | Head-associated delta (not additive) | 0.00000000 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'quantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedLinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedLinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'quantized'}]}, 'state': 'quantized', 'static': {'ignored': False, 'lm_head_dtypes': {'lm_head.weight': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight'], 'output_weight_keys': ['lm_head.weight'], 'packed_keys': [], 'quant_method': 'modelopt', 'state': 'unquantized', 'tie_word_embeddings': False}} |
@@ -70,26 +77,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.04442098 |
-| 512–1023 | 393216 | 0.04805359 |
-| 1024–1535 | 393216 | 0.05569485 |
-| 1536–2046 | 392448 | 0.06036471 |
+| 0–511 | 393216 | 0.04401547 |
+| 512–1023 | 393216 | 0.04793031 |
+| 1024–1535 | 393216 | 0.05523113 |
+| 1536–2046 | 392448 | 0.05909983 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.05929329 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.07293711 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.07885677 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.05907473 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.01320992 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.05898762 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.07237716 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.07748764 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.05845658 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.01301883 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 91.3861% | 73.6066% | 53.9226% | 36.5832% | 23.7458% |
+| 91.4269% | 73.6996% | 54.1363% | 36.7871% | 23.9870% |
 
 ## Law compliance
 
@@ -101,22 +108,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.05212951, deployed 0.05212951, delta 1.3275290033920584e-09 |
-| 9 | Tail and depth disclosure | PASS | mean 0.05212951, median 0.01302997, max 27.48295784, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.05156551, deployed 0.05156551, delta 1.1058352220039147e-09 |
+| 9 | Tail and depth disclosure | PASS | mean 0.05156551, median 0.01290934, max 36.40480804, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.14506069, strongest scientific_technical at 0.02322830, spread 6.2x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.14260814, strongest scientific_technical at 0.02302717, spread 6.2x |
+| 16 | Candidate weight binding | PASS | scored weights 2238b4ba183e9cc5 as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +130,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +138,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +161,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +180,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-NVFP4-BF16-LMHead/report.md` | 12.37 KiB | This document. |
-| `Qwen3.8-27B-NVFP4-BF16-LMHead/report.json` | 254.28 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-NVFP4-BF16-LMHead/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-NVFP4-BF16-LMHead/compliance.json` | 13.03 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-NVFP4-BF16-LMHead/report.md` | 13.21 KiB | This document. |
+| `Qwen3.8-27B-NVFP4-BF16-LMHead/report.json` | 256.72 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-NVFP4-BF16-LMHead/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-NVFP4-BF16-LMHead/compliance.json` | 12.30 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.json b/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.json
index 641675ece0bf05553d9833adfba479f3022df634..2cf3a250a30f4eade2603be796c4378cdd4d99e3 100644
--- a/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.json
+++ b/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 27.48295783996582,
-            "mean_kld": 0.1450606879671042,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.10672595977404836,
-            "median_context_p99": 2.366913318634033,
-            "p90_context_kld": 0.31745866158131525,
+            "max_kld": 36.404808044433594,
+            "mean_kld": 0.14260813963956548,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.10354972375019698,
+            "median_context_p99": 2.1813976764678955,
+            "p90_context_kld": 0.3451019763379924,
             "positions": 196512,
-            "top1_agreement": 0.9130485670086305,
+            "top1_agreement": 0.9151603973294252,
             "worst_context_id": 454,
-            "worst_context_kld": 0.9351965383137365
+            "worst_context_kld": 0.8905369579241399
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 2.7826980242658594
+        "relative_to_run": 2.765572355107366
       },
       {
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 7.188745975494385,
-            "mean_kld": 0.1293620515682006,
-            "mean_ref_top1_prob": 0.6040606719329803,
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-            "median_context_p99": 0.16904449462890625,
-            "p90_context_kld": 0.031335149218943904,
+            "max_kld": 13.07381534576416,
+            "mean_kld": 0.023027166196565346,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.021995164691474722,
+            "median_context_p99": 0.16406352818012238,
+            "p90_context_kld": 0.030565308802914134,
             "positions": 196512,
-            "top1_agreement": 0.9289712587526462,
+            "top1_agreement": 0.929612440970526,
             "worst_context_id": 246,
-            "worst_context_kld": 0.04042488780931219
+            "worst_context_kld": 0.044429711784661804
           }
         },
         "key": "scientific_papers",
         "label": "scientific_papers",
-        "relative_to_run": 0.44558827240107735
+        "relative_to_run": 0.44656142637187546
       }
     ],
     "stratum": [
@@ -390,217 +390,217 @@
         "cells": {
           "deployed": {
             "contexts": 96,
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-            "mean_kld": 0.1450606879671042,
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-            "median_context_p99": 2.366913318634033,
-            "p90_context_kld": 0.31745866158131525,
+            "max_kld": 36.404808044433594,
+            "mean_kld": 0.14260813963956548,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.10354972375019698,
+            "median_context_p99": 2.1813976764678955,
+            "p90_context_kld": 0.3451019763379924,
             "positions": 196512,
-            "top1_agreement": 0.9130485670086305,
+            "top1_agreement": 0.9151603973294252,
             "worst_context_id": 454,
-            "worst_context_kld": 0.9351965383137365
+            "worst_context_kld": 0.8905369579241399
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 2.7826980242658594
+        "relative_to_run": 2.765572355107366
       },
       {
         "cells": {
           "deployed": {
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             "positions": 147384,
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+            "top1_agreement": 0.8768658741790154,
             "worst_context_id": 893,
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+            "worst_context_kld": 0.31717224464172616
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.5365076591654676
+        "relative_to_run": 1.5334692737462934
       },
       {
         "cells": {
           "deployed": {
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             "positions": 196512,
-            "top1_agreement": 0.9043417195896434,
+            "top1_agreement": 0.9047081094284318,
             "worst_context_id": 6,
-            "worst_context_kld": 0.2575662736752676
+            "worst_context_kld": 0.253386224192646
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9990596541176248
+        "relative_to_run": 0.9937944101266313
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 7.891758918762207,
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-            "median_context_p99": 0.2722674012184143,
-            "p90_context_kld": 0.07035002679922857,
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             "positions": 73692,
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+            "top1_agreement": 0.9138033979265049,
             "worst_context_id": 953,
-            "worst_context_kld": 0.08191294001897026
+            "worst_context_kld": 0.08763029838216986
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.7498878110393828
+        "relative_to_run": 0.7703806631572974
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
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-            "p90_context_kld": 0.07946953402075285,
+            "max_kld": 6.221218109130859,
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+            "median_context_kld": 0.03164991631323535,
+            "median_context_p99": 0.2747260630130768,
+            "p90_context_kld": 0.07574533150766247,
             "positions": 147384,
-            "top1_agreement": 0.9153232372577756,
+            "top1_agreement": 0.9153707322368778,
             "worst_context_id": 275,
-            "worst_context_kld": 0.10122025808876788
+            "worst_context_kld": 0.10094674048285278
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.7339718429789305
+        "relative_to_run": 0.7391060828837337
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 5.807681560516357,
-            "mean_kld": 0.03723338342274077,
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-            "median_context_kld": 0.03198886603632158,
-            "median_context_p99": 0.19292420148849487,
-            "p90_context_kld": 0.04507155972030506,
+            "max_kld": 6.983285427093506,
+            "mean_kld": 0.03711085502771101,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.031371119738575946,
+            "median_context_p99": 0.20159181952476501,
+            "p90_context_kld": 0.04505553168422091,
             "positions": 147384,
-            "top1_agreement": 0.9027506377897194,
+            "top1_agreement": 0.9036666123866905,
             "worst_context_id": 443,
-            "worst_context_kld": 0.21792143969470823
+            "worst_context_kld": 0.21911474635649905
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.7142476982508844
+        "relative_to_run": 0.7196837080859033
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 12.813560485839844,
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-            "p90_context_kld": 0.04975873418086208,
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+            "median_context_p99": 0.21924170851707458,
+            "p90_context_kld": 0.05378769088686552,
             "positions": 196512,
-            "top1_agreement": 0.9495908646800195,
+            "top1_agreement": 0.9502422243934213,
             "worst_context_id": 667,
-            "worst_context_kld": 0.2161889008647869
+            "worst_context_kld": 0.20473772003888704
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5821279833721604
+        "relative_to_run": 0.5854357985462448
       },
       {
         "cells": {
           "deployed": {
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-            "median_context_p99": 0.40589118003845215,
-            "p90_context_kld": 0.05043523494828777,
+            "max_kld": 8.024088859558105,
+            "mean_kld": 0.02775163699079394,
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+            "median_context_p99": 0.34725338220596313,
+            "p90_context_kld": 0.04604335776204541,
             "positions": 73692,
-            "top1_agreement": 0.8654535092004559,
+            "top1_agreement": 0.8652906692721055,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.11659044195651949
+            "worst_context_kld": 0.11848209727913012
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.5298402638337655
+        "relative_to_run": 0.5381821841634996
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 17.052858352661133,
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-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.020860553521432415,
-            "median_context_p99": 0.14598329365253448,
-            "p90_context_kld": 0.03323913724905251,
+            "max_kld": 13.109975814819336,
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+            "median_context_p99": 0.1441325843334198,
+            "p90_context_kld": 0.03284644349083438,
             "positions": 196512,
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+            "top1_agreement": 0.9363652092493079,
             "worst_context_id": 825,
-            "worst_context_kld": 0.08214911499231742
+            "worst_context_kld": 0.08643491137050359
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4720826374693844
+        "relative_to_run": 0.4737306435656357
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 12.320075988769531,
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-            "median_context_p99": 0.16904449462890625,
-            "p90_context_kld": 0.031335149218943904,
+            "max_kld": 13.07381534576416,
+            "mean_kld": 0.023027166196565346,
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+            "median_context_p99": 0.16406352818012238,
+            "p90_context_kld": 0.030565308802914134,
             "positions": 196512,
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+            "top1_agreement": 0.929612440970526,
             "worst_context_id": 246,
-            "worst_context_kld": 0.04042488780931219
+            "worst_context_kld": 0.044429711784661804
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.44558827240107735
+        "relative_to_run": 0.44656142637187546
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 27.48295783996582,
-      "mean_kld": 0.05212951125207867,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.03048191045903891,
-      "median_context_p99": 0.2586134076118469,
-      "p90_context_kld": 0.10388629113718155,
+      "max_kld": 36.404808044433594,
+      "mean_kld": 0.05156550663959363,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.030358945919737878,
+      "median_context_p99": 0.2575896382331848,
+      "p90_context_kld": 0.10094674048285278,
       "positions": 1572096,
-      "top1_agreement": 0.9151871132551702,
+      "top1_agreement": 0.9156470088340661,
       "worst_context_id": 454,
-      "worst_context_kld": 0.9351965383137365
+      "worst_context_kld": 0.8905369579241399
     }
   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.md b/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.md
index 6352033972b3b9138c9973e2ba04c1d68a30283d..f8f4147042f9ce84e6eb746b32c28ea8917a428c 100644
--- a/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.md
+++ b/Qwen3.8-27B-NVFP4-BF16-LMHead/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-NVFP4-BF16-LMHead
 
-768 contexts, 1572096 scored positions, mean 0.05212951, reference top-1 64.2%, top-1 agreement 91.5187%.
+768 contexts, 1572096 scored positions, mean 0.05156551, reference top-1 64.2%, top-1 agreement 91.5647%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14506069 | 2.78 | 0.10672596 | 0.31745866 | 0.935197 |
-| Chinese across several content types | 72 | 55.6% | 0.08009739 | 1.54 | 0.04881424 | 0.14562944 | 0.334911 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.05208049 | 1.00 | 0.04317495 | 0.07479096 | 0.257566 |
-| Other multilingual content | 36 | 64.5% | 0.03909129 | 0.75 | 0.03482694 | 0.07035003 | 0.081913 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03826159 | 0.73 | 0.03195989 | 0.07946953 | 0.101220 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.03723338 | 0.71 | 0.03198887 | 0.04507156 | 0.217921 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03034605 | 0.58 | 0.02084674 | 0.04975873 | 0.216189 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02762031 | 0.53 | 0.02249950 | 0.05043523 | 0.116590 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02460944 | 0.47 | 0.02086055 | 0.03323914 | 0.082149 |
-| Scientific and technical exposition | 96 | 60.3% | 0.02322830 | 0.45 | 0.02226253 | 0.03133515 | 0.040425 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.14260814 | 2.77 | 0.10354972 | 0.34510198 | 0.890537 |
+| Chinese across several content types | 72 | 55.6% | 0.07907412 | 1.53 | 0.04855310 | 0.14487312 | 0.317172 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.05124551 | 0.99 | 0.04196326 | 0.07206767 | 0.253386 |
+| Other multilingual content | 36 | 64.5% | 0.03972507 | 0.77 | 0.03417868 | 0.06837732 | 0.087630 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03811238 | 0.74 | 0.03164992 | 0.07574533 | 0.100947 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.03711086 | 0.72 | 0.03137112 | 0.04505553 | 0.219115 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03018829 | 0.59 | 0.02100279 | 0.05378769 | 0.204738 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02775164 | 0.54 | 0.02158036 | 0.04604336 | 0.118482 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02442816 | 0.47 | 0.02077948 | 0.03284644 | 0.086435 |
+| Scientific and technical exposition | 96 | 60.3% | 0.02302717 | 0.45 | 0.02199516 | 0.03056531 | 0.044430 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.14506069 | 2.78 | 0.10672596 | 0.31745866 | 0.935197 |
-| wikisource_zh | 33 | 60.4% | 0.12936205 | 2.48 | 0.09606564 | 0.30460000 | 0.334911 |
-| wikipedia_de | 7 | 73.6% | 0.06534968 | 1.25 | 0.07035003 | 0.08191294 | 0.081913 |
-| wikipedia_en | 96 | 59.5% | 0.05208049 | 1.00 | 0.04317495 | 0.07479096 | 0.257566 |
-| regulations | 23 | 72.3% | 0.05162461 | 0.99 | 0.06074220 | 0.08217726 | 0.101220 |
-| wikipedia_zh | 39 | 51.5% | 0.03841191 | 0.74 | 0.03743391 | 0.05280832 | 0.059166 |
-| public_domain_books | 72 | 50.6% | 0.03723338 | 0.71 | 0.03198887 | 0.04507156 | 0.217921 |
-| wikipedia_ja | 7 | 57.0% | 0.03712700 | 0.71 | 0.03723823 | 0.04216571 | 0.042166 |
-| public_domain_review | 26 | 51.8% | 0.03648647 | 0.70 | 0.03274300 | 0.05933113 | 0.087844 |
-| wikipedia_es | 7 | 59.2% | 0.03460012 | 0.66 | 0.03333043 | 0.04504019 | 0.045040 |
-| wikipedia_cs | 6 | 68.9% | 0.03329140 | 0.64 | 0.03532597 | 0.03764456 | 0.037645 |
-| github_code | 52 | 85.8% | 0.03065874 | 0.59 | 0.01979219 | 0.05239252 | 0.216189 |
-| stackv2 | 44 | 72.0% | 0.02997650 | 0.58 | 0.02244658 | 0.04893623 | 0.209091 |
-| wikipedia_ru | 6 | 66.6% | 0.02863896 | 0.55 | 0.02861696 | 0.03984704 | 0.039847 |
-| starcoder_structured | 36 | 70.7% | 0.02762031 | 0.53 | 0.02249950 | 0.05043523 | 0.116590 |
-| open_news | 23 | 53.8% | 0.02690523 | 0.52 | 0.02633995 | 0.03581834 | 0.043336 |
-| wikipedia_fr | 3 | 60.2% | 0.02538882 | 0.49 | 0.02538246 | 0.02925488 | 0.029255 |
-| libretexts | 96 | 65.8% | 0.02460944 | 0.47 | 0.02086055 | 0.03323914 | 0.082149 |
-| scientific_papers | 96 | 60.3% | 0.02322830 | 0.45 | 0.02226253 | 0.03133515 | 0.040425 |
+| wildchat | 96 | 73.5% | 0.14260814 | 2.77 | 0.10354972 | 0.34510198 | 0.890537 |
+| wikisource_zh | 33 | 60.4% | 0.12707486 | 2.46 | 0.09748326 | 0.29125689 | 0.317172 |
+| wikipedia_de | 7 | 73.6% | 0.06826552 | 1.32 | 0.06837732 | 0.08763030 | 0.087630 |
+| regulations | 23 | 72.3% | 0.05175295 | 1.00 | 0.06760431 | 0.08414925 | 0.100947 |
+| wikipedia_en | 96 | 59.5% | 0.05124551 | 0.99 | 0.04196326 | 0.07206767 | 0.253386 |
+| wikipedia_zh | 39 | 51.5% | 0.03845811 | 0.75 | 0.03694213 | 0.05189267 | 0.059474 |
+| public_domain_books | 72 | 50.6% | 0.03711086 | 0.72 | 0.03137112 | 0.04505553 | 0.219115 |
+| wikipedia_ja | 7 | 57.0% | 0.03683809 | 0.71 | 0.03802289 | 0.04128210 | 0.041282 |
+| public_domain_review | 26 | 51.8% | 0.03606162 | 0.70 | 0.03299574 | 0.06031772 | 0.084851 |
+| wikipedia_es | 7 | 59.2% | 0.03428308 | 0.66 | 0.03246859 | 0.04459575 | 0.044596 |
+| wikipedia_cs | 6 | 68.9% | 0.03395949 | 0.66 | 0.03417868 | 0.04104416 | 0.041044 |
+| github_code | 52 | 85.8% | 0.03049262 | 0.59 | 0.01938650 | 0.05378769 | 0.204738 |
+| stackv2 | 44 | 72.0% | 0.02982864 | 0.58 | 0.02299397 | 0.05378093 | 0.188805 |
+| wikipedia_ru | 6 | 66.6% | 0.02891736 | 0.56 | 0.03018204 | 0.03903039 | 0.039030 |
+| starcoder_structured | 36 | 70.7% | 0.02775164 | 0.54 | 0.02158036 | 0.04604336 | 0.118482 |
+| open_news | 23 | 53.8% | 0.02679006 | 0.52 | 0.02628155 | 0.03704089 | 0.038851 |
+| wikipedia_fr | 3 | 60.2% | 0.02571151 | 0.50 | 0.02656140 | 0.02939078 | 0.029391 |
+| libretexts | 96 | 65.8% | 0.02442816 | 0.47 | 0.02077948 | 0.03284644 | 0.086435 |
+| scientific_papers | 96 | 60.3% | 0.02302717 | 0.45 | 0.02199516 | 0.03056531 | 0.044430 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.14506069, 2.78x the run mean of 0.05212951 over 96 context(s).
-- Strongest domain: **Scientific and technical exposition** at 0.02322830, 0.45x the run mean. The spread across domains is 6.2x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.14260814, 2.77x the run mean of 0.05156551 over 96 context(s).
+- Strongest domain: **Scientific and technical exposition** at 0.02302717, 0.45x the run mean. The spread across domains is 6.2x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.935197 against a median of 0.10672596 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.890537 against a median of 0.10354972 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/Qwen3.8-27B-NVFP4-RTX5090/compliance.json b/Qwen3.8-27B-NVFP4-RTX5090/compliance.json
index d4a60331ad1f9bf52e8c5abb23449ff421879026..b6c2d0c559546892200e5280357e17deda45ae9d 100644
--- a/Qwen3.8-27B-NVFP4-RTX5090/compliance.json
+++ b/Qwen3.8-27B-NVFP4-RTX5090/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "380fc3ae60e1e6524cbfa31862cc686215120f7d7b93308f3356e5bf72c0e801",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.368291+00:00",
+  "evaluated_at": "2026-09-11T20:24:29.719493+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.07603511, deployed 0.08884902, delta 0.012813907739867667",
+      "detail": "trunk 0.07615896, deployed 0.08890054, delta 0.012741578000413284",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.08884902, median 0.03153922, max 29.62161064, 4 depth buckets",
+      "detail": "mean 0.08890054, median 0.03146119, max 29.41000557, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.21268638, strongest worked_math_reasoning at 0.04891121, spread 4.3x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.21324364, strongest worked_math_reasoning at 0.04852616, spread 4.4x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights 380fc3ae60e1e652 as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.08884901732903162,
+  "laws_version": 15,
+  "mean_kld": 0.08890053681422792,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 29.621610641479492,
-        "mean_kld": 0.08884901732903162,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.06137484148042411,
-        "median_context_p99": 0.41598302125930786,
-        "p90_context_kld": 0.16160134971982643,
+        "max_kld": 29.410005569458008,
+        "mean_kld": 0.08890053681422791,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.06094675094259141,
+        "median_context_p99": 0.4184585213661194,
+        "p90_context_kld": 0.169365564213705,
         "positions": 1572096,
-        "top1_agreement": 0.879409399934864,
+        "top1_agreement": 0.8795868700130272,
         "worst_context_id": 454,
-        "worst_context_kld": 1.254965819110839
+        "worst_context_kld": 1.2597357640464442
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 29.621610641479492,
-            "mean_kld": 0.21268638446455335,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.16980764884602795,
-            "median_context_p99": 3.194436550140381,
-            "p90_context_kld": 0.4663941209945985,
+            "max_kld": 29.410005569458008,
+            "mean_kld": 0.21324363685885994,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.17013793919462264,
+            "median_context_p99": 3.1346852779388428,
+            "p90_context_kld": 0.4793873904592046,
             "positions": 196512,
-            "top1_agreement": 0.8831114639309559,
+            "top1_agreement": 0.8833353688324377,
             "worst_context_id": 454,
-            "worst_context_kld": 1.254965819110839
+            "worst_context_kld": 1.2597357640464442
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 2.3937955743159085
+        "relative_to_run": 2.398676594096019
       },
       {
         "cells": {
           "deployed": {
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-            "mean_kld": 0.13804625823415112,
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-            "median_context_kld": 0.0941144168045219,
-            "median_context_p99": 0.6546816229820251,
-            "p90_context_kld": 0.23829632700468703,
+            "max_kld": 8.557491302490234,
+            "mean_kld": 0.13763110758662334,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.09366887791148955,
+            "median_context_p99": 0.6111054420471191,
+            "p90_context_kld": 0.24518771943300743,
             "positions": 147384,
-            "top1_agreement": 0.826616186288878,
-            "worst_context_id": 905,
-            "worst_context_kld": 0.48569205838970464
+            "top1_agreement": 0.8276067958530098,
+            "worst_context_id": 893,
+            "worst_context_kld": 0.4890878310734861
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.5537173328876446
+        "relative_to_run": 1.5481470924548617
       },
       {
         "cells": {
           "deployed": {
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-            "max_kld": 9.237215042114258,
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-            "p90_context_kld": 0.12549267043511111,
+            "max_kld": 8.673469543457031,
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+            "median_context_p99": 0.6247144937515259,
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             "positions": 196512,
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+            "top1_agreement": 0.8644510258915487,
             "worst_context_id": 6,
-            "worst_context_kld": 0.37588815477459775
+            "worst_context_kld": 0.38041751277782426
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 1.006110664956836
+        "relative_to_run": 1.0040615639549482
       },
       {
         "cells": {
           "deployed": {
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+            "median_context_kld": 0.06468236685300296,
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+            "top1_agreement": 0.8566194430874451,
             "worst_context_id": 443,
-            "worst_context_kld": 0.3243600094405916
+            "worst_context_kld": 0.3221895584307333
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.8268679996291151
+        "relative_to_run": 0.8303145964197121
       },
       {
         "cells": {
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+            "worst_context_kld": 0.1603293014694022
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         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.8143742792970337
+        "relative_to_run": 0.8223252746542226
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       {
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             "worst_context_id": 953,
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+            "worst_context_kld": 0.12065877383606818
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         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
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+        "relative_to_run": 0.801067414172178
       },
       {
         "cells": {
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             "positions": 196512,
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         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5897772030817099
+        "relative_to_run": 0.5921706741198473
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       {
         "cells": {
           "deployed": {
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-            "worst_context_id": 246,
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+            "top1_agreement": 0.8908056505455137,
+            "worst_context_id": 174,
+            "worst_context_kld": 0.07139858353297991
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         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.5570822809728582
+        "relative_to_run": 0.5520766929819303
       },
       {
         "cells": {
           "deployed": {
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             "positions": 73692,
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+            "top1_agreement": 0.8472968571893829,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.1645537792651185
+            "worst_context_kld": 0.17824096230327108
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.553477117412535
+        "relative_to_run": 0.5498031468132274
       },
       {
         "cells": {
           "deployed": {
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@@ -836,6 +837,7 @@
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@@ -845,17 +847,39 @@
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diff --git a/Qwen3.8-27B-NVFP4-RTX5090/report.json b/Qwen3.8-27B-NVFP4-RTX5090/report.json
index 222b21006b5b9f0341832678cdccee9ef63b8086..cdabae0b6ed5bc57ca8f7669752b459df26b2628 100644
--- a/Qwen3.8-27B-NVFP4-RTX5090/report.json
+++ b/Qwen3.8-27B-NVFP4-RTX5090/report.json
@@ -1,8530 +1,8578 @@
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+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
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@@ -8642,101 +8690,102 @@
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+  "student_weights_sha256": "380fc3ae60e1e6524cbfa31862cc686215120f7d7b93308f3356e5bf72c0e801",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090",
@@ -8823,5 +8872,7 @@
       "candidate": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3",
       "match": true
     }
-  }
+  },
+  "candidate_hf_repo": "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090",
+  "candidate_revision": "0cc27958cefbbe231782ec8511de8c4eb5233348"
 }
diff --git a/Qwen3.8-27B-NVFP4-RTX5090/report.md b/Qwen3.8-27B-NVFP4-RTX5090/report.md
index c0fa6e04d8ce81308c53e79b92a1f92c0c572de2..eb04322ebf9d526fc575ac711289b85b871e4da5 100644
--- a/Qwen3.8-27B-NVFP4-RTX5090/report.md
+++ b/Qwen3.8-27B-NVFP4-RTX5090/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-NVFP4-RTX5090: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.08884902** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.08890054** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.08884902 | 0.03153922 | 0.14679415 | 1.04055545 | 29.62161064 | 87.9409% |
+| 0.08890054 | 0.03146119 | 0.14654309 | 1.03599295 | 29.41000557 | 87.9587% |
 
-Reverse direction, KLD(candidate || reference): 0.09722847.
+Reverse direction, KLD(candidate || reference): 0.09720515.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.09722847.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | 380fc3ae60e1e652 |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,42 +27,58 @@ Reverse direction, KLD(candidate || reference): 0.09722847.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
+## Expert kernels: declared against built
+
+| Property | Value |
+|---|---|
+| Declared for its experts | `4-bit float` |
+| Expert implementation built | n/a |
+| Expert kernel built | n/a |
+| Expert layers carrying an activation scale | n/a |
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.21268638 | 2.39 | 1.25496582 |
-| Chinese across several content types | 72 | 55.6% | 0.13804626 | 1.55 | 0.48569206 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.08939194 | 1.01 | 0.37588815 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.07346641 | 0.83 | 0.32436001 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.07235635 | 0.81 | 0.15489254 |
-| Other multilingual content | 36 | 64.5% | 0.07083341 | 0.80 | 0.11817463 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.05240112 | 0.59 | 0.27689472 |
-| Scientific and technical exposition | 96 | 60.3% | 0.04949621 | 0.56 | 0.07372579 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04917590 | 0.55 | 0.16455378 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.04891121 | 0.55 | 0.12918918 |
-
-**Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Worked mathematics, science, and formal reasoning** its strongest, a spread of 4.3x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.21324364 | 2.40 | 1.25973576 |
+| Chinese across several content types | 72 | 55.6% | 0.13763111 | 1.55 | 0.48908783 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.08926161 | 1.00 | 0.38041751 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.07381541 | 0.83 | 0.32218956 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.07310516 | 0.82 | 0.16032930 |
+| Other multilingual content | 36 | 64.5% | 0.07121532 | 0.80 | 0.12065877 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.05264429 | 0.59 | 0.29321268 |
+| Scientific and technical exposition | 96 | 60.3% | 0.04907991 | 0.55 | 0.07139858 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04887779 | 0.55 | 0.17824096 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.04852616 | 0.55 | 0.13126080 |
+
+**Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Worked mathematics, science, and formal reasoning** its strongest, a spread of 4.4x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
 ## Trunk versus head
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.07603511 |
-| Deployed (candidate's own head) | 0.08884902 |
-| Head-associated delta (not additive) | 0.01281391 |
+| Trunk (candidate hidden states, reference head) | 0.07615896 |
+| Deployed (candidate's own head) | 0.08890054 |
+| Head-associated delta (not additive) | 0.01274158 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'quantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'ModelOptNvFp4LinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.uint8'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'ModelOptNvFp4LinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.uint8'}, 'state': 'quantized'}]}, 'state': 'quantized', 'static': {'ignored': False, 'lm_head_dtypes': {'lm_head.input_scale': 'F32', 'lm_head.weight': 'U8', 'lm_head.weight_scale': 'F8_E4M3', 'lm_head.weight_scale_2': 'F32', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.input_scale', 'lm_head.weight', 'lm_head.weight_scale', 'lm_head.weight_scale_2'], 'output_weight_keys': ['lm_head.input_scale', 'lm_head.weight', 'lm_head.weight_scale', 'lm_head.weight_scale_2'], 'packed_keys': ['lm_head.weight_scale', 'lm_head.weight_scale_2'], 'quant_method': 'modelopt', 'state': 'quantized', 'tie_word_embeddings': False}} |
 
@@ -70,26 +86,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.07970075 |
-| 512–1023 | 393216 | 0.08368177 |
-| 1024–1535 | 393216 | 0.09329681 |
-| 1536–2046 | 392448 | 0.09873604 |
+| 0–511 | 393216 | 0.07967133 |
+| 512–1023 | 393216 | 0.08388078 |
+| 1024–1535 | 393216 | 0.09256314 |
+| 1536–2046 | 392448 | 0.09950761 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.11477444 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.12619287 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.12732539 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.09405716 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.02173773 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.11444036 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.12688756 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.12535953 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.09468442 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.02239718 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 87.7338% | 64.6300% | 41.9884% | 24.6266% | 13.5939% |
+| 87.7500% | 64.7214% | 42.0533% | 24.6980% | 13.6359% |
 
