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PROJECT_STATUS.md

@@ -37,7 +37,7 @@ The current no-new-episode enhancement layer records how to push the selected
 | Public package policy | Verified | `DATA_NOTICE.md`, `REPRODUCIBILITY.md` | Raw Xperience-10M data, private gated files, large archives, credentials, and full Qwen weights are not redistributed. |
 | Reproducibility | Verified for the public sample | `REPRODUCIBILITY.md`, `docs/data/reproducibility_matrix.json`, `notes/reproducibility_audit.md` | The public sample workflow has explicit commands, expected outputs, and exact-match reproduction evidence. |
 | 128-episode aligned baselines | Verified companion result | `results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md`, `results/omni_finetune/multi_episode_128_task_baselines/summary_report.json`, `scripts/omni/run_128_task_baselines.py` | The earlier simple and neural baseline framing is aligned to the same selected 96/16/16 episode split used by the Qwen3-Omni pilot. JSON-supported tasks have metadata/text simple and neural MLP metrics; raw-feature-only tasks are explicitly marked unsupported until 128-run sensor feature blocks are available. |
-| Qwen3-Omni fine-tuning | Final verified diagnostic held-out result; JSON target met | `docs/data/omni_finetune_verified_result.json`, `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/`, `scripts/omni/package_verified_omni_result.py`, `scripts/omni/audit_verified_omni_package.py`, `scripts/omni/analyze_qwen3_omni_errors.py` | The selected 96/16/16 episode split produced a current public-safe v4 four-epoch held-out package with 3,808 exported windows, 512 validation windows, 448 test predictions, validation/audit summaries, and the public LoRA adapter. JSON validity is 100.00%, meeting the 98% target; transition accuracy is 97.32%, contact accuracy is 72.99%, object micro-F1 is 31.10%, next-action accuracy is 3.35%, and action/subtask metrics remain weak, so it is still a diagnostic baseline rather than a strong model-quality claim. |
+| Qwen3-Omni fine-tuning | Latest v6 diagnostic branch verified; JSON target met | `docs/data/omni_finetune_verified_result.json`, `docs/data/qwen3_v5_v6_comparison.json`, `results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md`, `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/`, `scripts/omni/package_verified_omni_result.py`, `scripts/omni/audit_verified_omni_package.py`, `scripts/omni/analyze_qwen3_omni_errors.py` | The selected 96/16/16 episode split now has a current public-safe v6 rank64/lr5e-5 held-out package with 34,269 exported windows and 4,032 test predictions. JSON validity is 99.90%, meeting the 98% target; transition accuracy is 98.98%, contact accuracy is 81.77%, object micro-F1 is 30.65%, next-action accuracy is 4.31%, and action/subtask metrics remain weak. v6 improves action macro-F1 and contact accuracy versus v5, but v5 remains stronger on JSON validity, subtask, next-action, transition, and object metrics. |
 | Raw Xperience-10M redistribution | Not included | `DATA_NOTICE.md`, `docs/data/publication_audit.json` | Raw MP4, HDF5, RRD files, private gated data, and full Qwen weights are intentionally excluded. |
 
 ## Fast Research Route
@@ -79,10 +79,11 @@ The current no-new-episode enhancement layer records how to push the selected
   current results use one public sample episode.
 - Public-facing fine-tuning results should come from the verified result
   package, not from live process logs or setup-only artifacts.
-- The final Qwen3-Omni v4 held-out package verifies the pipeline and meets the
-  strict-JSON target, but not strong action/subtask model quality: JSON
-  validity is 100.00%, action macro-F1 is 0.0019, and subtask accuracy is
-  0.0000.
+- The latest Qwen3-Omni v6 held-out package verifies the current dense
+  multiscale branch and meets the strict-JSON target, but not strong
+  action/subtask model quality: JSON validity is 99.90%, action macro-F1 is
+  0.0029, and subtask accuracy is 0.0037. v5 remains the pinned prior release
+  row because it is still stronger on several metrics.
 - The current 128-episode task suite can be pushed further without more raw
   episodes by using dense/multiscale windows, hierarchical action/subtask
   targets, stronger label-normalized scoring, and compact raw-feature shards

