Frozen manifests, predictions, latency probes, results.json, dataset card (run of 2026-09-27)

.gitattributes

@@ -58,3 +58,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+manifests/banking77.jsonl filter=lfs diff=lfs merge=lfs -text
+predictions/jev.jsonl filter=lfs diff=lfs merge=lfs -text
+predictions/julia-onnx.jsonl filter=lfs diff=lfs merge=lfs -text
+predictions/laya.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,80 @@
+---
+license: cc-by-4.0
+task_categories:
+  - text-classification
+language:
+  - en
+  - multilingual
+tags:
+  - benchmark
+  - decision-models
+  - jev
+  - laya
+  - julia-1
+  - calibration
+size_categories:
+  - 10K<n<100K
+---
+
+# Decision-Model Benchmark 2026-09-27: jev vs laya vs Julia-1-ONNX
+
+Raw data behind the head-to-head benchmark of three decision engines: 19,776 decisions × 3 engines
+(59,328 inferences), 0 inference failures. Everything needed to audit or re-score the run.
+
+## Contents
+
+- `manifests/*.jsonl` — frozen test manifests (19,776 decisions): question wording, criteria, option
+  order (`keys`), gold key, per-dataset row ids. `manifests/meta.json` records source URLs and
+  SHA-256 of every dataset file (typed-decisions pinned rev `c76749ec…`).
+- `predictions/{jev,laya,julia-onnx}.jsonl` — 19,776 rows each: predicted key, full probability
+  vector over `keys`, gold, latency_ms, engine-specific traces (julia tournament rounds/final
+  candidates; jev input_tokens).
+- `predictions/*.latency.jsonl` — 250 rows per engine from dedicated serial runs (one engine at a
+  time, jev single in-flight). The only latency numbers reported publicly come from these files.
+- `results.json` — all computed metrics: accuracy, macro-F1, Brier, clipped NLL, ECE, p(gold)=0,
+  per-dataset and per-slice breakdowns, paired-bootstrap deltas (20,000 resamples,
+  target-stratified, seed 20260927).
+
+## Test sets (gold comes from the datasets themselves)
+
+| Dataset | Decisions | Source |
+|---|---:|---|
+| typed-decisions | 2,000 | `LocalLLaMA/typed-decisions` test parquet (official TypeSafe/Julia suite: 600 choice, 800 score, 600 noul) |
+| AG News | 7,600 | full test split |
+| DAIR Emotion | 2,000 | full test split |
+| Banking77 | 3,076 | full test split (77 options per call) |
+| MASSIVE scenario | 5,100 | 51 locales × 100 rows, deterministic SHA256-based selection, seed 20260927 |
+
+Question wording and criteria are identical across engines and frozen in the manifests.
+
+## Engines
+
+- **jev-1.13.0** — TypeSafe hosted API (`POST /v1/systemone`)
+- **laya 0.3.20** — `NandhaKishorM/laya` shipped `Router`, base zero-shot (the `laya-typed-decisions`
+  fine-tuned checkpoint was deliberately excluded: train-on-test on this suite)
+- **Julia-1-ONNX** — `SupersonicLabs/Julia-1-ONNX` published `model.onnx` + `model.onnx.data` on
+  onnxruntime CPU, native `julia.data.sequence` encoding (strict, max_length=1024, head_length=512);
+  >20 options via the official `julia.router.Router` tournament (width=20, survivors=2).
+  ONNX↔torch parity verified: argmax 40/40, max |Δlogit| 0.000258.
+
+## Scoring conventions (matters for calibration metrics)
+
+- jev and laya return display-rounded probabilities (sums off by up to 0.01); all vectors are
+  renormalized before scoring. jev affected 416 rows.
+- julia Banking77 probabilities are conditional on the final tournament candidate set
+  (`probability_scope=final_candidates`) — the vendor's documented Router behavior.
+- NLL is clipped; ECE is 15 equal-width bins.
+
+## Caveats
+
+- MASSIVE uses bare label names as criteria (hard zero-shot). The Julia-1 card's 71.5% used scenario
+  descriptions — not comparable to the 0.402 measured here.
+- Reported latency is end-to-end deployment latency on different hardware (jev: hosted API +
+  network RTT; laya: Apple MPS, torch 2.14; julia: onnxruntime CPU), not compute-normalized speed.
+- Vendor-claim checks and the full write-up live in the companion report (`BENCHMARK.md` in the
+  release zip).
+
+## License
+
+Published dataset rows remain under their original dataset licenses (typed-decisions, AG News,
+DAIR Emotion, Banking77, MASSIVE). Prediction and manifest files are CC-BY-4.0.

