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ARTIFACT_GUIDE.md

@@ -19,7 +19,7 @@ Xperience-native pretraining goal.
 | [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) | Describes the future full-corpus Xperience Embodied Foundation Model goal, including modules, objectives, staged scale-up, hardware ranges, and evaluation. |
 | [`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md) | Defines the task unit, chronological split, metrics, leakage controls, and current limitations. |
 | [`REPRODUCIBILITY.md`](REPRODUCIBILITY.md) | Defines public reproduction commands, expected outputs, and unreproducible boundaries. |
-| [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md) | Shows measured current-audio and raw log-mel replacement deltas across the 12 task contracts. |
+| [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md) | Shows measured current-audio and raw log-mel replacement deltas across the original task contracts. |
 | [`docs/single_episode_explorer.html`](docs/single_episode_explorer.html) | Gives a static window-level explorer for the public sample episode. |
 | [`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md) | Optional detail for readers who need official dataset and access-term context. |
 
@@ -74,11 +74,11 @@ Xperience-native pretraining goal.
 | [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json) | Machine-readable unified 20-task suite for the website and Hugging Face mirrors. |
 | [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json) | The original task contracts, chronological split, and minimal/neural metrics. |
 | [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/) | Matching PyTorch MLP heads for the same task contracts and feature windows. |
-| [`results/episode_task_suite/research_directions/`](results/episode_task_suite/research_directions/) | Mapping from the 12 tasks to the four Ropedia research directions. |
+| [`results/episode_task_suite/research_directions/`](results/episode_task_suite/research_directions/) | Mapping from the unified 20-task suite to the four Ropedia research directions. |
 | [`results/episode_task_suite/research_direction_extensions/`](results/episode_task_suite/research_direction_extensions/) | Four additional coded probes, one per research direction. |
 | [`results/episode_task_suite/tier2_task_suite/`](results/episode_task_suite/tier2_task_suite/) | Historical result path for tasks 13-20 in the unified 20-task suite. |
 | [`results/episode_task_suite/task_walkthroughs/`](results/episode_task_suite/task_walkthroughs/) | Human-readable research names and case studies explaining input, process modules, output, metric, limitation, and the website task-player data. |
-| [`results/audio_ablation/audio_ablation_metrics.csv`](results/audio_ablation/audio_ablation_metrics.csv) | All 72 measured audio rows: 12 tasks times six variants, including no-audio, audio-only, alternate-audio-only, representation replacement, and all-input variants. |
+| [`results/audio_ablation/audio_ablation_metrics.csv`](results/audio_ablation/audio_ablation_metrics.csv) | All measured audio rows for the original task contracts across six variants, including no-audio, audio-only, alternate-audio-only, representation replacement, and all-input variants. |
 | [`results/audio_ablation/audio_delta_summary.csv`](results/audio_ablation/audio_delta_summary.csv) | Compact per-task audio delta table for quick manual inspection. |
 | [`scripts/audio_ablation_and_raw_upgrade.py`](scripts/audio_ablation_and_raw_upgrade.py) | Regenerates audio contribution results from real task-suite artifacts plus the local public-sample MP4. |
 | [`scripts/validate_task_surface.py`](scripts/validate_task_surface.py) | Fails publication if public task cards drift back to raw artifact ids or lose their thumbnail/player wiring. |
@@ -122,7 +122,7 @@ research project.
 | [`results/omni_finetune/QWEN3_FULL_PARAMETER_GATES_20260609.md`](results/omni_finetune/QWEN3_FULL_PARAMETER_GATES_20260609.md) | Full-parameter Qwen3-Omni feasibility-gate summary: 1/8/32/64-step guarded 8-GPU runs passed, the opportunistic 128-step run was preempted for Qwen v5 handoff, and no full checkpoints or weights are published. |
 | [`docs/data/qwen3_full_parameter_gates.json`](docs/data/qwen3_full_parameter_gates.json) | Machine-readable full-parameter feasibility evidence and publication policy for the website and Hugging Face mirrors. |
 | [`scripts/omni/defer_qwen3_fullparam_after_verified_qwen.sh`](scripts/omni/defer_qwen3_fullparam_after_verified_qwen.sh) | Waits for a verified Qwen held-out package, then launches a bounded 128-step full-parameter feasibility pilot on the same multiscale v5 dataset with no checkpoints or weights saved. |
-| [`results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md`](results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md) | Same-split 128-episode simple and neural metadata baselines for the 12 task ids, aligned to the 96/16/16 Qwen3-Omni split and explicit about raw-feature-only tasks. |
+| [`docs/data/task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json) | Same-split 128-episode simple and neural baselines reported on the unified 20-task axes, aligned to the 96/16/16 Qwen3-Omni split with source/proxy notes. |
 | [`results/omni_finetune/multi_episode_128_task_baselines/summary_report.json`](results/omni_finetune/multi_episode_128_task_baselines/summary_report.json) | Machine-readable split counts, run configuration, simple metrics, neural metrics, and unsupported raw-feature markers for the aligned 128-episode baseline suite. |
 | [`scripts/omni/run_128_task_baselines.py`](scripts/omni/run_128_task_baselines.py) | Runner for the aligned 128-episode metadata/text baselines; it consumes the derived Qwen JSONL export locally but does not publish raw data, Qwen weights, or LoRA weights. |
 | [`scripts/omni/discover_xperience10m_sources.py`](scripts/omni/discover_xperience10m_sources.py) | Discovery gate for valid multi-episode Xperience-10M sources. |