RESEARCH_ROADMAP.md

@@ -11,7 +11,7 @@ should exist before the stage is treated as complete.
 | --- | --- | --- | --- | --- |
 | Public-Sample Task Lab | Implemented | One public Xperience-10M sample episode is available. | 1,161 aligned windows, 12 task contracts, minimal heads, neural MLP heads, modality atlas, task walkthroughs, and derived figures. | `PROJECT_STATUS.md`, `EVALUATION_PROTOCOL.md`, `RESEARCH_TAKEAWAYS.md`, `docs/data/summary_metrics.json`, `results/episode_task_suite/summary_report.json` |
 | Multi-Episode Data Preparation | Implemented for first selected pilot | Gated dataset availability and enough storage for selected episodes. | 128 selected episodes, episode manifest, missing-view manifest, held-out episode split, and source-discovery report. | `results/omni_finetune/DATA_ACCESS_STATUS.md`, `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`, `results/omni_finetune/xperience10m_128_episode_selection.json` |
-| Qwen3-Omni LoRA Final Diagnostic Result | Verified baseline | Selected episodes prepared locally with no train/test episode leakage. | Dataset JSONL/media manifests, LoRA adapter checkpoint, progress logs, validation monitoring, held-out predictions, metrics, confusion matrices, run report, and public LoRA adapter repo. | `docs/data/omni_finetune_verified_result.json`, `results/omni_finetune/verified_public/`, `metrics.json`, `predictions.jsonl`, `RUN_REPORT.md`, `https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep` |
+| Qwen3-Omni LoRA Latest Diagnostic Branch | Verified latest branch | Selected episodes prepared locally with no train/test episode leakage. | Dataset JSONL/media manifests, LoRA adapter checkpoint, progress logs, validation monitoring, held-out predictions, metrics, confusion matrices, v5/v6 comparison, run report, and public LoRA adapter repo. | `docs/data/omni_finetune_verified_result.json`, `docs/data/qwen3_v5_v6_comparison.json`, `results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md`, `results/omni_finetune/verified_public/`, `metrics.json`, `predictions.jsonl`, `RUN_REPORT.md`, `https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep` |
 | 128-Episode Same-Split Simple/NN Baselines | Verified companion result | Derived Qwen JSONL export for the selected 96/16/16 split. | Same 12 task ids, simple metadata/text baselines, neural MLP baselines where JSON labels support them, and explicit unsupported markers for tasks that still require raw 128 feature blocks. | `results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md`, `summary_report.json`, `scripts/omni/run_128_task_baselines.py` |
 | 128-Episode Task Suite Enhancement Pack | Current no-new-episode plan | Same selected 96/16/16 split and current public 3,808-window export. | Dense-window and multiscale export estimates, hierarchical action/subtask target contract, raw-feature shard priorities for unsupported tasks, Qwen v5 and Cosmos continuation run cards, and publication-ready artifacts. | `TASK_SUITE_ENHANCEMENT_128.md`, `docs/data/task_suite_enhancement_128.json`, `results/omni_finetune/task_suite_enhancement_128_v1_20260608/enhancement_plan.json`, `scripts/omni/build_task_suite_enhancement_128.py` |
 | Action/Subtask Error-Analysis Pass | Active next step | The final diagnostic package meets strict JSON validity but has weak action/subtask held-out quality. | Same 96/16/16 split, action/subtask confusion analysis, unseen-label analysis, object/action family breakdowns, and comparison to the final verified Qwen baseline. | Updated error-analysis tables, held-out metrics by failure type, and verified public package. |
@@ -24,8 +24,8 @@ should exist before the stage is treated as complete.
 
 The useful next decision is model-quality improvement plus backbone fit without
 requiring more raw episodes first: keep the public-sample task suite as the
-development harness, use the verified Qwen3-Omni final diagnostic result as the
-first cross-episode baseline, then improve action/subtask quality before
+development harness, use the verified Qwen3-Omni v6 diagnostic branch plus the
+pinned v5 row as the current cross-episode references, then improve action/subtask quality before
 claiming model quality. The earlier simple and neural baseline framing is now
 aligned to the same 96/16/16 split through metadata/text baselines for
 JSON-supported task ids; raw-feature-only tasks remain marked as needing the

data/mirror_parity.json

@@ -1,9 +1,9 @@
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data/omni_finetune_verified_result.json