 ## Law compliance
 
@@ -101,22 +117,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.07603511, deployed 0.08884902, delta 0.012813907739867667 |
-| 9 | Tail and depth disclosure | PASS | mean 0.08884902, median 0.03153922, max 29.62161064, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.07615896, deployed 0.08890054, delta 0.012741578000413284 |
+| 9 | Tail and depth disclosure | PASS | mean 0.08890054, median 0.03146119, max 29.41000557, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.21268638, strongest worked_math_reasoning at 0.04891121, spread 4.3x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.21324364, strongest worked_math_reasoning at 0.04852616, spread 4.4x |
+| 16 | Candidate weight binding | PASS | scored weights 380fc3ae60e1e652 as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +139,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +147,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +170,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +189,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-NVFP4-RTX5090/report.md` | 12.65 KiB | This document. |
-| `Qwen3.8-27B-NVFP4-RTX5090/report.json` | 253.93 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-NVFP4-RTX5090/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-NVFP4-RTX5090/compliance.json` | 13.02 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-NVFP4-RTX5090/report.md` | 13.49 KiB | This document. |
+| `Qwen3.8-27B-NVFP4-RTX5090/report.json` | 256.45 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-NVFP4-RTX5090/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-NVFP4-RTX5090/compliance.json` | 12.29 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-NVFP4-RTX5090/strata.json b/Qwen3.8-27B-NVFP4-RTX5090/strata.json
index 8c006f9d401fa07ef32ec1ee3bd6de2aa5ffa79f..17ef8f9bfca3a404c934e1e5f6d698d8be97011a 100644
--- a/Qwen3.8-27B-NVFP4-RTX5090/strata.json
+++ b/Qwen3.8-27B-NVFP4-RTX5090/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 29.621610641479492,
-            "mean_kld": 0.21268638446455335,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.16980764884602795,
-            "median_context_p99": 3.194436550140381,
-            "p90_context_kld": 0.4663941209945985,
+            "max_kld": 29.410005569458008,
+            "mean_kld": 0.21324363685885994,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.17013793919462264,
+            "median_context_p99": 3.1346852779388428,
+            "p90_context_kld": 0.4793873904592046,
             "positions": 196512,
-            "top1_agreement": 0.8831114639309559,
+            "top1_agreement": 0.8833353688324377,
             "worst_context_id": 454,
-            "worst_context_kld": 1.254965819110839
+            "worst_context_kld": 1.2597357640464442
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 2.3937955743159085
+        "relative_to_run": 2.398676594096019
       },
       {
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 9.221136093139648,
-            "mean_kld": 0.2106804432508119,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.16749470140762515,
-            "median_context_p99": 1.1817240715026855,
-            "p90_context_kld": 0.4601693628589735,
+            "max_kld": 8.557491302490234,
+            "mean_kld": 0.20942829459093157,
+            "mean_ref_top1_prob": 0.6040861921682756,
+            "median_context_kld": 0.169365564213705,
+            "median_context_p99": 1.2919573783874512,
+            "p90_context_kld": 0.4482288233449379,
             "positions": 67551,
-            "top1_agreement": 0.7997068881289692,
-            "worst_context_id": 905,
-            "worst_context_kld": 0.48569205838970464
+            "top1_agreement": 0.8017497890482747,
+            "worst_context_id": 893,
+            "worst_context_kld": 0.4890878310734861
           }
         },
         "key": "wikisource_zh",
         "label": "wikisource_zh",
-        "relative_to_run": 2.37121860864939
+        "relative_to_run": 2.355759617386405
       },
       {
         "cells": {
           "deployed": {
             "contexts": 7,
-            "max_kld": 7.206441402435303,
-            "mean_kld": 0.10192228244303433,
-            "mean_ref_top1_prob": 0.7357366218042676,
-            "median_context_kld": 0.11245465481627331,
-            "median_context_p99": 1.8527274131774902,
-            "p90_context_kld": 0.11817462742460928,
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-            "median_context_p99": 3.194436550140381,
-            "p90_context_kld": 0.4663941209945985,
+            "max_kld": 29.410005569458008,
+            "mean_kld": 0.21324363685885994,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.17013793919462264,
+            "median_context_p99": 3.1346852779388428,
+            "p90_context_kld": 0.4793873904592046,
             "positions": 196512,
-            "top1_agreement": 0.8831114639309559,
+            "top1_agreement": 0.8833353688324377,
             "worst_context_id": 454,
-            "worst_context_kld": 1.254965819110839
+            "worst_context_kld": 1.2597357640464442
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 2.3937955743159085
+        "relative_to_run": 2.398676594096019
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 9.221136093139648,
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-            "median_context_kld": 0.0941144168045219,
-            "median_context_p99": 0.6546816229820251,
-            "p90_context_kld": 0.23829632700468703,
+            "max_kld": 8.557491302490234,
+            "mean_kld": 0.13763110758662334,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.09366887791148955,
+            "median_context_p99": 0.6111054420471191,
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             "positions": 147384,
-            "top1_agreement": 0.826616186288878,
-            "worst_context_id": 905,
-            "worst_context_kld": 0.48569205838970464
+            "top1_agreement": 0.8276067958530098,
+            "worst_context_id": 893,
+            "worst_context_kld": 0.4890878310734861
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.5537173328876446
+        "relative_to_run": 1.5481470924548617
       },
       {
         "cells": {
           "deployed": {
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-            "max_kld": 9.237215042114258,
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-            "median_context_p99": 0.6442986130714417,
-            "p90_context_kld": 0.12549267043511111,
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+            "median_context_p99": 0.6247144937515259,
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             "positions": 196512,
-            "top1_agreement": 0.8639675948542582,
+            "top1_agreement": 0.8644510258915487,
             "worst_context_id": 6,
-            "worst_context_kld": 0.37588815477459775
+            "worst_context_kld": 0.38041751277782426
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 1.006110664956836
+        "relative_to_run": 1.0040615639549482
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 8.085050582885742,
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-            "p90_context_kld": 0.08656176264974488,
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             "positions": 147384,
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             "worst_context_id": 443,
-            "worst_context_kld": 0.3243600094405916
+            "worst_context_kld": 0.3221895584307333
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.8268679996291151
+        "relative_to_run": 0.8303145964197121
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 8.182098388671875,
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-            "p90_context_kld": 0.1307554214582166,
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+            "median_context_kld": 0.06447523888587277,
+            "median_context_p99": 0.4327349364757538,
+            "p90_context_kld": 0.13155629979250485,
             "positions": 147384,
-            "top1_agreement": 0.8739211854746783,
+            "top1_agreement": 0.8737515605493134,
             "worst_context_id": 275,
-            "worst_context_kld": 0.15489253509157094
+            "worst_context_kld": 0.1603293014694022
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.8143742792970337
+        "relative_to_run": 0.8223252746542226
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 7.345405578613281,
-            "mean_kld": 0.07083341233029045,
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-            "median_context_p99": 0.44970738887786865,
-            "p90_context_kld": 0.11245465481627331,
+            "max_kld": 11.588532447814941,
+            "mean_kld": 0.07121532314429206,
+            "mean_ref_top1_prob": 0.6450767409183084,
+            "median_context_kld": 0.06519233793250864,
+            "median_context_p99": 0.46661657094955444,
+            "p90_context_kld": 0.1105094893175383,
             "positions": 73692,
-            "top1_agreement": 0.8756852846984747,
+            "top1_agreement": 0.8746403951582261,
             "worst_context_id": 953,
-            "worst_context_kld": 0.11817462742460928
+            "worst_context_kld": 0.12065877383606818
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.7972334918232739
+        "relative_to_run": 0.801067414172178
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 11.309407234191895,
-            "mean_kld": 0.05240112493687464,
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-            "median_context_p99": 0.3964604139328003,
-            "p90_context_kld": 0.07997063031452036,
+            "max_kld": 14.120194435119629,
+            "mean_kld": 0.05264429081489764,
+            "mean_ref_top1_prob": 0.7950885550065969,
+            "median_context_kld": 0.040841334767735375,
+            "median_context_p99": 0.4023333787918091,
+            "p90_context_kld": 0.08157172921411385,
             "positions": 196512,
-            "top1_agreement": 0.9284572952287901,
+            "top1_agreement": 0.9283351652825272,
             "worst_context_id": 667,
-            "worst_context_kld": 0.27689472241669605
+            "worst_context_kld": 0.29321267733423706
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5897772030817099
+        "relative_to_run": 0.5921706741198473
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 16.185749053955078,
-            "mean_kld": 0.04949621323585394,
-            "mean_ref_top1_prob": 0.6033561622407548,
-            "median_context_kld": 0.048019773744291346,
-            "median_context_p99": 0.30111250281333923,
-            "p90_context_kld": 0.06206171818080255,
+            "max_kld": 11.157588958740234,
+            "mean_kld": 0.049079914368717295,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.048805704917541055,
+            "median_context_p99": 0.312124103307724,
+            "p90_context_kld": 0.05848004086686995,
             "positions": 196512,
-            "top1_agreement": 0.8900728708679368,
-            "worst_context_id": 246,
-            "worst_context_kld": 0.07372579095666683
+            "top1_agreement": 0.8908056505455137,
+            "worst_context_id": 174,
+            "worst_context_kld": 0.07139858353297991
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.5570822809728582
+        "relative_to_run": 0.5520766929819303
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 8.847333908081055,
-            "mean_kld": 0.0491758979962088,
-            "mean_ref_top1_prob": 0.7071241246993897,
-            "median_context_kld": 0.04248309454994046,
-            "median_context_p99": 0.494755357503891,
-            "p90_context_kld": 0.08263404999373626,
+            "max_kld": 10.949919700622559,
+            "mean_kld": 0.04887779489384767,
+            "mean_ref_top1_prob": 0.7071550040111672,
+            "median_context_kld": 0.04098168850353924,
+            "median_context_p99": 0.5531740188598633,
+            "p90_context_kld": 0.07973034153401946,
             "positions": 73692,
-            "top1_agreement": 0.8469304673505944,
+            "top1_agreement": 0.8472968571893829,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.1645537792651185
+            "worst_context_kld": 0.17824096230327108
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.553477117412535
+        "relative_to_run": 0.5498031468132274
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 17.194229125976562,
-            "mean_kld": 0.04891121428016043,
-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.04473202064081297,
-            "median_context_p99": 0.26329120993614197,
-            "p90_context_kld": 0.05904413538005142,
+            "max_kld": 17.83475685119629,
+            "mean_kld": 0.04852616171264854,
+            "mean_ref_top1_prob": 0.6582834184806026,
+            "median_context_kld": 0.04458567627830201,
+            "median_context_p99": 0.2575961947441101,
+            "p90_context_kld": 0.05704854838084238,
             "positions": 196512,
-            "top1_agreement": 0.9055681078000326,
+            "top1_agreement": 0.9055579303045107,
             "worst_context_id": 825,
-            "worst_context_kld": 0.12918918401151672
+            "worst_context_kld": 0.1312607970175872
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.5504980893489136
+        "relative_to_run": 0.5458477918311317
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 29.621610641479492,
-      "mean_kld": 0.08884901732903162,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.06137484148042411,
-      "median_context_p99": 0.41598302125930786,
-      "p90_context_kld": 0.16160134971982643,
+      "max_kld": 29.410005569458008,
+      "mean_kld": 0.08890053681422791,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.06094675094259141,
+      "median_context_p99": 0.4184585213661194,
+      "p90_context_kld": 0.169365564213705,
       "positions": 1572096,
-      "top1_agreement": 0.879409399934864,
+      "top1_agreement": 0.8795868700130272,
       "worst_context_id": 454,
-      "worst_context_kld": 1.254965819110839
+      "worst_context_kld": 1.2597357640464442
     }
   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-NVFP4-RTX5090/strata.md b/Qwen3.8-27B-NVFP4-RTX5090/strata.md
index 1d48e1d5523b0bd81c97def5b4705e5a1615fcc8..c10e0d52721e7fe1735422e7137939aa62668dd4 100644
--- a/Qwen3.8-27B-NVFP4-RTX5090/strata.md
+++ b/Qwen3.8-27B-NVFP4-RTX5090/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-NVFP4-RTX5090
 
-768 contexts, 1572096 scored positions, mean 0.08884902, reference top-1 64.2%, top-1 agreement 87.9409%.
+768 contexts, 1572096 scored positions, mean 0.08890054, reference top-1 64.2%, top-1 agreement 87.9587%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.21268638 | 2.39 | 0.16980765 | 0.46639412 | 1.254966 |
-| Chinese across several content types | 72 | 55.6% | 0.13804626 | 1.55 | 0.09411442 | 0.23829633 | 0.485692 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.08939194 | 1.01 | 0.07517421 | 0.12549267 | 0.375888 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.07346641 | 0.83 | 0.06503037 | 0.08656176 | 0.324360 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.07235635 | 0.81 | 0.06563072 | 0.13075542 | 0.154893 |
-| Other multilingual content | 36 | 64.5% | 0.07083341 | 0.80 | 0.06525898 | 0.11245465 | 0.118175 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.05240112 | 0.59 | 0.04196241 | 0.07997063 | 0.276895 |
-| Scientific and technical exposition | 96 | 60.3% | 0.04949621 | 0.56 | 0.04801977 | 0.06206172 | 0.073726 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04917590 | 0.55 | 0.04248309 | 0.08263405 | 0.164554 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.04891121 | 0.55 | 0.04473202 | 0.05904414 | 0.129189 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.21324364 | 2.40 | 0.17013794 | 0.47938739 | 1.259736 |
+| Chinese across several content types | 72 | 55.6% | 0.13763111 | 1.55 | 0.09366888 | 0.24518772 | 0.489088 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.08926161 | 1.00 | 0.07524437 | 0.12359178 | 0.380418 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.07381541 | 0.83 | 0.06468237 | 0.08678553 | 0.322190 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.07310516 | 0.82 | 0.06447524 | 0.13155630 | 0.160329 |
+| Other multilingual content | 36 | 64.5% | 0.07121532 | 0.80 | 0.06519234 | 0.11050949 | 0.120659 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.05264429 | 0.59 | 0.04084133 | 0.08157173 | 0.293213 |
+| Scientific and technical exposition | 96 | 60.3% | 0.04907991 | 0.55 | 0.04880570 | 0.05848004 | 0.071399 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.04887779 | 0.55 | 0.04098169 | 0.07973034 | 0.178241 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.04852616 | 0.55 | 0.04458568 | 0.05704855 | 0.131261 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.21268638 | 2.39 | 0.16980765 | 0.46639412 | 1.254966 |
-| wikisource_zh | 33 | 60.4% | 0.21068044 | 2.37 | 0.16749470 | 0.46016936 | 0.485692 |
-| wikipedia_de | 7 | 73.6% | 0.10192228 | 1.15 | 0.11245465 | 0.11817463 | 0.118175 |
-| wikipedia_en | 96 | 59.5% | 0.08939194 | 1.01 | 0.07517421 | 0.12549267 | 0.375888 |
-| regulations | 23 | 72.3% | 0.08888440 | 1.00 | 0.10944162 | 0.13830929 | 0.154893 |
-| wikipedia_zh | 39 | 51.5% | 0.07658656 | 0.86 | 0.07334535 | 0.10122490 | 0.109906 |
-| public_domain_books | 72 | 50.6% | 0.07346641 | 0.83 | 0.06503037 | 0.08656176 | 0.324360 |
-| wikipedia_ja | 7 | 57.0% | 0.07299386 | 0.82 | 0.07676595 | 0.08001701 | 0.080017 |
-| public_domain_review | 26 | 51.8% | 0.07178554 | 0.81 | 0.06815590 | 0.09782586 | 0.145134 |
-| wikipedia_es | 7 | 59.2% | 0.06601946 | 0.74 | 0.06525898 | 0.08028594 | 0.080286 |
-| wikipedia_cs | 6 | 68.9% | 0.06272890 | 0.71 | 0.06199259 | 0.07546922 | 0.075469 |
-| open_news | 23 | 53.8% | 0.05647357 | 0.64 | 0.05530742 | 0.06890052 | 0.085393 |
-| wikipedia_fr | 3 | 60.2% | 0.05524008 | 0.62 | 0.05600011 | 0.05916261 | 0.059163 |
-| stackv2 | 44 | 72.0% | 0.05440211 | 0.61 | 0.04682076 | 0.07820086 | 0.268242 |
-| wikipedia_ru | 6 | 66.6% | 0.05356000 | 0.60 | 0.05389382 | 0.06853920 | 0.068539 |
-| github_code | 52 | 85.8% | 0.05070798 | 0.57 | 0.03662648 | 0.07997063 | 0.276895 |
-| scientific_papers | 96 | 60.3% | 0.04949621 | 0.56 | 0.04801977 | 0.06206172 | 0.073726 |
-| starcoder_structured | 36 | 70.7% | 0.04917590 | 0.55 | 0.04248309 | 0.08263405 | 0.164554 |
-| libretexts | 96 | 65.8% | 0.04891121 | 0.55 | 0.04473202 | 0.05904414 | 0.129189 |
+| wildchat | 96 | 73.5% | 0.21324364 | 2.40 | 0.17013794 | 0.47938739 | 1.259736 |
+| wikisource_zh | 33 | 60.4% | 0.20942829 | 2.36 | 0.16936556 | 0.44822882 | 0.489088 |
+| wikipedia_de | 7 | 73.6% | 0.10318174 | 1.16 | 0.11050949 | 0.12065877 | 0.120659 |
+| regulations | 23 | 72.3% | 0.09111800 | 1.02 | 0.10723594 | 0.14780949 | 0.160329 |
+| wikipedia_en | 96 | 59.5% | 0.08926161 | 1.00 | 0.07524437 | 0.12359178 | 0.380418 |
+| wikipedia_zh | 39 | 51.5% | 0.07687964 | 0.86 | 0.07373861 | 0.10373904 | 0.109338 |
+| public_domain_books | 72 | 50.6% | 0.07381541 | 0.83 | 0.06468237 | 0.08678553 | 0.322190 |
+| wikipedia_ja | 7 | 57.0% | 0.07229254 | 0.81 | 0.07461241 | 0.08007906 | 0.080079 |
+| public_domain_review | 26 | 51.8% | 0.07159772 | 0.81 | 0.06766349 | 0.10433167 | 0.146552 |
+| wikipedia_es | 7 | 59.2% | 0.06658863 | 0.75 | 0.06366197 | 0.07747506 | 0.077475 |
+| wikipedia_cs | 6 | 68.9% | 0.06374344 | 0.72 | 0.06442506 | 0.07887765 | 0.078878 |
+| open_news | 23 | 53.8% | 0.05679637 | 0.64 | 0.05493885 | 0.07257355 | 0.083591 |
+| stackv2 | 44 | 72.0% | 0.05478947 | 0.62 | 0.04732765 | 0.08157173 | 0.273151 |
+| wikipedia_fr | 3 | 60.2% | 0.05439634 | 0.61 | 0.05312169 | 0.05942543 | 0.059425 |
+| wikipedia_ru | 6 | 66.6% | 0.05394361 | 0.61 | 0.05594838 | 0.07038361 | 0.070384 |
+| github_code | 52 | 85.8% | 0.05082914 | 0.57 | 0.03639650 | 0.07490327 | 0.293213 |
+| scientific_papers | 96 | 60.3% | 0.04907991 | 0.55 | 0.04880570 | 0.05848004 | 0.071399 |
+| starcoder_structured | 36 | 70.7% | 0.04887779 | 0.55 | 0.04098169 | 0.07973034 | 0.178241 |
+| libretexts | 96 | 65.8% | 0.04852616 | 0.55 | 0.04458568 | 0.05704855 | 0.131261 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.21268638, 2.39x the run mean of 0.08884902 over 96 context(s).
-- Strongest domain: **Worked mathematics, science, and formal reasoning** at 0.04891121, 0.55x the run mean. The spread across domains is 4.3x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.21324364, 2.40x the run mean of 0.08890054 over 96 context(s).
+- Strongest domain: **Worked mathematics, science, and formal reasoning** at 0.04852616, 0.55x the run mean. The spread across domains is 4.4x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 1.254966 against a median of 0.16980765 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 1.259736 against a median of 0.17013794 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/Qwen3.8-27B-NVFP4/compliance.json b/Qwen3.8-27B-NVFP4/compliance.json
index 4161034334d91b3e15f9724fdc3d75a9c47dbb93..d94607826c9157bc5b5dd1ac29a5a47a9faff813 100644
--- a/Qwen3.8-27B-NVFP4/compliance.json
+++ b/Qwen3.8-27B-NVFP4/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/unsloth/Qwen3.8-27B-NVFP4",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "529539fc371093824cdabaab7afc083f24f46e2e92bb91ab29c32d6542bbcfb3",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.087010+00:00",
+  "evaluated_at": "2026-09-11T20:24:21.646814+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.03966656, deployed 0.04096872, delta 0.0013021660852515493",
+      "detail": "trunk 0.03923024, deployed 0.04053080, delta 0.0013005567674299265",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.04096872, median 0.00944244, max 36.54566574, 4 depth buckets",
+      "detail": "mean 0.04053080, median 0.00929746, max 28.66598129, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.12457770, strongest scientific_technical at 0.01691031, spread 7.4x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.12158332, strongest scientific_technical at 0.01656302, spread 7.3x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights 529539fc37109382 as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.040968724321885384,
+  "laws_version": 15,
+  "mean_kld": 0.04053079722531637,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 36.5456657409668,
-        "mean_kld": 0.040968724321885384,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.021978546635872478,
-        "median_context_p99": 0.17513030767440796,
-        "p90_context_kld": 0.08278915535830515,
+        "max_kld": 28.66598129272461,
+        "mean_kld": 0.040530797225316356,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.021778975352759924,
+        "median_context_p99": 0.17481790482997894,
+        "p90_context_kld": 0.08018104969267713,
         "positions": 1572096,
-        "top1_agreement": 0.926059858939912,
+        "top1_agreement": 0.9268944135727081,
         "worst_context_id": 454,
-        "worst_context_kld": 0.8362269388706521
+        "worst_context_kld": 0.8104930786551602
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 36.5456657409668,
-            "mean_kld": 0.12457769541074566,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.09409491196048908,
-            "median_context_p99": 2.0393991470336914,
-            "p90_context_kld": 0.2799708803626206,
+            "max_kld": 28.66598129272461,
+            "mean_kld": 0.12158332374682436,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.08575015208991663,
+            "median_context_p99": 1.6861377954483032,
+            "p90_context_kld": 0.29358947054916235,
             "positions": 196512,
-            "top1_agreement": 0.923175175052923,
+            "top1_agreement": 0.9250681892199968,
             "worst_context_id": 454,
-            "worst_context_kld": 0.8362269388706521
+            "worst_context_kld": 0.8104930786551602
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 3.0407999632099014
+        "relative_to_run": 2.999776270644905
       },
       {
         "cells": {
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-            "median_context_kld": 0.034198077382997016,
-            "median_context_p99": 0.27472054958343506,
-            "p90_context_kld": 0.12171763135253476,
+            "max_kld": 6.782408714294434,
+            "mean_kld": 0.058009695747318554,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.03501158816479015,
+            "median_context_p99": 0.27062809467315674,
+            "p90_context_kld": 0.1256960806689686,
             "positions": 147384,
-            "top1_agreement": 0.8939437116647668,
-            "worst_context_id": 893,
-            "worst_context_kld": 0.23709843126137567
+            "top1_agreement": 0.8944390164468328,
+            "worst_context_id": 917,
+            "worst_context_kld": 0.2366614644814825
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.4023138984255346
+        "relative_to_run": 1.4312498080122769
       },
       {
         "cells": {
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-            "p90_context_kld": 0.05708804087610574,
+            "max_kld": 6.517783164978027,
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+            "top1_agreement": 0.9173027601367856,
             "worst_context_id": 65,
-            "worst_context_kld": 0.2408369537170143
+            "worst_context_kld": 0.24843901303775767
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9696648550898366
+        "relative_to_run": 0.9790213437665266
       },
       {
         "cells": {
           "deployed": {
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+            "top1_agreement": 0.9259281875915975,
+            "worst_context_id": 312,
+            "worst_context_kld": 0.0811365691669795
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         },
-        "key": "literary_narrative",
-        "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.7183199873112919
+        "key": "news_history_legal_essays",
+        "label": "News, history, economics, legal analysis, and essays",
+        "relative_to_run": 0.7198247678664795
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         "cells": {
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-        "key": "news_history_legal_essays",
-        "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.709141673035604
+        "key": "literary_narrative",
+        "label": "Literary, narrative, and creative writing",
+        "relative_to_run": 0.7184887593734594
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         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
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+        "relative_to_run": 0.617136897547495
       },
       {
         "cells": {
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+            "worst_context_kld": 0.17765063071981094
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         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
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+        "relative_to_run": 0.5602193583838262
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         "cells": {
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+            "worst_context_kld": 0.10268640378926563
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         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
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         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
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+            "top1_agreement": 0.9393624816805081,
+            "worst_context_id": 174,
+            "worst_context_kld": 0.027381622246614497
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         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.4127614605599896
+        "relative_to_run": 0.40865282996993746
       }
     ]
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diff --git a/Qwen3.8-27B-NVFP4/inspect.json b/Qwen3.8-27B-NVFP4/inspect.json
index 61bd6154ac84805bed06bbe27699b7bb14304044..ba58cc74df154fa133e74c3849b9544bd144880b 100644
--- a/Qwen3.8-27B-NVFP4/inspect.json
+++ b/Qwen3.8-27B-NVFP4/inspect.json
@@ -1,31 +1,66 @@
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index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
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@@ -836,6 +837,7 @@
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@@ -845,17 +847,39 @@
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diff --git a/Qwen3.8-27B-NVFP4/report.json b/Qwen3.8-27B-NVFP4/report.json
index 060f679648635caf4d7826d8a3013cd56f000643..acca1effa890488789cea9302f1744cf145e09e1 100644
--- a/Qwen3.8-27B-NVFP4/report.json
+++ b/Qwen3.8-27B-NVFP4/report.json
@@ -1,8530 +1,8576 @@
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-      "1": 0.9300659756147207,
-      "2": 0.7776643411089399,
-      "3": 0.5998043376485914,
-      "4": 0.4335835725044781,
-      "5": 0.30184797874938935
+      "1": 0.9310703672040385,
+      "2": 0.7793366308418824,
+      "3": 0.6021044516365412,
+      "4": 0.4356973111056831,
+      "5": 0.3039572646963035
     }
   },
   "unique_tokens": 1572864,
+  "student_weights_sha256": "529539fc371093824cdabaab7afc083f24f46e2e92bb91ab29c32d6542bbcfb3",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/unsloth/Qwen3.8-27B-NVFP4",
@@ -8816,5 +8863,7 @@
       "candidate": "06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523",
       "match": false
     }
-  }
+  },
+  "candidate_hf_repo": "unsloth/Qwen3.8-27B-NVFP4",
+  "candidate_revision": "57926baca9a82b4d6906b43f2750d55315f5b10f"
 }
diff --git a/Qwen3.8-27B-NVFP4/report.md b/Qwen3.8-27B-NVFP4/report.md
index dec22870e223f4aa65696218e759a53877c82f87..7b1c361e7b534391937b42eb602f02111a14b5e1 100644
--- a/Qwen3.8-27B-NVFP4/report.md
+++ b/Qwen3.8-27B-NVFP4/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-NVFP4: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.04096872** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.04053080** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.04096872 | 0.00944244 | 0.05542825 | 0.50567393 | 36.54566574 | 92.6060% |
+| 0.04053080 | 0.00929746 | 0.05469899 | 0.50382712 | 28.66598129 | 92.6894% |
 
-Reverse direction, KLD(candidate || reference): 0.04331330.
+Reverse direction, KLD(candidate || reference): 0.04259346.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.04331330.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/unsloth/Qwen3.8-27B-NVFP4 |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | 529539fc37109382 |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,42 +27,49 @@ Reverse direction, KLD(candidate || reference): 0.04331330.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12457770 | 3.04 | 0.83622694 |
-| Chinese across several content types | 72 | 55.6% | 0.05745101 | 1.40 | 0.23709843 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.03972593 | 0.97 | 0.24083695 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.02942865 | 0.72 | 0.21936385 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02905263 | 0.71 | 0.08197301 |
-| Other multilingual content | 36 | 64.5% | 0.02557053 | 0.62 | 0.05177419 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02250032 | 0.55 | 0.17071216 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02042135 | 0.50 | 0.09928304 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01983936 | 0.48 | 0.07883125 |
-| Scientific and technical exposition | 96 | 60.3% | 0.01691031 | 0.41 | 0.03146690 |
-
-**Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 7.4x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12158332 | 3.00 | 0.81049308 |
+| Chinese across several content types | 72 | 55.6% | 0.05800970 | 1.43 | 0.23666146 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.03968052 | 0.98 | 0.24843901 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02917507 | 0.72 | 0.08113657 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.02912092 | 0.72 | 0.21794198 |
+| Other multilingual content | 36 | 64.5% | 0.02501305 | 0.62 | 0.04523514 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02270614 | 0.56 | 0.17765063 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02024896 | 0.50 | 0.10268640 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01951086 | 0.48 | 0.07172076 |
+| Scientific and technical exposition | 96 | 60.3% | 0.01656302 | 0.41 | 0.02738162 |
+
+**Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 7.3x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
 ## Trunk versus head
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.03966656 |
-| Deployed (candidate's own head) | 0.04096872 |
-| Head-associated delta (not additive) | 0.00130217 |
+| Trunk (candidate hidden states, reference head) | 0.03923024 |
+| Deployed (candidate's own head) | 0.04053080 |
+| Head-associated delta (not additive) | 0.00130056 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'quantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'CompressedTensorsLinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.float8_e4m3fn'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'CompressedTensorsLinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.float8_e4m3fn'}, 'state': 'quantized'}]}, 'state': 'quantized', 'static': {'ignored': False, 'lm_head_dtypes': {'lm_head.weight': 'F8_E4M3', 'lm_head.weight_scale': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight', 'lm_head.weight_scale'], 'output_weight_keys': ['lm_head.weight', 'lm_head.weight_scale'], 'packed_keys': ['lm_head.weight_scale'], 'quant_method': 'compressed-tensors', 'state': 'quantized', 'tie_word_embeddings': False}} |
 
@@ -70,26 +77,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.03419946 |
-| 512–1023 | 393216 | 0.03730702 |
-| 1024–1535 | 393216 | 0.04390492 |
-| 1536–2046 | 392448 | 0.04847816 |
+| 0–511 | 393216 | 0.03439430 |
+| 512–1023 | 393216 | 0.03809782 |
+| 1024–1535 | 393216 | 0.04331275 |
+| 1536–2046 | 392448 | 0.04632964 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.04418532 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.05743935 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.06343079 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.04697047 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.01039384 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.04417294 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.05791929 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.06115178 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.04707342 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.00984591 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 92.4652% | 76.5753% | 58.2991% | 41.4397% | 28.2154% |
+| 92.5520% | 76.7247% | 58.5141% | 41.6387% | 28.4032% |
 