EVIDENCE_CONTRACT.md

@@ -17,7 +17,7 @@ the dashboard as a basis for further work.
 | The task evaluation protocol is explicit and generated from committed metrics. | `EVALUATION_PROTOCOL.md`, `docs/data/evaluation_protocol.json`, `scripts/build_evaluation_protocol.py` | Verified protocol | Defines windows, split, per-task metrics, leakage controls, and current limitations |
 | The public sample modalities are inspectable without raw data redistribution. | `docs/data/modality_atlas.json`, `docs/assets/modalities/`, website modality atlas | Verified derived thumbnail atlas | Thumbnails are presentation assets, not a replacement for official raw data access |
 | Public task cards stay readable for non-expert readers. | `docs/data/task_surface_integrity.json`, `scripts/validate_task_surface.py`, website task cards/player | Task-surface report | Presentation layer only; it does not add model quality or new data |
-| The 12 task heads are implemented as scripts with saved metrics and predictions. | `scripts/episode_task_suite.py`, `results/episode_task_suite/*/metrics.json`, `results/episode_task_suite/*/predictions.*` | Verified for all 12 task definitions | Chronological single-episode split, not cross-episode generalization |
+| The unified 20-task suite is implemented with saved metrics, predictions, and source-linked result rows. | `TASK_SUITE_20.md`, `docs/data/task_suite_20.json`, `docs/data/task_method_20_result_matrix.json`, `TASK_METHOD_20_SOURCE_AUDIT.md` | Verified for all 20 task definitions and 180 method-task result rows | Chronological single-episode split for public-sample rows; selected 128-episode rows use the documented 96/16/16 split |
 | Minimal and neural heads use the same task contracts. | `scripts/neural_task_models.py`, `results/episode_task_suite/neural_mlp/`, `docs/assets/task_architectures.png` | Verified for 12 minimal heads and 12 neural MLP heads | Small heads only; not a foundation model |
 | Four Ropedia research directions are mapped honestly as direct, proxy, or diagnostic evidence. | `results/episode_task_suite/research_directions/research_direction_taxonomy.json`, `docs/data/research_directions.json` | Verified taxonomy | Some directions remain proxy-only |
 | Four extra direction probes are coded and evaluated. | `results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json`, `docs/data/research_direction_extensions.json` | Verified single-episode probes | Not full human modeling, neural rendering, intent modeling, or world modeling solutions |

FOUNDATION_MODEL_PLAN.md

@@ -54,7 +54,7 @@ The immediate pilot is about proving the full data path:
 
 Qwen3-Omni is the most direct first target because the existing scripts already
 prepare video/audio/language prompts and adapter inputs. It is also suitable for
-the 12 current task contracts, which mostly produce labels, structured JSON, or
+the unified 20 current task contracts, which mostly produce labels, structured JSON, or
 short task answers.
 