@@ -1,98 +1,96 @@
 {
-    "title": "Verified Qwen3-Omni LoRA 128-Episode Held-Out Result",
-    "status": "verified_full_128_episode_diagnostic_result",
-    "status_date": "2026-06-08",
-    "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
-    "adapter": "Qwen3-Omni LoRA",
-    "dataset": "Ropedia Xperience-10M selected 128-episode pilot",
-    "split_policy": {
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-            "train": 96,
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-        "skipped_selected_episodes": 9,
-        "leakage_policy": "Train, validation, and test are separated by episode/session; test windows are used only for held-out evaluation."
+  "title": "Verified Qwen3-Omni LoRA 128-Episode Held-Out Result",
+  "status": "verified_latest_qwen3_v6_diagnostic_result",
+  "status_date": "2026-06-14",
+  "backbone": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+  "adapter": "Qwen3-Omni LoRA",
+  "dataset": "Ropedia Xperience-10M selected 128-episode pilot",
+  "split_policy": {
+    "unit": "episode",
+    "selected_episode_counts": {
+      "train": 96,
+      "val": 16,
+      "test": 16
     },
-    "training": {
-        "num_processes": 8,
-        "epochs": 4,
-        "lora_rank": 16,
-        "lora_alpha": 32,
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-        "loss": "answer-token cross entropy over supervised JSON tokens",
-        "note": "This current Qwen3-Omni LoRA result is the v4 four-epoch full held-out evaluation on the selected 96/16/16 episode setup."
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+      "val": 4608,
+      "test": 4032
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-        "previous_structured_json_v2_json_validity_rate": 0.9977678571428571
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+      "train": 89,
+      "val": 16,
+      "test": 14
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-    "interpretation": "This is the current verified Qwen3-Omni LoRA diagnostic result for the selected 128-episode setup. The v4 four-epoch package reaches 100% JSON validity and slightly improves next-action, contact, and object metrics versus the prior strict-label v3 package, while action and subtask classification remain weak on held-out episodes. Treat it as a diagnostic baseline and error-analysis source, not as a strong Xperience-10M action recognizer.",
-    "public_package": {
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-        "adapter_weights_repo": "cy0307/ropedia-qwen3-omni-lora-128ep"
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+    "leakage_policy": "Train, validation, and test are separated by episode/session; test windows are used only for held-out evaluation."
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+    "epochs": 2,
+    "lora_rank": 64,
+    "lora_alpha": 128,
+    "lora_dropout": 0.05,
+    "learning_rate": 0.00005,
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+    "num_val_samples": 2048,
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+        "global_step": 6408
+      }
+    ],
+    "loss": "answer-token cross entropy over supervised JSON tokens",
+    "note": "This current Qwen3-Omni LoRA result is the v6 rank64/lr5e-5 dense multiscale held-out evaluation on the selected 96/16/16 episode setup."
+  },
+  "evaluation": {
+    "split": "test",
+    "num_samples": 4032,
+    "held_out_episode_count": 14,
+    "json_validity_rate": 0.9990079365079365,
+    "action_macro_f1": 0.0028830723979596335,
+    "subtask_accuracy": 0.0037313432835820895,
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+    "next_action_accuracy": 0.04305335446381405,
+    "contact_accuracy": 0.8177083333333334,
+    "object_micro_f1": 0.3064982378331287,
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+      "status": "met"
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-    "required_next_steps": [
-        "Use the v4 predictions for action/subtask error analysis, unseen-label debugging, and hierarchical action-family scoring.",
-        "Use TASK_SUITE_ENHANCEMENT_128.md and docs/data/task_suite_enhancement_128.json for the no-new-episode suite push before requesting more storage.",
-        "Keep the existing Qwen LoRA adapter repository as the weight-bearing artifact and publish future Qwen v5 runs as separate verified packages.",
-        "Use the verified Cosmos3-Super Forward-Dynamics LoRA package as a separate world-model branch: it updates adapter weights over camera-pose proxy future-vision-velocity targets, not Qwen-style JSON action labels."
-    ]
+    "previous_v5_json_validity_rate": 1.0,
+    "previous_v5_action_macro_f1": 0.002289711036077459,
+    "previous_v5_subtask_accuracy": 0.011194029850746268,
+    "previous_v5_next_action_accuracy": 0.053618594823032224,
+    "previous_v5_contact_accuracy": 0.7864583333333334,
+    "previous_v5_object_micro_f1": 0.31614599936244814
+  },
+  "interpretation": "This is the latest verified Qwen3-Omni LoRA diagnostic result for the selected 128-episode setup. The v6 rank64/lr5e-5 package keeps JSON validity above the 98% target and improves action macro-F1 and contact accuracy versus the pinned v5 release row, but slightly regresses JSON validity, subtask accuracy, next-action accuracy, transition accuracy, and object micro-F1. Treat it as the latest diagnostic branch, not as a globally stronger replacement for v5.",
+  "public_package": {
+    "path": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full",
+    "audit_status": "pass",
+    "contains_raw_xperience10m_data": false,
+    "contains_qwen_base_weights": false,
+    "contains_lora_weights": false,
+    "adapter_weights_repo": "cy0307/ropedia-qwen3-omni-lora-128ep"
+  },
+  "release_policy": {
+    "latest_verified_qwen_row": "xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full",
+    "pinned_release_tag": "ropedia-xperience-10m-v5",
+    "pinned_release_reason": "v5 remains the prior stable release tag; v6 is published on main/HF as the latest verified branch and can receive a separate v6 release tag."
+  },
+  "required_next_steps": [
+    "Use results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md before deciding whether v6 should become a formal release tag.",
+    "Use the v6 predictions for action/contact error analysis, and compare v5 for subtask, next-action, and object regressions.",
+    "Keep full-parameter Qwen runs as feasibility gates until there is a storage plan for checkpoints or mergeable full-weight deltas.",
+    "Use the verified Cosmos3-Super Forward-Dynamics LoRA package as a separate world-model branch: it updates adapter weights over camera-pose proxy future-vision-velocity targets, not Qwen-style JSON action labels."
+  ]
 }

data/project_packet.json

@@ -1,6 +1,6 @@
 {
     "title": "Ropedia Xperience-10M Task Suite Project Packet",
-    "version": "2026-06-08",
+    "version": "2026-06-14",
     "scope_status": {
         "validated_data": "one public Xperience-10M sample episode",
         "aligned_frames": 5821,
@@ -12,7 +12,7 @@
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         "audio_feature_status": "Audio is one of the synchronized source modalities in the current task representation.",
         "qwen3_omni_32_episode_claim": false,
-        "qwen3_omni_status": "The selected 96/16/16 Qwen3-Omni v4 final diagnostic result is verified, meets the strict-JSON target, and still has weak action/subtask metrics that guide the next error-analysis pass.",
+        "qwen3_omni_status": "The selected 96/16/16 Qwen3-Omni v6 diagnostic branch is verified, meets the strict-JSON target, improves action macro-F1/contact accuracy versus v5, and still has weak action/subtask metrics that guide the next error-analysis pass.",
         "cosmos3_super_forward_dynamics_lora_status": "The first Cosmos3-Super fine-tuned adapter branch is verified as a forward-dynamics LoRA over camera-pose proxy targets; it reports loss metrics, not JSON action-label accuracy.",
         "task_suite_enhancement_128_status": "Current no-new-episode enhancement pack recommends multiscale_20s10_40s20_80s40, hierarchical action/subtask targets, label-normalized scoring, and raw-feature shards before adding more episodes."
     },
@@ -118,7 +118,7 @@
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                 "docs/data/omni_finetune_verified_result.json"
             ],
-            "readout": "The selected-episode held-out Qwen3-Omni final diagnostic result is verified and JSON-format reliability meets the 98% target. The same public comparison also includes the verified 128-episode baselines, Cosmos3-Nano compatibility result, Cosmos3-Super Reasoner evaluation, and Cosmos3-Super Forward-Dynamics LoRA package. The next milestone is action/subtask error analysis and stronger model-quality runs on the same split."
+            "readout": "The selected-episode held-out Qwen3-Omni v6 diagnostic branch is verified and JSON-format reliability meets the 98% target. The same public comparison also includes the verified 128-episode baselines, Cosmos3-Nano compatibility result, Cosmos3-Super Reasoner evaluation, and Cosmos3-Super Forward-Dynamics LoRA package. The next milestone is action/subtask error analysis and stronger model-quality runs on the same split."
         },
         {
             "step": 9,
@@ -155,7 +155,7 @@
         "hf_model_baselines": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines"
     },
     "current_reading_notes": [
-        "The first cross-episode Qwen3-Omni v4 diagnostic pilot is verified, but strong model quality is not yet shown; action/subtask metrics remain weak.",
+        "The latest cross-episode Qwen3-Omni v6 diagnostic branch is verified, but strong model quality is not yet shown; action/subtask metrics remain weak and v5 remains stronger on several non-contact metrics.",
         "The current 128-episode suite has a no-new-episode enhancement plan: multiscale_20s10_40s20_80s40 windows, hierarchical labels, label-normalized scoring, and raw-feature shard export.",
         "Cosmos3-Super Forward-Dynamics LoRA is verified as a loss-based world-model adapter branch, not as JSON action-token prediction.",
         "Older Qwen3-Omni setup artifacts are separate from the verified selected-episode diagnostic package.",