manifests/banking77.jsonl

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manifests/meta.json

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+{
+  "seed": "20260927",
+  "massive_per_locale": 100,
+  "datasets": {
+    "typed-decisions": {
+      "decisions": 2000,
+      "by_type": {
+        "choice": 600,
+        "noul": 600,
+        "score": 800
+      },
+      "manifest_sha256": "bb9f6ab598384003651b064bd5362bf7a95d32273f1a625c94c3a69ffba461a0",
+      "sources": [
+        {
+          "url": "https://huggingface.co/datasets/LocalLLaMA/typed-decisions/resolve/c76749ec58bd8c3d2ea706b31c333a9059c38f90/all/test-00000-of-00001.parquet",
+          "sha256": "4f294f218ea1da27f3efef936359389c62ea4d3973a41457732990f1d31b647c"
+        }
+      ],
+      "instructions": "What should the observability system do with this trace?"
+    },
+    "agnews": {
+      "decisions": 7600,
+      "by_type": {
+        "choice": 7600
+      },
+      "manifest_sha256": "38bc639a98429fc526f17689ae44a0dc3976d618497cc9546cb4e35fa29ccf67",
+      "sources": [
+        {
+          "url": "https://huggingface.co/datasets/fancyzhx/ag_news/resolve/refs%2Fconvert%2Fparquet/default/test/0000.parquet",
+          "sha256": "71de87ec66bc5737752a2502204dfa6d7fe9856ade3ea444dc6317789a4f13fb"
+        }
+      ],
+      "instructions": "Which topic category does this news article belong to?"
+    },
+    "emotion": {
+      "decisions": 2000,
+      "by_type": {
+        "choice": 2000
+      },
+      "manifest_sha256": "536d6bc799808ac1787b9678bde54d939ff39c4dff161f81c6e86c42a8e8c0ed",
+      "sources": [
+        {
+          "url": "https://huggingface.co/datasets/dair-ai/emotion/resolve/refs%2Fconvert%2Fparquet/split/test/0000.parquet",
+          "sha256": "6f8407fa1ca9c310f55781f082ed73812f6551e8dda2c61973123a121869245b"
+        }
+      ],
+      "instructions": "Which emotion does this message express?"
+    },
+    "banking77": {
+      "decisions": 3076,
+      "by_type": {
+        "choice": 3076
+      },
+      "manifest_sha256": "eda4e9a6544d75f0f3d92e54146b17f25778ce61ea95d7f6cdd2b7e15723746d",
+      "sources": [
+        {
+          "url": "https://huggingface.co/datasets/mteb/banking77/resolve/refs%2Fconvert%2Fparquet/default/test/0000.parquet",
+          "sha256": "9575f636fdeae0c94a0f3f2d926ca8f9a2833b998cf94108936dfd5d72322bf9"
+        }
+      ],
+      "instructions": "Which banking service intent does this customer message express?"
+    },
+    "massive": {
+      "decisions": 5100,
+      "by_type": {
+        "choice": 5100
+      },
+      "manifest_sha256": "c7f7a6f0844c9ba85f1a140d4127ba410370c15d6aa2ffccdc325283c2d464b7",
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+          "sha256": "a82023816556874e78f0a4ab638fbcda0c3d7886cbf6eeea8cf5f5643b166e3b"
+        },
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+          "sha256": "df12b62eff94d3dc3e0bbae4acf04ef301c0cc72883ef31e3ae8ad8422ce153c"
+        },
+        {
+          "url": "https://huggingface.co/datasets/mteb/amazon_massive_scenario/resolve/main/test/zh-CN.json.gz",
+          "sha256": "8ef951eb9f0efc24432a95244f8d8b1a716d9cc78b63f8f3e47995b6e1b08124"
+        },
+        {
+          "url": "https://huggingface.co/datasets/mteb/amazon_massive_scenario/resolve/main/test/zh-TW.json.gz",
+          "sha256": "1017fa19bc358294589171f7a4b4529573e2562f3ecfc0faa4be86a7aa85355f"
+        }
+      ],
+      "instructions": "Which scenario does this utterance belong to?"
+    }
+  }
+}

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predictions/julia-onnx.jsonl

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+        "ece": 0.38127786939912045
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+    },
+    "massive-locale-macro-accuracy": {
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+      "laya": 0.3015686274509804,
+      "julia-onnx": 0.40215686274509804
+    }
+  }
+}