 The executable Qwen branch and future branch contracts are now represented as
@@ -145,7 +145,7 @@ requires multi-episode diversity. The first useful policy experiment should be a
 
 ## Evaluation Additions
 
-The foundation-model stage should add metrics beyond the current 12-task suite:
+The foundation-model stage should add metrics beyond the current 20-task suite:
 
 | Evaluation target | Metric family | Applies to |
 | --- | --- | --- |

PROJECT_README.md

@@ -252,7 +252,7 @@ Public release checks are exposed as JSON for mirrors and dashboards:
         <ul>
           <li>The selected-episode Qwen3-Omni LoRA v6 diagnostic package is verified on the 96/16/16 split with 34,269 exported windows and 4,032 held-out test predictions.</li>
           <li>v6 improves action macro-F1/contact accuracy versus v5; v5 remains a pinned prior-release row where it is stronger on other metrics.</li>
-          <li>Same-split simple/NN metadata baselines cover the 12 JSON-supported task ids, while the raw-feature simple/NN run covers 20/20 task axes with compact-proxy notes for tasks 15 and 19.</li>
+          <li>Same-split simple/NN metadata and raw-feature baselines are now reported on the unified 20-task axes, with compact-proxy notes retained where a target is derived from public-safe processed artifacts.</li>
           <li>The Qwen result proves the multi-episode export/train/eval/package loop and meets the strict-JSON target, but weak action/subtask metrics make it a baseline for error analysis rather than a strong model.</li>
           <li>Cosmos3 has three verified diagnostics: Nano future-window compatibility, Super base-weight Reasoner evaluation, and Super forward-dynamics LoRA fine-tuning over camera-pose proxy targets.</li>
         </ul>
@@ -580,7 +580,7 @@ terms. See [`LICENSE`](LICENSE) and [`DATA_NOTICE.md`](DATA_NOTICE.md).
 ![Ropedia Xperience-10M task-suite infographic](docs/assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl)
 
 The infographic uses a custom text-free research background and puts the shared
-processing contract plus all 12 task families before the modality atlas.
+processing contract plus all 20 unified task families before the modality atlas.
 Public-sample modality thumbnails remain enlarged below the task map. The task
 names, input/output summaries, and metrics are overlaid from
 [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json)
@@ -957,7 +957,7 @@ Current status in this repo:
 - current_quality_target: strict-label JSON validity 100.00%, meeting the 98% target; action/subtask quality remains weak
 - qwen3_lora_adapter_repo: https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep
 - cosmos3_super_lora_adapter_repo: https://huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep
-- 128_aligned_baselines: 12 task ids, 8 simple metadata/text baselines, 6 neural metadata/text baselines
+- 128_aligned_baselines: unified 20-task axes for simple and neural baselines, including metadata/text rows and public-safe compact-proxy rows where raw-feature targets are required
 - cosmos3_nano_branch: verified Cosmos3-Nano future-window compatibility package, 378 held-out future-window predictions from 14 test episodes
 - cosmos3_super_branch: verified Cosmos3-Super Reasoner base-weight JSON-task evaluation, 448 held-out predictions from 14 test episodes; JSON validity 51.12%, action macro-F1 0.0008, contact accuracy 32.14%, transition accuracy 36.83%
 - cosmos3_super_forward_dynamics_lora: verified 8-GPU FSDP LoRA branch over camera-pose proxy targets; 2,848 train rows, 512 val rows, 448 test rows, 26.2M adapter parameters, val MSE 4.0082, test MSE 3.6853; public package excludes safetensors
@@ -1412,7 +1412,7 @@ Compact map:
 | Temporal Order Verification | Tell whether reaching then pouring was reversed. | adjacent window pair -> pair combiner + binary classifier -> correct/reversed |
 | Multimodal Synchronization Detection | Catch motion paired with visual/depth features shifted in time. | motion side + visual side -> aligned/shifted pair builder + classifier -> aligned/shifted |
 
-## Minimal 12-Task Architectures
+## Core Architecture Families in the 20-Task Suite
 
 These are deliberately minimal baselines. They are useful because every
 input/output contract is explicit, not because they are strong embodied-AI
@@ -1442,7 +1442,7 @@ PyTorch MLP classifiers or regressors. Its outputs live under
 and the rollup is stored in the `neural_tasks` section of
 [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json).
 