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-12T18:14:49+00:00",
+  "generated_at_utc": "2026-06-13T17:46:37+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,7 +18,7 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-12T17:58:57+00:00"
+          "generated_at_utc": "2026-06-13T17:46:31+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-12T17:58:56+00:00"
+          "generated_at_utc": "2026-06-13T17:42:17+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-12T17:58:56+00:00"
+          "generated_at_utc": "2026-06-13T17:42:17+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-12T17:58:56+00:00"
+          "generated_at_utc": "2026-06-13T17:46:07+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-12T17:59:02+00:00"
+          "generated_at_utc": "2026-06-13T17:44:16+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-12T17:59:14+00:00"
+          "generated_at_utc": "2026-06-12T18:14:59+00:00"
         },
         "live_publication": {
           "exists": true,

data/qwen3_v5_v6_comparison.json

@@ -0,0 +1,68 @@
+{
+  "title": "Qwen3-Omni v5 versus v6 verified comparison",
+  "status": "pass",
+  "generated_at_utc": "2026-06-14T00:00:00+00:00",
+  "comparison_scope": "Verified Qwen3-Omni LoRA held-out test packages on the same dense multiscale selected 128-episode dataset.",
+  "release_policy": {
+    "latest_verified_qwen_row": "v6",
+    "pinned_release_tag": "ropedia-xperience-10m-v5",
+    "recommendation": "Publish v6 as the latest verified branch and create a separate v6 tag only if the project wants a formal experimental release; do not move the v5 tag."
+  },
+  "runs": {
+    "v5": {
+      "eval_run_id": "xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_eval_test_full",
+      "train_run_id": "xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora",
+      "epochs": 1,
+      "eval_samples": 4032,
+      "held_out_episode_count": 14,
+      "metrics": {
+        "json_validity_rate": 1.0,
+        "action_macro_f1": 0.002289711036077459,
+        "subtask_accuracy": 0.011194029850746268,
+        "transition_accuracy": 0.9908234126984127,
+        "next_action_accuracy": 0.053618594823032224,
+        "contact_accuracy": 0.7864583333333334,
+        "object_micro_f1": 0.31614599936244814
+      }
+    },
+    "v6": {
+      "eval_run_id": "xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full",
+      "train_run_id": "xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora",
+      "epochs": 2,
+      "lora_rank": 64,
+      "learning_rate": 0.00005,
+      "eval_samples": 4032,
+      "held_out_episode_count": 14,
+      "metrics": {
+        "json_validity_rate": 0.9990079365079365,
+        "action_macro_f1": 0.0028830723979596335,
+        "subtask_accuracy": 0.0037313432835820895,
+        "transition_accuracy": 0.9898313492063492,
+        "next_action_accuracy": 0.04305335446381405,
+        "contact_accuracy": 0.8177083333333334,
+        "object_micro_f1": 0.3064982378331287
+      }
+    }
+  },
+  "deltas_v6_minus_v5": {
+    "json_validity_rate": -0.0009920634920634888,
+    "action_macro_f1": 0.0005933613618821745,
+    "subtask_accuracy": -0.007462686567164178,
+    "transition_accuracy": -0.0009920634920634888,
+    "next_action_accuracy": -0.010565240359218173,
+    "contact_accuracy": 0.03125,
+    "object_micro_f1": -0.009647761529319436
+  },
+  "wins_for_v6": [
+    "action_macro_f1",
+    "contact_accuracy"
+  ],
+  "wins_for_v5": [
+    "json_validity_rate",
+    "subtask_accuracy",
+    "transition_accuracy",
+    "next_action_accuracy",
+    "object_micro_f1"
+  ],
+  "interpretation": "v6 is the newest verified Qwen LoRA branch and is better for action macro-F1 and contact accuracy, but v5 remains the safer pinned release row for JSON perfection, subtask/next-action accuracy, transition accuracy, and object micro-F1."
+}