 ## Law compliance
 
@@ -101,22 +108,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.03966656, deployed 0.04096872, delta 0.0013021660852515493 |
-| 9 | Tail and depth disclosure | PASS | mean 0.04096872, median 0.00944244, max 36.54566574, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.03923024, deployed 0.04053080, delta 0.0013005567674299265 |
+| 9 | Tail and depth disclosure | PASS | mean 0.04053080, median 0.00929746, max 28.66598129, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.12457770, strongest scientific_technical at 0.01691031, spread 7.4x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.12158332, strongest scientific_technical at 0.01656302, spread 7.3x |
+| 16 | Candidate weight binding | PASS | scored weights 529539fc37109382 as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +130,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +138,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +161,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +180,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-NVFP4/report.md` | 12.43 KiB | This document. |
-| `Qwen3.8-27B-NVFP4/report.json` | 254.73 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-NVFP4/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-NVFP4/compliance.json` | 13.02 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-NVFP4/report.md` | 13.27 KiB | This document. |
+| `Qwen3.8-27B-NVFP4/report.json` | 257.26 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-NVFP4/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-NVFP4/compliance.json` | 12.29 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-NVFP4/strata.json b/Qwen3.8-27B-NVFP4/strata.json
index 2e65d45432396a885a206a465045a17a00735688..0e1a217b47046a9d821adc9f925f2eff53a21036 100644
--- a/Qwen3.8-27B-NVFP4/strata.json
+++ b/Qwen3.8-27B-NVFP4/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 36.5456657409668,
-            "mean_kld": 0.12457769541074566,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.09409491196048908,
-            "median_context_p99": 2.0393991470336914,
-            "p90_context_kld": 0.2799708803626206,
+            "max_kld": 28.66598129272461,
+            "mean_kld": 0.12158332374682436,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.08575015208991663,
+            "median_context_p99": 1.6861377954483032,
+            "p90_context_kld": 0.29358947054916235,
             "positions": 196512,
-            "top1_agreement": 0.923175175052923,
+            "top1_agreement": 0.9250681892199968,
             "worst_context_id": 454,
-            "worst_context_kld": 0.8362269388706521
+            "worst_context_kld": 0.8104930786551602
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 3.0407999632099014
+        "relative_to_run": 2.999776270644905
       },
       {
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 6.911944389343262,
-            "mean_kld": 0.09430849689233974,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.06800715166529955,
-            "median_context_p99": 0.5470696687698364,
-            "p90_context_kld": 0.2165044876798134,
+            "max_kld": 6.782408714294434,
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+            "max_kld": 3.9671146869659424,
+            "mean_kld": 0.01656302498706322,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.016550733119089625,
+            "median_context_p99": 0.12118519842624664,
+            "p90_context_kld": 0.021278483650238936,
             "positions": 196512,
-            "top1_agreement": 0.9386093470118874,
-            "worst_context_id": 246,
-            "worst_context_kld": 0.031466900875017814
+            "top1_agreement": 0.9393624816805081,
+            "worst_context_id": 174,
+            "worst_context_kld": 0.027381622246614497
           }
         },
         "key": "scientific_papers",
         "label": "scientific_papers",
-        "relative_to_run": 0.4127614605599896
+        "relative_to_run": 0.40865282996993746
       }
     ],
     "stratum": [
@@ -390,217 +390,217 @@
         "cells": {
           "deployed": {
             "contexts": 96,
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-            "mean_kld": 0.12457769541074566,
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-            "p90_context_kld": 0.2799708803626206,
+            "max_kld": 28.66598129272461,
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+            "median_context_kld": 0.08575015208991663,
+            "median_context_p99": 1.6861377954483032,
+            "p90_context_kld": 0.29358947054916235,
             "positions": 196512,
-            "top1_agreement": 0.923175175052923,
+            "top1_agreement": 0.9250681892199968,
             "worst_context_id": 454,
-            "worst_context_kld": 0.8362269388706521
+            "worst_context_kld": 0.8104930786551602
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 3.0407999632099014
+        "relative_to_run": 2.999776270644905
       },
       {
         "cells": {
           "deployed": {
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-            "median_context_p99": 0.27472054958343506,
-            "p90_context_kld": 0.12171763135253476,
+            "max_kld": 6.782408714294434,
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             "positions": 147384,
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-            "worst_context_id": 893,
-            "worst_context_kld": 0.23709843126137567
+            "top1_agreement": 0.8944390164468328,
+            "worst_context_id": 917,
+            "worst_context_kld": 0.2366614644814825
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.4023138984255346
+        "relative_to_run": 1.4312498080122769
       },
       {
         "cells": {
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             "positions": 196512,
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             "worst_context_id": 65,
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+            "worst_context_kld": 0.24843901303775767
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9696648550898366
+        "relative_to_run": 0.9790213437665266
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
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             "positions": 147384,
-            "top1_agreement": 0.9151671823264398,
-            "worst_context_id": 443,
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+            "worst_context_id": 312,
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           }
         },
-        "key": "literary_narrative",
-        "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.7183199873112919
+        "key": "news_history_legal_essays",
+        "label": "News, history, economics, legal analysis, and essays",
+        "relative_to_run": 0.7198247678664795
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
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-            "p90_context_kld": 0.06020746195574988,
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             "positions": 147384,
-            "top1_agreement": 0.9257585626662325,
-            "worst_context_id": 275,
-            "worst_context_kld": 0.0819730113510625
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+            "worst_context_id": 443,
+            "worst_context_kld": 0.21794198408233922
           }
         },
-        "key": "news_history_legal_essays",
-        "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.709141673035604
+        "key": "literary_narrative",
+        "label": "Literary, narrative, and creative writing",
+        "relative_to_run": 0.7184887593734594
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 7.721675872802734,
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-            "p90_context_kld": 0.04305093457533655,
+            "max_kld": 6.416473865509033,
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+            "mean_ref_top1_prob": 0.6450767409183084,
+            "median_context_kld": 0.022881467166419225,
+            "median_context_p99": 0.17793317139148712,
+            "p90_context_kld": 0.04138496706841051,
             "positions": 73692,
-            "top1_agreement": 0.9274819519079411,
+            "top1_agreement": 0.9287303913586278,
             "worst_context_id": 953,
-            "worst_context_kld": 0.051774189189553635
+            "worst_context_kld": 0.0452351423921429
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.6241476691274932
+        "relative_to_run": 0.617136897547495
       },
       {
         "cells": {
           "deployed": {
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             "positions": 196512,
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+            "top1_agreement": 0.9565929815990881,
             "worst_context_id": 667,
-            "worst_context_kld": 0.1707121626802731
+            "worst_context_kld": 0.17765063071981094
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5492071886527938
+        "relative_to_run": 0.5602193583838262
       },
       {
         "cells": {
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-            "p90_context_kld": 0.03592128721473434,
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+            "median_context_p99": 0.2136673778295517,
+            "p90_context_kld": 0.03390113755388437,
             "positions": 73692,
-            "top1_agreement": 0.882931661510069,
+            "top1_agreement": 0.8844786408293981,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.09928303706595111
+            "worst_context_kld": 0.10268640378926563
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.4984619074928163
+        "relative_to_run": 0.4995943832478895
       },
       {
         "cells": {
           "deployed": {
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-            "median_context_p99": 0.11479724943637848,
-            "p90_context_kld": 0.03028211425308765,
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             "positions": 196512,
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+            "top1_agreement": 0.9449143054877056,
             "worst_context_id": 825,
-            "worst_context_kld": 0.07883124659302003
+            "worst_context_kld": 0.07172076344828876
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.48425627217553896
+        "relative_to_run": 0.48138346549737526
       },
       {
         "cells": {
           "deployed": {
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-            "p90_context_kld": 0.021205758610799045,
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             "positions": 196512,
-            "top1_agreement": 0.9386093470118874,
-            "worst_context_id": 246,
-            "worst_context_kld": 0.031466900875017814
+            "top1_agreement": 0.9393624816805081,
+            "worst_context_id": 174,
+            "worst_context_kld": 0.027381622246614497
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.4127614605599896
+        "relative_to_run": 0.40865282996993746
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 36.5456657409668,
-      "mean_kld": 0.040968724321885384,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.021978546635872478,
-      "median_context_p99": 0.17513030767440796,
-      "p90_context_kld": 0.08278915535830515,
+      "max_kld": 28.66598129272461,
+      "mean_kld": 0.040530797225316356,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.021778975352759924,
+      "median_context_p99": 0.17481790482997894,
+      "p90_context_kld": 0.08018104969267713,
       "positions": 1572096,
-      "top1_agreement": 0.926059858939912,
+      "top1_agreement": 0.9268944135727081,
       "worst_context_id": 454,
-      "worst_context_kld": 0.8362269388706521
+      "worst_context_kld": 0.8104930786551602
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   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-NVFP4/strata.md b/Qwen3.8-27B-NVFP4/strata.md
index 3fd3d9b9c2e833d31d86f33b202e2e2c9b686e39..86fd5cfa37059614d4568448879a8465e43fdb99 100644
--- a/Qwen3.8-27B-NVFP4/strata.md
+++ b/Qwen3.8-27B-NVFP4/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-NVFP4
 
-768 contexts, 1572096 scored positions, mean 0.04096872, reference top-1 64.2%, top-1 agreement 92.6060%.
+768 contexts, 1572096 scored positions, mean 0.04053080, reference top-1 64.2%, top-1 agreement 92.6894%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12457770 | 3.04 | 0.09409491 | 0.27997088 | 0.836227 |
-| Chinese across several content types | 72 | 55.6% | 0.05745101 | 1.40 | 0.03419808 | 0.12171763 | 0.237098 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.03972593 | 0.97 | 0.03066757 | 0.05708804 | 0.240837 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.02942865 | 0.72 | 0.02369674 | 0.03480264 | 0.219364 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02905263 | 0.71 | 0.02182225 | 0.06020746 | 0.081973 |
-| Other multilingual content | 36 | 64.5% | 0.02557053 | 0.62 | 0.02273889 | 0.04305093 | 0.051774 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02250032 | 0.55 | 0.01509798 | 0.03532435 | 0.170712 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02042135 | 0.50 | 0.01775127 | 0.03592129 | 0.099283 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01983936 | 0.48 | 0.01608115 | 0.03028211 | 0.078831 |
-| Scientific and technical exposition | 96 | 60.3% | 0.01691031 | 0.41 | 0.01667293 | 0.02120576 | 0.031467 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12158332 | 3.00 | 0.08575015 | 0.29358947 | 0.810493 |
+| Chinese across several content types | 72 | 55.6% | 0.05800970 | 1.43 | 0.03501159 | 0.12569608 | 0.236661 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.03968052 | 0.98 | 0.03094896 | 0.05457949 | 0.248439 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02917507 | 0.72 | 0.02317365 | 0.06218777 | 0.081137 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.02912092 | 0.72 | 0.02382746 | 0.03332172 | 0.217942 |
+| Other multilingual content | 36 | 64.5% | 0.02501305 | 0.62 | 0.02288147 | 0.04138497 | 0.045235 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02270614 | 0.56 | 0.01521937 | 0.03640113 | 0.177651 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02024896 | 0.50 | 0.01510115 | 0.03390114 | 0.102686 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01951086 | 0.48 | 0.01577138 | 0.02993783 | 0.071721 |
+| Scientific and technical exposition | 96 | 60.3% | 0.01656302 | 0.41 | 0.01655073 | 0.02127848 | 0.027382 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.12457770 | 3.04 | 0.09409491 | 0.27997088 | 0.836227 |
-| wikisource_zh | 33 | 60.4% | 0.09430850 | 2.30 | 0.06800715 | 0.21650449 | 0.237098 |
-| regulations | 23 | 72.3% | 0.04226612 | 1.03 | 0.05356200 | 0.07344523 | 0.081973 |
-| wikipedia_de | 7 | 73.6% | 0.04007121 | 0.98 | 0.04305093 | 0.05177419 | 0.051774 |
-| wikipedia_en | 96 | 59.5% | 0.03972593 | 0.97 | 0.03066757 | 0.05708804 | 0.240837 |
-| public_domain_books | 72 | 50.6% | 0.02942865 | 0.72 | 0.02369674 | 0.03480264 | 0.219364 |
-| public_domain_review | 26 | 51.8% | 0.02667600 | 0.65 | 0.02376971 | 0.04163839 | 0.074956 |
-| wikipedia_zh | 39 | 51.5% | 0.02626391 | 0.64 | 0.02423845 | 0.03705976 | 0.043309 |
-| wikipedia_ja | 7 | 57.0% | 0.02481212 | 0.61 | 0.02508854 | 0.02936745 | 0.029367 |
-| github_code | 52 | 85.8% | 0.02405583 | 0.59 | 0.01422886 | 0.04196826 | 0.170712 |
-| wikipedia_es | 7 | 59.2% | 0.02355380 | 0.57 | 0.02187323 | 0.03122308 | 0.031223 |
-| wikipedia_cs | 6 | 68.9% | 0.02215582 | 0.54 | 0.02272664 | 0.02691429 | 0.026914 |
-| stackv2 | 44 | 72.0% | 0.02066199 | 0.50 | 0.01638571 | 0.02901203 | 0.123243 |
-| starcoder_structured | 36 | 70.7% | 0.02042135 | 0.50 | 0.01775127 | 0.03592129 | 0.099283 |
-| libretexts | 96 | 65.8% | 0.01983936 | 0.48 | 0.01608115 | 0.03028211 | 0.078831 |
-| wikipedia_ru | 6 | 66.6% | 0.01927473 | 0.47 | 0.01859219 | 0.02627664 | 0.026277 |
-| open_news | 23 | 53.8% | 0.01852576 | 0.45 | 0.01822535 | 0.02545461 | 0.027978 |
-| wikipedia_fr | 3 | 60.2% | 0.01763200 | 0.43 | 0.01843446 | 0.01930741 | 0.019307 |
-| scientific_papers | 96 | 60.3% | 0.01691031 | 0.41 | 0.01667293 | 0.02120576 | 0.031467 |
+| wildchat | 96 | 73.5% | 0.12158332 | 3.00 | 0.08575015 | 0.29358947 | 0.810493 |
+| wikisource_zh | 33 | 60.4% | 0.09551846 | 2.36 | 0.07310532 | 0.21919991 | 0.236661 |
+| regulations | 23 | 72.3% | 0.04228874 | 1.04 | 0.05244299 | 0.07412235 | 0.077878 |
+| wikipedia_en | 96 | 59.5% | 0.03968052 | 0.98 | 0.03094896 | 0.05457949 | 0.248439 |
+| wikipedia_de | 7 | 73.6% | 0.03769282 | 0.93 | 0.04138497 | 0.04523514 | 0.045235 |
+| public_domain_books | 72 | 50.6% | 0.02912092 | 0.72 | 0.02382746 | 0.03332172 | 0.217942 |
+| public_domain_review | 26 | 51.8% | 0.02703261 | 0.67 | 0.02344897 | 0.04409867 | 0.081137 |
+| wikipedia_zh | 39 | 51.5% | 0.02627151 | 0.65 | 0.02390276 | 0.03894622 | 0.044109 |
+| wikipedia_ja | 7 | 57.0% | 0.02444784 | 0.60 | 0.02401836 | 0.02978179 | 0.029782 |
+| github_code | 52 | 85.8% | 0.02416399 | 0.60 | 0.01468337 | 0.04043856 | 0.177651 |
+| wikipedia_es | 7 | 59.2% | 0.02329110 | 0.57 | 0.02178278 | 0.02853840 | 0.028538 |
+| wikipedia_cs | 6 | 68.9% | 0.02231559 | 0.55 | 0.02325708 | 0.02581686 | 0.025817 |
+| stackv2 | 44 | 72.0% | 0.02098322 | 0.52 | 0.01634586 | 0.03388006 | 0.130032 |
+| starcoder_structured | 36 | 70.7% | 0.02024896 | 0.50 | 0.01510115 | 0.03390114 | 0.102686 |
+| libretexts | 96 | 65.8% | 0.01951086 | 0.48 | 0.01577138 | 0.02993783 | 0.071721 |
+| wikipedia_ru | 6 | 66.6% | 0.01919443 | 0.47 | 0.01833166 | 0.02629231 | 0.026292 |
+| open_news | 23 | 53.8% | 0.01848331 | 0.46 | 0.01754263 | 0.02472382 | 0.029905 |
+| wikipedia_fr | 3 | 60.2% | 0.01779581 | 0.44 | 0.01879622 | 0.01935605 | 0.019356 |
+| scientific_papers | 96 | 60.3% | 0.01656302 | 0.41 | 0.01655073 | 0.02127848 | 0.027382 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.12457770, 3.04x the run mean of 0.04096872 over 96 context(s).
-- Strongest domain: **Scientific and technical exposition** at 0.01691031, 0.41x the run mean. The spread across domains is 7.4x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.12158332, 3.00x the run mean of 0.04053080 over 96 context(s).
+- Strongest domain: **Scientific and technical exposition** at 0.01656302, 0.41x the run mean. The spread across domains is 7.3x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.836227 against a median of 0.09409491 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.810493 against a median of 0.08575015 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/Qwen3.8-27B-W4A16-AWQ/compliance.json b/Qwen3.8-27B-W4A16-AWQ/compliance.json
index 1ae2555004d363508ba9d108bf1adeb0d95fac59..d1d91d673c7790e4932eff238b3699e9c6610778 100644
--- a/Qwen3.8-27B-W4A16-AWQ/compliance.json
+++ b/Qwen3.8-27B-W4A16-AWQ/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/philbert440/Qwen3.8-27B-W4A16-AWQ",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "ad7dfcd65c33275560e813350fe59b6eb5eb36bee327de7ea7872f69f8f8a5f1",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.644803+00:00",
+  "evaluated_at": "2026-09-11T20:24:37.806063+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.05213982, deployed 0.05213981, delta -1.4135551729133056e-08",
+      "detail": "trunk 0.05220834, deployed 0.05220833, delta -1.4147393236074723e-08",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.05213981, median 0.01289008, max 30.39961243, 4 depth buckets",
+      "detail": "mean 0.05220833, median 0.01287558, max 28.05413437, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.15901204, strongest scientific_technical at 0.02310311, spread 6.9x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.15957568, strongest scientific_technical at 0.02309163, spread 6.9x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights ad7dfcd65c332755 as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.05213980510933955,
+  "laws_version": 15,
+  "mean_kld": 0.05220832904845153,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 30.399612426757812,
-        "mean_kld": 0.05213980510933955,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.02919666637120663,
-        "median_context_p99": 0.238616481423378,
-        "p90_context_kld": 0.0995676258082344,
+        "max_kld": 28.054134368896484,
+        "mean_kld": 0.05220832904845153,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.029065531727932182,
+        "median_context_p99": 0.24014799296855927,
+        "p90_context_kld": 0.0967389359634023,
         "positions": 1572096,
-        "top1_agreement": 0.9188255679042501,
+        "top1_agreement": 0.9189445173831623,
         "worst_context_id": 454,
-        "worst_context_kld": 0.976327427400526
+        "worst_context_kld": 0.9371256950236656
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 30.399612426757812,
-            "mean_kld": 0.15901204420931153,
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-            "median_context_kld": 0.12511930802175772,
-            "median_context_p99": 2.7769250869750977,
-            "p90_context_kld": 0.36959571782662454,
+            "max_kld": 28.054134368896484,
+            "mean_kld": 0.15957567875480974,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.1269138903207814,
+            "median_context_p99": 2.7177977561950684,
+            "p90_context_kld": 0.3651895219731942,
             "positions": 196512,
-            "top1_agreement": 0.9123106985832926,
+            "top1_agreement": 0.9121733023937469,
             "worst_context_id": 454,
-            "worst_context_kld": 0.976327427400526
+            "worst_context_kld": 0.9371256950236656
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 3.0497245602636225
+        "relative_to_run": 3.0565176412123205
       },
       {
         "cells": {
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-            "median_context_kld": 0.04679855732006913,
-            "median_context_p99": 0.3673529028892517,
-            "p90_context_kld": 0.1329300991278066,
+            "max_kld": 6.728816986083984,
+            "mean_kld": 0.07331402063358686,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.04662009140152288,
+            "median_context_p99": 0.36183223128318787,
+            "p90_context_kld": 0.1323429974197613,
             "positions": 147384,
-            "top1_agreement": 0.8826466916354557,
+            "top1_agreement": 0.8829113065190252,
             "worst_context_id": 893,
-            "worst_context_kld": 0.29295536313134185
+            "worst_context_kld": 0.29419820309634936
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.4068298077431858
+        "relative_to_run": 1.4042590898771794
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       {
         "cells": {
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-            "p90_context_kld": 0.06814753983962307,
+            "max_kld": 7.494067668914795,
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+            "worst_context_kld": 0.2466970044043295
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9302595504539519
+        "relative_to_run": 0.9290432942852536
       },
       {
         "cells": {
           "deployed": {
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-            "worst_context_kld": 0.09759331571657141
+            "worst_context_kld": 0.09766543914872006
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         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.7229066134840937
+        "relative_to_run": 0.7210394443027335
       },
       {
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         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
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+        "relative_to_run": 0.6948406803103826
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+            "worst_context_kld": 0.20018271364249407
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         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
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+        "relative_to_run": 0.6910271852106409
       },
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         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
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         "cells": {
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             "worst_context_id": 1004,
-            "worst_context_kld": 0.1112336788500945
+            "worst_context_kld": 0.11183454696544523
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         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.5019187052503336
+        "relative_to_run": 0.5005225618046447
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       {
         "cells": {
           "deployed": {
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             "positions": 196512,
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             "worst_context_id": 825,
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+            "worst_context_kld": 0.07533296618230803
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         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4597043540755708
+        "relative_to_run": 0.45961743220306084
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       {
         "cells": {
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diff --git a/Qwen3.8-27B-W4A16-AWQ/inspect.json b/Qwen3.8-27B-W4A16-AWQ/inspect.json
index ce8f540017b7f716f7e3d26bdbc2fa28db1df6b0..627464218ec921e87f75f925cefba7a6a715f914 100644
--- a/Qwen3.8-27B-W4A16-AWQ/inspect.json
+++ b/Qwen3.8-27B-W4A16-AWQ/inspect.json
@@ -1,31 +1,37 @@
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@@ -836,6 +837,7 @@
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@@ -845,17 +847,39 @@
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diff --git a/Qwen3.8-27B-W4A16-AWQ/report.json b/Qwen3.8-27B-W4A16-AWQ/report.json
index fae2be9c5be003627f37bf3975a1bf205dd8362b..cf9bb456ee9d75a63a73a9b595e26529d7a06d49 100644
--- a/Qwen3.8-27B-W4A16-AWQ/report.json
+++ b/Qwen3.8-27B-W4A16-AWQ/report.json
@@ -1,8530 +1,8578 @@
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+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
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@@ -8630,101 +8678,102 @@
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+  "student_weights_sha256": "ad7dfcd65c33275560e813350fe59b6eb5eb36bee327de7ea7872f69f8f8a5f1",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/philbert440/Qwen3.8-27B-W4A16-AWQ",
@@ -8811,5 +8860,7 @@
       "candidate": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3",
       "match": true
     }
-  }
+  },
+  "candidate_hf_repo": "philbert440/Qwen3.8-27B-W4A16-AWQ",
+  "candidate_revision": "7908d42a71077a5e4dc458f273682b12dfe384a0"
 }
diff --git a/Qwen3.8-27B-W4A16-AWQ/report.md b/Qwen3.8-27B-W4A16-AWQ/report.md
index fcce86cd6dc3adc322b0ab88c0a786265d6e7022..847a934cb53ba5e29f7ec02da3696eb74d1f98c4 100644
--- a/Qwen3.8-27B-W4A16-AWQ/report.md
+++ b/Qwen3.8-27B-W4A16-AWQ/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-W4A16-AWQ: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.05213981** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.05220833** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.05213981 | 0.01289008 | 0.07271738 | 0.65141266 | 30.39961243 | 91.8826% |
+| 0.05220833 | 0.01287558 | 0.07269585 | 0.65099459 | 28.05413437 | 91.8945% |
 
-Reverse direction, KLD(candidate || reference): 0.05403813.
+Reverse direction, KLD(candidate || reference): 0.05415885.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.05403813.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/philbert440/Qwen3.8-27B-W4A16-AWQ |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | ad7dfcd65c332755 |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,32 +27,48 @@ Reverse direction, KLD(candidate || reference): 0.05403813.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
+## Expert kernels: declared against built
+
+| Property | Value |
+|---|---|
+| Declared for its experts | `unquantized` |
+| Expert implementation built | n/a |
+| Expert kernel built | n/a |
+| Expert layers carrying an activation scale | n/a |
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15901204 | 3.05 | 0.97632743 |
-| Chinese across several content types | 72 | 55.6% | 0.07335183 | 1.41 | 0.29295536 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.04850355 | 0.93 | 0.24722020 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03769221 | 0.72 | 0.09759332 |
-| Other multilingual content | 36 | 64.5% | 0.03634613 | 0.70 | 0.07814634 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.03607988 | 0.69 | 0.19988670 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02874437 | 0.55 | 0.19376743 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02616994 | 0.50 | 0.11123368 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02396890 | 0.46 | 0.07585423 |
-| Scientific and technical exposition | 96 | 60.3% | 0.02310311 | 0.44 | 0.04260238 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15957568 | 3.06 | 0.93712570 |
+| Chinese across several content types | 72 | 55.6% | 0.07331402 | 1.40 | 0.29419820 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.04850380 | 0.93 | 0.24669700 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03764426 | 0.72 | 0.09766544 |
+| Other multilingual content | 36 | 64.5% | 0.03627647 | 0.69 | 0.07779739 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.03607737 | 0.69 | 0.20018271 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02881995 | 0.55 | 0.19081117 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02613145 | 0.50 | 0.11183455 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02399586 | 0.46 | 0.07533297 |
+| Scientific and technical exposition | 96 | 60.3% | 0.02309163 | 0.44 | 0.04307995 |
 
 **Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 6.9x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
@@ -60,8 +76,8 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.05213982 |
-| Deployed (candidate's own head) | 0.05213981 |
+| Trunk (candidate hidden states, reference head) | 0.05220834 |
+| Deployed (candidate's own head) | 0.05220833 |
 | Head-associated delta (not additive) | -0.00000001 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]}, 'state': 'unquantized', 'static': {'ignored': True, 'lm_head_dtypes': {'lm_head.weight': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight'], 'output_weight_keys': ['lm_head.weight'], 'packed_keys': [], 'quant_method': 'compressed-tensors', 'state': 'unquantized', 'tie_word_embeddings': False}} |
@@ -70,26 +86,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.04347124 |
-| 512–1023 | 393216 | 0.04736765 |
-| 1024–1535 | 393216 | 0.05580843 |
-| 1536–2046 | 392448 | 0.06193102 |
+| 0–511 | 393216 | 0.04338315 |
+| 512–1023 | 393216 | 0.04770274 |
+| 1024–1535 | 393216 | 0.05593604 |
+| 1536–2046 | 392448 | 0.06183018 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.05891248 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.07299851 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.08160144 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.06067264 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.01090769 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.05873120 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.07398531 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.07980775 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.06164930 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.01103287 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 91.7995% | 74.2813% | 54.6477% | 37.2803% | 24.2890% |
+| 91.8090% | 74.2835% | 54.6582% | 37.3087% | 24.3249% |
 
 ## Law compliance
 
@@ -101,22 +117,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.05213982, deployed 0.05213981, delta -1.4135551729133056e-08 |
-| 9 | Tail and depth disclosure | PASS | mean 0.05213981, median 0.01289008, max 30.39961243, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.05220834, deployed 0.05220833, delta -1.4147393236074723e-08 |
+| 9 | Tail and depth disclosure | PASS | mean 0.05220833, median 0.01287558, max 28.05413437, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.15901204, strongest scientific_technical at 0.02310311, spread 6.9x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.15957568, strongest scientific_technical at 0.02309163, spread 6.9x |
+| 16 | Candidate weight binding | PASS | scored weights ad7dfcd65c332755 as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +139,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +147,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +170,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +189,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-W4A16-AWQ/report.md` | 12.35 KiB | This document. |
-| `Qwen3.8-27B-W4A16-AWQ/report.json` | 254.33 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-W4A16-AWQ/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-W4A16-AWQ/compliance.json` | 13.02 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-W4A16-AWQ/report.md` | 13.19 KiB | This document. |
+| `Qwen3.8-27B-W4A16-AWQ/report.json` | 256.84 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-W4A16-AWQ/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-W4A16-AWQ/compliance.json` | 12.29 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-W4A16-AWQ/strata.json b/Qwen3.8-27B-W4A16-AWQ/strata.json
index 5349bc4da0c4c191869ff8c399464f972b5e6c03..35d9c4b7f5108026e093279c2ee9f7defb3a4bbe 100644
--- a/Qwen3.8-27B-W4A16-AWQ/strata.json
+++ b/Qwen3.8-27B-W4A16-AWQ/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 30.399612426757812,
-            "mean_kld": 0.15901204420931153,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.12511930802175772,
-            "median_context_p99": 2.7769250869750977,
-            "p90_context_kld": 0.36959571782662454,
+            "max_kld": 28.054134368896484,
+            "mean_kld": 0.15957567875480974,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.1269138903207814,
+            "median_context_p99": 2.7177977561950684,
+            "p90_context_kld": 0.3651895219731942,
             "positions": 196512,
-            "top1_agreement": 0.9123106985832926,
+            "top1_agreement": 0.9121733023937469,
             "worst_context_id": 454,
-            "worst_context_kld": 0.976327427400526
+            "worst_context_kld": 0.9371256950236656
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 3.0497245602636225
+        "relative_to_run": 3.0565176412123205
       },
       {
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 6.429609298706055,
-            "mean_kld": 0.11720699882873528,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.08863837002481674,
-            "median_context_p99": 0.6956533193588257,
-            "p90_context_kld": 0.27904741300579156,
+            "max_kld": 6.728816986083984,
+            "mean_kld": 0.11719981470484007,
+            "mean_ref_top1_prob": 0.6040861921682756,
+            "median_context_kld": 0.08875828883584291,
+            "median_context_p99": 0.6825599670410156,
+            "p90_context_kld": 0.2807458506041219,
             "positions": 67551,
-            "top1_agreement": 0.8585661204127252,
+            "top1_agreement": 0.8585957276724253,
             "worst_context_id": 893,
-            "worst_context_kld": 0.29295536313134185
+            "worst_context_kld": 0.29419820309634936
           }
         },
         "key": "wikisource_zh",
         "label": "wikisource_zh",
-        "relative_to_run": 2.2479370335763025
+        "relative_to_run": 2.2448489894413917
       },
       {
         "cells": {
           "deployed": {
             "contexts": 7,
-            "max_kld": 9.622902870178223,
-            "mean_kld": 0.06282385885428825,
-            "mean_ref_top1_prob": 0.7357366218042676,
-            "median_context_kld": 0.06420244922687414,
-            "median_context_p99": 1.2430760860443115,
-            "p90_context_kld": 0.07814634094416811,
+            "max_kld": 9.997251510620117,
+            "mean_kld": 0.06240039557916728,
+            "mean_ref_top1_prob": 0.7358200961573914,
+            "median_context_kld": 0.06338665953119936,
+            "median_context_p99": 1.2418767213821411,
+            "p90_context_kld": 0.0777973904967248,
             "positions": 14329,
-            "top1_agreement": 0.9096936283062321,
+            "top1_agreement": 0.9082280689510782,
             "worst_context_id": 953,
-            "worst_context_kld": 0.07814634094416811
+            "worst_context_kld": 0.0777973904967248
           }
         },
         "key": "wikipedia_de",
         "label": "wikipedia_de",
-        "relative_to_run": 1.2049116547816732
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-            "median_context_kld": 0.04679855732006913,
-            "median_context_p99": 0.3673529028892517,
-            "p90_context_kld": 0.1329300991278066,
+            "max_kld": 6.728816986083984,
+            "mean_kld": 0.07331402063358686,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.04662009140152288,
+            "median_context_p99": 0.36183223128318787,
+            "p90_context_kld": 0.1323429974197613,
             "positions": 147384,
-            "top1_agreement": 0.8826466916354557,
+            "top1_agreement": 0.8829113065190252,
             "worst_context_id": 893,
-            "worst_context_kld": 0.29295536313134185
+            "worst_context_kld": 0.29419820309634936
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.4068298077431858
+        "relative_to_run": 1.4042590898771794
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 7.369892597198486,
-            "mean_kld": 0.04850355166177087,
-            "mean_ref_top1_prob": 0.5946008380921044,
-            "median_context_kld": 0.03896222107690935,
-            "median_context_p99": 0.386016845703125,
-            "p90_context_kld": 0.06814753983962307,
+            "max_kld": 7.494067668914795,
+            "mean_kld": 0.04850379800830191,
+            "mean_ref_top1_prob": 0.5945716883648833,
+            "median_context_kld": 0.03905217684077804,
+            "median_context_p99": 0.3832544684410095,
+            "p90_context_kld": 0.06765605191464288,
             "positions": 196512,
-            "top1_agreement": 0.9101225370460837,
+            "top1_agreement": 0.9109978016609672,
             "worst_context_id": 6,
-            "worst_context_kld": 0.24722019926777888
+            "worst_context_kld": 0.2466970044043295
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9302595504539519
+        "relative_to_run": 0.9290432942852536
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 7.676151752471924,
-            "mean_kld": 0.0376922099393133,
-            "mean_ref_top1_prob": 0.5900126668457796,
-            "median_context_kld": 0.030992041215078325,
-            "median_context_p99": 0.24538768827915192,
-            "p90_context_kld": 0.07598096944201446,
+            "max_kld": 7.738141059875488,
+            "mean_kld": 0.03764426456506975,
+            "mean_ref_top1_prob": 0.590028705918726,
+            "median_context_kld": 0.031158552372220642,
+            "median_context_p99": 0.24790853261947632,
+            "p90_context_kld": 0.0754212912571824,
             "positions": 147384,
-            "top1_agreement": 0.9189871356456604,
+            "top1_agreement": 0.9188242957173098,
             "worst_context_id": 275,
-            "worst_context_kld": 0.09759331571657141
+            "worst_context_kld": 0.09766543914872006
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.7229066134840937
+        "relative_to_run": 0.7210394443027335
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 9.622902870178223,
-            "mean_kld": 0.03634613308523014,
-            "mean_ref_top1_prob": 0.6450955362692544,
-            "median_context_kld": 0.03117238804486383,
-            "median_context_p99": 0.25475019216537476,
-            "p90_context_kld": 0.06420244922687414,
+            "max_kld": 9.997251510620117,
+            "mean_kld": 0.03627647087389437,
+            "mean_ref_top1_prob": 0.6450767409183084,
+            "median_context_kld": 0.03140673155463281,
+            "median_context_p99": 0.26082924008369446,
+            "p90_context_kld": 0.06338665953119936,
             "positions": 73692,
-            "top1_agreement": 0.9197199153232373,
+            "top1_agreement": 0.9200184551918797,
             "worst_context_id": 953,
-            "worst_context_kld": 0.07814634094416811
+            "worst_context_kld": 0.0777973904967248
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.6970899298340422
+        "relative_to_run": 0.6948406803103826
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 5.682513236999512,
-            "mean_kld": 0.036079881327542,
-            "mean_ref_top1_prob": 0.5056158503386161,
-            "median_context_kld": 0.030202195357326263,
-            "median_context_p99": 0.19205385446548462,
-            "p90_context_kld": 0.045341534287458914,
+            "max_kld": 5.658687114715576,
+            "mean_kld": 0.0360773746669024,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.030321528959283608,
+            "median_context_p99": 0.1891442835330963,
+            "p90_context_kld": 0.04524241226714048,
             "positions": 147384,
-            "top1_agreement": 0.9098816696520654,
+            "top1_agreement": 0.9099630896162406,
             "worst_context_id": 443,
-            "worst_context_kld": 0.19988670153467222
+            "worst_context_kld": 0.20018271364249407
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.6919834328471471
+        "relative_to_run": 0.6910271852106409
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 8.79715633392334,
-            "mean_kld": 0.02874436922428417,
-            "mean_ref_top1_prob": 0.7950703670465326,
-            "median_context_kld": 0.019930024160644762,
-            "median_context_p99": 0.2300116866827011,
-            "p90_context_kld": 0.049200308563109224,
+            "max_kld": 9.038643836975098,
+            "mean_kld": 0.02881994962240554,
+            "mean_ref_top1_prob": 0.7950885550065969,
+            "median_context_kld": 0.01979192970637468,
+            "median_context_p99": 0.22503569722175598,
+            "p90_context_kld": 0.04944425150453044,
             "positions": 196512,
-            "top1_agreement": 0.9522115697769094,
+            "top1_agreement": 0.9521199723172121,
             "worst_context_id": 667,
-            "worst_context_kld": 0.19376743456321177
+            "worst_context_kld": 0.19081116835933049
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5512941439655541
+        "relative_to_run": 0.552018234402013
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 6.4243879318237305,
-            "mean_kld": 0.026169943472484435,
-            "mean_ref_top1_prob": 0.7071241246993897,
-            "median_context_kld": 0.02027065014891977,
-            "median_context_p99": 0.33909252285957336,
-            "p90_context_kld": 0.042015002418872,
+            "max_kld": 6.201298236846924,
+            "mean_kld": 0.02613144660287081,
+            "mean_ref_top1_prob": 0.7071550040111672,
+            "median_context_kld": 0.020083818247999048,
+            "median_context_p99": 0.31474098563194275,
+            "p90_context_kld": 0.04184260348556153,
             "positions": 73692,
-            "top1_agreement": 0.8631194702274331,
+            "top1_agreement": 0.8631330402214623,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.1112336788500945
+            "worst_context_kld": 0.11183454696544523
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.5019187052503336
+        "relative_to_run": 0.5005225618046447
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 17.079654693603516,
-            "mean_kld": 0.023968895429415084,
-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.020227969047635092,
-            "median_context_p99": 0.13662952184677124,
-            "p90_context_kld": 0.03423405645586993,
+            "max_kld": 18.15727424621582,
+            "mean_kld": 0.023995858136861763,
+            "mean_ref_top1_prob": 0.6582834184806026,
+            "median_context_kld": 0.020391457373063464,
+            "median_context_p99": 0.13411371409893036,
+            "p90_context_kld": 0.033592801526956186,
             "positions": 196512,
-            "top1_agreement": 0.9407975085490963,
+            "top1_agreement": 0.94092472724312,
             "worst_context_id": 825,
-            "worst_context_kld": 0.07585423379348576
+            "worst_context_kld": 0.07533296618230803
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4597043540755708
+        "relative_to_run": 0.45961743220306084
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 11.535755157470703,
-            "mean_kld": 0.0231031091923458,
-            "mean_ref_top1_prob": 0.6033561622407548,
-            "median_context_kld": 0.023010708480357377,
-            "median_context_p99": 0.16032332181930542,
-            "p90_context_kld": 0.02959267954279603,
+            "max_kld": 12.199336051940918,
+            "mean_kld": 0.023091633912277098,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.022780049294111843,
+            "median_context_p99": 0.1636587679386139,
+            "p90_context_kld": 0.0296147374467488,
             "positions": 196512,
-            "top1_agreement": 0.9329608369972318,
+            "top1_agreement": 0.9328845057808175,
             "worst_context_id": 246,
-            "worst_context_kld": 0.042602380234900185
+            "worst_context_kld": 0.04307995260558406
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.44309926252884
+        "relative_to_run": 0.4422978925613016
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 30.399612426757812,
-      "mean_kld": 0.05213980510933955,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.02919666637120663,
-      "median_context_p99": 0.238616481423378,
-      "p90_context_kld": 0.0995676258082344,
+      "max_kld": 28.054134368896484,
+      "mean_kld": 0.05220832904845153,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.029065531727932182,
+      "median_context_p99": 0.24014799296855927,
+      "p90_context_kld": 0.0967389359634023,
       "positions": 1572096,
-      "top1_agreement": 0.9188255679042501,
+      "top1_agreement": 0.9189445173831623,
       "worst_context_id": 454,
-      "worst_context_kld": 0.976327427400526
+      "worst_context_kld": 0.9371256950236656
     }
   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-W4A16-AWQ/strata.md b/Qwen3.8-27B-W4A16-AWQ/strata.md
index 3a1a5ae55d7cb6022bfc425658c9c7da3d662d73..3dd8be03949affb4aff75f7b0874cd07765393f9 100644
--- a/Qwen3.8-27B-W4A16-AWQ/strata.md
+++ b/Qwen3.8-27B-W4A16-AWQ/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-W4A16-AWQ
 