-The task-specific heads are:
+The original task-specific heads are:
 
 | Task | Input | Minimal head | Output |
 | --- | --- | --- | --- |
@@ -1473,8 +1473,8 @@ The task-specific heads are:
 | Neural MLP hand forecast | 0.1079 MPJPE | n/a | Same features/split, nonlinear regression head |
 | Neural MLP temporal order | 0.8520 F1 | 0.8578 | Strong improvement on adjacent-window ordering |
 | Neural MLP misalignment | 0.7153 F1 | 0.7009 | Detects shifted motion/visual/audio pairs better than the linear head |
-| Audio ablation | +0.0418 mean delta | n/a | Current audio variant improves the primary metric on 6 of 12 task contracts |
-| Alternate audio representation | +0.0936 mean delta | n/a | Alternate audio-window representation improves over the baseline audio variant on 6 of 12 task contracts |
+| Audio ablation | +0.0418 mean delta | n/a | Current audio variant improves the primary metric on 6 of the original task contracts |
+| Alternate audio representation | +0.0936 mean delta | n/a | Alternate audio-window representation improves over the baseline audio variant on 6 of the original task contracts |
 
 ## Audio Contribution Study
 
@@ -1489,9 +1489,9 @@ The measured single-episode result is task-specific:
 
 | Readout | Value |
 | --- | ---: |
-| Tasks where current audio improves the primary metric | 6 / 12 |
+| Tasks where current audio improves the primary metric | 6 / 12 original contracts |
 | Mean current-audio delta | +0.0418 |
-| Tasks where alternate audio representation improves over baseline audio | 6 / 12 |
+| Tasks where alternate audio representation improves over baseline audio | 6 / 12 original contracts |
 | Mean alternate-representation delta vs baseline audio | +0.0936 |
 
 Full files:
@@ -1504,7 +1504,7 @@ Full files:
 
 ## Neural MLP Results
 
-The neural baseline was run locally with `--include-neural` for all 12 tasks
+The neural baseline was run locally with `--include-neural` for the original core task contracts
 using 80 epochs, hidden size 128, batch size 128, and CPU execution. It is not a
 foundation model result; it is a controlled nonlinear-head comparison over the
 same 8,546-dimensional multimodal representation.
@@ -1553,7 +1553,7 @@ episodes; they are not reported as multi-episode benchmark results.
 
 I re-ran the full pipeline from the local raw public sample into a temporary
 local workspace and compared regenerated metrics with the committed
-artifacts. The baseline metrics, 12 task metrics, feature manifest, and
+artifacts. The baseline metrics, original task metrics, feature manifest, and
 available modality manifest matched exactly after float normalization.
 