docs/index.html

@@ -2327,8 +2327,8 @@
             <p>The first selected-episode LoRA pilot is packaged with real held-out predictions and metrics. It proves the pipeline, while the weak scores make it a baseline for error analysis.</p>
             <div class="snapshot-meta">
               <span>split <strong>96 / 16 / 16</strong></span>
-              <span>test windows <strong>448</strong></span>
-              <span>JSON validity <strong>100.00%</strong></span>
+              <span>test windows <strong>4,032</strong></span>
+              <span>JSON validity <strong>99.90%</strong></span>
             </div>
           </article>
           <article class="snapshot-card gated">
@@ -2368,10 +2368,10 @@
       <div class="wrap">
         <div class="section-head">
           <h2>Research roadmap.</h2>
-          <p>The project path moves from the current public-sample task lab to a final verified Qwen3-Omni diagnostic result, same-split 128-episode baseline alignment, a no-new-episode enhancement pack, action/subtask error analysis, robustness runs, world/policy branches, and the future Xperience Embodied Foundation Model pretraining goal.</p>
+          <p>The project path moves from the current public-sample task lab to the latest verified Qwen3-Omni diagnostic branch, same-split 128-episode baseline alignment, a no-new-episode enhancement pack, action/subtask error analysis, robustness runs, world/policy branches, and the future Xperience Embodied Foundation Model pretraining goal.</p>
         </div>
         <div class="roadmap-grid" aria-label="Research roadmap stages">
-          <article class="roadmap-card" data-status="implemented_for_first_pilot">
+          <article class="roadmap-card" data-status="implemented">
             <span class="roadmap-status">implemented</span>
             <h3>Public-Sample Task Lab</h3>
             <p>One public episode is converted into aligned windows, task contracts, minimal baselines, neural heads, walkthroughs, and figures.</p>
@@ -2380,7 +2380,7 @@
               <strong>Evidence</strong><p>Status, protocol, takeaways, summary metrics, and episode-task outputs.</p>
             </div>
           </article>
-          <article class="roadmap-card" data-status="verified_baseline">
+          <article class="roadmap-card" data-status="implemented_for_first_pilot">
             <span class="roadmap-status">implemented</span>
             <h3>Multi-Episode Data Preparation</h3>
             <p>Prepare official gated episodes while preserving episode-level separation and recording missing-view coverage. The first selected split is available for Qwen3-Omni diagnostics.</p>
@@ -2389,13 +2389,13 @@
               <strong>Evidence</strong><p>Selected-episode plan, data boundary, preparation notes, and verified package summary.</p>
             </div>
           </article>
-          <article class="roadmap-card" data-status="implemented">
-            <span class="roadmap-status">verified baseline</span>
-            <h3>Qwen3-Omni LoRA Final Diagnostic Result</h3>
+          <article class="roadmap-card" data-status="verified_latest_branch">
+            <span class="roadmap-status">verified latest branch</span>
+            <h3>Qwen3-Omni LoRA Latest Diagnostic Branch</h3>
             <p>Train lightweight adapters on selected prepared episodes and evaluate on held-out episodes with committed predictions, metrics, and run reports.</p>
             <div class="roadmap-meta">
               <strong>Entry</strong><p>Selected episodes prepared with no train/test episode leakage.</p>
-              <strong>Evidence</strong><p>Verified result summary, dataset manifest, training metadata, progress logs, metrics, and predictions.</p>
+              <strong>Evidence</strong><p>Verified result summary, v5/v6 comparison, dataset manifest, training metadata, progress logs, metrics, and predictions.</p>
             </div>
           </article>
           <article class="roadmap-card" data-status="verified_companion_result">
@@ -2749,7 +2749,7 @@
           <article class="artifact primary-artifact"><div><h3>Official dataset</h3><p>Xperience-10M is a gated large-scale egocentric multimodal dataset for embodied AI, robotics, spatial intelligence, and world modeling.</p></div><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official HF dataset</a></article>
           <article class="artifact"><h3>Public sample</h3><p>The current task suite is built from one public sample episode, not from the entire gated dataset.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
           <article class="artifact"><h3>Modalities</h3><p>The sample exposes synchronized video, audio, depth, pose/SLAM, motion capture, inertial signals, calibration, and language annotations.</p><a href="data/modality_atlas.json">modality atlas</a></article>
-          <article class="artifact"><h3>Multi-episode pilot</h3><p>The selected 128-episode Qwen3-Omni LoRA v4 diagnostic result is verified with 448 held-out test predictions and 100.00% JSON validity. Action/subtask metrics are still weak, so this remains a baseline for error analysis.</p><a href="https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep">LoRA adapter</a></article>
+          <article class="artifact"><h3>Multi-episode pilot</h3><p>The selected 128-episode Qwen3-Omni LoRA v6 diagnostic branch is verified with 4,032 held-out test predictions and 99.90% JSON validity. Action/subtask metrics are still weak, so this remains a baseline for error analysis.</p><a href="https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep">LoRA adapter</a></article>
           <article class="artifact"><h3>Data boundary</h3><p>Raw MP4, HDF5, RRD files, private gated data, and full Qwen weights are not redistributed in this project.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">data notice</a></article>
           <article class="artifact"><h3>Current project subset</h3><p>One public sample episode, 5,821 frames, 1,161 aligned windows, 8,546-dimensional task inputs, and no raw-data redistribution.</p><a href="data/modality_atlas.json">modality atlas</a></article>
           <article class="artifact"><h3>Covered now</h3><p>Action/subtask labels, next-action prediction, temporal diagnostics, hand trajectory, contact, object relevance, caption grounding, retrieval, reconstruction, and misalignment.</p><a href="data/summary_metrics.json">summary metrics</a></article>
@@ -3234,13 +3234,13 @@
     <section id="omni-scale-up" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>Qwen3-Omni diagnostic pilot is verified.</h2>
-          <p>The selected pilot uses 128 source-balanced episodes across 128 different session UUIDs. The first held-out package is verified, and its weak metrics define the next structured-output and error-analysis pass.</p>
+          <h2>Qwen3-Omni diagnostic branch is verified.</h2>
+          <p>The selected pilot uses 128 source-balanced episodes across 128 different session UUIDs. The latest v6 held-out package is verified, and its weak metrics define the next structured-output and error-analysis pass.</p>
         </div>
         <div class="artifact-grid">
           <article class="artifact"><h3>Selection</h3><p>128 complete episodes selected from 128 unique top-level sessions, balanced across episode-size bands and split 96/16/16 for train/val/test.</p></article>
           <article class="artifact"><h3>Transfer</h3><p>Download raw episodes only from official gated sources, exclude visualization.rrd, validate files, then stage them for training.</p></article>
-          <article class="artifact"><h3>Current LoRA artifact</h3><p>The current Qwen3-Omni LoRA artifact is the selected 128-episode diagnostic adapter. The 1-episode Qwen entry is only a sensor-adapter smoke test.</p><a href="data/omni_model_comparison.json">model groups</a></article>
+          <article class="artifact"><h3>Current LoRA artifact</h3><p>The current Qwen3-Omni LoRA artifact is the verified v6 selected 128-episode diagnostic adapter. The v5 row remains pinned as the prior release, and the 1-episode Qwen entry is only a sensor-adapter smoke test.</p><a href="data/omni_model_comparison.json">model groups</a></article>
           <article class="artifact"><h3>128-Episode Task Suite Enhancement Pack</h3><p>The next suite push does not need more episodes first: use `multiscale_20s10_40s20_80s40`, hierarchical action/subtask targets, and raw-feature shards while keeping the held-out split fixed.</p><a href="data/task_suite_enhancement_128.json">task_suite_enhancement_128.json</a></article>
           <article class="artifact"><h3>Backbone branches</h3><p>Qwen3-Omni uses a separate LoRA model repo; Cosmos3-Nano remains a compatibility package; Cosmos3-Super now has a verified forward-dynamics LoRA branch with weights in a dedicated model repo.</p><a href="https://huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep">Cosmos3-Super weights</a></article>
           <article class="artifact"><h3>Native foundation model</h3><p>The long-term goal is a full-corpus Xperience Embodied Foundation Model trained on synchronized perception, geometry, motion, inertial, audio, and language streams after smaller scaling stages validate the approach.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>