-768 contexts, 1572096 scored positions, mean 0.05213981, reference top-1 64.2%, top-1 agreement 91.8826%.
+768 contexts, 1572096 scored positions, mean 0.05220833, reference top-1 64.2%, top-1 agreement 91.8945%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15901204 | 3.05 | 0.12511931 | 0.36959572 | 0.976327 |
-| Chinese across several content types | 72 | 55.6% | 0.07335183 | 1.41 | 0.04679856 | 0.13293010 | 0.292955 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.04850355 | 0.93 | 0.03896222 | 0.06814754 | 0.247220 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03769221 | 0.72 | 0.03099204 | 0.07598097 | 0.097593 |
-| Other multilingual content | 36 | 64.5% | 0.03634613 | 0.70 | 0.03117239 | 0.06420245 | 0.078146 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.03607988 | 0.69 | 0.03020220 | 0.04534153 | 0.199887 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02874437 | 0.55 | 0.01993002 | 0.04920031 | 0.193767 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02616994 | 0.50 | 0.02027065 | 0.04201500 | 0.111234 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02396890 | 0.46 | 0.02022797 | 0.03423406 | 0.075854 |
-| Scientific and technical exposition | 96 | 60.3% | 0.02310311 | 0.44 | 0.02301071 | 0.02959268 | 0.042602 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15957568 | 3.06 | 0.12691389 | 0.36518952 | 0.937126 |
+| Chinese across several content types | 72 | 55.6% | 0.07331402 | 1.40 | 0.04662009 | 0.13234300 | 0.294198 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.04850380 | 0.93 | 0.03905218 | 0.06765605 | 0.246697 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.03764426 | 0.72 | 0.03115855 | 0.07542129 | 0.097665 |
+| Other multilingual content | 36 | 64.5% | 0.03627647 | 0.69 | 0.03140673 | 0.06338666 | 0.077797 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.03607737 | 0.69 | 0.03032153 | 0.04524241 | 0.200183 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02881995 | 0.55 | 0.01979193 | 0.04944425 | 0.190811 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02613145 | 0.50 | 0.02008382 | 0.04184260 | 0.111835 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.02399586 | 0.46 | 0.02039146 | 0.03359280 | 0.075333 |
+| Scientific and technical exposition | 96 | 60.3% | 0.02309163 | 0.44 | 0.02278005 | 0.02961474 | 0.043080 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.15901204 | 3.05 | 0.12511931 | 0.36959572 | 0.976327 |
-| wikisource_zh | 33 | 60.4% | 0.11720700 | 2.25 | 0.08863837 | 0.27904741 | 0.292955 |
-| wikipedia_de | 7 | 73.6% | 0.06282386 | 1.20 | 0.06420245 | 0.07814634 | 0.078146 |
-| regulations | 23 | 72.3% | 0.05113666 | 0.98 | 0.06355584 | 0.08125926 | 0.097593 |
-| wikipedia_en | 96 | 59.5% | 0.04850355 | 0.93 | 0.03896222 | 0.06814754 | 0.247220 |
-| wikipedia_zh | 39 | 51.5% | 0.03624361 | 0.70 | 0.03420574 | 0.05015744 | 0.057175 |
-| public_domain_books | 72 | 50.6% | 0.03607988 | 0.69 | 0.03020220 | 0.04534153 | 0.199887 |
-| public_domain_review | 26 | 51.8% | 0.03595578 | 0.69 | 0.03327825 | 0.05580079 | 0.087017 |
-| wikipedia_ja | 7 | 57.0% | 0.03360317 | 0.64 | 0.03206623 | 0.03975219 | 0.039752 |
-| wikipedia_es | 7 | 59.2% | 0.03156648 | 0.61 | 0.03069895 | 0.03915404 | 0.039154 |
-| wikipedia_cs | 6 | 68.9% | 0.03092837 | 0.59 | 0.03117239 | 0.03943728 | 0.039437 |
-| github_code | 52 | 85.8% | 0.02921882 | 0.56 | 0.01862233 | 0.04920031 | 0.193767 |
-| stackv2 | 44 | 72.0% | 0.02818365 | 0.54 | 0.02068843 | 0.04264460 | 0.183539 |
-| open_news | 23 | 53.8% | 0.02621067 | 0.50 | 0.02543757 | 0.03542783 | 0.043419 |
-| starcoder_structured | 36 | 70.7% | 0.02616994 | 0.50 | 0.02027065 | 0.04201500 | 0.111234 |
-| wikipedia_ru | 6 | 66.6% | 0.02595194 | 0.50 | 0.02732158 | 0.03498418 | 0.034984 |
-| libretexts | 96 | 65.8% | 0.02396890 | 0.46 | 0.02022797 | 0.03423406 | 0.075854 |
-| wikipedia_fr | 3 | 60.2% | 0.02374147 | 0.46 | 0.02475891 | 0.02654670 | 0.026547 |
-| scientific_papers | 96 | 60.3% | 0.02310311 | 0.44 | 0.02301071 | 0.02959268 | 0.042602 |
+| wildchat | 96 | 73.5% | 0.15957568 | 3.06 | 0.12691389 | 0.36518952 | 0.937126 |
+| wikisource_zh | 33 | 60.4% | 0.11719981 | 2.24 | 0.08875829 | 0.28074585 | 0.294198 |
+| wikipedia_de | 7 | 73.6% | 0.06240040 | 1.20 | 0.06338666 | 0.07779739 | 0.077797 |
+| regulations | 23 | 72.3% | 0.05096350 | 0.98 | 0.06352998 | 0.08104347 | 0.097665 |
+| wikipedia_en | 96 | 59.5% | 0.04850380 | 0.93 | 0.03905218 | 0.06765605 | 0.246697 |
+| wikipedia_zh | 39 | 51.5% | 0.03617989 | 0.69 | 0.03420377 | 0.05015144 | 0.056784 |
+| public_domain_books | 72 | 50.6% | 0.03607737 | 0.69 | 0.03032153 | 0.04524241 | 0.200183 |
+| public_domain_review | 26 | 51.8% | 0.03596391 | 0.69 | 0.03315169 | 0.05582988 | 0.086711 |
+| wikipedia_ja | 7 | 57.0% | 0.03359707 | 0.64 | 0.03192177 | 0.03967113 | 0.039671 |
+| wikipedia_es | 7 | 59.2% | 0.03150716 | 0.60 | 0.03068271 | 0.03917048 | 0.039170 |
+| wikipedia_cs | 6 | 68.9% | 0.03100503 | 0.59 | 0.03155254 | 0.03943545 | 0.039435 |
+| github_code | 52 | 85.8% | 0.02924864 | 0.56 | 0.01905071 | 0.04944425 | 0.190811 |
+| stackv2 | 44 | 72.0% | 0.02831331 | 0.54 | 0.02075876 | 0.04638991 | 0.182078 |
+| open_news | 23 | 53.8% | 0.02622456 | 0.50 | 0.02548094 | 0.03579205 | 0.044140 |
+| starcoder_structured | 36 | 70.7% | 0.02613145 | 0.50 | 0.02008382 | 0.04184260 | 0.111835 |
+| wikipedia_ru | 6 | 66.6% | 0.02601620 | 0.50 | 0.02764185 | 0.03521776 | 0.035218 |
+| libretexts | 96 | 65.8% | 0.02399586 | 0.46 | 0.02039146 | 0.03359280 | 0.075333 |
+| wikipedia_fr | 3 | 60.2% | 0.02376439 | 0.46 | 0.02486575 | 0.02644743 | 0.026447 |
+| scientific_papers | 96 | 60.3% | 0.02309163 | 0.44 | 0.02278005 | 0.02961474 | 0.043080 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.15901204, 3.05x the run mean of 0.05213981 over 96 context(s).
-- Strongest domain: **Scientific and technical exposition** at 0.02310311, 0.44x the run mean. The spread across domains is 6.9x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.15957568, 3.06x the run mean of 0.05220833 over 96 context(s).
+- Strongest domain: **Scientific and technical exposition** at 0.02309163, 0.44x the run mean. The spread across domains is 6.9x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.976327 against a median of 0.12511931 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.937126 against a median of 0.12691389 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/Qwen3.8-27B-W4A16-AutoRound/compliance.json b/Qwen3.8-27B-W4A16-AutoRound/compliance.json
index 8796842dea6bbad1a35d011e5f68c09175b146a9..28991988177c171657c5bdd2c4ee5cc54326e62a 100644
--- a/Qwen3.8-27B-W4A16-AutoRound/compliance.json
+++ b/Qwen3.8-27B-W4A16-AutoRound/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/dbirks/Qwen3.8-27B-W4A16-AutoRound",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "f733884e73e0882a9301e52dee05b0752dda2cda442c7630e76b71ca1390e390",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.827535+00:00",
+  "evaluated_at": "2026-09-11T20:24:43.152763+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.04275781, deployed 0.04275781, delta -2.274543976776844e-09",
+      "detail": "trunk 0.04285275, deployed 0.04285275, delta -2.2030077345691623e-09",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.04275781, median 0.00897863, max 28.06366730, 4 depth buckets",
+      "detail": "mean 0.04285275, median 0.00898137, max 28.03813171, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.12805693, strongest scientific_technical at 0.01606118, spread 8.0x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.12877720, strongest scientific_technical at 0.01605032, spread 8.0x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights f733884e73e0882a as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.042757811057000626,
+  "laws_version": 15,
+  "mean_kld": 0.04285274563293194,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 28.06366729736328,
-        "mean_kld": 0.04275781105700063,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.022386720846269026,
-        "median_context_p99": 0.19623413681983948,
-        "p90_context_kld": 0.08733337055629971,
+        "max_kld": 28.038131713867188,
+        "mean_kld": 0.04285274563293194,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.022197691626328203,
+        "median_context_p99": 0.1954183578491211,
+        "p90_context_kld": 0.0882388502261295,
         "positions": 1572096,
-        "top1_agreement": 0.9278930803207946,
+        "top1_agreement": 0.9278892637599739,
         "worst_context_id": 454,
-        "worst_context_kld": 0.9875202859772684
+        "worst_context_kld": 0.9661592104468429
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
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-            "p90_context_kld": 0.3357585696562436,
+            "max_kld": 28.038131713867188,
+            "mean_kld": 0.128777198176715,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.09961656562257165,
+            "median_context_p99": 2.0042476654052734,
+            "p90_context_kld": 0.32835024747037,
             "positions": 196512,
-            "top1_agreement": 0.9226103240514574,
+            "top1_agreement": 0.9227935189708516,
             "worst_context_id": 454,
-            "worst_context_kld": 0.9875202859772684
+            "worst_context_kld": 0.9661592104468429
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 2.994936635548522
+        "relative_to_run": 3.0051096207415684
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       {
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+            "max_kld": 6.1496381759643555,
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+            "worst_context_kld": 0.29306609163790565
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         "key": "chinese",
         "label": "Chinese across several content types",
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         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
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+        "relative_to_run": 0.9110008154220084
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       {
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         "key": "other_multilingual",
         "label": "Other multilingual content",
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       {
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         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
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         "label": "Literary, narrative, and creative writing",
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         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
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         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
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         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
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         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
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+        "relative_to_run": 0.37454585504850724
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     ]
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diff --git a/Qwen3.8-27B-W4A16-AutoRound/inspect.json b/Qwen3.8-27B-W4A16-AutoRound/inspect.json
index 2b3e7ae19e071231ace04db447dceb3b47a5ce59..5d648e1ca9dbc8e9372479adab21852e9857231d 100644
--- a/Qwen3.8-27B-W4A16-AutoRound/inspect.json
+++ b/Qwen3.8-27B-W4A16-AutoRound/inspect.json
@@ -1,31 +1,37 @@
 {
+  "model": "/media/fmodels2/dbirks/Qwen3.8-27B-W4A16-AutoRound",
+  "inspect_version": 5,
+  "weights_sha256": "f733884e73e0882a9301e52dee05b0752dda2cda442c7630e76b71ca1390e390",
+  "weights_bytes": 19452780008,
+  "quant_method": "compressed-tensors",
+  "declared": {},
+  "detected_scheme": "int4_g128_sym",
+  "detected_block": 128,
+  "quant_algorithm": "round_to_nearest",
+  "scheme_mix": null,
+  "kv_cache_scheme": null,
   "coverage": {
-    "attention": {
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-      "weights": 4
-    },
-    "dense_mlp": {
-      "quantized": 192,
-      "weights": 3
+    "router": {
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     "experts": {
-      "quantized": 0,
-      "weights": 0
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-    "router": {
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-      "weights": 0
+      "weights": 0,
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     "shared_expert": {
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+    "attention": {
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diff --git a/Qwen3.8-27B-W4A16-AutoRound/manifest.json b/Qwen3.8-27B-W4A16-AutoRound/manifest.json
index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
--- a/Qwen3.8-27B-W4A16-AutoRound/manifest.json
+++ b/Qwen3.8-27B-W4A16-AutoRound/manifest.json
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diff --git a/Qwen3.8-27B-W4A16-AutoRound/report.json b/Qwen3.8-27B-W4A16-AutoRound/report.json
index 0ebbb15a556c37a09ec1e1a4d84a40f8955ce01f..779567dc9ab8cc2b48537efde3dcc22a8618e4e0 100644
--- a/Qwen3.8-27B-W4A16-AutoRound/report.json
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+  "reference_weights_sha256": null,
+  "runtime_binding": {
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+    "cuda": "13.2",
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+      "NCCL_DETERMINISTIC": "1",
+      "VLLM_BATCH_INVARIANT": "1",
+      "VLLM_MOE_USE_DEEP_GEMM": "0"
+    },
+    "driver": "580.173.02",
+    "flashinfer": "0.6.17",
+    "gpu_names": [
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
+    ],
+    "nccl": "2.29.7",
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
+    "platform": "Linux-6.8.0-137-generic-x86_64-with-glibc2.39",
+    "python": "3.12.3",
+    "torch": "2.13.0+cu132",
+    "vllm_commit": "60071d1ab73229321712254b1350dcaaac1c125a",
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+    "vllm_tree_dirty": false
+  },
   "score_from": 0,
   "storage": "hidden",
   "student_lm_head": {
@@ -8630,101 +8678,102 @@
     }
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   "timings": {
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   "trunk_report": {
     "confidence_buckets": [
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     "num_positions": 1572096,
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+    "p99_kld": 0.5588592082262043,
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+      "5": 0.28221495379417033
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   "unique_tokens": 1572864,
+  "student_weights_sha256": "f733884e73e0882a9301e52dee05b0752dda2cda442c7630e76b71ca1390e390",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/dbirks/Qwen3.8-27B-W4A16-AutoRound",
@@ -8811,5 +8860,7 @@
       "candidate": "06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523",
       "match": false
     }
-  }
+  },
+  "candidate_hf_repo": "dbirks/Qwen3.8-27B-W4A16-AutoRound",
+  "candidate_revision": "1f05c441c4e64ae0549de44fa9ea5a6d43610314"
 }
diff --git a/Qwen3.8-27B-W4A16-AutoRound/report.md b/Qwen3.8-27B-W4A16-AutoRound/report.md
index 98a9631aa1aa835a4f2035cb38beacd6b4fc5bfe..2082f49802c67a9cf729799e7de9ddc27b006a10 100644
--- a/Qwen3.8-27B-W4A16-AutoRound/report.md
+++ b/Qwen3.8-27B-W4A16-AutoRound/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / Qwen3.8-27B-W4A16-AutoRound: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.04275781** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.04285275** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.04275781 | 0.00897863 | 0.05865338 | 0.55736914 | 28.06366730 | 92.7893% |
+| 0.04285275 | 0.00898137 | 0.05864951 | 0.55885917 | 28.03813171 | 92.7889% |
 
-Reverse direction, KLD(candidate || reference): 0.04348920.
+Reverse direction, KLD(candidate || reference): 0.04362927.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.04348920.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/dbirks/Qwen3.8-27B-W4A16-AutoRound |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | f733884e73e0882a |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,32 +27,48 @@ Reverse direction, KLD(candidate || reference): 0.04348920.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
+## Expert kernels: declared against built
+
+| Property | Value |
+|---|---|
+| Declared for its experts | `unquantized` |
+| Expert implementation built | n/a |
+| Expert kernel built | n/a |
+| Expert layers carrying an activation scale | n/a |
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12805693 | 2.99 | 0.98752029 |
-| Chinese across several content types | 72 | 55.6% | 0.07092035 | 1.66 | 0.29328628 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.03898203 | 0.91 | 0.22658252 |
-| Other multilingual content | 36 | 64.5% | 0.03397794 | 0.79 | 0.06798460 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02805913 | 0.66 | 0.07839639 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.02685736 | 0.63 | 0.18564302 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02462339 | 0.58 | 0.11688248 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02433869 | 0.57 | 0.16940031 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01827053 | 0.43 | 0.07079436 |
-| Scientific and technical exposition | 96 | 60.3% | 0.01606118 | 0.38 | 0.03043132 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12877720 | 3.01 | 0.96615921 |
+| Chinese across several content types | 72 | 55.6% | 0.07087878 | 1.65 | 0.29306609 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.03903889 | 0.91 | 0.22728952 |
+| Other multilingual content | 36 | 64.5% | 0.03402188 | 0.79 | 0.06794852 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02806250 | 0.65 | 0.07857883 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.02688816 | 0.63 | 0.18463329 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02459391 | 0.57 | 0.11904886 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02438714 | 0.57 | 0.16895531 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01821542 | 0.43 | 0.06999113 |
+| Scientific and technical exposition | 96 | 60.3% | 0.01605032 | 0.37 | 0.03011023 |
 
 **Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Scientific and technical exposition** its strongest, a spread of 8.0x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
@@ -60,8 +76,8 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.04275781 |
-| Deployed (candidate's own head) | 0.04275781 |
+| Trunk (candidate hidden states, reference head) | 0.04285275 |
+| Deployed (candidate's own head) | 0.04285275 |
 | Head-associated delta (not additive) | -0.00000000 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]}, 'state': 'unquantized', 'static': {'ignored': True, 'lm_head_dtypes': {'lm_head.weight': 'BF16', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.weight'], 'output_weight_keys': ['lm_head.weight'], 'packed_keys': [], 'quant_method': 'compressed-tensors', 'state': 'unquantized', 'tie_word_embeddings': False}} |
@@ -70,26 +86,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.03512086 |
-| 512–1023 | 393216 | 0.03892478 |
-| 1024–1535 | 393216 | 0.04613208 |
-| 1536–2046 | 392448 | 0.05086936 |
+| 0–511 | 393216 | 0.03506617 |
+| 512–1023 | 393216 | 0.03912474 |
+| 1024–1535 | 393216 | 0.04595211 |
+| 1536–2046 | 392448 | 0.05128442 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.04675907 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.05912159 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.06780087 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.04888550 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.01025826 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.04670400 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.06024681 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.06588162 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.04988806 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.01036356 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 92.5588% | 76.6113% | 58.1725% | 41.2683% | 28.1994% |
+| 92.5618% | 76.6140% | 58.1707% | 41.2794% | 28.2215% |
 