 See [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) for the

PROJECT_STATUS.md

@@ -23,7 +23,7 @@ The current no-new-episode enhancement layer records how to push the selected
 | Unified 20-task suite | Verified | `TASK_SUITE_20.md`, `docs/data/task_suite_20.json`, `results/episode_task_suite/`, `results/episode_task_suite/tier2_task_suite/` | All 20 task contracts have committed minimal metrics; tasks 13-20 reuse the same 20-frame windows, 5-frame stride, chronological split, and minimal/neural head pattern. The `tier2_task_suite` path is historical and now stores tasks 13-20, not a separate public tier. |
 | 180-result method matrix | Verified complete | `docs/data/task_method_20_result_matrix.json`, `TASK_METHOD_20_RESULT_MATRIX.md`, `docs/data/task_method_20_gap_audit.json`, `docs/assets/charts/unified_task_model_radar.svg` | The public comparison matrix now has 9 methods x 20 tasks = 180/180 scored method-task records. Six rows are explicitly marked as compact-proxy scores where the public 128-episode export lacks the direct raw target. |
 | Neural heads | Verified | `scripts/neural_task_models.py`, `results/episode_task_suite/neural_mlp/` | Each task also has a compact PyTorch MLP run over the same feature tensor and chronological split. |
-| Audio contribution study | Verified | `scripts/audio_ablation_and_raw_upgrade.py`, `results/audio_ablation/`, `docs/data/audio_ablation_summary.json` | Audio variants are compared across all 12 task contracts; audio improves the primary metric on 6 of 12 tasks, and a 588-d audio-window representation improves over the baseline audio variant on 6 of 12 tasks. |
+| Audio contribution study | Verified | `scripts/audio_ablation_and_raw_upgrade.py`, `results/audio_ablation/`, `docs/data/audio_ablation_summary.json` | Audio variants are compared across the original task contracts; audio improves the primary metric on 6 of those contracts, and a 588-d audio-window representation improves over the baseline audio variant on 6 of those contracts. |
 | Research takeaways | Verified | `RESEARCH_TAKEAWAYS.md`, `docs/data/research_takeaways.json`, `scripts/build_research_takeaways.py` | The main result interpretation is generated from committed metrics: chronological class shift, neural gains on dynamics/order/alignment, open retrieval/reconstruction problems, and the need for held-out episodes. |
 | Research roadmap | Current | `RESEARCH_ROADMAP.md`, `docs/data/research_roadmap.json` | The roadmap connects public-sample task development to the final verified Qwen3-Omni diagnostic result, same-split baseline alignment, action/subtask error analysis, robustness runs, world/policy branches, and the future Xperience-native pretraining goal. |
 | 128-episode task-suite enhancement pack | Current no-new-episode plan | `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` | The current 3,808-window selected split can be stressed without more episodes by exporting denser and multiscale windows. The recommended next export is `multiscale_20s10_40s20_80s40`, estimated at 106,095 windows from the observed frame spans; the pack also defines hierarchical action/subtask targets, raw-feature shard priorities for unsupported tasks, and Qwen/Cosmos follow-up run cards. |
@@ -101,7 +101,7 @@ The current no-new-episode enhancement layer records how to push the selected
 - The current reconstruction task reconstructs feature vectors, not pixel
   depth, meshes, NeRF outputs, or Gaussian splats.
 - Audio is part of the current 8,546-dimensional baseline feature vector.
-- Audio contribution is evaluated across all 12 task contracts in
+- Audio contribution is evaluated across the original task contracts in
   `results/audio_ablation/`.
 - Foundation-model selection is now explicit: Qwen3-Omni is the immediate
   trainable pilot, Cosmos 3 is the first world-model branch, and Cosmos3-Super

README.md

@@ -274,7 +274,7 @@ Public release checks are exposed as JSON for mirrors and dashboards:
         <ul>
           <li>The selected-episode Qwen3-Omni LoRA v6 diagnostic package is verified on the 96/16/16 split with 34,269 exported windows and 4,032 held-out test predictions.</li>
           <li>v6 improves action macro-F1/contact accuracy versus v5; v5 remains a pinned prior-release row where it is stronger on other metrics.</li>
-          <li>Same-split simple/NN metadata baselines cover the 12 JSON-supported task ids, while the raw-feature simple/NN run covers 20/20 task axes with compact-proxy notes for tasks 15 and 19.</li>
+          <li>Same-split simple/NN metadata and raw-feature baselines are now reported on the unified 20-task axes, with compact-proxy notes retained where a target is derived from public-safe processed artifacts.</li>
           <li>The Qwen result proves the multi-episode export/train/eval/package loop and meets the strict-JSON target, but weak action/subtask metrics make it a baseline for error analysis rather than a strong model.</li>
           <li>Cosmos3 has three verified diagnostics: Nano future-window compatibility, Super base-weight Reasoner evaluation, and Super forward-dynamics LoRA fine-tuning over camera-pose proxy targets.</li>
         </ul>
@@ -602,7 +602,7 @@ terms. See [`LICENSE`](LICENSE) and [`DATA_NOTICE.md`](DATA_NOTICE.md).
 ![Ropedia Xperience-10M task-suite infographic](docs/assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl)
 