scripts/build_artifact_index.py

@@ -193,6 +193,22 @@ ARTIFACTS = [
         "surface": "website_hf",
         "shows": "Machine-readable summary of full-parameter feasibility evidence and publication policy for website and Hugging Face mirrors.",
     },
+    {
+        "id": "qwen3_v5_v6_comparison",
+        "title": "Qwen3-Omni v5/v6 comparison",
+        "path": "results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md",
+        "kind": "scaleup_status",
+        "surface": "repo_hf",
+        "shows": "Reader-facing comparison of the verified Qwen3 v5 release row and the latest verified v6 row, including metric deltas and release-tag policy.",
+    },
+    {
+        "id": "qwen3_v5_v6_comparison_json",
+        "title": "Qwen3-Omni v5/v6 comparison JSON",
+        "path": "docs/data/qwen3_v5_v6_comparison.json",
+        "kind": "scaleup_status",
+        "surface": "website_hf",
+        "shows": "Machine-readable v5/v6 metric deltas and publication recommendation for website and Hugging Face mirrors.",
+    },
     {
         "id": "qwen3_full_parameter_gates_builder",
         "title": "Qwen3-Omni full-parameter gate summary builder",

scripts/publish_hf_bundles.py

@@ -314,6 +314,9 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--artifact-repo", default=DEFAULT_ARTIFACT_REPO)
     parser.add_argument("--model-repo", default=DEFAULT_MODEL_REPO)
     parser.add_argument("--token", default=os.environ.get("HF_TOKEN", "").strip())
+    parser.add_argument("--skip-space", action="store_true")
+    parser.add_argument("--skip-artifacts", action="store_true")
+    parser.add_argument("--skip-model", action="store_true")
     return parser.parse_args()
 
 
@@ -348,6 +351,19 @@ def upload_folder(
     ignore_patterns: list[str] | None = None,
 ):
     print(f"Uploading {folder} -> {repo_id}")
+    effective_repo_type = repo_type or "model"
+    effective_ignore_patterns = COMMON_IGNORE + (ignore_patterns or [])
+    if effective_repo_type != "space" and hasattr(api, "upload_large_folder"):
+        return api.upload_large_folder(
+            repo_id=repo_id,
+            repo_type=effective_repo_type,
+            folder_path=str(folder),
+            allow_patterns=allow_patterns,
+            ignore_patterns=effective_ignore_patterns,
+            num_workers=8,
+            print_report=True,
+            print_report_every=60,
+        )
     return api.upload_folder(
         repo_id=repo_id,
         repo_type=repo_type,
@@ -355,7 +371,7 @@ def upload_folder(
         commit_message=message,
         token=token,
         allow_patterns=allow_patterns,
-        ignore_patterns=COMMON_IGNORE + (ignore_patterns or []),
+        ignore_patterns=effective_ignore_patterns,
     )
 
 
@@ -458,50 +474,53 @@ def main() -> int:
     api.create_repo(artifact_repo, repo_type="dataset", exist_ok=True, token=token)
     api.create_repo(model_repo, repo_type=None, exist_ok=True, token=token)
 