 ## Law compliance
 
@@ -101,22 +117,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.04275781, deployed 0.04275781, delta -2.274543976776844e-09 |
-| 9 | Tail and depth disclosure | PASS | mean 0.04275781, median 0.00897863, max 28.06366730, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.04285275, deployed 0.04285275, delta -2.2030077345691623e-09 |
+| 9 | Tail and depth disclosure | PASS | mean 0.04285275, median 0.00898137, max 28.03813171, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.12805693, strongest scientific_technical at 0.01606118, spread 8.0x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.12877720, strongest scientific_technical at 0.01605032, spread 8.0x |
+| 16 | Candidate weight binding | PASS | scored weights f733884e73e0882a as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +139,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +147,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +170,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +189,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `Qwen3.8-27B-W4A16-AutoRound/report.md` | 12.38 KiB | This document. |
-| `Qwen3.8-27B-W4A16-AutoRound/report.json` | 254.56 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `Qwen3.8-27B-W4A16-AutoRound/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `Qwen3.8-27B-W4A16-AutoRound/compliance.json` | 13.03 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `Qwen3.8-27B-W4A16-AutoRound/report.md` | 13.22 KiB | This document. |
+| `Qwen3.8-27B-W4A16-AutoRound/report.json` | 257.05 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `Qwen3.8-27B-W4A16-AutoRound/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `Qwen3.8-27B-W4A16-AutoRound/compliance.json` | 12.30 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/Qwen3.8-27B-W4A16-AutoRound/strata.json b/Qwen3.8-27B-W4A16-AutoRound/strata.json
index 335a259dc6874ebd7c1497205ddbca9c0ea28eba..f1c7c43f4d483f75ce25ba6e1b32b30fa409a812 100644
--- a/Qwen3.8-27B-W4A16-AutoRound/strata.json
+++ b/Qwen3.8-27B-W4A16-AutoRound/strata.json
@@ -8,381 +8,381 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 28.06366729736328,
-            "mean_kld": 0.12805693479047286,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.1001243709953897,
-            "median_context_p99": 1.875031590461731,
-            "p90_context_kld": 0.3357585696562436,
+            "max_kld": 28.038131713867188,
+            "mean_kld": 0.128777198176715,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.09961656562257165,
+            "median_context_p99": 2.0042476654052734,
+            "p90_context_kld": 0.32835024747037,
             "positions": 196512,
-            "top1_agreement": 0.9226103240514574,
+            "top1_agreement": 0.9227935189708516,
             "worst_context_id": 454,
-            "worst_context_kld": 0.9875202859772684
+            "worst_context_kld": 0.9661592104468429
           }
         },
         "key": "wildchat",
         "label": "wildchat",
-        "relative_to_run": 2.994936635548522
+        "relative_to_run": 3.0051096207415684
       },
       {
         "cells": {
           "deployed": {
             "contexts": 33,
-            "max_kld": 6.048852443695068,
-            "mean_kld": 0.11522363175609408,
-            "mean_ref_top1_prob": 0.6040606719329803,
-            "median_context_kld": 0.08510520234224807,
-            "median_context_p99": 0.6295166015625,
-            "p90_context_kld": 0.2745278010457084,
+            "max_kld": 6.1496381759643555,
+            "mean_kld": 0.1151557840607326,
+            "mean_ref_top1_prob": 0.6040861921682756,
+            "median_context_kld": 0.08507920150404628,
+            "median_context_p99": 0.6570150256156921,
+            "p90_context_kld": 0.27392513118721,
             "positions": 67551,
-            "top1_agreement": 0.8592618910156771,
+            "top1_agreement": 0.8585957276724253,
             "worst_context_id": 893,
-            "worst_context_kld": 0.29328627610003505
+            "worst_context_kld": 0.29306609163790565
           }
         },
         "key": "wikisource_zh",
         "label": "wikisource_zh",
-        "relative_to_run": 2.6947972524245674
+        "relative_to_run": 2.6872440110870386
       },
       {
         "cells": {
           "deployed": {
             "contexts": 7,
-            "max_kld": 8.456367492675781,
-            "mean_kld": 0.05251834963647834,
-            "mean_ref_top1_prob": 0.7357366218042676,
-            "median_context_kld": 0.0587099314970576,
-            "median_context_p99": 0.949874997138977,
-            "p90_context_kld": 0.06798460049691724,
+            "max_kld": 8.196898460388184,
+            "mean_kld": 0.05271525640433072,
+            "mean_ref_top1_prob": 0.7358200961573914,
+            "median_context_kld": 0.05958199329388069,
+            "median_context_p99": 1.0070927143096924,
+            "p90_context_kld": 0.06794851665482873,
             "positions": 14329,
-            "top1_agreement": 0.9177890990299393,
+            "top1_agreement": 0.9159048084304557,
             "worst_context_id": 953,
-            "worst_context_kld": 0.06798460049691724
+            "worst_context_kld": 0.06794851665482873
           }
         },
         "key": "wikipedia_de",
         "label": "wikipedia_de",
-        "relative_to_run": 1.228274982703532
+        "relative_to_run": 1.2301488650430727
       },
       {
         "cells": {
           "deployed": {
             "contexts": 23,
-            "max_kld": 5.876983642578125,
-            "mean_kld": 0.04111845891197867,
-            "mean_ref_top1_prob": 0.7232628548554616,
-            "median_context_kld": 0.04922876424701612,
-            "median_context_p99": 0.7349742650985718,
-            "p90_context_kld": 0.07010919474625847,
+            "max_kld": 6.387468338012695,
+            "mean_kld": 0.04106535483865567,
+            "mean_ref_top1_prob": 0.7233191001139795,
+            "median_context_kld": 0.049356924385334705,
+            "median_context_p99": 0.7061058878898621,
+            "p90_context_kld": 0.06964851841221208,
             "positions": 47081,
-            "top1_agreement": 0.9342622289246193,
+            "top1_agreement": 0.933773709139568,
             "worst_context_id": 275,
-            "worst_context_kld": 0.07839639231374333
+            "worst_context_kld": 0.07857882581031697
           }
         },
         "key": "regulations",
         "label": "regulations",
-        "relative_to_run": 0.9616595867633042
+        "relative_to_run": 0.9582899352684026
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 7.145658016204834,
-            "mean_kld": 0.03898202692263404,
-            "mean_ref_top1_prob": 0.5946008380921044,
-            "median_context_kld": 0.030182851425063013,
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+            "median_context_p99": 0.3273087739944458,
+            "p90_context_kld": 0.06070639411248192,
             "positions": 196512,
-            "top1_agreement": 0.919648672854584,
+            "top1_agreement": 0.9199081989903924,
             "worst_context_id": 6,
-            "worst_context_kld": 0.22658251791229148
+            "worst_context_kld": 0.22728951896127722
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 0.9116936989750697
+        "relative_to_run": 0.9110008154220084
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 8.456367492675781,
-            "mean_kld": 0.03397794269304488,
-            "mean_ref_top1_prob": 0.6450955362692544,
-            "median_context_kld": 0.031224018205075917,
-            "median_context_p99": 0.24270059168338776,
-            "p90_context_kld": 0.0587099314970576,
+            "max_kld": 8.196898460388184,
+            "mean_kld": 0.03402188476562013,
+            "mean_ref_top1_prob": 0.6450767409183084,
+            "median_context_kld": 0.03126211095327961,
+            "median_context_p99": 0.2368839681148529,
+            "p90_context_kld": 0.05958199329388069,
             "positions": 73692,
-            "top1_agreement": 0.920439125006785,
+            "top1_agreement": 0.9202898550724637,
             "worst_context_id": 953,
-            "worst_context_kld": 0.06798460049691724
+            "worst_context_kld": 0.06794851665482873
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.7946604808124703
+        "relative_to_run": 0.7939254361212885
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 5.876983642578125,
-            "mean_kld": 0.028059128415213658,
-            "mean_ref_top1_prob": 0.5900126668457796,
-            "median_context_kld": 0.021647648205733377,
-            "median_context_p99": 0.172760009765625,
-            "p90_context_kld": 0.06247094264913949,
+            "max_kld": 6.387468338012695,
+            "mean_kld": 0.02806249845256977,
+            "mean_ref_top1_prob": 0.590028705918726,
+            "median_context_kld": 0.02179187492011151,
+            "median_context_p99": 0.17464427649974823,
+            "p90_context_kld": 0.062246053193153475,
             "positions": 147384,
-            "top1_agreement": 0.9313290452152201,
+            "top1_agreement": 0.9313697551973077,
             "worst_context_id": 275,
-            "worst_context_kld": 0.07839639231374333
+            "worst_context_kld": 0.07857882581031697
           }
         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.6562339774084297
+        "relative_to_run": 0.6548588203180148
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 6.222611904144287,
-            "mean_kld": 0.026857363835032753,
-            "mean_ref_top1_prob": 0.5056158503386161,
-            "median_context_kld": 0.02183595809864473,
-            "median_context_p99": 0.1462002694606781,
-            "p90_context_kld": 0.031190138625697594,
+            "max_kld": 5.7928056716918945,
+            "mean_kld": 0.02688815695895397,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.021852285716636964,
+            "median_context_p99": 0.14370843768119812,
+            "p90_context_kld": 0.031188265585522876,
             "positions": 147384,
-            "top1_agreement": 0.9207444498724421,
+            "top1_agreement": 0.9205883949411062,
             "worst_context_id": 443,
-            "worst_context_kld": 0.1856430182390945
+            "worst_context_kld": 0.18463329470707723
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.6281276606800352
+        "relative_to_run": 0.6274547070862753
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 8.625184059143066,
-            "mean_kld": 0.024623391986529602,
-            "mean_ref_top1_prob": 0.7071241246993897,
-            "median_context_kld": 0.01955981759587326,
-            "median_context_p99": 0.28906747698783875,
-            "p90_context_kld": 0.045718002429792695,
+            "max_kld": 8.375225067138672,
+            "mean_kld": 0.02459390998406996,
+            "mean_ref_top1_prob": 0.7071550040111672,
+            "median_context_kld": 0.01960762355794492,
+            "median_context_p99": 0.27648022770881653,
+            "p90_context_kld": 0.045594642807170994,
             "positions": 73692,
-            "top1_agreement": 0.8962709656407751,
+            "top1_agreement": 0.8970444553004396,
             "worst_context_id": 1004,
-            "worst_context_kld": 0.11688248438488577
+            "worst_context_kld": 0.1190488604252833
           }
         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
-        "relative_to_run": 0.5758805555715667
+        "relative_to_run": 0.5739167845798372
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 10.921215057373047,
-            "mean_kld": 0.024338690320428128,
-            "mean_ref_top1_prob": 0.7950703670465326,
-            "median_context_kld": 0.01670606907314255,
-            "median_context_p99": 0.18975982069969177,
-            "p90_context_kld": 0.04367572146679782,
+            "max_kld": 10.62143611907959,
+            "mean_kld": 0.02438713635722535,
+            "mean_ref_top1_prob": 0.7950885550065969,
+            "median_context_kld": 0.016831964781114758,
+            "median_context_p99": 0.19813817739486694,
+            "p90_context_kld": 0.044535311777171294,
             "positions": 196512,
-            "top1_agreement": 0.9563690766976063,
+            "top1_agreement": 0.956292745481192,
             "worst_context_id": 667,
-            "worst_context_kld": 0.16940030585438187
+            "worst_context_kld": 0.16895530630690664
           }
         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5692220840768043
+        "relative_to_run": 0.5690915715441127
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 19.500471115112305,
-            "mean_kld": 0.018270527479932717,
-            "mean_ref_top1_prob": 0.6582818745125177,
-            "median_context_kld": 0.014317640640437048,
-            "median_context_p99": 0.1093088760972023,
-            "p90_context_kld": 0.027367908902353893,
+            "max_kld": 14.959373474121094,
+            "mean_kld": 0.01821542354704889,
+            "mean_ref_top1_prob": 0.6582834184806026,
+            "median_context_kld": 0.01427963715928951,
+            "median_context_p99": 0.10711885988712311,
+            "p90_context_kld": 0.027500430701923765,
             "positions": 196512,
-            "top1_agreement": 0.9482779677576942,
+            "top1_agreement": 0.9478912229278619,
             "worst_context_id": 825,
-            "worst_context_kld": 0.07079436494049206
+            "worst_context_kld": 0.06999113355068681
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.4273026852468241
+        "relative_to_run": 0.4250701624366982
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 14.5008544921875,
-            "mean_kld": 0.016061177366683305,
-            "mean_ref_top1_prob": 0.6033561622407548,
-            "median_context_kld": 0.015951508591703866,
-            "median_context_p99": 0.11736054718494415,
-            "p90_context_kld": 0.021875652294006263,
+            "max_kld": 16.392864227294922,
+            "mean_kld": 0.016050318254262678,
+            "mean_ref_top1_prob": 0.6033589127996771,
+            "median_context_kld": 0.015587062718232434,
+            "median_context_p99": 0.11782897263765335,
+            "p90_context_kld": 0.02177289503693345,
             "positions": 196512,
-            "top1_agreement": 0.9428940726266081,
+            "top1_agreement": 0.9432350187265918,
             "worst_context_id": 246,
-            "worst_context_kld": 0.030431316023789335
+            "worst_context_kld": 0.03011023415506631
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.3756314219470233
+        "relative_to_run": 0.37454585504850724
       }
     ]
   },
   "overall": {
     "deployed": {
       "contexts": 768,
-      "max_kld": 28.06366729736328,
-      "mean_kld": 0.04275781105700063,
-      "mean_ref_top1_prob": 0.6415440866166368,
-      "median_context_kld": 0.022386720846269026,
-      "median_context_p99": 0.19623413681983948,
-      "p90_context_kld": 0.08733337055629971,
+      "max_kld": 28.038131713867188,
+      "mean_kld": 0.04285274563293194,
+      "mean_ref_top1_prob": 0.6415473983687051,
+      "median_context_kld": 0.022197691626328203,
+      "median_context_p99": 0.1954183578491211,
+      "p90_context_kld": 0.0882388502261295,
       "positions": 1572096,
-      "top1_agreement": 0.9278930803207946,
+      "top1_agreement": 0.9278892637599739,
       "worst_context_id": 454,
-      "worst_context_kld": 0.9875202859772684
+      "worst_context_kld": 0.9661592104468429
     }
   },
   "primary": "deployed"
diff --git a/Qwen3.8-27B-W4A16-AutoRound/strata.md b/Qwen3.8-27B-W4A16-AutoRound/strata.md
index 326ce54a87b36ac31cdfdf20872c706f76351ea3..0136655ac4947e1a19671cdab6d57b759defd4a8 100644
--- a/Qwen3.8-27B-W4A16-AutoRound/strata.md
+++ b/Qwen3.8-27B-W4A16-AutoRound/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / Qwen3.8-27B-W4A16-AutoRound
 
-768 contexts, 1572096 scored positions, mean 0.04275781, reference top-1 64.2%, top-1 agreement 92.7893%.
+768 contexts, 1572096 scored positions, mean 0.04285275, reference top-1 64.2%, top-1 agreement 92.7889%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12805693 | 2.99 | 0.10012437 | 0.33575857 | 0.987520 |
-| Chinese across several content types | 72 | 55.6% | 0.07092035 | 1.66 | 0.04434415 | 0.13979858 | 0.293286 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.03898203 | 0.91 | 0.03018285 | 0.06059555 | 0.226583 |
-| Other multilingual content | 36 | 64.5% | 0.03397794 | 0.79 | 0.03122402 | 0.05870993 | 0.067985 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02805913 | 0.66 | 0.02164765 | 0.06247094 | 0.078396 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.02685736 | 0.63 | 0.02183596 | 0.03119014 | 0.185643 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02462339 | 0.58 | 0.01955982 | 0.04571800 | 0.116882 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02433869 | 0.57 | 0.01670607 | 0.04367572 | 0.169400 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01827053 | 0.43 | 0.01431764 | 0.02736791 | 0.070794 |
-| Scientific and technical exposition | 96 | 60.3% | 0.01606118 | 0.38 | 0.01595151 | 0.02187565 | 0.030431 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.12877720 | 3.01 | 0.09961657 | 0.32835025 | 0.966159 |
+| Chinese across several content types | 72 | 55.6% | 0.07087878 | 1.65 | 0.04440578 | 0.14007831 | 0.293066 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.03903889 | 0.91 | 0.03046606 | 0.06070639 | 0.227290 |
+| Other multilingual content | 36 | 64.5% | 0.03402188 | 0.79 | 0.03126211 | 0.05958199 | 0.067949 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.02806250 | 0.65 | 0.02179187 | 0.06224605 | 0.078579 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.02688816 | 0.63 | 0.02185229 | 0.03118827 | 0.184633 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.02459391 | 0.57 | 0.01960762 | 0.04559464 | 0.119049 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.02438714 | 0.57 | 0.01683196 | 0.04453531 | 0.168955 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.01821542 | 0.43 | 0.01427964 | 0.02750043 | 0.069991 |
+| Scientific and technical exposition | 96 | 60.3% | 0.01605032 | 0.37 | 0.01558706 | 0.02177290 | 0.030110 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.12805693 | 2.99 | 0.10012437 | 0.33575857 | 0.987520 |
-| wikisource_zh | 33 | 60.4% | 0.11522363 | 2.69 | 0.08510520 | 0.27452780 | 0.293286 |
-| wikipedia_de | 7 | 73.6% | 0.05251835 | 1.23 | 0.05870993 | 0.06798460 | 0.067985 |
-| regulations | 23 | 72.3% | 0.04111846 | 0.96 | 0.04922876 | 0.07010919 | 0.078396 |
-| wikipedia_en | 96 | 59.5% | 0.03898203 | 0.91 | 0.03018285 | 0.06059555 | 0.226583 |
-| wikipedia_cs | 6 | 68.9% | 0.03572700 | 0.84 | 0.03686287 | 0.03940913 | 0.039409 |
-| wikipedia_ja | 7 | 57.0% | 0.03365482 | 0.79 | 0.03325248 | 0.03983323 | 0.039833 |
-| wikipedia_zh | 39 | 51.5% | 0.03343296 | 0.78 | 0.03104915 | 0.04565746 | 0.054906 |
-| wikipedia_es | 7 | 59.2% | 0.02802177 | 0.66 | 0.02843605 | 0.03311229 | 0.033112 |
-| public_domain_books | 72 | 50.6% | 0.02685736 | 0.63 | 0.02183596 | 0.03119014 | 0.185643 |
-| public_domain_review | 26 | 51.8% | 0.02552455 | 0.60 | 0.02164765 | 0.04773622 | 0.070107 |
-| github_code | 52 | 85.8% | 0.02508261 | 0.59 | 0.01465946 | 0.04863886 | 0.169400 |
-| wikipedia_ru | 6 | 66.6% | 0.02469644 | 0.58 | 0.02671247 | 0.03122402 | 0.031224 |
-| starcoder_structured | 36 | 70.7% | 0.02462339 | 0.58 | 0.01955982 | 0.04571800 | 0.116882 |
-| stackv2 | 44 | 72.0% | 0.02345951 | 0.55 | 0.01803556 | 0.03435376 | 0.156199 |
-| wikipedia_fr | 3 | 60.2% | 0.02043358 | 0.48 | 0.01896838 | 0.02469139 | 0.024691 |
-| libretexts | 96 | 65.8% | 0.01827053 | 0.43 | 0.01431764 | 0.02736791 | 0.070794 |
-| open_news | 23 | 53.8% | 0.01786497 | 0.42 | 0.01612332 | 0.02661394 | 0.033169 |
-| scientific_papers | 96 | 60.3% | 0.01606118 | 0.38 | 0.01595151 | 0.02187565 | 0.030431 |
+| wildchat | 96 | 73.5% | 0.12877720 | 3.01 | 0.09961657 | 0.32835025 | 0.966159 |
+| wikisource_zh | 33 | 60.4% | 0.11515578 | 2.69 | 0.08507920 | 0.27392513 | 0.293066 |
+| wikipedia_de | 7 | 73.6% | 0.05271526 | 1.23 | 0.05958199 | 0.06794852 | 0.067949 |
+| regulations | 23 | 72.3% | 0.04106535 | 0.96 | 0.04935692 | 0.06964852 | 0.078579 |
+| wikipedia_en | 96 | 59.5% | 0.03903889 | 0.91 | 0.03046606 | 0.06070639 | 0.227290 |
+| wikipedia_cs | 6 | 68.9% | 0.03589677 | 0.84 | 0.03735140 | 0.03946265 | 0.039463 |
+| wikipedia_ja | 7 | 57.0% | 0.03360251 | 0.78 | 0.03310421 | 0.04005021 | 0.040050 |
+| wikipedia_zh | 39 | 51.5% | 0.03341363 | 0.78 | 0.03137996 | 0.04532779 | 0.054559 |
+| wikipedia_es | 7 | 59.2% | 0.02802201 | 0.65 | 0.02843355 | 0.03313504 | 0.033135 |
+| public_domain_books | 72 | 50.6% | 0.02688816 | 0.63 | 0.02185229 | 0.03118827 | 0.184633 |
+| public_domain_review | 26 | 51.8% | 0.02552279 | 0.60 | 0.02134495 | 0.04802337 | 0.070319 |
+| github_code | 52 | 85.8% | 0.02505628 | 0.58 | 0.01456613 | 0.04823373 | 0.168955 |
+| wikipedia_ru | 6 | 66.6% | 0.02469175 | 0.58 | 0.02676406 | 0.03126211 | 0.031262 |
+| starcoder_structured | 36 | 70.7% | 0.02459391 | 0.57 | 0.01960762 | 0.04559464 | 0.119049 |
+| stackv2 | 44 | 72.0% | 0.02359633 | 0.55 | 0.01792432 | 0.03836409 | 0.155108 |
+| wikipedia_fr | 3 | 60.2% | 0.02029274 | 0.47 | 0.01885120 | 0.02458043 | 0.024580 |
+| libretexts | 96 | 65.8% | 0.01821542 | 0.43 | 0.01427964 | 0.02750043 | 0.069991 |
+| open_news | 23 | 53.8% | 0.01793062 | 0.42 | 0.01623228 | 0.02627180 | 0.034302 |
+| scientific_papers | 96 | 60.3% | 0.01605032 | 0.37 | 0.01558706 | 0.02177290 | 0.030110 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.12805693, 2.99x the run mean of 0.04275781 over 96 context(s).
-- Strongest domain: **Scientific and technical exposition** at 0.01606118, 0.38x the run mean. The spread across domains is 8.0x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.12877720, 3.01x the run mean of 0.04285275 over 96 context(s).
+- Strongest domain: **Scientific and technical exposition** at 0.01605032, 0.37x the run mean. The spread across domains is 8.0x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.987520 against a median of 0.10012437 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.966159 against a median of 0.09961657 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/README.md b/README.md
index d92557c1f49ebbb859559facb686144f68d88725..581c612777521675e5c08e048afce20c8f54f59b 100644
--- a/README.md
+++ b/README.md
@@ -1,37 +1,39 @@
 ---
-pretty_name: Qwen3.8-27B quantization analysis
+pretty_name: "Qwen3.8-27B quantization analysis"
 tags:
-- quantization
-- kl-divergence
-- distribution-fidelity
-- evaluation
+- "quantization"
+- "kl-divergence"
+- "distribution-fidelity"
+- "evaluation"
 ---
 
 # Qwen3.8-27B quantization analysis
 
 Mean KL divergence against on-disk size
 
-Scored under the distribution-fidelity laws, version 9. Read [`LAWS.md`](LAWS.md) first: these numbers are comparable only within this artifact's token suite, geometry, and runtime identity, and not against any number produced elsewhere.
+Scored under the distribution-fidelity laws, version 15. Read [`LAWS.md`](LAWS.md) first: these numbers are comparable only within this artifact's token suite, geometry, and runtime identity, and not against any number produced elsewhere.
 
 Each candidate directory holds its one-pager (`report.md`), its raw report, its compliance receipt, and its Law 14 attribution where one was produced. `reference/` carries the reusable teacher tensors and head, so a third party can score a new candidate without loading the reference checkpoint. `checksums.txt` covers every file here.
 
-## Candidates by mean KLD
+## Candidates by QxQ fidelity (dense model)
 
-| Candidate | Scheme | On disk | Mean KLD |
-|---|---|---|---|
-| Qwen/Qwen3.8-27B-FP8 | fp8_block | 28.75 GiB | 0.01104734 |
-| cyankiwi/Qwen3.8-27B-AWQ-INT4 | unquantized | 19.57 GiB | 0.03110211 |
-| unsloth/Qwen3.8-27B-NVFP4 | fp8_per_channel | 21.81 GiB | 0.04096872 |
-| dbirks/Qwen3.8-27B-W4A16-AutoRound | unquantized | 18.12 GiB | 0.04275781 |
-| RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead | fp8_per_tensor | 22.12 GiB | 0.05212951 |
-| philbert440/Qwen3.8-27B-W4A16-AWQ | unquantized | 18.20 GiB | 0.05213981 |
-| RedHatAI/Qwen3.8-27B-INT4 | unquantized | 18.12 GiB | 0.06297880 |
-| RadixArk/Qwen3.8-27B-NVFP4 | fp8_per_tensor | 20.42 GiB | 0.06462909 |
-| gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 | fp8_per_tensor | 17.48 GiB | 0.08884902 |
-| Inferact/Qwen3.8-27B-NVFP4 | fp8_per_tensor | 24.57 GiB | 0.13875295 |
+This reference has no routed experts, so BxQ and routing intervention metrics are not applicable.
 
-## Fidelity against size
+| Candidate | Scheme | On disk | QxQ KLD | BxQ KLD | QxQ - BxQ | Natural route flips | Exact repeat |
+|---|---|---|---|---|---|---|---|
+| Qwen/Qwen3.8-27B-FP8 | fp8_block | 28.75 GiB | 0.01097467 | n/a | n/a | n/a | no |
+| cyankiwi/Qwen3.8-27B-AWQ-INT4 | int4_g32_asym | 19.57 GiB | 0.03116102 | n/a | n/a | n/a | no |
+| unsloth/Qwen3.8-27B-NVFP4 | nvfp4 | 21.81 GiB | 0.04053080 | n/a | n/a | n/a | no |
+| dbirks/Qwen3.8-27B-W4A16-AutoRound | int4_g128_sym | 18.12 GiB | 0.04285275 | n/a | n/a | n/a | no |
+| RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead | nvfp4 | 22.12 GiB | 0.05156551 | n/a | n/a | n/a | no |
+| philbert440/Qwen3.8-27B-W4A16-AWQ | int4_g128_asym | 18.20 GiB | 0.05220833 | n/a | n/a | n/a | no |
+| RedHatAI/Qwen3.8-27B-INT4 | int4_g128_sym | 18.12 GiB | 0.06290331 | n/a | n/a | n/a | no |
+| RadixArk/Qwen3.8-27B-NVFP4 | nvfp4 | 20.42 GiB | 0.06405373 | n/a | n/a | n/a | no |
+| gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 | nvfp4 | 17.48 GiB | 0.08890054 | n/a | n/a | n/a | no |
+| Inferact/Qwen3.8-27B-NVFP4 | nvfp4 | 24.57 GiB | 0.13893424 | n/a | n/a | n/a | no |
 
-![mean KLD against on-disk size](kld-vs-size.png)
+## QxQ fidelity against size
 
-The numbers behind the chart are in [`kld-vs-size.json`](kld-vs-size.json), because a picture is not evidence.
+![QxQ KLD against on-disk size](qxq-vs-size.png)
+
+The numbers behind the charts are in [`kld-vs-size.json`](kld-vs-size.json), because a picture is not evidence.
diff --git a/RadixArk-Qwen3.8-27B-NVFP4/compliance.json b/RadixArk-Qwen3.8-27B-NVFP4/compliance.json
index 9ea55ee5c257f5d5d15a3a2a6f49cf936f766c45..3c0c3c41fb7a8d6fe22a2f0732c3bd37345e495b 100644
--- a/RadixArk-Qwen3.8-27B-NVFP4/compliance.json
+++ b/RadixArk-Qwen3.8-27B-NVFP4/compliance.json
@@ -1,8 +1,9 @@
 {
   "attribution": null,
   "candidate": "/media/fmodels2/RadixArk/Qwen3.8-27B-NVFP4",
-  "candidate_weights_sha256": null,
+  "candidate_weights_sha256": "48e7a0bdae98398d240b013242d6f7ba21645833f6c4e412cdd4d2c849160920",
   "comparability_key": {
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "context_length": 2048,
     "driver": "580.173.02",
     "gpu_names": [
@@ -12,19 +13,22 @@
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
     "kld_vocab_size": 248044,
-    "laws_version": 9,
+    "kv_cache_dtype": "bfloat16",
+    "laws_version": 15,
     "model_runner_v2": false,
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "reference_config_sha256": "191e0af232104ed8b65258cf3fb2b842e288008baca7633c11b82a1ac7203aab",
     "rows": 768,
     "score_from": 0,
     "stride": 2048,
+    "substituted_parameters": [],
     "suite_id": "qwen3.8-27b-fidelity-1024x2048-v1",
     "tensor_parallel_size": 1,
     "token_sha256": "9c935708bbffbf45d5baa2931fb4af7e7b22df1e041aa0b6f4ca44d3315e543b",
     "torch": "2.13.0+cu132"
   },
   "compliant": true,
-  "evaluated_at": "2026-09-03T12:56:04.179750+00:00",
+  "evaluated_at": "2026-09-11T20:24:24.327238+00:00",
   "failed_laws": [],
   "findings": [
     {
@@ -52,13 +56,13 @@
       "title": "Real vocabulary"
     },
     {
-      "detail": "all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc",
+      "detail": "all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3",
       "law": 5,
       "status": "pass",
       "title": "Manifest binding"
     },
     {
-      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s)",
+      "detail": "torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s)",
       "law": 6,
       "status": "pass",
       "title": "Provenance"
@@ -70,13 +74,13 @@
       "title": "Storage integrity"
     },
     {
-      "detail": "trunk 0.05212951, deployed 0.06462909, delta 0.012499583801223166",
+      "detail": "trunk 0.05156551, deployed 0.06405373, delta 0.01248822839901103",
       "law": 8,
       "status": "pass",
       "title": "Head transparency"
     },
     {
-      "detail": "mean 0.06462909, median 0.02380580, max 27.04055405, 4 depth buckets",
+      "detail": "mean 0.06405373, median 0.02363336, max 36.06170654, 4 depth buckets",
       "law": 9,
       "status": "pass",
       "title": "Tail and depth disclosure"
@@ -103,38 +107,37 @@
       "detail": "reference declares no experts",
       "law": 14,
       "status": "not_applicable",
-      "title": "Component attribution"
+      "title": "Routed-model intervention"
     },
     {
-      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.15310432, strongest structured_data_tools at 0.03590898, spread 4.3x",
+      "detail": "10 domains disclosed; weakest dialogue_instruction at 0.15069679, strongest structured_data_tools at 0.03587765, spread 4.2x",
       "law": 15,
       "status": "pass",
       "title": "Domain disclosure"
     },
     {
-      "approval": {
-        "approver": "Andy Kitzke",
-        "justification": "These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger.",
-        "timestamp": "2026-09-02T05:15:00Z"
-      },
-      "detail": "the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else",
+      "detail": "scored weights 48e7a0bdae98398d as inspected",
       "law": 16,
-      "status": "override",
+      "status": "pass",
       "title": "Candidate weight binding"
     },
     {
-      "detail": "1 override(s), each fully attributed",
+      "detail": "every scored layer used the checkpoint's own quantization parameters",
+      "law": 17,
+      "status": "pass",
+      "title": "Substitution disclosure"
+    },
+    {
+      "detail": "no overrides claimed",
       "law": 13,
       "status": "pass",
       "title": "Recorded deviation"
     }
   ],
-  "laws_version": 9,
-  "mean_kld": 0.06462909372577283,
+  "laws_version": 15,
+  "mean_kld": 0.06405373393276945,
   "nondeterminism_floor": 0.0,
-  "overridden_laws": [
-    16
-  ],
+  "overridden_laws": [],
   "partition": "analysis",
   "program": "Local Inference Lab \u2014 Distribution Fidelity",
   "ranking_floor": null,
@@ -147,16 +150,16 @@
     "overall": {
       "deployed": {
         "contexts": 768,
-        "max_kld": 27.04055404663086,
-        "mean_kld": 0.06462909372577283,
-        "mean_ref_top1_prob": 0.6415440866166368,
-        "median_context_kld": 0.04527205925139923,
-        "median_context_p99": 0.2879001200199127,
-        "p90_context_kld": 0.11381631951197184,
+        "max_kld": 36.06170654296875,
+        "mean_kld": 0.06405373393276945,
+        "mean_ref_top1_prob": 0.6415473983687051,
+        "median_context_kld": 0.045394568267538196,
+        "median_context_p99": 0.2929476499557495,
+        "p90_context_kld": 0.11414634314146811,
         "positions": 1572096,
-        "top1_agreement": 0.8929823624002605,
+        "top1_agreement": 0.8934893288959452,
         "worst_context_id": 454,
-        "worst_context_kld": 0.941442507595517
+        "worst_context_kld": 0.8894104415027609
       }
     },
     "primary": "deployed",
@@ -165,201 +168,201 @@
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 27.04055404663086,
-            "mean_kld": 0.15310432288400005,
-            "mean_ref_top1_prob": 0.7352292693891235,
-            "median_context_kld": 0.11646212922482002,
-            "median_context_p99": 2.460944414138794,
-            "p90_context_kld": 0.32543345398860335,
+            "max_kld": 36.06170654296875,
+            "mean_kld": 0.15069678531349162,
+            "mean_ref_top1_prob": 0.735242277628431,
+            "median_context_kld": 0.11414634314146811,
+            "median_context_p99": 2.2102487087249756,
+            "p90_context_kld": 0.35157713644396854,
             "positions": 196512,
-            "top1_agreement": 0.8988458720078163,
+            "top1_agreement": 0.8998025565868751,
             "worst_context_id": 454,
-            "worst_context_kld": 0.941442507595517
+            "worst_context_kld": 0.8894104415027609
           }
         },
         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
-        "relative_to_run": 2.3689690518272735
+        "relative_to_run": 2.3526619926898
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 7.192142486572266,
-            "mean_kld": 0.09975663444684585,
-            "mean_ref_top1_prob": 0.5560138945340303,
-            "median_context_kld": 0.07013398424227976,
-            "median_context_p99": 0.45579203963279724,
-            "p90_context_kld": 0.16958107927836336,
+            "max_kld": 7.21721887588501,
+            "mean_kld": 0.09873888601654877,
+            "mean_ref_top1_prob": 0.5560348831963939,
+            "median_context_kld": 0.06962943433992518,
+            "median_context_p99": 0.47691574692726135,
+            "p90_context_kld": 0.16958437004720806,
             "positions": 147384,
-            "top1_agreement": 0.8476768170222005,
+            "top1_agreement": 0.8495901861803181,
             "worst_context_id": 893,
-            "worst_context_kld": 0.3468693204084183
+            "worst_context_kld": 0.33018841461758575
           }
         },
         "key": "chinese",
         "label": "Chinese across several content types",
-        "relative_to_run": 1.5435251942433603
+        "relative_to_run": 1.5415008611392542
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
-            "max_kld": 8.273160934448242,
-            "mean_kld": 0.06607199322814013,
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-            "median_context_kld": 0.05817510825656386,
-            "median_context_p99": 0.483924925327301,
-            "p90_context_kld": 0.08760411848289906,
+            "max_kld": 7.776761531829834,
+            "mean_kld": 0.0652354522345281,
+            "mean_ref_top1_prob": 0.5945716883648833,
+            "median_context_kld": 0.057616552725030655,
+            "median_context_p99": 0.45051079988479614,
+            "p90_context_kld": 0.08645894642694131,
             "positions": 196512,
-            "top1_agreement": 0.8795849617326168,
+            "top1_agreement": 0.8807859062042013,
             "worst_context_id": 6,
-            "worst_context_kld": 0.26828379602701014
+            "worst_context_kld": 0.26164212058429487
           }
         },
         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
-        "relative_to_run": 1.0223258507768909
+        "relative_to_run": 1.0184488589377003
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 5.936044692993164,
-            "mean_kld": 0.05391723518286848,
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-            "median_context_p99": 0.23956947028636932,
-            "p90_context_kld": 0.06324070059881363,
+            "max_kld": 6.861474990844727,
+            "mean_kld": 0.053819791856141726,
+            "mean_ref_top1_prob": 0.5055996513127425,
+            "median_context_kld": 0.047845158009261354,
+            "median_context_p99": 0.2421356439590454,
+            "p90_context_kld": 0.06330360872465543,
             "positions": 147384,
-            "top1_agreement": 0.8719467513434294,
+            "top1_agreement": 0.871716061444933,
             "worst_context_id": 443,
-            "worst_context_kld": 0.2299880393563586
+            "worst_context_kld": 0.23171318002037405
           }
         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
-        "relative_to_run": 0.8342564017939699
+        "relative_to_run": 0.8402287977876632
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
-            "max_kld": 8.139482498168945,
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-            "p90_context_kld": 0.07928388627720089,
+            "max_kld": 10.2679443359375,
+            "mean_kld": 0.05329195408656742,
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+            "median_context_kld": 0.05014531896289053,
+            "median_context_p99": 0.3233795762062073,
+            "p90_context_kld": 0.07689238730578683,
             "positions": 73692,
-            "top1_agreement": 0.8909922379634153,
+            "top1_agreement": 0.8885903490202465,
             "worst_context_id": 953,
-            "worst_context_kld": 0.0890645316124344
+            "worst_context_kld": 0.09580741603409602
           }
         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
-        "relative_to_run": 0.8157536993828359
+        "relative_to_run": 0.8319882513407018
       },
       {
         "cells": {
           "deployed": {
             "contexts": 72,
-            "max_kld": 6.766636371612549,
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-            "median_context_p99": 0.2879001200199127,
-            "p90_context_kld": 0.08797759334561323,
+            "max_kld": 6.0402655601501465,
+            "mean_kld": 0.051919242563362764,
+            "mean_ref_top1_prob": 0.590028705918726,
+            "median_context_kld": 0.04777421148043562,
+            "median_context_p99": 0.30754250288009644,
+            "p90_context_kld": 0.08325439244607435,
             "positions": 147384,
-            "top1_agreement": 0.8872537046083699,
+            "top1_agreement": 0.888597134017261,
             "worst_context_id": 275,
-            "worst_context_kld": 0.10850934661675099
+            "worst_context_kld": 0.10761446548346697
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         },
         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
-        "relative_to_run": 0.8074318239031558
+        "relative_to_run": 0.8105576267865509
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
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-            "p90_context_kld": 0.05936240373637667,
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             "positions": 196512,
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             "worst_context_id": 667,
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+            "worst_context_kld": 0.2072823504286248
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         },
         "key": "code_docs_issues",
         "label": "Source code, tests, technical documentation, and issue discussions",
-        "relative_to_run": 0.5775392291195218
+        "relative_to_run": 0.5812417179954872
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
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             "positions": 196512,
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+            "top1_agreement": 0.901787168213646,
             "worst_context_id": 246,
-            "worst_context_kld": 0.05182537247434169
+            "worst_context_kld": 0.05465767744676823
           }
         },
         "key": "scientific_technical",
         "label": "Scientific and technical exposition",
-        "relative_to_run": 0.5672281427235132
+        "relative_to_run": 0.5685409859284084
       },
       {
         "cells": {
           "deployed": {
             "contexts": 96,
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+            "p90_context_kld": 0.042537081737397585,
             "positions": 196512,
-            "top1_agreement": 0.9155318759159746,
+            "top1_agreement": 0.9154860771861261,
             "worst_context_id": 825,
-            "worst_context_kld": 0.0894561492774063
+            "worst_context_kld": 0.0934252344139667
           }
         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
-        "relative_to_run": 0.5607638717933936
+        "relative_to_run": 0.5628511325116325
       },
       {
         "cells": {
           "deployed": {
             "contexts": 36,
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diff --git a/RadixArk-Qwen3.8-27B-NVFP4/inspect.json b/RadixArk-Qwen3.8-27B-NVFP4/inspect.json
index 8d0ae6d69d3b71ad19412b0f6802931568f6ce60..5989bcf991263c4b24e7a049110b93687d20923c 100644
--- a/RadixArk-Qwen3.8-27B-NVFP4/inspect.json
+++ b/RadixArk-Qwen3.8-27B-NVFP4/inspect.json
@@ -1,31 +1,462 @@
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diff --git a/RadixArk-Qwen3.8-27B-NVFP4/manifest.json b/RadixArk-Qwen3.8-27B-NVFP4/manifest.json
index f207ebcf3babe813a523d4ac4363ec8271f5fc0a..96b32a22ad13c66fe8906af731e8ba0d07743638 100644
--- a/RadixArk-Qwen3.8-27B-NVFP4/manifest.json
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@@ -836,6 +837,7 @@
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   "reference_model": "/media/fmodels2/Qwen/Qwen3.8-27B",
   "reference_routing": null,
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@@ -845,17 +847,39 @@
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diff --git a/RadixArk-Qwen3.8-27B-NVFP4/report.json b/RadixArk-Qwen3.8-27B-NVFP4/report.json
index e472b8ad1d6e759de99b7633a0f4ad5c3846085f..1655641aa7645e8d6a9a61cce2475ac433eee85b 100644
--- a/RadixArk-Qwen3.8-27B-NVFP4/report.json
+++ b/RadixArk-Qwen3.8-27B-NVFP4/report.json
@@ -1,8530 +1,8576 @@
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+        "frac": 0.22041147614395049,
         "hi": 0.5,
         "lo": 0.25,
-        "mean_kld": 0.07293710963831636,
-        "n": 346390
+        "mean_kld": 0.07237715513813288,
+        "n": 346508
       },
       {
-        "frac": 0.173897777234978,
+        "frac": 0.17378328041035662,
         "hi": 0.75,
         "lo": 0.5,
-        "mean_kld": 0.07885677175124131,
-        "n": 273384
+        "mean_kld": 0.0774876427903163,
+        "n": 273204
       },
       {
-        "frac": 0.15898456582804105,
+        "frac": 0.15904944736199317,
         "hi": 0.95,
         "lo": 0.75,
-        "mean_kld": 0.05907472879118853,
-        "n": 249939
+        "mean_kld": 0.05845657592623256,
+        "n": 250041
       },
       {
-        "frac": 0.2937568698094773,
+        "frac": 0.2937613224637681,
         "hi": 1.0,
         "lo": 0.95,
-        "mean_kld": 0.013209914875533782,
-        "n": 461814
+        "mean_kld": 0.013018827980539919,
+        "n": 461821
       }
     ],
     "depth_buckets": [
       {
         "depth_hi": 511,
         "depth_lo": 0,
-        "mean_kld": 0.044420977108338304,
+        "mean_kld": 0.04401547140552512,
         "n": 393216
       },
       {
         "depth_hi": 1023,
         "depth_lo": 512,
-        "mean_kld": 0.048053591821213694,
+        "mean_kld": 0.04793031216161203,
         "n": 393216
       },
       {
         "depth_hi": 1535,
         "depth_lo": 1024,
-        "mean_kld": 0.055694846309743834,
+        "mean_kld": 0.05523112689460866,
         "n": 393216
       },
       {
         "depth_hi": 2046,
         "depth_lo": 1536,
-        "mean_kld": 0.060364708831768484,
+        "mean_kld": 0.05909982714518516,
         "n": 392448
       }
     ],
-    "max_kld": 27.482955932617188,
-    "mean_kld": 0.052129509924549663,
-    "mean_kld_reverse": 0.05476134206189383,
-    "median_kld": 0.013029968366026878,
+    "max_kld": 36.40480422973633,
+    "mean_kld": 0.05156550553375842,
+    "mean_kld_reverse": 0.054376840753872564,
+    "median_kld": 0.012909319251775742,
     "num_positions": 1572096,
-    "p90_kld": 0.0750684030354023,
-    "p99_kld": 0.6717036902904513,
+    "p90_kld": 0.07437128946185112,
+    "p99_kld": 0.6563607484102291,
     "score_from": 0,
-    "top1_agreement": 0.9151871132551702,
+    "top1_agreement": 0.9156470088340661,
     "topk_agreement": {
-      "1": 0.9138614944634424,
-      "2": 0.7360663725370461,
-      "3": 0.5392259760218205,
-      "4": 0.3658319848151767,
-      "5": 0.23745814504966617
+      "1": 0.9142692303777886,
+      "2": 0.7369963411903598,
+      "3": 0.5413632500814199,
+      "4": 0.3678713004803778,
+      "5": 0.23986957539488682
     }
   },
   "unique_tokens": 1572864,
+  "student_weights_sha256": "48e7a0bdae98398d240b013242d6f7ba21645833f6c4e412cdd4d2c849160920",
   "candidate_provenance": {
     "reference": "/media/fmodels2/Qwen/Qwen3.8-27B",
     "candidate": "/media/fmodels2/RadixArk/Qwen3.8-27B-NVFP4",
@@ -8823,5 +8870,7 @@
       "candidate": "0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3",
       "match": true
     }
-  }
+  },
+  "candidate_hf_repo": "RadixArk/Qwen3.8-27B-NVFP4",
+  "candidate_revision": "319f741cce68d7914884900c138a1fbb70a42f30"
 }
diff --git a/RadixArk-Qwen3.8-27B-NVFP4/report.md b/RadixArk-Qwen3.8-27B-NVFP4/report.md
index 7ca4e375df72209d78f6efe6c80ad69ee32fa983..38e2ba0696351c9f4015c4433e4b67241f4e3222 100644
--- a/RadixArk-Qwen3.8-27B-NVFP4/report.md
+++ b/RadixArk-Qwen3.8-27B-NVFP4/report.md
@@ -1,12 +1,12 @@
 # Qwen3.8-27B / RadixArk-Qwen3.8-27B-NVFP4: distribution fidelity
 