 The infographic uses a custom text-free research background and puts the shared
-processing contract plus all 12 task families before the modality atlas.
+processing contract plus all 20 unified task families before the modality atlas.
 Public-sample modality thumbnails remain enlarged below the task map. The task
 names, input/output summaries, and metrics are overlaid from
 [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json)
@@ -979,7 +979,7 @@ Current status in this repo:
 - current_quality_target: strict-label JSON validity 100.00%, meeting the 98% target; action/subtask quality remains weak
 - qwen3_lora_adapter_repo: https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep
 - cosmos3_super_lora_adapter_repo: https://huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep
-- 128_aligned_baselines: 12 task ids, 8 simple metadata/text baselines, 6 neural metadata/text baselines
+- 128_aligned_baselines: unified 20-task axes for simple and neural baselines, including metadata/text rows and public-safe compact-proxy rows where raw-feature targets are required
 - cosmos3_nano_branch: verified Cosmos3-Nano future-window compatibility package, 378 held-out future-window predictions from 14 test episodes
 - cosmos3_super_branch: verified Cosmos3-Super Reasoner base-weight JSON-task evaluation, 448 held-out predictions from 14 test episodes; JSON validity 51.12%, action macro-F1 0.0008, contact accuracy 32.14%, transition accuracy 36.83%
 - cosmos3_super_forward_dynamics_lora: verified 8-GPU FSDP LoRA branch over camera-pose proxy targets; 2,848 train rows, 512 val rows, 448 test rows, 26.2M adapter parameters, val MSE 4.0082, test MSE 3.6853; public package excludes safetensors
@@ -1434,7 +1434,7 @@ Compact map:
 | Temporal Order Verification | Tell whether reaching then pouring was reversed. | adjacent window pair -> pair combiner + binary classifier -> correct/reversed |
 | Multimodal Synchronization Detection | Catch motion paired with visual/depth features shifted in time. | motion side + visual side -> aligned/shifted pair builder + classifier -> aligned/shifted |
 
-## Minimal 12-Task Architectures
+## Core Architecture Families in the 20-Task Suite
 
 These are deliberately minimal baselines. They are useful because every
 input/output contract is explicit, not because they are strong embodied-AI
@@ -1464,7 +1464,7 @@ PyTorch MLP classifiers or regressors. Its outputs live under
 and the rollup is stored in the `neural_tasks` section of
 [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json).
 
-The task-specific heads are:
+The original task-specific heads are:
 
 | Task | Input | Minimal head | Output |
 | --- | --- | --- | --- |
@@ -1495,8 +1495,8 @@ The task-specific heads are:
 | Neural MLP hand forecast | 0.1079 MPJPE | n/a | Same features/split, nonlinear regression head |
 | Neural MLP temporal order | 0.8520 F1 | 0.8578 | Strong improvement on adjacent-window ordering |
 | Neural MLP misalignment | 0.7153 F1 | 0.7009 | Detects shifted motion/visual/audio pairs better than the linear head |
-| Audio ablation | +0.0418 mean delta | n/a | Current audio variant improves the primary metric on 6 of 12 task contracts |
-| Alternate audio representation | +0.0936 mean delta | n/a | Alternate audio-window representation improves over the baseline audio variant on 6 of 12 task contracts |
+| Audio ablation | +0.0418 mean delta | n/a | Current audio variant improves the primary metric on 6 of the original task contracts |
+| Alternate audio representation | +0.0936 mean delta | n/a | Alternate audio-window representation improves over the baseline audio variant on 6 of the original task contracts |
 
 ## Audio Contribution Study
 
@@ -1511,9 +1511,9 @@ The measured single-episode result is task-specific:
 
 | Readout | Value |
 | --- | ---: |
-| Tasks where current audio improves the primary metric | 6 / 12 |
+| Tasks where current audio improves the primary metric | 6 / 12 original contracts |
 | Mean current-audio delta | +0.0418 |
-| Tasks where alternate audio representation improves over baseline audio | 6 / 12 |
+| Tasks where alternate audio representation improves over baseline audio | 6 / 12 original contracts |
 | Mean alternate-representation delta vs baseline audio | +0.0936 |
 
 Full files:
@@ -1526,7 +1526,7 @@ Full files:
 
 ## Neural MLP Results
 
-The neural baseline was run locally with `--include-neural` for all 12 tasks
+The neural baseline was run locally with `--include-neural` for the original core task contracts
 using 80 epochs, hidden size 128, batch size 128, and CPU execution. It is not a
 foundation model result; it is a controlled nonlinear-head comparison over the
 same 8,546-dimensional multimodal representation.
@@ -1575,7 +1575,7 @@ episodes; they are not reported as multi-episode benchmark results.
 