-    upload_folder(
-        api,
-        token,
-        space_repo,
-        "space",
-        hf_root / "space",
-        "Publish Ropedia Xperience-10M task-suite Space",
-    )
-    for path_in_repo in STALE_SPACE_REMOTE_FILES:
-        delete_remote_file_if_present(api, token, space_repo, "space", path_in_repo)
-    upload_folder(
-        api,
-        token,
-        artifact_repo,
-        "dataset",
-        hf_root / "artifacts",
-        "Publish Ropedia Xperience-10M derived artifacts",
-        ignore_patterns=["**/*.pt", "**/*.npz"],
-    )
-    upload_allowlisted_artifact_binaries(api, token, artifact_repo, hf_root / "artifacts")
-    for path_in_repo in STALE_ARTIFACT_REMOTE_FILES:
-        delete_remote_file_if_present(api, token, artifact_repo, "dataset", path_in_repo)
-    for path_in_repo in STALE_ARTIFACT_REMOTE_FOLDERS:
-        delete_remote_folder_if_present(api, token, artifact_repo, "dataset", path_in_repo)
-    upload_folder(
-        api,
-        token,
-        model_repo,
-        None,
-        hf_root / "model",
-        "Publish Ropedia Xperience-10M task baseline cards",
-        ignore_patterns=["**/*.pt", "**/*.npz"],
-    )
-    for path_in_repo in STALE_MODEL_REMOTE_FILES:
-        delete_remote_file_if_present(api, token, model_repo, "model", path_in_repo)
-    upload_folder(
-        api,
-        token,
-        model_repo,
-        None,
-        hf_root / "model",
-        "Publish Ropedia Xperience-10M model binaries",
-        allow_patterns=["**/*.npz", "**/*.pt"],
-    )
+    if not args.skip_space:
+        upload_folder(
+            api,
+            token,
+            space_repo,
+            "space",
+            hf_root / "space",
+            "Publish Ropedia Xperience-10M task-suite Space",
+        )
+        for path_in_repo in STALE_SPACE_REMOTE_FILES:
+            delete_remote_file_if_present(api, token, space_repo, "space", path_in_repo)
+    if not args.skip_artifacts:
+        upload_folder(
+            api,
+            token,
+            artifact_repo,
+            "dataset",
+            hf_root / "artifacts",
+            "Publish Ropedia Xperience-10M derived artifacts",
+            ignore_patterns=["**/*.pt", "**/*.npz"],
+        )
+        upload_allowlisted_artifact_binaries(api, token, artifact_repo, hf_root / "artifacts")
+        for path_in_repo in STALE_ARTIFACT_REMOTE_FILES:
+            delete_remote_file_if_present(api, token, artifact_repo, "dataset", path_in_repo)
+        for path_in_repo in STALE_ARTIFACT_REMOTE_FOLDERS:
+            delete_remote_folder_if_present(api, token, artifact_repo, "dataset", path_in_repo)
+    if not args.skip_model:
+        upload_folder(
+            api,
+            token,
+            model_repo,
+            None,
+            hf_root / "model",
+            "Publish Ropedia Xperience-10M task baseline cards",
+            ignore_patterns=["**/*.pt", "**/*.npz"],
+        )
+        for path_in_repo in STALE_MODEL_REMOTE_FILES:
+            delete_remote_file_if_present(api, token, model_repo, "model", path_in_repo)
+        upload_folder(
+            api,
+            token,
+            model_repo,
+            None,
+            hf_root / "model",
+            "Publish Ropedia Xperience-10M model binaries",
+            allow_patterns=["**/*.npz", "**/*.pt"],
+        )
 
     try:
         collection = api.create_collection(

scripts/validate_mirror_parity.py

@@ -39,6 +39,7 @@ DATA_FILES = [
     "publication_audit.json",
     "public_surface_qa.json",
     "qwen3_full_parameter_gates.json",
+    "qwen3_v5_v6_comparison.json",
     "quality_gates.json",
     "rendered_site_check.json",
     "reproducibility_matrix.json",
@@ -162,6 +163,7 @@ RESULT_FILES = [
     "omni_finetune/task_suite_enhancement_128_v1_20260608/task_bottlenecks.csv",
     "omni_finetune/OMNI_MODEL_COMPARISON.md",
     "omni_finetune/QWEN3_FULL_PARAMETER_GATES_20260609.md",
+    "omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md",
     "omni_finetune/xperience10m_qwen3_omni_128ep_fullparam_smoke_preemptible_8gpu_20260609/fullparam_feasibility_summary.json",
     "omni_finetune/xperience10m_qwen3_omni_128ep_fullparam_smoke_preemptible_8gpu_20260609/progress.jsonl",
     "omni_finetune/xperience10m_qwen3_omni_128ep_fullparam_smoke_preemptible_8gpu_20260609/training_metadata.json",

scripts/validate_scope_claims.py

@@ -171,67 +171,88 @@ def build_report() -> dict:
     expected_json_validity = float(verified_evaluation.get("json_validity_rate", 0.0))
 
     reading_notes = " ".join(project_packet.get("current_reading_notes", []))
+    has_verified_qwen_note = (
+        "diagnostic pilot is verified" in reading_notes
+        or "diagnostic branch is verified" in reading_notes
+        or "diagnostic result is verified" in reading_notes
+    )
     checks.append(
         check(
             "project_packet_records_verified_diagnostic_status",
-            "diagnostic pilot is verified" in reading_notes and "strong model quality is not yet shown" in reading_notes,
+            has_verified_qwen_note and "strong model quality is not yet shown" in reading_notes,
             "project packet describes the verified diagnostic pilot and quality boundary",
             ["docs/data/project_packet.json"],
         )
     )
 
     current_scope = summary_metrics.get("omni_relay", {}).get("current_scope", "")
+    has_verified_scope = (
+        "diagnostic pilot is verified" in current_scope
+        or "diagnostic branch is verified" in current_scope
+        or "diagnostic result is verified" in current_scope
+    )
     checks.append(
         check(
             "summary_metrics_preserves_verified_diagnostic_status",
-            "diagnostic pilot is verified" in current_scope and "98% target" in current_scope,
+            has_verified_scope and "98% target" in current_scope,
             current_scope,
             ["docs/data/summary_metrics.json"],
         )
     )
 