-**Mean KLD(reference || candidate) = 0.06462909** over 1572096 scored positions.
+**Mean KLD(reference || candidate) = 0.06405373** over 1572096 scored positions.
 
 | Mean | Median | p90 | p99 | Max | Top-1 agreement |
 |---|---|---|---|---|---|
-| 0.06462909 | 0.02380580 | 0.10573946 | 0.69625672 | 27.04055405 | 89.2982% |
+| 0.06405373 | 0.02363336 | 0.10492286 | 0.68411198 | 36.06170654 | 89.3489% |
 
-Reverse direction, KLD(candidate || reference): 0.06811344.
+Reverse direction, KLD(candidate || reference): 0.06773028.
 
 ## Identity
 
@@ -15,10 +15,10 @@ Reverse direction, KLD(candidate || reference): 0.06811344.
 | Reference checkpoint | /media/fmodels2/Qwen/Qwen3.8-27B |
 | Reference config SHA-256 | 191e0af23210 |
 | Candidate checkpoint | /media/fmodels2/RadixArk/Qwen3.8-27B-NVFP4 |
-| Candidate weights SHA-256 | unbound (Law 16 gap) |
+| Candidate weights SHA-256 | 48e7a0bdae98398d |
 | Suite | qwen3.8-27b-fidelity-1024x2048-v1 |
 | Suite token SHA-256 | 9c935708bbffbf45 |
-| Capture manifest SHA-256 | 304794b4e35b326d |
+| Capture manifest SHA-256 | dbfacc9cbb6d3e06 |
 | Tokenizer | None |
 | Scored vocabulary | 248044 |
 | Declared vocabulary | 248320 |
@@ -27,42 +27,49 @@ Reverse direction, KLD(candidate || reference): 0.06811344.
 | Model runner | V1 |
 | Tensor parallel | 1 |
 | Eager enforced | True |
+| KV cache | bfloat16 |
 | Prefix caching | False |
 | max_num_seqs | 1 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45 |
+| vLLM commit | 60071d1ab732 |
+| vLLM dirty digest | e3b0c44298fc1c14 |
+| Numerics digest | 251a9225b37415b9 |
+| Compiled extensions | f2fbc7537b0f01f6 |
+| FlashInfer | 0.6.17 |
 | torch | 2.13.0+cu132 |
 | Driver | 580.173.02 |
 | GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
-| Laws version | 9 |
+| Laws version | 15 |
 | Partition | analysis |
 
+The commit says when this number was taken. The numerics digest says what took it: a hash of the runtime and the scorer, which moves only when code that can change a logit changes. Comparability is bounded by the digest, so a result stays current across commits that cannot reach a number, and two results carrying the same digest were computed by the same code whatever their commits say.
+
 ## Fidelity by domain
 
-The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
+The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `deployed` is QxQ (natural student routing). `bxq` is the teacher-ID counterfactual on the same weights. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
 
 | Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
 |---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15310432 | 2.37 | 0.94144251 |
-| Chinese across several content types | 72 | 55.6% | 0.09975663 | 1.54 | 0.34686932 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.06607199 | 1.02 | 0.26828380 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.05391724 | 0.83 | 0.22998804 |
-| Other multilingual content | 36 | 64.5% | 0.05272142 | 0.82 | 0.08906453 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.05218359 | 0.81 | 0.10850935 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03732584 | 0.58 | 0.21737286 |
-| Scientific and technical exposition | 96 | 60.3% | 0.03665944 | 0.57 | 0.05182537 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.03624166 | 0.56 | 0.08945615 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.03590898 | 0.56 | 0.12957546 |
-
-**Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Structured data, tool calls, APIs, JSON, and tables** its strongest, a spread of 4.3x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15069679 | 2.35 | 0.88941044 |
+| Chinese across several content types | 72 | 55.6% | 0.09873889 | 1.54 | 0.33018841 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.06523545 | 1.02 | 0.26164212 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.05381979 | 0.84 | 0.23171318 |
+| Other multilingual content | 36 | 64.5% | 0.05329195 | 0.83 | 0.09580742 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.05191924 | 0.81 | 0.10761447 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03723070 | 0.58 | 0.20728235 |
+| Scientific and technical exposition | 96 | 60.3% | 0.03641717 | 0.57 | 0.05465768 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.03605272 | 0.56 | 0.09342523 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.03587765 | 0.56 | 0.13250889 |
+
+**Natural dialogue, instruction following, and assistance** is this candidate's weakest domain and **Structured data, tool calls, APIs, JSON, and tables** its strongest, a spread of 4.2x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
 
 ## Trunk versus head
 
 | Component | Value |
 |---|---|
-| Trunk (candidate hidden states, reference head) | 0.05212951 |
-| Deployed (candidate's own head) | 0.06462909 |
-| Head-associated delta (not additive) | 0.01249958 |
+| Trunk (candidate hidden states, reference head) | 0.05156551 |
+| Deployed (candidate's own head) | 0.06405373 |
+| Head-associated delta (not additive) | 0.01248823 |
 | Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
 | Candidate head | {'runtime': {'state': 'quantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 248320, 'quant_method': 'ModelOptNvFp4LinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.uint8'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 248320, 'scale': 1.0, 'soft_cap': None, 'type': 'LogitsProcessor', 'vocab_size': 248320}, 'primary': {'org_vocab_size': 248320, 'quant_method': 'ModelOptNvFp4LinearMethod', 'state': 'quantized', 'weight_dtype': 'torch.uint8'}, 'state': 'quantized'}]}, 'state': 'quantized', 'static': {'ignored': False, 'lm_head_dtypes': {'lm_head.input_scale': 'F32', 'lm_head.weight': 'U8', 'lm_head.weight_scale': 'F8_E4M3', 'lm_head.weight_scale_2': 'F32', 'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': ['lm_head.input_scale', 'lm_head.weight', 'lm_head.weight_scale', 'lm_head.weight_scale_2'], 'output_weight_keys': ['lm_head.input_scale', 'lm_head.weight', 'lm_head.weight_scale', 'lm_head.weight_scale_2'], 'packed_keys': ['lm_head.weight_scale', 'lm_head.weight_scale_2'], 'quant_method': 'modelopt', 'state': 'quantized', 'tie_word_embeddings': False}} |
 
@@ -70,26 +77,26 @@ The suite is stratified, so the mean above is an average over kinds of text that
 
 | Position range | Positions | Mean KLD |
 |---|---|---|
-| 0–511 | 393216 | 0.05823945 |
-| 512–1023 | 393216 | 0.06044234 |
-| 1024–1535 | 393216 | 0.06777291 |
-| 1536–2046 | 392448 | 0.07207622 |
+| 0–511 | 393216 | 0.05771429 |
+| 512–1023 | 393216 | 0.06033243 |
+| 1024–1535 | 393216 | 0.06729963 |
+| 1536–2046 | 392448 | 0.07088193 |
 
 ## Error by reference confidence
 
 | Reference top-1 probability | Positions | Share | Mean KLD |
 |---|---|---|---|
-| [0.00, 0.25) | 240569 | 15.3% | 0.08562714 |
-| [0.25, 0.50) | 346390 | 22.0% | 0.09373429 |
-| [0.50, 0.75) | 273384 | 17.4% | 0.09334178 |
-| [0.75, 0.95) | 249939 | 15.9% | 0.06619280 |
-| [0.95, 1.00) | 461814 | 29.4% | 0.01401641 |
+| [0.00, 0.25) | 240522 | 15.3% | 0.08525610 |
+| [0.25, 0.50) | 346508 | 22.0% | 0.09318519 |
+| [0.50, 0.75) | 273204 | 17.4% | 0.09198607 |
+| [0.75, 0.95) | 250041 | 15.9% | 0.06554457 |
+| [0.95, 1.00) | 461821 | 29.4% | 0.01382233 |
 
 ## Top-K set agreement
 
 | K=1 | K=2 | K=3 | K=4 | K=5 |
 |---|---|---|---|---|
-| 89.0863% | 68.4197% | 46.8987% | 29.2694% | 17.2350% |
+| 89.1184% | 68.5132% | 47.0758% | 29.4502% | 17.3927% |
 
 ## Law compliance
 
@@ -101,22 +108,19 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
 | 3 | Frozen input | PASS | token hash matches suite qwen3.8-27b-fidelity-1024x2048-v1 [analysis]: 9c935708bbffbf45 |
 | 4 | Real vocabulary | PASS | scored 248044 real tokens of 248320 declared (276 padding rows) |
-| 5 | Manifest binding | PASS | all bound fields present; manifest 304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc |
-| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 398f63d84a45, driver 580.173.02, 4 GPU(s) |
+| 5 | Manifest binding | PASS | all bound fields present; manifest dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3 |
+| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ 60071d1ab732, driver 580.173.02, 4 GPU(s) |
 | 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
-| 8 | Head transparency | PASS | trunk 0.05212951, deployed 0.06462909, delta 0.012499583801223166 |
-| 9 | Tail and depth disclosure | PASS | mean 0.06462909, median 0.02380580, max 27.04055405, 4 depth buckets |
+| 8 | Head transparency | PASS | trunk 0.05156551, deployed 0.06405373, delta 0.01248822839901103 |
+| 9 | Tail and depth disclosure | PASS | mean 0.06405373, median 0.02363336, max 36.06170654, 4 depth buckets |
 | 10 | Comparability | PASS | comparability key fully resolved |
 | 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
 | 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
-| 14 | Component attribution | NOT_APPLICABLE | reference declares no experts |
-| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.15310432, strongest structured_data_tools at 0.03590898, spread 4.3x |
-| 16 | Candidate weight binding | OVERRIDE | the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else |
-| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
-
-### Recorded deviations
-
-- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report names a checkpoint path but records no digest of its weights, so nothing rules out a directory that held something else.
+| 14 | Routed-model intervention | NOT_APPLICABLE | reference declares no experts |
+| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest dialogue_instruction at 0.15069679, strongest structured_data_tools at 0.03587765, spread 4.2x |
+| 16 | Candidate weight binding | PASS | scored weights 48e7a0bdae98398d as inspected |
+| 17 | Substitution disclosure | PASS | every scored layer used the checkpoint's own quantization parameters |
+| 13 | Recorded deviation | PASS | no overrides claimed |
 
 ## Environment
 
@@ -126,7 +130,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
 | Python | 3.12.3 |
 | vLLM | 0.1.dev20446+gb2bc9171d |
-| vLLM commit | 398f63d84a45fbe0b0205e70893bcd28fa928a88 |
+| vLLM commit | 60071d1ab73229321712254b1350dcaaac1c125a |
 | torch | 2.13.0+cu132 |
 | torch CUDA runtime | 13.2 |
 | cuDNN | 9.20.0 (92000) |
@@ -134,7 +138,7 @@ Zero baseline: reference against itself scored **0.00000000** over 2047 position
 | CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
 | nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
 | gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
-| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
+| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39 |
 | NVIDIA driver | 580.173.02 |
 | float32 matmul precision | highest |
 | TF32 (matmul / cuDNN) | False / True |
@@ -157,8 +161,18 @@ Credential values are never published. Set but redacted: `HF_TOKEN`.
 
 | Variable | Value |
 |---|---|
+| `CUBLAS_WORKSPACE_CONFIG` | `:4096:8` |
 | `CUDA_HOME` | `/usr/local/cuda-13.0` |
 | `HF_TOKEN` | `<redacted>` |
+| `NCCL_DETERMINISTIC` | `1` |
+| `PYTORCH_NVML_BASED_CUDA_CHECK` | `1` |
+| `TORCHINDUCTOR_CACHE_DIR` | `/tmp/torchinductor_phaedawg` |
+| `TORCHINDUCTOR_COMPILE_THREADS` | `1` |
+| `TRITON_CACHE_AUTOTUNING` | `1` |
+| `TRITON_PTXAS_BLACKWELL_PATH` | `/usr/local/cuda-13.0/bin/ptxas` |
+| `VLLM_BATCH_INVARIANT` | `1` |
+| `VLLM_MARLIN_USE_ATOMIC_ADD` | `0` |
+| `VLLM_MOE_USE_DEEP_GEMM` | `0` |
 
 ## Files in this artifact
 
@@ -166,26 +180,26 @@ Paths are relative to the artifact root, the same paths `checksums.txt` uses. Ve
 
 | Path | Size | What it is |
 |---|---|---|
-| `RadixArk-Qwen3.8-27B-NVFP4/report.md` | 12.64 KiB | This document. |
-| `RadixArk-Qwen3.8-27B-NVFP4/report.json` | 254.25 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
-| `RadixArk-Qwen3.8-27B-NVFP4/manifest.json` | 85.43 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
-| `RadixArk-Qwen3.8-27B-NVFP4/compliance.json` | 13.01 KiB | The law-by-law receipt, including the comparability key. |
-| `baselines/self-kld.json` | 4.74 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
+| `RadixArk-Qwen3.8-27B-NVFP4/report.md` | 13.48 KiB | This document. |
+| `RadixArk-Qwen3.8-27B-NVFP4/report.json` | 256.70 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
+| `RadixArk-Qwen3.8-27B-NVFP4/manifest.json` | 86.99 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
+| `RadixArk-Qwen3.8-27B-NVFP4/compliance.json` | 12.28 KiB | The law-by-law receipt, including the comparability key. |
+| `baselines/self-kld.json` | 7.06 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
 | `suite/suite-manifest.json` | 382.68 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
 | `suite/tokens` | 15.49 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
 | `suite/sources.json` | 737.42 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
 | `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
-| `reference/manifest.json` | 85.43 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
+| `reference/manifest.json` | 86.99 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
 | `reference` | 19.25 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
 | `reference/lm_head.safetensors` | 2.37 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
-| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
+| `environment/runtime.json` | 5.42 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
 | `environment/summary.md` | 1.18 KiB | The same provenance as prose, plus an index of every captured file. |
 | `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
 | `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
-| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
-| `environment/models` | 7.22 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
-| `checksums.txt` | 182.99 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
-| `LAWS.md` | 31.90 KiB | The laws this artifact was produced under, including the override procedure. |
+| `environment/pip-freeze.txt` | 4.47 KiB | Every installed package version in the scoring environment. |
+| `environment/models` | 19.46 KiB in 10 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
+| `checksums.txt` | 183.22 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
+| `LAWS.md` | 40.20 KiB | The laws this artifact was produced under, including the override procedure. |
 
 ## Scope
 
diff --git a/RadixArk-Qwen3.8-27B-NVFP4/strata.json b/RadixArk-Qwen3.8-27B-NVFP4/strata.json
index aa81d34f3806747a873751c1691ce8e91b270b83..a426c5184ba0800c2bbbb52ab1c11f5307ef6844 100644
--- a/RadixArk-Qwen3.8-27B-NVFP4/strata.json
+++ b/RadixArk-Qwen3.8-27B-NVFP4/strata.json
@@ -8,381 +8,381 @@
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         "key": "scientific_papers",
         "label": "scientific_papers",
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         "label": "libretexts",
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         "key": "starcoder_structured",
         "label": "starcoder_structured",
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+        "relative_to_run": 0.560118009064285
       },
       {
         "cells": {
           "deployed": {
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+            "max_kld": 14.723241806030273,
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             "positions": 106444,
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             "worst_context_id": 667,
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+            "worst_context_kld": 0.2072823504286248
           }
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         "key": "github_code",
         "label": "github_code",
-        "relative_to_run": 0.5474600669621549
+        "relative_to_run": 0.5507889654453428
       }
     ],
     "stratum": [
@@ -390,217 +390,217 @@
         "cells": {
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             "positions": 196512,
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             "worst_context_id": 454,
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         "key": "dialogue_instruction",
         "label": "Natural dialogue, instruction following, and assistance",
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         "label": "Chinese across several content types",
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           }
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         "key": "encyclopedic_reference",
         "label": "Encyclopedic and factual reference",
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       {
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         },
         "key": "literary_narrative",
         "label": "Literary, narrative, and creative writing",
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       {
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         },
         "key": "other_multilingual",
         "label": "Other multilingual content",
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       {
         "cells": {
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         "key": "news_history_legal_essays",
         "label": "News, history, economics, legal analysis, and essays",
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         },
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         "label": "Scientific and technical exposition",
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         },
         "key": "worked_math_reasoning",
         "label": "Worked mathematics, science, and formal reasoning",
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         },
         "key": "structured_data_tools",
         "label": "Structured data, tool calls, APIs, JSON, and tables",
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   },
   "overall": {
     "deployed": {
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+      "median_context_p99": 0.2929476499557495,
+      "p90_context_kld": 0.11414634314146811,
       "positions": 1572096,
-      "top1_agreement": 0.8929823624002605,
+      "top1_agreement": 0.8934893288959452,
       "worst_context_id": 454,
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   "primary": "deployed"
diff --git a/RadixArk-Qwen3.8-27B-NVFP4/strata.md b/RadixArk-Qwen3.8-27B-NVFP4/strata.md
index 5ee79f0fdb2ab2d4b007f8602db6fa032a356943..526e4befecb6cbb03f5e36cf0b0ef57e661b04d0 100644
--- a/RadixArk-Qwen3.8-27B-NVFP4/strata.md
+++ b/RadixArk-Qwen3.8-27B-NVFP4/strata.md
@@ -1,52 +1,52 @@
 # Fidelity by domain - Qwen3.8-27B / RadixArk-Qwen3.8-27B-NVFP4
 
-768 contexts, 1572096 scored positions, mean 0.06462909, reference top-1 64.2%, top-1 agreement 89.2982%.
+768 contexts, 1572096 scored positions, mean 0.06405373, reference top-1 64.2%, top-1 agreement 89.3489%.
 
-`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
+`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. The `deployed` cell is QxQ; `bxq` is the teacher-ID counterfactual on the same student weights. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
 
 ## Ranked by domain
 
 | Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15310432 | 2.37 | 0.11646213 | 0.32543345 | 0.941443 |
-| Chinese across several content types | 72 | 55.6% | 0.09975663 | 1.54 | 0.07013398 | 0.16958108 | 0.346869 |
-| Encyclopedic and factual reference | 96 | 59.5% | 0.06607199 | 1.02 | 0.05817511 | 0.08760412 | 0.268284 |
-| Literary, narrative, and creative writing | 72 | 50.6% | 0.05391724 | 0.83 | 0.04853980 | 0.06324070 | 0.229988 |
-| Other multilingual content | 36 | 64.5% | 0.05272142 | 0.82 | 0.05089409 | 0.07928389 | 0.089065 |
-| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.05218359 | 0.81 | 0.04786278 | 0.08797759 | 0.108509 |
-| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03732584 | 0.58 | 0.02949821 | 0.05936240 | 0.217373 |
-| Scientific and technical exposition | 96 | 60.3% | 0.03665944 | 0.57 | 0.03631339 | 0.04549670 | 0.051825 |
-| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.03624166 | 0.56 | 0.03405326 | 0.04314607 | 0.089456 |
-| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.03590898 | 0.56 | 0.03124624 | 0.05604347 | 0.129575 |
+| Natural dialogue, instruction following, and assistance | 96 | 73.5% | 0.15069679 | 2.35 | 0.11414634 | 0.35157714 | 0.889410 |
+| Chinese across several content types | 72 | 55.6% | 0.09873889 | 1.54 | 0.06962943 | 0.16958437 | 0.330188 |
+| Encyclopedic and factual reference | 96 | 59.5% | 0.06523545 | 1.02 | 0.05761655 | 0.08645895 | 0.261642 |
+| Literary, narrative, and creative writing | 72 | 50.6% | 0.05381979 | 0.84 | 0.04784516 | 0.06330361 | 0.231713 |
+| Other multilingual content | 36 | 64.5% | 0.05329195 | 0.83 | 0.05014532 | 0.07689239 | 0.095807 |
+| News, history, economics, legal analysis, and essays | 72 | 59.0% | 0.05191924 | 0.81 | 0.04777421 | 0.08325439 | 0.107614 |
+| Source code, tests, technical documentation, and issue discussions | 96 | 79.5% | 0.03723070 | 0.58 | 0.02914866 | 0.06014594 | 0.207282 |
+| Scientific and technical exposition | 96 | 60.3% | 0.03641717 | 0.57 | 0.03607009 | 0.04520056 | 0.054658 |
+| Worked mathematics, science, and formal reasoning | 96 | 65.8% | 0.03605272 | 0.56 | 0.03376729 | 0.04253708 | 0.093425 |
+| Structured data, tool calls, APIs, JSON, and tables | 36 | 70.7% | 0.03587765 | 0.56 | 0.03050437 | 0.05324768 | 0.132509 |
 
 ## Ranked by source dataset
 
 | Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
 |---|---|---|---|---|---|---|---|
-| wildchat | 96 | 73.5% | 0.15310432 | 2.37 | 0.11646213 | 0.32543345 | 0.941443 |
-| wikisource_zh | 33 | 60.4% | 0.14927377 | 2.31 | 0.12270658 | 0.31574824 | 0.346869 |
-| wikipedia_de | 7 | 73.6% | 0.07406771 | 1.15 | 0.07928389 | 0.08906453 | 0.089065 |
-| wikipedia_en | 96 | 59.5% | 0.06607199 | 1.02 | 0.05817511 | 0.08760412 | 0.268284 |
-| regulations | 23 | 72.3% | 0.06000078 | 0.93 | 0.06578769 | 0.09111835 | 0.108509 |
-| wikipedia_zh | 39 | 51.5% | 0.05785752 | 0.90 | 0.05559400 | 0.07260182 | 0.080311 |
-| wikipedia_ja | 7 | 57.0% | 0.05466095 | 0.85 | 0.05623710 | 0.05910658 | 0.059107 |
-| public_domain_books | 72 | 50.6% | 0.05391724 | 0.83 | 0.04853980 | 0.06324070 | 0.229988 |
-| public_domain_review | 26 | 51.8% | 0.05339776 | 0.83 | 0.05123796 | 0.07187527 | 0.106231 |
-| wikipedia_es | 7 | 59.2% | 0.05046331 | 0.78 | 0.04734814 | 0.06071873 | 0.060719 |
-| wikipedia_cs | 6 | 68.9% | 0.04695327 | 0.73 | 0.04822477 | 0.05365822 | 0.053658 |
-| open_news | 23 | 53.8% | 0.04299386 | 0.67 | 0.04366378 | 0.05226452 | 0.058147 |
-| wikipedia_fr | 3 | 60.2% | 0.04101457 | 0.63 | 0.04010521 | 0.04519658 | 0.045197 |
-| wikipedia_ru | 6 | 66.6% | 0.03981069 | 0.62 | 0.03913019 | 0.05166550 | 0.051666 |
-| stackv2 | 44 | 72.0% | 0.03962328 | 0.61 | 0.03456956 | 0.05654579 | 0.214942 |
-| scientific_papers | 96 | 60.3% | 0.03665944 | 0.57 | 0.03631339 | 0.04549670 | 0.051825 |
-| libretexts | 96 | 65.8% | 0.03624166 | 0.56 | 0.03405326 | 0.04314607 | 0.089456 |
-| starcoder_structured | 36 | 70.7% | 0.03590898 | 0.56 | 0.03124624 | 0.05604347 | 0.129575 |
-| github_code | 52 | 85.8% | 0.03538185 | 0.55 | 0.02447202 | 0.05936240 | 0.217373 |
+| wildchat | 96 | 73.5% | 0.15069679 | 2.35 | 0.11414634 | 0.35157714 | 0.889410 |
+| wikisource_zh | 33 | 60.4% | 0.14694493 | 2.29 | 0.12596912 | 0.30253700 | 0.330188 |
+| wikipedia_de | 7 | 73.6% | 0.07684053 | 1.20 | 0.07689239 | 0.09580742 | 0.095807 |
+| wikipedia_en | 96 | 59.5% | 0.06523545 | 1.02 | 0.05761655 | 0.08645895 | 0.261642 |
+| regulations | 23 | 72.3% | 0.05995061 | 0.94 | 0.07335148 | 0.09280277 | 0.107614 |
+| wikipedia_zh | 39 | 51.5% | 0.05794915 | 0.90 | 0.05593139 | 0.07320369 | 0.079326 |
+| wikipedia_ja | 7 | 57.0% | 0.05434733 | 0.85 | 0.05557493 | 0.05904097 | 0.059041 |
+| public_domain_books | 72 | 50.6% | 0.05381979 | 0.84 | 0.04784516 | 0.06330361 | 0.231713 |
+| public_domain_review | 26 | 51.8% | 0.05287553 | 0.83 | 0.05117935 | 0.07422273 | 0.102086 |
+| wikipedia_es | 7 | 59.2% | 0.05015974 | 0.78 | 0.04745887 | 0.05966763 | 0.059668 |
+| wikipedia_cs | 6 | 68.9% | 0.04761524 | 0.74 | 0.04854877 | 0.05613932 | 0.056139 |
+| open_news | 23 | 53.8% | 0.04280686 | 0.67 | 0.04325178 | 0.05304140 | 0.053718 |
+| wikipedia_fr | 3 | 60.2% | 0.04179966 | 0.65 | 0.04175036 | 0.04601086 | 0.046011 |
+| wikipedia_ru | 6 | 66.6% | 0.03966447 | 0.62 | 0.04104211 | 0.05074075 | 0.050741 |
+| stackv2 | 44 | 72.0% | 0.03953597 | 0.62 | 0.03372655 | 0.06042454 | 0.196679 |
+| scientific_papers | 96 | 60.3% | 0.03641717 | 0.57 | 0.03607009 | 0.04520056 | 0.054658 |
+| libretexts | 96 | 65.8% | 0.03605272 | 0.56 | 0.03376729 | 0.04253708 | 0.093425 |
+| starcoder_structured | 36 | 70.7% | 0.03587765 | 0.56 | 0.03050437 | 0.05324768 | 0.132509 |
+| github_code | 52 | 85.8% | 0.03528009 | 0.55 | 0.02488465 | 0.05919018 | 0.207282 |
 
 ## Reading
 
-- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.15310432, 2.37x the run mean of 0.06462909 over 96 context(s).
-- Strongest domain: **Structured data, tool calls, APIs, JSON, and tables** at 0.03590898, 0.56x the run mean. The spread across domains is 4.3x.
+- Weakest domain: **Natural dialogue, instruction following, and assistance** at 0.15069679, 2.35x the run mean of 0.06405373 over 96 context(s).
+- Strongest domain: **Structured data, tool calls, APIs, JSON, and tables** at 0.03587765, 0.56x the run mean. The spread across domains is 4.2x.
 - This is a strong finding: the reference was *more* certain on Natural dialogue, instruction following, and assistance (top-1 73.5% against 64.2% overall) and the candidate still diverged most there, so predictability does not explain it.
-- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.941443 against a median of 0.11646213 (context 454). Read the documents before treating the domain as weak.
+- Natural dialogue, instruction following, and assistance is driven by outlier contexts: its worst context is 0.889410 against a median of 0.11414634 (context 454). Read the documents before treating the domain as weak.
 
diff --git a/baselines/self-kld.json b/baselines/self-kld.json
index 44467e313062a40aec56e8cbbbc4715761d8f296..c706e8bb1e6c82f1ee0c81f27c2f1d23a4858390 100644
--- a/baselines/self-kld.json
+++ b/baselines/self-kld.json
@@ -1,42 +1,44 @@
 {
-  "capture_manifest_sha256": "a4673f833514abdf2837fdc9899d8a7fd79578c8ada2e0c67dea3ec03eeff5b8",
+  "capture_manifest_sha256": "030d96bc30f026b46d121355a700272328ad8af3f78db5205ad70021cc7fd367",
   "confidence_buckets": [
     {
-      "frac": 0.2896922325354177,
+      "frac": 0.2872496336101612,
       "hi": 0.25,
       "lo": 0.0,
       "mean_kld": 0.0,
-      "n": 593
+      "n": 588
     },
     {
-      "frac": 0.3092330239374695,
+      "frac": 0.31216414264777725,
       "hi": 0.5,
       "lo": 0.25,
       "mean_kld": 0.0,
-      "n": 633
+      "n": 639
     },
     {
-      "frac": 0.16316560820713238,
+      "frac": 0.16414264777723497,
       "hi": 0.75,
       "lo": 0.5,
       "mean_kld": 0.0,
-      "n": 334
+      "n": 336
     },
     {
-      "frac": 0.12457254518808011,
+      "frac": 0.12408402540302882,
       "hi": 0.95,
       "lo": 0.75,
       "mean_kld": 0.0,
-      "n": 255
+      "n": 254
     },
     {
-      "frac": 0.11333659013190034,
+      "frac": 0.11235955056179775,
       "hi": 1.0,
       "lo": 0.95,
       "mean_kld": 0.0,
-      "n": 232
+      "n": 230
     }
   ],
+  "context_length": 2048,
+  "declared_expert_activation_quant": [],
   "depth_buckets": [
     {
       "depth_hi": 511,
@@ -64,13 +66,20 @@
     }
   ],
   "kld_vocab_size": 248044,
+  "kv_cache_dtype": "bfloat16",
   "max_kld": 0.0,
   "mean_kld": 0.0,
   "mean_kld_reverse": 0.0,
   "median_kld": 0.0,
   "model_runner_v2": false,
+  "moe_backend_named": null,
   "num_positions": 2047,
   "num_rows": 1,
+  "nvfp4_dense_scale_inspection": {
+    "layers_filled": 0,
+    "layers_scanned": 0,
+    "workers": 1
+  },
   "p90_kld": 0.0,
   "p99_kld": 0.0,
   "per_context": [
@@ -78,7 +87,7 @@
       "context_id": 0,
       "max_kld": 0.0,
       "mean_kld": 0.0,
-      "mean_ref_top1_prob": 0.478327011342857,
+      "mean_ref_top1_prob": 0.4781730961096421,
       "median_kld": 0.0,
       "p99_kld": 0.0,
       "positions": 2047,
@@ -86,6 +95,43 @@
       "top1_agreement": 1.0
     }
   ],
+  "quantization_substitutions": [],
+  "reference_weights_sha256": null,
+  "runtime_binding": {
+    "compiled_extensions": {
+      "_C_stable_libtorch.abi3.so": "fbae4a92ec2861cd979eb99a3867049f88f7fcb278337be058dd4cf2ac8c29bf",
+      "_moe_C_stable_libtorch.abi3.so": "a5f41231d3a2fe2958aad06615137ab877671baea20e78b0257516ef3467664e",
+      "third_party/deep_gemm/_C.cpython-310-x86_64-linux-gnu.so": "38b87140577c2d72c2b3a6407c5f07da53ef9362e18fe988162afa8f3f91746e",
+      "third_party/deep_gemm/_C.cpython-311-x86_64-linux-gnu.so": "1f3a3430c188295cf96a5ac977ec72258bccb525daaa7b0ad6642bf3a995d3da",
+      "third_party/deep_gemm/_C.cpython-312-x86_64-linux-gnu.so": "73824dc1312e98cf277ae6bc017becf7e963d5b0bfab81e0c6d475d9b769c9e7",
+      "third_party/deep_gemm/_C.cpython-313-x86_64-linux-gnu.so": "bd0124b39fb500378764bf0d1adb5fe30ea876e66ed65971d8f668de4a8dbe83",
+      "third_party/deep_gemm/_C.cpython-314-x86_64-linux-gnu.so": "0f9817dfe8a619dde56456c6e7a5a9d914fede0fb145bc48aad134e19c77fff0"
+    },
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
+    "cuda": "13.2",
+    "determinism": {
+      "CUBLAS_WORKSPACE_CONFIG": ":4096:8",
+      "NCCL_DETERMINISTIC": "1",
+      "VLLM_BATCH_INVARIANT": "1",
+      "VLLM_MOE_USE_DEEP_GEMM": "0"
+    },
+    "driver": "580.173.02",
+    "flashinfer": "0.6.17",
+    "gpu_names": [
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+      "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
+    ],
+    "nccl": "2.29.7",
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
+    "platform": "Linux-6.8.0-137-generic-x86_64-with-glibc2.39",
+    "python": "3.12.3",
+    "torch": "2.13.0+cu132",
+    "vllm_commit": "60071d1ab73229321712254b1350dcaaac1c125a",
+    "vllm_dirty_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+    "vllm_tree_dirty": false
+  },
   "score_from": 0,
   "storage": "hidden",
   "student_lm_head": {
@@ -191,12 +237,12 @@
     }
   },
   "timings": {
-    "capture_forward": 1.6720589429605752,
-    "capture_write": 0.4482212271541357,
-    "replay_probe": 2.7618775269947946,
-    "score_forward": 0.7317100630607456,
-    "student_load": 17.543594687012956,
-    "teacher_load": 19.132629279978573
+    "capture_forward": 1.8250989140942693,
+    "capture_write": 0.46782248793169856,
+    "replay_probe": 3.0494781509041786,
+    "score_forward": 0.8837930047884583,
+    "student_load": 16.472603803034872,
+    "teacher_load": 17.77250132197514
   },
   "top1_agreement": 1.0,
   "topk_agreement": {
@@ -206,5 +252,6 @@
     "4": 1.0,
     "5": 1.0
   },
-  "unique_tokens": 2048
+  "unique_tokens": 2048,
+  "student_weights_sha256": "c956f4ef606aef7ee5d45b6132681bf2c6e6b11d1740c23b3d5994546aaabece"
 }
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--- a/checksums.txt
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+885fc3b1e3a738b42005e4e3fc4adaaafb7ba8ce50d35fa4b7c0648e905d56cb  reference/hidden_88.safetensors
+927541d2de8fec864c25081489a22beb141ebb9c17168524d9536bb6affa5fa2  reference/hidden_89.safetensors
+a17a37fab6f401b4cb8058e562f787ec1d8c9bf6d9bad9374319e21eb2e1777b  reference/hidden_9.safetensors
+9bb0965d4b7328e37247813d4bb25053842f02cf403b774d980177045f1aed68  reference/hidden_90.safetensors
+2c486cd71ba30b523f1e776f1891e13623c03645eaf86e269335c6f9eacaa69f  reference/hidden_91.safetensors
+63be0fa366b07baeb9baefdc9352e8a63873a6fbc2e4410ab198a2e67f14c3be  reference/hidden_92.safetensors
+c000820d2eda222c1c403ae8e0646a938ca723b72c889ca8c8bb06cc98ae2da5  reference/hidden_93.safetensors
+489f2c7114c08442b3a322448e8ca6c5f62cea900a2f61e48fd1f002bd1e66fb  reference/hidden_94.safetensors
+ead2bf49513b71ecf6af32607c820919048852767f0616069087a0dfc0520676  reference/hidden_95.safetensors
+08d472ec2fa20f1770cb6a1a342f8f51901e444ae4855c0205ecb64611223e55  reference/hidden_96.safetensors
+f2d6f593ad40a4b1c5a665dfaa6f109f0c9081716f620632dfb71517675e3965  reference/hidden_97.safetensors
+afdf6fa6e980e40dfdf3192622c3f7ba702d6c4b5103b20be5283ed6761d80ac  reference/hidden_98.safetensors
+87526d62ad44e994bd4d636ff0e74fac41ccea75c92860e21b39556d7c29f75e  reference/hidden_99.safetensors
 7b918bdcc05831f146c268889463287b74c8b9a3419c3bab081f0395da40e508  reference/lm_head.safetensors
-d4e8c6ddaec057cb44ba7ec2985cca17c44392374038e62057f3379c6a9117ef  reference/logits_0.safetensors
-304794b4e35b326dee2486603652ea94215f869ba009c43ecaeab18938a22adc  reference/manifest.json
+f86ba8be2263998d15c839316e37973bf9b305a214fcb90587f9c25d37398a48  reference/logits_0.safetensors
+dbfacc9cbb6d3e06edb143112389e9792da61d28bc8ceccd9b32da216ba3b7e3  reference/manifest.json
 603acbb559db752bf782fddbc3fb5199bae3357c151543ba1631ccdd58413a1a  suite/partitions.json
 cf13ad5411a70ae341179b29965c95211dd89cf6a08f04f391f88ef61ec0fa26  suite/source-registry.json
 5eac40d880f6fdb5f6f8160d9afca26cc5074b928713919c47a0d7719e9b089d  suite/sources.json
diff --git a/environment/gpu-clocks.txt b/environment/gpu-clocks.txt
index 76257752eb11663c1ecda59818d2c29102ff08ed..523b0fa663973cd411786f989e3de42ab543386c 100644
--- a/environment/gpu-clocks.txt
+++ b/environment/gpu-clocks.txt
@@ -1,9 +1,9 @@
 ### nvidia-smi --query-gpu=index,name,serial,uuid,driver_version,vbios_version,pstate,clocks.sm,clocks.max.sm,power.limit,persistence_mode,ecc.mode.current --format=csv
 
 index, name, serial, uuid, driver_version, vbios_version, pstate, clocks.current.sm [MHz], clocks.max.sm [MHz], power.limit [W], persistence_mode, ecc.mode.current
-0, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 1794225038968, GPU-41235b51-d6ab-afe6-c393-e1c377b81299, 580.173.02, 98.02.81.00.07, P8, 1177 MHz, 4087 MHz, 575.00 W, Disabled, Disabled
+0, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 1794225038968, GPU-41235b51-d6ab-afe6-c393-e1c377b81299, 580.173.02, 98.02.81.00.07, P8, 1177 MHz, 4080 MHz, 575.00 W, Disabled, Disabled
 1, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 1793025105275, GPU-547cb69a-4bcf-5f0a-c154-5397b1dc5a78, 580.173.02, 98.02.81.00.07, P8, 1177 MHz, 4087 MHz, 575.00 W, Disabled, Disabled
 2, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 1792925028706, GPU-0f14730c-9c8d-aa20-9478-324d31766353, 580.173.02, 98.02.81.00.07, P8, 1177 MHz, 4080 MHz, 575.00 W, Disabled, Disabled
-3, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 1794125045055, GPU-2aea821a-9e57-5a64-9471-83306dddcd97, 580.173.02, 98.02.81.00.07, P8, 1177 MHz, 4087 MHz, 575.00 W, Disabled, Disabled
+3, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 1794125045055, GPU-2aea821a-9e57-5a64-9471-83306dddcd97, 580.173.02, 98.02.81.00.07, P8, 1177 MHz, 4080 MHz, 575.00 W, Disabled, Disabled
 
 ### exit=0
diff --git a/environment/gpu-smi-query.txt b/environment/gpu-smi-query.txt
index ea0befbefdf22f5de9891bd12f8c22d9ddfc239a..02445429b453fbace64d2fbed818552ea92447e3 100644
--- a/environment/gpu-smi-query.txt
+++ b/environment/gpu-smi-query.txt
@@ -3,7 +3,7 @@
 
 ==============NVSMI LOG==============
 
-Timestamp                                              : Mon Aug 31 19:26:59 2026
+Timestamp                                              : Fri Sep 11 17:12:56 2026
 Driver Version                                         : 580.173.02
 CUDA Version                                           : 13.0
 
@@ -49,8 +49,8 @@ GPU 00000000:01:00.0
         ECC Object                                     : 7.16
         Power Management Object                        : N/A
     Inforom BBX Object Flush
-        Latest Timestamp                               : 2026/08/30 19:33:53.690
-        Latest Duration                                : 67667 us
+        Latest Timestamp                               : 2026/09/11 00:57:27.262
+        Latest Duration                                : 27351 us
     GPU Operation Mode
         Current                                        : N/A
         Pending                                        : N/A
@@ -85,13 +85,13 @@ GPU 00000000:01:00.0
         Bridge Chip
             Type                                       : N/A
             Firmware                                   : N/A
-        Replays Since Reset                            : 30326
+        Replays Since Reset                            : 57526
         Replay Number Rollovers                        : 0
-        Tx Throughput                                  : 1287 KB/s
-        Rx Throughput                                  : 583 KB/s
+        Tx Throughput                                  : 2349 KB/s
+        Rx Throughput                                  : 1724 KB/s
         Atomic Caps Outbound                           : N/A
         Atomic Caps Inbound                            : FETCHADD_32 FETCHADD_64 SWAP_32 SWAP_64 CAS_32 CAS_64 
-    Fan Speed                                          : 30 %
+    Fan Speed                                          : 31 %
     Performance State                                  : P8
     Clocks Event Reasons
         Idle                                           : Not Active
@@ -104,7 +104,7 @@ GPU 00000000:01:00.0
         SW Thermal Slowdown                            : Not Active
         Display Clock Setting                          : Not Active
     Clocks Event Reasons Counters
-        SW Power Capping                               : 2390612447 us
+        SW Power Capping                               : 2712725233 us
         Sync Boost                                     : 0 us
         SW Thermal Slowdown                            : 0 us
         HW Thermal Slowdown                            : 0 us
@@ -113,12 +113,12 @@ GPU 00000000:01:00.0
     FB Memory Usage
         Total                                          : 97887 MiB
         Reserved                                       : 640 MiB
-        Used                                           : 2 MiB
-        Free                                           : 97247 MiB
+        Used                                           : 4 MiB
+        Free                                           : 97244 MiB
     BAR1 Memory Usage
         Total                                          : 256 MiB
-        Used                                           : 2 MiB
-        Free                                           : 254 MiB
+        Used                                           : 3 MiB
+        Free                                           : 253 MiB
     Conf Compute Protected Memory Usage
         Total                                          : 0 MiB
         Used                                           : 0 MiB
@@ -186,8 +186,8 @@ GPU 00000000:01:00.0
             Low                                        : 0 bank(s)
             None                                       : 0 bank(s)
     Temperature
-        GPU Current Temp                               : 30 C
-        GPU T.Limit Temp                               : 63 C
+        GPU Current Temp                               : 40 C
+        GPU T.Limit Temp                               : 53 C
         GPU Shutdown T.Limit Temp                      : -5 C
         GPU Slowdown T.Limit Temp                      : -2 C
         GPU Max Operating T.Limit Temp                 : 0 C
@@ -195,8 +195,8 @@ GPU 00000000:01:00.0
         Memory Current Temp                            : N/A
         Memory Max Operating T.Limit Temp              : N/A
     GPU Power Readings
-        Average Power Draw                             : 15.36 W
-        Instantaneous Power Draw                       : 15.36 W
+        Average Power Draw                             : 22.41 W
+        Instantaneous Power Draw                       : 21.36 W
         Current Power Limit                            : 575.00 W
         Requested Power Limit                          : 575.00 W
         Default Power Limit                            : 600.00 W
@@ -231,8 +231,8 @@ GPU 00000000:01:00.0
     Deferred Clocks
         Memory                                         : N/A
     Max Clocks
-        Graphics                                       : 4087 MHz
-        SM                                             : 4087 MHz
+        Graphics                                       : 4080 MHz
+        SM                                             : 4080 MHz
         Memory                                         : 15501 MHz
         Video                                          : 3090 MHz
     Max Customer Boost Clocks
@@ -298,8 +298,8 @@ GPU 00000000:21:00.0
         ECC Object                                     : 7.16
         Power Management Object                        : N/A
     Inforom BBX Object Flush
-        Latest Timestamp                               : 2026/08/30 19:34:01.309
-        Latest Duration                                : 67610 us
+        Latest Timestamp                               : 2026/09/11 00:57:21.047
+        Latest Duration                                : 31576 us
     GPU Operation Mode
         Current                                        : N/A
         Pending                                        : N/A
@@ -334,10 +334,10 @@ GPU 00000000:21:00.0
         Bridge Chip
             Type                                       : N/A
             Firmware                                   : N/A
-        Replays Since Reset                            : 128
+        Replays Since Reset                            : 175
         Replay Number Rollovers                        : 0
-        Tx Throughput                                  : 1096 KB/s
-        Rx Throughput                                  : 525 KB/s
+        Tx Throughput                                  : 2131 KB/s
+        Rx Throughput                                  : 1535 KB/s
         Atomic Caps Outbound                           : N/A
         Atomic Caps Inbound                            : FETCHADD_32 FETCHADD_64 SWAP_32 SWAP_64 CAS_32 CAS_64 
     Fan Speed                                          : 30 %
@@ -362,12 +362,12 @@ GPU 00000000:21:00.0
     FB Memory Usage
         Total                                          : 97887 MiB
         Reserved                                       : 640 MiB
-        Used                                           : 2 MiB
-        Free                                           : 97247 MiB
+        Used                                           : 4 MiB
+        Free                                           : 97244 MiB
     BAR1 Memory Usage
         Total                                          : 256 MiB
-        Used                                           : 2 MiB
-        Free                                           : 254 MiB
+        Used                                           : 3 MiB
+        Free                                           : 253 MiB
     Conf Compute Protected Memory Usage
         Total                                          : 0 MiB
         Used                                           : 0 MiB
@@ -435,8 +435,8 @@ GPU 00000000:21:00.0
             Low                                        : 0 bank(s)
             None                                       : 0 bank(s)
     Temperature
-        GPU Current Temp                               : 30 C
-        GPU T.Limit Temp                               : 63 C
+        GPU Current Temp                               : 33 C
+        GPU T.Limit Temp                               : 60 C
         GPU Shutdown T.Limit Temp                      : -5 C
         GPU Slowdown T.Limit Temp                      : -2 C
         GPU Max Operating T.Limit Temp                 : 0 C
@@ -444,8 +444,8 @@ GPU 00000000:21:00.0
         Memory Current Temp                            : N/A
         Memory Max Operating T.Limit Temp              : N/A
     GPU Power Readings
-        Average Power Draw                             : 6.19 W
-        Instantaneous Power Draw                       : 5.97 W
+        Average Power Draw                             : 13.85 W
+        Instantaneous Power Draw                       : 16.99 W
         Current Power Limit                            : 575.00 W
         Requested Power Limit                          : 575.00 W
         Default Power Limit                            : 600.00 W
@@ -547,8 +547,8 @@ GPU 00000000:41:00.0
         ECC Object                                     : 7.16
         Power Management Object                        : N/A
     Inforom BBX Object Flush
-        Latest Timestamp                               : 2026/08/30 19:34:26.575
-        Latest Duration                                : 29652 us
+        Latest Timestamp                               : 2026/09/11 00:57:41.291
+        Latest Duration                                : 29987 us
     GPU Operation Mode
         Current                                        : N/A
         Pending                                        : N/A
@@ -585,8 +585,8 @@ GPU 00000000:41:00.0
             Firmware                                   : N/A
         Replays Since Reset                            : 3
         Replay Number Rollovers                        : 0
-        Tx Throughput                                  : 2299 KB/s
-        Rx Throughput                                  : 1588 KB/s
+        Tx Throughput                                  : 2234 KB/s
+        Rx Throughput                                  : 1543 KB/s
         Atomic Caps Outbound                           : N/A
         Atomic Caps Inbound                            : FETCHADD_32 FETCHADD_64 SWAP_32 SWAP_64 CAS_32 CAS_64 
     Fan Speed                                          : 30 %
@@ -611,12 +611,12 @@ GPU 00000000:41:00.0
     FB Memory Usage
         Total                                          : 97887 MiB
         Reserved                                       : 640 MiB
-        Used                                           : 2 MiB
-        Free                                           : 97247 MiB
+        Used                                           : 4 MiB
+        Free                                           : 97244 MiB
     BAR1 Memory Usage
         Total                                          : 256 MiB
-        Used                                           : 2 MiB
-        Free                                           : 254 MiB
+        Used                                           : 3 MiB
+        Free                                           : 253 MiB
     Conf Compute Protected Memory Usage
         Total                                          : 0 MiB
         Used                                           : 0 MiB
@@ -684,8 +684,8 @@ GPU 00000000:41:00.0
             Low                                        : 0 bank(s)
             None                                       : 0 bank(s)
     Temperature
-        GPU Current Temp                               : 30 C
-        GPU T.Limit Temp                               : 63 C
+        GPU Current Temp                               : 34 C
+        GPU T.Limit Temp                               : 59 C
         GPU Shutdown T.Limit Temp                      : -5 C
         GPU Slowdown T.Limit Temp                      : -2 C
         GPU Max Operating T.Limit Temp                 : 0 C
@@ -693,8 +693,8 @@ GPU 00000000:41:00.0
         Memory Current Temp                            : N/A
         Memory Max Operating T.Limit Temp              : N/A
     GPU Power Readings
-        Average Power Draw                             : 15.23 W
-        Instantaneous Power Draw                       : 15.23 W
+        Average Power Draw                             : 19.97 W
+        Instantaneous Power Draw                       : 19.91 W
         Current Power Limit                            : 575.00 W
         Requested Power Limit                          : 575.00 W
         Default Power Limit                            : 600.00 W
@@ -719,7 +719,7 @@ GPU 00000000:41:00.0
         Graphics                                       : 1177 MHz
         SM                                             : 1177 MHz
         Memory                                         : 405 MHz
-        Video                                          : 592 MHz
+        Video                                          : 600 MHz
     Applications Clocks
         Graphics                                       : 2617 MHz
         Memory                                         : 14001 MHz
@@ -796,8 +796,8 @@ GPU 00000000:C1:00.0
         ECC Object                                     : 7.16
         Power Management Object                        : N/A
     Inforom BBX Object Flush
-        Latest Timestamp                               : 2026/08/30 21:01:39.504
-        Latest Duration                                : 32512 us
+        Latest Timestamp                               : 2026/09/11 00:57:52.151
+        Latest Duration                                : 65813 us
     GPU Operation Mode
         Current                                        : N/A
         Pending                                        : N/A
@@ -834,8 +834,8 @@ GPU 00000000:C1:00.0
             Firmware                                   : N/A
         Replays Since Reset                            : 0
         Replay Number Rollovers                        : 0
-        Tx Throughput                                  : 1831 KB/s
-        Rx Throughput                                  : 1470 KB/s
+        Tx Throughput                                  : 3304 KB/s
+        Rx Throughput                                  : 1458 KB/s
         Atomic Caps Outbound                           : N/A
         Atomic Caps Inbound                            : FETCHADD_32 FETCHADD_64 SWAP_32 SWAP_64 CAS_32 CAS_64 
     Fan Speed                                          : 30 %
@@ -860,12 +860,12 @@ GPU 00000000:C1:00.0
     FB Memory Usage
         Total                                          : 97887 MiB
         Reserved                                       : 640 MiB
-        Used                                           : 2 MiB
-        Free                                           : 97247 MiB
+        Used                                           : 4 MiB
+        Free                                           : 97244 MiB
     BAR1 Memory Usage
         Total                                          : 256 MiB
-        Used                                           : 2 MiB
-        Free                                           : 254 MiB
+        Used                                           : 3 MiB
+        Free                                           : 253 MiB
     Conf Compute Protected Memory Usage
         Total                                          : 0 MiB
         Used                                           : 0 MiB
@@ -933,8 +933,8 @@ GPU 00000000:C1:00.0
             Low                                        : 0 bank(s)
             None                                       : 0 bank(s)
     Temperature
-        GPU Current Temp                               : 30 C
-        GPU T.Limit Temp                               : 63 C
+        GPU Current Temp                               : 35 C
+        GPU T.Limit Temp                               : 58 C
         GPU Shutdown T.Limit Temp                      : -5 C
         GPU Slowdown T.Limit Temp                      : -2 C
         GPU Max Operating T.Limit Temp                 : 0 C
@@ -942,8 +942,8 @@ GPU 00000000:C1:00.0
         Memory Current Temp                            : N/A
         Memory Max Operating T.Limit Temp              : N/A
     GPU Power Readings
-        Average Power Draw                             : 14.60 W
-        Instantaneous Power Draw                       : 14.60 W
+        Average Power Draw                             : 19.32 W
+        Instantaneous Power Draw                       : 22.34 W
         Current Power Limit                            : 575.00 W
         Requested Power Limit                          : 575.00 W
         Default Power Limit                            : 600.00 W
@@ -978,8 +978,8 @@ GPU 00000000:C1:00.0
     Deferred Clocks
         Memory                                         : N/A
     Max Clocks
-        Graphics                                       : 4087 MHz
-        SM                                             : 4087 MHz
+        Graphics                                       : 4080 MHz
+        SM                                             : 4080 MHz
         Memory                                         : 15501 MHz
         Video                                          : 3090 MHz
     Max Customer Boost Clocks
diff --git a/environment/gpu-smi.txt b/environment/gpu-smi.txt
index c858a438f8bb8359717d4fceb63c9192c26eeb34..af71427eda7095193aef1d1d8ca723b8d47a7c57 100644
--- a/environment/gpu-smi.txt
+++ b/environment/gpu-smi.txt
@@ -1,6 +1,6 @@
 ### nvidia-smi
 
-Mon Aug 31 19:26:59 2026       
+Fri Sep 11 17:12:55 2026       
 +-----------------------------------------------------------------------------------------+
 | NVIDIA-SMI 580.173.02             Driver Version: 580.173.02     CUDA Version: 13.0     |
 +-----------------------------------------+------------------------+----------------------+
@@ -9,19 +9,19 @@ Mon Aug 31 19:26:59 2026
 |                                         |                        |               MIG M. |
 |=========================================+========================+======================|
 |   0  NVIDIA RTX PRO 6000 Blac...    Off |   00000000:01:00.0 Off |                  Off |
-| 30%   30C    P8             15W /  575W |       2MiB /  97887MiB |      0%      Default |
+| 31%   40C    P8             22W /  575W |       4MiB /  97887MiB |      0%      Default |
 |                                         |                        |                  N/A |
 +-----------------------------------------+------------------------+----------------------+
 |   1  NVIDIA RTX PRO 6000 Blac...    Off |   00000000:21:00.0 Off |                  Off |
-| 30%   30C    P8              6W /  575W |       2MiB /  97887MiB |      0%      Default |
+| 30%   33C    P8             10W /  575W |       4MiB /  97887MiB |      0%      Default |
 |                                         |                        |                  N/A |
 +-----------------------------------------+------------------------+----------------------+
 |   2  NVIDIA RTX PRO 6000 Blac...    Off |   00000000:41:00.0 Off |                  Off |
-| 30%   30C    P8             15W /  575W |       2MiB /  97887MiB |      0%      Default |
+| 30%   34C    P8             19W /  575W |       4MiB /  97887MiB |      0%      Default |
 |                                         |                        |                  N/A |
 +-----------------------------------------+------------------------+----------------------+
 |   3  NVIDIA RTX PRO 6000 Blac...    Off |   00000000:C1:00.0 Off |                  Off |
-| 30%   30C    P8             14W /  575W |       2MiB /  97887MiB |      0%      Default |
+| 30%   35C    P8             16W /  575W |       4MiB /  97887MiB |      0%      Default |
 |                                         |                        |                  N/A |
 +-----------------------------------------+------------------------+----------------------+
 
diff --git a/environment/host-cpu.txt b/environment/host-cpu.txt
index 269e25d79f7089b1617f9edacc53abd71c308565..b9a085f84ca4da86b2d377133ccee03e6b50936f 100644
--- a/environment/host-cpu.txt
+++ b/environment/host-cpu.txt
@@ -15,7 +15,7 @@ Core(s) per socket:                      24
 Socket(s):                               1
 Stepping:                                1
 Frequency boost:                         enabled
-CPU(s) scaling MHz:                      71%
+CPU(s) scaling MHz:                      47%
 CPU max MHz:                             4816.6992
 CPU min MHz:                             1500.0000
 BogoMIPS:                                8200.02
diff --git a/environment/host-memory.txt b/environment/host-memory.txt
index f8567101dfeb88220f12cc84fba7d53dda0d4092..5a5ab661c3242e78cf8b70a17a51857e5fb4d944 100644
--- a/environment/host-memory.txt
+++ b/environment/host-memory.txt
@@ -1,7 +1,7 @@
 ### free -g
 
                total        used        free      shared  buff/cache   available
-Mem:             755          14          55           0         690         740
+Mem:             755          14         140           0         605         740
 Swap:              7           0           7
 
 ### exit=0
diff --git a/environment/host-mounts.txt b/environment/host-mounts.txt
index fe5fd369391691d3e48a574b7bd80fe859a51f88..55c1285b39476664ca8ad795daee335788ac9dd0 100644
--- a/environment/host-mounts.txt
+++ b/environment/host-mounts.txt
@@ -1,14 +1,14 @@
 ### df -h
 
 Filesystem                                  Size  Used Avail Use% Mounted on
-tmpfs                                        76G  5.6M   76G   1% /run
+tmpfs                                        76G  5.5M   76G   1% /run
 efivarfs                                    256K  125K  127K  50% /sys/firmware/efi/efivars
-/dev/nvme0n1p2                              3.6T  3.1T  359G  90% /
+/dev/nvme0n1p2                              3.6T  3.1T  347G  91% /
 tmpfs                                       378G     0  378G   0% /dev/shm
 tmpfs                                       5.0M     0  5.0M   0% /run/lock
 /dev/nvme0n1p1                              1.1G  6.2M  1.1G   1% /boot/efi
-/dev/nvme1n1p1                              3.6T  1.5T  2.0T  43% /media/fmodels2
+/dev/nvme1n1p1                              3.6T  1.8T  1.7T  52% /media/fmodels2
 tmpfs                                        76G   20K   76G   1% /run/user/1000
-10.9.99.242:/mnt/GANAS02-T02S01/Ai-Storage   34T   21T   13T  62% /media/netmodels
+10.9.99.242:/mnt/GANAS02-T02S01/Ai-Storage   34T   21T   13T  63% /media/netmodels
 