     split_counts = dataset_manifest.get("split_counts", {})
+    expected_split_counts = verified_result.get("split_policy", {}).get("exported_window_counts", {})
+    expected_dataset_samples = sum(expected_split_counts.values()) if expected_split_counts else None
     checks.append(
         check(
             "verified_package_dataset_has_expected_windows",
             dataset_manifest.get("num_episodes") == 119
-            and dataset_manifest.get("num_samples") == 3808
-            and split_counts == {"train": 2848, "val": 512, "test": 448},
+            and dataset_manifest.get("num_samples") == expected_dataset_samples
+            and split_counts == expected_split_counts,
             (
                 f"episodes={dataset_manifest.get('num_episodes')}, "
-                f"samples={dataset_manifest.get('num_samples')}, split_counts={split_counts}"
+                f"samples={dataset_manifest.get('num_samples')}, split_counts={split_counts}, "
+                f"expected_samples={expected_dataset_samples}, expected_split_counts={expected_split_counts}"
             ),
             [f"{package_path}/dataset/dataset_manifest.json"],
         )
     )
 
+    expected_train = verified_result.get("training", {}).get("num_train_samples")
+    expected_val = verified_result.get("training", {}).get("num_val_samples")
+    expected_processes = verified_result.get("training", {}).get("num_processes")
     checks.append(
         check(
             "verified_package_training_records_8_processes",
-            training_metadata.get("num_train_samples") == 2848
-            and training_metadata.get("num_val_samples") == 512
-            and training_metadata.get("num_processes") == 8,
+            training_metadata.get("num_train_samples") == expected_train
+            and training_metadata.get("num_val_samples") == expected_val
+            and training_metadata.get("num_processes") == expected_processes,
             (
                 f"train={training_metadata.get('num_train_samples')}, "
                 f"val={training_metadata.get('num_val_samples')}, "
-                f"processes={training_metadata.get('num_processes')}"
+                f"processes={training_metadata.get('num_processes')}, "
+                f"expected_train={expected_train}, expected_val={expected_val}, "
+                f"expected_processes={expected_processes}"
             ),
             [f"{package_path}/training/training_metadata.json"],
         )
     )
 
+    expected_eval_samples = verified_evaluation.get("num_samples")
+    expected_eval_episodes = verified_evaluation.get("held_out_episode_count")
     checks.append(
         check(
             "verified_package_eval_records_real_held_out_metrics",
-            eval_metrics.get("num_samples") == 448
+            eval_metrics.get("num_samples") == expected_eval_samples
             and eval_metrics.get("eval_split") == "test"
-            and eval_metrics.get("held_out_episode_count", eval_metrics.get("num_eval_episodes")) == 14
+            and eval_metrics.get("held_out_episode_count", eval_metrics.get("num_eval_episodes")) == expected_eval_episodes
             and abs(float(eval_metrics.get("json_validity_rate", 0.0)) - expected_json_validity) < 1e-12,
             (
                 f"samples={eval_metrics.get('num_samples')}, "
                 f"split={eval_metrics.get('eval_split')}, "
                 f"held_out={eval_metrics.get('held_out_episode_count', eval_metrics.get('num_eval_episodes'))}, "
-                f"json_validity={eval_metrics.get('json_validity_rate')}"
+                f"json_validity={eval_metrics.get('json_validity_rate')}, "
+                f"expected_samples={expected_eval_samples}, expected_held_out={expected_eval_episodes}"
             ),
             [f"{package_path}/eval/metrics.json"],
         )

scripts/verify_live_publication.py

@@ -345,9 +345,10 @@ MARKER_CHECKS = [
             "data/task_walkthroughs.json",
             "research_roadmap.html",
             "research_roadmap_interactive.json",
-            "Qwen3-Omni LoRA Final Diagnostic Result",
+            "Qwen3-Omni LoRA Latest Diagnostic Branch",
             "Action/Subtask Error-Analysis Pass",
-            "100.00%",
+            "99.90%",
+            "qwen3_v5_v6_comparison.json",
             "omni_model_comparison.json",
             "task_suite_enhancement_128.json",
             "128-Episode Task Suite Enhancement Pack",
@@ -378,9 +379,10 @@ MARKER_CHECKS = [
             "data/task_walkthroughs.json",
             "research_roadmap.html",
             "research_roadmap_interactive.json",
-            "Qwen3-Omni LoRA Final Diagnostic Result",
+            "Qwen3-Omni LoRA Latest Diagnostic Branch",
             "Action/Subtask Error-Analysis Pass",
-            "100.00%",
+            "99.90%",
+            "qwen3_v5_v6_comparison.json",
             "omni_model_comparison.json",
             "task_suite_enhancement_128.json",
             "128-Episode Task Suite Enhancement Pack",
@@ -403,7 +405,8 @@ MARKER_CHECKS = [
             "docs/data/omni_finetune_verified_result.json",
             "docs/data/omni_model_comparison.json",
             "docs/data/task_suite_enhancement_128.json",
-            "100.00% JSON validity",
+            "99.90% JSON validity",
+            "qwen3_v5_v6_comparison.json",
             "Cosmos3-Super",
             "ropedia-qwen3-omni-lora-128ep",
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",
@@ -452,7 +455,8 @@ MARKER_CHECKS = [
             "docs/data/omni_finetune_verified_result.json",
             "docs/data/omni_model_comparison.json",
             "docs/data/task_suite_enhancement_128.json",
-            "100.00%",
+            "99.90%",
+            "qwen3_v5_v6_comparison.json",
             "Cosmos3-Super",
             "ropedia-qwen3-omni-lora-128ep",
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",