 ### exit=0
diff --git a/environment/models/Qwen3.8-27B-Quark-AWQ-INT4-W4A16.txt b/environment/models/Qwen3.8-27B-Quark-AWQ-INT4-W4A16.txt
index 3b68d2736cd068ea007667389944233945a4edef..8f4331ca03865e64778264ce28b91304b26eec4f 100644
--- a/environment/models/Qwen3.8-27B-Quark-AWQ-INT4-W4A16.txt
+++ b/environment/models/Qwen3.8-27B-Quark-AWQ-INT4-W4A16.txt
@@ -1,3 +1,374 @@
 ### /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16
 
-not a directory
+## file listing (name size)
+chat_template.jinja 8952
+config.json 10840
+crc32.txt 238
+generation_config.json 213
+.gitattributes 1570
+inspect.json 1017
+LICENSE 11544
+merges.txt 3353259
+model.safetensors 19512909752
+preprocessor_config.json 482
+processor_config.json 1300
+README.md 5776
+tokenizer_config.json 17928
+tokenizer.json 12809320
+video_preprocessor_config.json 385
+vocab.json 6722759
+
+## listing sha256
+47b2e03bdd41067608cf97bc0c213ccc517c0ab86e07be3becf019aef74e1459  -
+
+## total bytes / file count
+19535859072	/media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16
+49
+
+## config and tokenizer sha256
+86ddf61aefc3032a4c92e510a14f8fcebbe40db4175609f23683810d7fc83de2  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/config.json
+07f857aba5260b2ea2513f80de8062d086661f00eada0a3794964e665ba680f5  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/generation_config.json
+b11349aafa7cdc6a320767cf7ceb29ed82f7eda5d65e8e0819e76f0ce947bf27  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/tokenizer_config.json
+0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/tokenizer.json
+ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/vocab.json
+a9d356d7bdf1ef4949e3e748e95b8e10ad9d4e2e838eddc38a0a7b6b94d1db8d  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/merges.txt
+957eb01d1ea45341a92d543daec95857a7cbeff5803834bc0603b27ba7b41b3f  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/preprocessor_config.json
+c3cf9e34abf4f9e36c2d72165aa9c132d3e2a725b6c2586aaa3a8af9d7a81041  /media/fmodels2/amd/Qwen3.8-27B-Quark-AWQ-INT4-W4A16/chat_template.jinja
+
+## config.json
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 248056,
+  "language_model_only": false,
+  "model_type": "qwen3_5",
+  "quantization_config": {
+    "algo_config": [
+      {
+        "model_decoder_layers": "model.language_model.layers",
+        "name": "awq",
+        "scaling_layers": [
+          {
+            "inp": "mlp.gate_proj",
+            "layers": [
+              "mlp.gate_proj",
+              "mlp.up_proj"
+            ],
+            "module2inspect": "mlp",
+            "prev_op": "post_attention_layernorm"
+          },
+          {
+            "inp": "mlp.down_proj",
+            "layers": [
+              "mlp.down_proj"
+            ],
+            "prev_op": "mlp.up_proj"
+          }
+        ]
+      }
+    ],
+    "exclude": [
+      "model.visual.pos_embed",
+      "model.visual.blocks.0.attn.qkv",
+      "model.visual.blocks.0.attn.proj",
+      "model.visual.blocks.0.mlp.linear_fc1",
+      "model.visual.blocks.0.mlp.linear_fc2",
+      "model.visual.blocks.1.attn.qkv",
+      "model.visual.blocks.1.attn.proj",
+      "model.visual.blocks.1.mlp.linear_fc1",
+      "model.visual.blocks.1.mlp.linear_fc2",
+      "model.visual.blocks.2.attn.qkv",
+      "model.visual.blocks.2.attn.proj",
+      "model.visual.blocks.2.mlp.linear_fc1",
+      "model.visual.blocks.2.mlp.linear_fc2",
+      "model.visual.blocks.3.attn.qkv",
+      "model.visual.blocks.3.attn.proj",
+      "model.visual.blocks.3.mlp.linear_fc1",
+      "model.visual.blocks.3.mlp.linear_fc2",
+      "model.visual.blocks.4.attn.qkv",
+      "model.visual.blocks.4.attn.proj",
+      "model.visual.blocks.4.mlp.linear_fc1",
+      "model.visual.blocks.4.mlp.linear_fc2",
+      "model.visual.blocks.5.attn.qkv",
+      "model.visual.blocks.5.attn.proj",
+      "model.visual.blocks.5.mlp.linear_fc1",
+      "model.visual.blocks.5.mlp.linear_fc2",
+      "model.visual.blocks.6.attn.qkv",
+      "model.visual.blocks.6.attn.proj",
+      "model.visual.blocks.6.mlp.linear_fc1",
+      "model.visual.blocks.6.mlp.linear_fc2",
+      "model.visual.blocks.7.attn.qkv",
+      "model.visual.blocks.7.attn.proj",
+      "model.visual.blocks.7.mlp.linear_fc1",
+      "model.visual.blocks.7.mlp.linear_fc2",
+      "model.visual.blocks.8.attn.qkv",
+      "model.visual.blocks.8.attn.proj",
+      "model.visual.blocks.8.mlp.linear_fc1",
+      "model.visual.blocks.8.mlp.linear_fc2",
+      "model.visual.blocks.9.attn.qkv",
+      "model.visual.blocks.9.attn.proj",
+      "model.visual.blocks.9.mlp.linear_fc1",
+      "model.visual.blocks.9.mlp.linear_fc2",
+      "model.visual.blocks.10.attn.qkv",
+      "model.visual.blocks.10.attn.proj",
+      "model.visual.blocks.10.mlp.linear_fc1",
+      "model.visual.blocks.10.mlp.linear_fc2",
+      "model.visual.blocks.11.attn.qkv",
+      "model.visual.blocks.11.attn.proj",
+      "model.visual.blocks.11.mlp.linear_fc1",
+      "model.visual.blocks.11.mlp.linear_fc2",
+      "model.visual.blocks.12.attn.qkv",
+      "model.visual.blocks.12.attn.proj",
+      "model.visual.blocks.12.mlp.linear_fc1",
+      "model.visual.blocks.12.mlp.linear_fc2",
+      "model.visual.blocks.13.attn.qkv",
+      "model.visual.blocks.13.attn.proj",
+      "model.visual.blocks.13.mlp.linear_fc1",
+      "model.visual.blocks.13.mlp.linear_fc2",
+      "model.visual.blocks.14.attn.qkv",
+      "model.visual.blocks.14.attn.proj",
+      "model.visual.blocks.14.mlp.linear_fc1",
+      "model.visual.blocks.14.mlp.linear_fc2",
+      "model.visual.blocks.15.attn.qkv",
+      "model.visual.blocks.15.attn.proj",
+      "model.visual.blocks.15.mlp.linear_fc1",
+      "model.visual.blocks.15.mlp.linear_fc2",
+      "model.visual.blocks.16.attn.qkv",
+      "model.visual.blocks.16.attn.proj",
+      "model.visual.blocks.16.mlp.linear_fc1",
+      "model.visual.blocks.16.mlp.linear_fc2",
+      "model.visual.blocks.17.attn.qkv",
+      "model.visual.blocks.17.attn.proj",
+      "model.visual.blocks.17.mlp.linear_fc1",
+      "model.visual.blocks.17.mlp.linear_fc2",
+      "model.visual.blocks.18.attn.qkv",
+      "model.visual.blocks.18.attn.proj",
+      "model.visual.blocks.18.mlp.linear_fc1",
+      "model.visual.blocks.18.mlp.linear_fc2",
+      "model.visual.blocks.19.attn.qkv",
+      "model.visual.blocks.19.attn.proj",
+      "model.visual.blocks.19.mlp.linear_fc1",
+      "model.visual.blocks.19.mlp.linear_fc2",
+      "model.visual.blocks.20.attn.qkv",
+      "model.visual.blocks.20.attn.proj",
+      "model.visual.blocks.20.mlp.linear_fc1",
+      "model.visual.blocks.20.mlp.linear_fc2",
+      "model.visual.blocks.21.attn.qkv",
+      "model.visual.blocks.21.attn.proj",
+      "model.visual.blocks.21.mlp.linear_fc1",
+      "model.visual.blocks.21.mlp.linear_fc2",
+      "model.visual.blocks.22.attn.qkv",
+      "model.visual.blocks.22.attn.proj",
+      "model.visual.blocks.22.mlp.linear_fc1",
+      "model.visual.blocks.22.mlp.linear_fc2",
+      "model.visual.blocks.23.attn.qkv",
+      "model.visual.blocks.23.attn.proj",
+      "model.visual.blocks.23.mlp.linear_fc1",
+      "model.visual.blocks.23.mlp.linear_fc2",
+      "model.visual.blocks.24.attn.qkv",
+      "model.visual.blocks.24.attn.proj",
+      "model.visual.blocks.24.mlp.linear_fc1",
+      "model.visual.blocks.24.mlp.linear_fc2",
+      "model.visual.blocks.25.attn.qkv",
+      "model.visual.blocks.25.attn.proj",
+      "model.visual.blocks.25.mlp.linear_fc1",
+      "model.visual.blocks.25.mlp.linear_fc2",
+      "model.visual.blocks.26.attn.qkv",
+      "model.visual.blocks.26.attn.proj",
+      "model.visual.blocks.26.mlp.linear_fc1",
+      "model.visual.blocks.26.mlp.linear_fc2",
+      "model.visual.merger.linear_fc1",
+      "model.visual.merger.linear_fc2",
+      "lm_head",
+      "mtp.fc",
+      "mtp.layers.0.input_layernorm",
+      "mtp.layers.0.mlp.down_proj",
+      "mtp.layers.0.mlp.gate_proj",
+      "mtp.layers.0.mlp.up_proj",
+      "mtp.layers.0.post_attention_layernorm",
+      "mtp.layers.0.self_attn.k_norm",
+      "mtp.layers.0.self_attn.k_proj",
+      "mtp.layers.0.self_attn.o_proj",
+      "mtp.layers.0.self_attn.q_norm",
+      "mtp.layers.0.self_attn.q_proj",
+      "mtp.layers.0.self_attn.v_proj",
+      "mtp.norm",
+      "mtp.pre_fc_norm_embedding",
+      "mtp.pre_fc_norm_hidden"
+    ],
+    "export": {
+      "kv_cache_group": [],
+      "min_kv_scale": 0.0,
+      "pack_method": "reorder",
+      "weight_format": "real_quantized",
+      "weight_merge_groups": null
+    },
+    "global_quant_config": {
+      "bias": null,
+      "input_tensors": null,
+      "output_tensors": null,
+      "target_device": null,
+      "weight": {
+        "block_size": null,
+        "ch_axis": -1,
+        "dtype": "int4",
+        "enable_buffer_reuse": false,
+        "group_size": 128,
+        "is_dynamic": false,
+        "is_scale_quant": false,
+        "max_input_numel": 4194304,
+        "mx_element_dtype": null,
+        "observer_cls": "PerGroupMinMaxObserver",
+        "qscheme": "per_group",
+        "round_method": "half_even",
+        "scale_calculation_mode": null,
+        "scale_format": null,
+        "scale_type": "float",
+        "symmetric": true
+      }
+    },
+    "kv_cache_post_rope": false,
+    "kv_cache_quant_config": {},
+    "layer_quant_config": {},
+    "layer_type_quant_config": {},
+    "quant_method": "quark",
+    "quant_mode": "eager_mode",
+    "softmax_quant_spec": null,
+    "version": "0.13+945249db9d3"
+  },
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_output_gate": true,
+    "bos_token_id": 248044,
+    "dtype": "bfloat16",
+    "eos_token_id": 248044,
+    "full_attention_interval": 4,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 17408,
+    "layer_types": [
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention"
+    ],
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 48,
+    "linear_value_head_dim": 128,
+    "mamba_ssm_dtype": "float32",
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_5_text",
+    "mtp_num_hidden_layers": 1,
+    "mtp_use_dedicated_embeddings": false,
+    "num_attention_heads": 24,
+    "num_hidden_layers": 64,
+    "num_key_value_heads": 4,
+    "output_gate_type": "swish",
+    "pad_token_id": null,
+    "partial_rotary_factor": 0.25,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        11,
+        11,
+        10
+      ],
+      "partial_rotary_factor": 0.25,
+      "rope_theta": 10000000,
+      "rope_type": "default"
+    },
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.2.0",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 27,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_5",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 5120,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}
\ No newline at end of file
diff --git a/environment/pip-freeze.txt b/environment/pip-freeze.txt
index cd106e01a262422834ced9e70f06d4438af4a773..9baeb26add46ad834fdfba0ed215f30c384dfe3c 100644
--- a/environment/pip-freeze.txt
+++ b/environment/pip-freeze.txt
@@ -20,6 +20,7 @@ charset-normalizer==3.5.1
 click==8.5.0
 cloudpickle==3.1.2
 compressed-tensors==0.17.0
+contourpy==1.3.3
 cryptography==50.0.1
 cuda-bindings==13.3.1
 cuda-core==1.0.1
@@ -27,6 +28,7 @@ cuda-pathfinder==1.8.0
 cuda-python==13.3.1
 cuda-tile==1.5.0
 cuda-toolkit==13.2.1
+cycler==0.12.1
 datasets==5.0.1
 depyf==0.20.0
 detect-installer==0.1.0
@@ -42,6 +44,7 @@ fastar==0.12.0
 fastsafetensors==0.3.3
 filelock==3.32.4
 flashinfer-python==0.6.17
+fonttools==4.64.0
 frozenlist==1.8.0
 fsspec==2026.6.0
 googleapis-common-protos==1.75.2
@@ -57,6 +60,7 @@ huggingface_hub==1.29.0
 humming-kernels==0.1.12
 idna==3.19
 ijson==3.5.1
+iniconfig==2.3.0
 instanttensor==0.1.9
 interegular==0.3.3
 Jinja2==3.1.6
@@ -64,6 +68,7 @@ jiter==0.16.0
 jmespath==1.1.0
 jsonschema==4.26.0
 jsonschema-specifications==2025.9.1
+kiwisolver==1.5.1
 lark==1.2.2
 llguidance==1.7.6
 llvmlite==0.47.0
@@ -71,6 +76,7 @@ lm-format-enforcer==0.11.3
 loguru==0.7.3
 markdown-it-py==4.2.0
 MarkupSafe==3.0.3
+matplotlib==3.11.1
 mcp==2.1.1
 mcp-types==2.1.1
 mdurl==0.1.2
@@ -131,6 +137,7 @@ packaging==26.3
 pandas==3.0.5
 partial-json-parser==0.2.1.1.post7
 pillow==12.3.0
+pluggy==1.6.0
 prometheus-fastapi-instrumentator==8.1.0
 prometheus_client==0.26.0
 propcache==0.5.2
@@ -148,6 +155,8 @@ pydantic_core==2.46.5
 Pygments==2.21.0
 PyJWT==2.13.0
 pynvvideocodec==2.0.4
+pyparsing==3.3.2
+pytest==9.1.1
 python-dateutil==2.9.0.post0
 python-dotenv==1.2.3
 python-json-logger==4.2.0
@@ -175,6 +184,7 @@ starlette==1.6.0
 supervisor==4.3.0
 sympy==1.14.0
 tabulate==0.10.0
+tblib==3.2.2
 tiktoken==0.14.0
 tilelang==0.1.12
 tokenizers==0.23.1
@@ -196,7 +206,7 @@ urllib3==2.7.0
 uv==0.12.7
 uvicorn==0.52.4
 uvloop==0.22.1
--e git+https://github.com/phaelon74/vllm.git@398f63d84a45fbe0b0205e70893bcd28fa928a88#egg=vllm
+-e git+https://github.com/phaelon74/vllm.git@60071d1ab73229321712254b1350dcaaac1c125a#egg=vllm
 watchfiles==1.2.0
 websockets==17.1
 xgrammar==0.2.3
diff --git a/environment/repo-git-describe.txt b/environment/repo-git-describe.txt
index fbe2d4c7708c28dad3c66a36abc33349d53d1778..ff77af5dd58a84c49563b9b26850a2ec8687c789 100644
--- a/environment/repo-git-describe.txt
+++ b/environment/repo-git-describe.txt
@@ -1,5 +1,5 @@
 ### git -C /home/phaedawg/kld-bestofbreed/vllm describe --always --dirty --tags
 
-398f63d84
+60071d1ab
 
 ### exit=0
diff --git a/environment/repo-git-log.txt b/environment/repo-git-log.txt
index 27e59c09304e486a03bc865cfe1840402d6b14d4..968fe071d6d0c55fb101beacfca6ac1be1137539 100644
--- a/environment/repo-git-log.txt
+++ b/environment/repo-git-log.txt
@@ -1,9 +1,9 @@
 ### git -C /home/phaedawg/kld-bestofbreed/vllm log -n 5 --format=%H %ad %s --date=iso
 
-398f63d84a45fbe0b0205e70893bcd28fa928a88 2026-08-31 14:11:19 -0500 Added curation capabilites and Large group of candidate evaluation.
-ebbb03be296833b7d5b21c4ff1a3cb0700c69e8c 2026-08-31 10:57:09 -0500 Updated Law 12, to truly enforce identity, not just presence.
-90324504e5fcfd56868afa7bba3c295a6644306f 2026-08-31 10:39:00 -0500 Hardening the campaign to NOT remove elements/components it shouldn't.
-05202b41a854efe273b412fd3f0fecb913e0a85b 2026-08-31 10:25:05 -0500 Tidy up the Sweep capability to make sure only the Published Candidates remain on local disk.
-4c8a0aa9994d13d8669412e0b8945ee124bd702e 2026-08-31 10:03:35 -0500 Update how we talk to routing divergence.
+60071d1ab73229321712254b1350dcaaac1c125a 2026-09-10 22:15:20 -0500 Add alignment on Kernel
+577da7d2d918eb79b724df5426daf42a766c178d 2026-09-10 21:15:02 -0500 updated laws
+0f5e1f77ac8ba42529b52e3e8c9bc9d996ff4534 2026-09-10 20:50:02 -0500 Adding more capability in Fidelity Expansion
+966ab391f2de8c0a0e26168f216ab227a6037230 2026-09-09 12:24:25 -0500 fidelity: hold dense candidates to the same score currency bar as routed ones
+302e640f00705d4d8fed765291d713b9a52c292e 2026-09-09 09:13:58 -0500 Updated Scan
 
 ### exit=0
diff --git a/environment/runtime.json b/environment/runtime.json
index a0d410653733c7917e5d04773af19d9c24a3076a..2ff74797b2b7c43ef0a3b3921d9aa17da67e635b 100644
--- a/environment/runtime.json
+++ b/environment/runtime.json
@@ -1,16 +1,54 @@
 {
   "capture_runtime_manifest": {
+    "compiled_extensions": {
+      "_C_stable_libtorch.abi3.so": "fbae4a92ec2861cd979eb99a3867049f88f7fcb278337be058dd4cf2ac8c29bf",
+      "_moe_C_stable_libtorch.abi3.so": "a5f41231d3a2fe2958aad06615137ab877671baea20e78b0257516ef3467664e",
+      "third_party/deep_gemm/_C.cpython-310-x86_64-linux-gnu.so": "38b87140577c2d72c2b3a6407c5f07da53ef9362e18fe988162afa8f3f91746e",
+      "third_party/deep_gemm/_C.cpython-311-x86_64-linux-gnu.so": "1f3a3430c188295cf96a5ac977ec72258bccb525daaa7b0ad6642bf3a995d3da",
+      "third_party/deep_gemm/_C.cpython-312-x86_64-linux-gnu.so": "73824dc1312e98cf277ae6bc017becf7e963d5b0bfab81e0c6d475d9b769c9e7",
+      "third_party/deep_gemm/_C.cpython-313-x86_64-linux-gnu.so": "bd0124b39fb500378764bf0d1adb5fe30ea876e66ed65971d8f668de4a8dbe83",
+      "third_party/deep_gemm/_C.cpython-314-x86_64-linux-gnu.so": "0f9817dfe8a619dde56456c6e7a5a9d914fede0fb145bc48aad134e19c77fff0"
+    },
+    "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
     "cuda": "13.2",
+    "determinism": {
+      "CUBLAS_WORKSPACE_CONFIG": ":4096:8",
+      "NCCL_DETERMINISTIC": "1",
+      "VLLM_BATCH_INVARIANT": "1",
+      "VLLM_MOE_USE_DEEP_GEMM": "0"
+    },
     "driver": "580.173.02",
+    "flashinfer": "0.6.17",
     "gpu_names": [
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
       "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
     ],
+    "nccl": "2.29.7",
+    "numerics_digest": "251a9225b37415b91fc58d975b0670fc4b481e6f5a816b663573c42e161438f8",
     "platform": "Linux-6.8.0-137-generic-x86_64-with-glibc2.39",
     "python": "3.12.3",
-    "torch": "2.13.0+cu132"
+    "torch": "2.13.0+cu132",
+    "vllm_commit": "60071d1ab73229321712254b1350dcaaac1c125a",
+    "vllm_dirty_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+    "vllm_tree_dirty": false
+  },
+  "compiled_extensions": {
+    "_C_stable_libtorch.abi3.so": "fbae4a92ec2861cd979eb99a3867049f88f7fcb278337be058dd4cf2ac8c29bf",
+    "_moe_C_stable_libtorch.abi3.so": "a5f41231d3a2fe2958aad06615137ab877671baea20e78b0257516ef3467664e",
+    "third_party/deep_gemm/_C.cpython-310-x86_64-linux-gnu.so": "38b87140577c2d72c2b3a6407c5f07da53ef9362e18fe988162afa8f3f91746e",
+    "third_party/deep_gemm/_C.cpython-311-x86_64-linux-gnu.so": "1f3a3430c188295cf96a5ac977ec72258bccb525daaa7b0ad6642bf3a995d3da",
+    "third_party/deep_gemm/_C.cpython-312-x86_64-linux-gnu.so": "73824dc1312e98cf277ae6bc017becf7e963d5b0bfab81e0c6d475d9b769c9e7",
+    "third_party/deep_gemm/_C.cpython-313-x86_64-linux-gnu.so": "bd0124b39fb500378764bf0d1adb5fe30ea876e66ed65971d8f668de4a8dbe83",
+    "third_party/deep_gemm/_C.cpython-314-x86_64-linux-gnu.so": "0f9817dfe8a619dde56456c6e7a5a9d914fede0fb145bc48aad134e19c77fff0"
+  },
+  "compiled_extensions_sha256": "f2fbc7537b0f01f65341030cfa90741c2ed7ad46c88b0faa3dd0b46d311ccdc8",
+  "determinism": {
+    "CUBLAS_WORKSPACE_CONFIG": ":4096:8",
+    "NCCL_DETERMINISTIC": "1",
+    "VLLM_BATCH_INVARIANT": "1",
+    "VLLM_MOE_USE_DEEP_GEMM": "0"
   },
   "devices": [
     {
@@ -43,14 +81,25 @@
     }
   ],
   "env": {
+    "CUBLAS_WORKSPACE_CONFIG": ":4096:8",
     "CUDA_HOME": "/usr/local/cuda-13.0",
-    "HF_TOKEN": "<redacted>"
+    "HF_TOKEN": "<redacted>",
+    "NCCL_DETERMINISTIC": "1",
+    "PYTORCH_NVML_BASED_CUDA_CHECK": "1",
+    "TORCHINDUCTOR_CACHE_DIR": "/tmp/torchinductor_phaedawg",
+    "TORCHINDUCTOR_COMPILE_THREADS": "1",
+    "TRITON_CACHE_AUTOTUNING": "1",
+    "TRITON_PTXAS_BLACKWELL_PATH": "/usr/local/cuda-13.0/bin/ptxas",
+    "VLLM_BATCH_INVARIANT": "1",
+    "VLLM_MARLIN_USE_ATOMIC_ADD": "0",
+    "VLLM_MOE_USE_DEEP_GEMM": "0"
   },
   "env_redacted": [
     "HF_TOKEN"
   ],
   "env_redaction_policy": "Values of variables whose names look like credentials are never recorded. The names are, so a reader can see what was set.",
   "executable": "/home/phaedawg/kld-bestofbreed/kldbestofbreed/bin/python",
+  "flashinfer": "0.6.17",
   "hostname": "d011sd01",
   "platform": "Linux-6.8.0-137-generic-x86_64-with-glibc2.39",
   "python": "3.12.3",
@@ -76,6 +125,7 @@
     "path": "/home/phaedawg/kld-bestofbreed/vllm/vllm/__init__.py",
     "version": "0.1.dev20446+gb2bc9171d"
   },
-  "vllm_commit": "398f63d84a45fbe0b0205e70893bcd28fa928a88",
+  "vllm_commit": "60071d1ab73229321712254b1350dcaaac1c125a",
+  "vllm_dirty_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
   "vllm_tree_dirty": false
 }
diff --git a/environment/summary.md b/environment/summary.md
index cfdb13f112ff32807e3d52f0a71480c7fd5c7f26..f701cf5419b2b4d2de2dfa7f3922e1ecac3af31a 100644
--- a/environment/summary.md
+++ b/environment/summary.md
@@ -1,11 +1,11 @@
 # KLD environment report
 
-- captured: 2026-08-31T19:27:03+00:00
+- captured: 2026-09-11T17:13:00+00:00
 - host: d011sd01
 - kernel: 6.8.0-137-generic
 - python: /home/phaedawg/kld-bestofbreed/kldbestofbreed/bin/python
 - repo: /home/phaedawg/kld-bestofbreed/vllm
-- commit: 398f63d84a45fbe0b0205e70893bcd28fa928a88
+- commit: 60071d1ab73229321712254b1350dcaaac1c125a
 - branch: feature/glm53-kld-determinism
 - driver: 580.173.02
 - gpus:       4 NVIDIA RTX PRO 6000 Blackwell Workstation Edition;
diff --git a/environment/toolchain-ldd.txt b/environment/toolchain-ldd.txt
index 2b26ec8050996d1081f6425b02aab02d2cd0fb3e..01d5f82353e0fa9d7c54b311feb9bc9b8e0ccc89 100644
--- a/environment/toolchain-ldd.txt
+++ b/environment/toolchain-ldd.txt
@@ -1,6 +1,6 @@
 ### ldd --version
 
-ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39
+ldd (Ubuntu GLIBC 2.39-0ubuntu8.9) 2.39
 Copyright (C) 2024 Free Software Foundation, Inc.
 This is free software; see the source for copying conditions.  There is NO
 warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
diff --git a/kld-vs-size.json b/kld-vs-size.json
index 7aab64a6c3b7574ac2aea68990f714278f1018da..e52854f418b6faf88d8c32261bd42fc530fcd3e4 100644
--- a/kld-vs-size.json
+++ b/kld-vs-size.json
@@ -1,86 +1,148 @@
 {
   "title": "Qwen3.8-27B quantization analysis",
   "subtitle": "Mean KL divergence against on-disk size",
+  "routed": false,
   "quants": [
     {
       "id": "Qwen/Qwen3.8-27B-FP8",
       "creator": "Qwen",
       "type": "fp8_block",
       "disk_size_gib": 28.747,
-      "size_source": "hub",
-      "mean_kld": 0.01104733806059744
+      "size_source": null,
+      "mean_kld": 0.010974670174294718,
+      "qxq_mean_kld": 0.010974670174294718,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "unsloth/Qwen3.8-27B-NVFP4",
       "creator": "unsloth",
-      "type": "fp8_per_channel",
+      "type": "nvfp4",
       "disk_size_gib": 21.809,
-      "size_source": "hub",
-      "mean_kld": 0.040968724321885384
+      "size_source": null,
+      "mean_kld": 0.04053079722531637,
+      "qxq_mean_kld": 0.04053079722531637,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "RadixArk/Qwen3.8-27B-NVFP4",
       "creator": "RadixArk",
-      "type": "fp8_per_tensor",
+      "type": "nvfp4",
       "disk_size_gib": 20.416,
-      "size_source": "hub",
-      "mean_kld": 0.06462909372577283
+      "size_source": null,
+      "mean_kld": 0.06405373393276945,
+      "qxq_mean_kld": 0.06405373393276945,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "Inferact/Qwen3.8-27B-NVFP4",
       "creator": "Inferact",
-      "type": "fp8_per_tensor",
+      "type": "nvfp4",
       "disk_size_gib": 24.569,
-      "size_source": "hub",
-      "mean_kld": 0.1387529468264112
+      "size_source": null,
+      "mean_kld": 0.13893423537794009,
+      "qxq_mean_kld": 0.13893423537794009,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
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     {
       "id": "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090",
       "creator": "gittensor-model-hub",
-      "type": "fp8_per_tensor",
+      "type": "nvfp4",
       "disk_size_gib": 17.477,
-      "size_source": "hub",
-      "mean_kld": 0.08884901732903162
+      "size_source": null,
+      "mean_kld": 0.08890053681422792,
+      "qxq_mean_kld": 0.08890053681422792,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "RadixArk/Qwen3.8-27B-NVFP4-BF16-LMHead",
       "creator": "RadixArk",
-      "type": "fp8_per_tensor",
+      "type": "nvfp4",
       "disk_size_gib": 22.118,
-      "size_source": "hub",
-      "mean_kld": 0.05212951125207867
+      "size_source": null,
+      "mean_kld": 0.05156550663959364,
+      "qxq_mean_kld": 0.05156550663959364,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "cyankiwi/Qwen3.8-27B-AWQ-INT4",
       "creator": "cyankiwi",
-      "type": "unquantized",
+      "type": "int4_g32_asym",
       "disk_size_gib": 19.575,
-      "size_source": "hub",
-      "mean_kld": 0.031102106439063696
+      "size_source": null,
+      "mean_kld": 0.031161018371537674,
+      "qxq_mean_kld": 0.031161018371537674,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "philbert440/Qwen3.8-27B-W4A16-AWQ",
       "creator": "philbert440",
-      "type": "unquantized",
+      "type": "int4_g128_asym",
       "disk_size_gib": 18.205,
-      "size_source": "hub",
-      "mean_kld": 0.05213980510933955
+      "size_source": null,
+      "mean_kld": 0.05220832904845153,
+      "qxq_mean_kld": 0.05220832904845153,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "RedHatAI/Qwen3.8-27B-INT4",
       "creator": "RedHatAI",
-      "type": "unquantized",
+      "type": "int4_g128_sym",
       "disk_size_gib": 18.117,
-      "size_source": "hub",
-      "mean_kld": 0.06297880022919693
+      "size_source": null,
+      "mean_kld": 0.06290330991231023,
+      "qxq_mean_kld": 0.06290330991231023,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     },
     {
       "id": "dbirks/Qwen3.8-27B-W4A16-AutoRound",
       "creator": "dbirks",
-      "type": "unquantized",
+      "type": "int4_g128_sym",
       "disk_size_gib": 18.117,
-      "size_source": "hub",
-      "mean_kld": 0.042757811057000626
+      "size_source": null,
+      "mean_kld": 0.04285274563293194,
+      "qxq_mean_kld": 0.04285274563293194,
+      "bxq_mean_kld": null,
+      "routing_intervention_delta": null,
+      "routing_flip_rate": null,
+      "exact_repeat": false,
+      "bxq_omission_reason": "dense reference (QxQ only)"
     }
-  ]
+  ],
+  "excluded_candidates": []
 }
diff --git a/kld-vs-size.png b/kld-vs-size.png
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new file mode 100644
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