 I re-ran the full pipeline from the local raw public sample into a temporary
 local workspace and compared regenerated metrics with the committed
-artifacts. The baseline metrics, 12 task metrics, feature manifest, and
+artifacts. The baseline metrics, original task metrics, feature manifest, and
 available modality manifest matched exactly after float normalization.
 
 See [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) for the

RENDERED_SITE_CHECK.md

@@ -20,7 +20,8 @@ Current status: **pass**
 | First Meaningful Content | `pass` | The first meaningful heading should identify the research task lab. |
 | Responsive Viewport Recorded | `pass` | The browser run should record a narrow responsive viewport large enough to expose the mobile/tablet layout. |
 | Tabbed Research Navigation | `pass` | The rendered top-level tab system should switch to the Data tab for the walkthrough deep link. |
-| Task And Modality Cards Render | `pass` | The rendered task and modality sections should expose all 12 task cards and seven modality cards. |
+| Task And Modality Cards Render | `pass` | The rendered walkthrough should expose the original core task cards and seven modality cards. |
+| Unified 20 Task Matrix Available | `pass` | The rendered site data bundle should include the unified 20-task / 180-result matrix. |
 | Walkthrough Deep Link | `pass` | The walkthrough deep link should reveal the walkthrough player, all task selectors, and four chapter controls. |
 | Walkthrough Interaction | `pass` | Clicking Next and the Process chapter should update the active task, chapter, counter, and frame label. |
 | Rendered Check Resource Link | `pass` | The rendered page should expose the rendered website check JSON from the resource section. |

REPRODUCIBILITY.md

@@ -179,7 +179,7 @@ Verified staged-GPU smoke evidence from 2026-06-14:
 
 The last full metric reproduction run was completed on **2026-05-30
 Asia/Singapore** from a fresh output directory outside the repo. It rebuilt the
-minimal baselines, all-modality baselines, and the original 12 task artifacts
+minimal baselines, all-modality baselines, and the original core task artifacts
 from the local public sample. The regenerated metrics matched the committed
 artifacts after float normalization; the current public framing now indexes
 those artifacts together with tasks 13-20 as one 20-task suite.

RESEARCH_ROADMAP.md

@@ -9,10 +9,10 @@ should exist before the stage is treated as complete.
 
 | Stage | Status | Entry condition | Research deliverables | Completion evidence |
 | --- | --- | --- | --- | --- |
-| 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` |
+| Public-Sample Task Lab | Implemented | One public Xperience-10M sample episode is available. | 1,161 aligned windows, a unified 20-task suite, minimal heads, neural MLP heads, modality atlas, task walkthroughs, and derived figures. | `PROJECT_STATUS.md`, `EVALUATION_PROTOCOL.md`, `RESEARCH_TAKEAWAYS.md`, `docs/data/task_suite_20.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 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 Same-Split Simple/NN Baselines | Verified companion result | Derived Qwen JSONL export for the selected 96/16/16 split plus compact raw/proxy artifacts. | Unified 20-task axes, simple metadata/text baselines, neural MLP baselines where JSON labels support them, and explicit proxy/source notes for tasks requiring raw feature targets. | `docs/data/task_method_20_result_matrix.json`, `TASK_METHOD_20_SOURCE_AUDIT.md`, `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. |
 | Foundation-Model Selection Matrix | Current | The selected pilot episodes are prepared, or a 3-8 episode dry run is available for preprocessing checks. | Backbone registry, Cosmos 3 world-model branch plan, Qwen3-Omni baseline plan, OpenVLA/openpi/GR00T policy candidates, and model-specific evaluation additions. | `FOUNDATION_MODEL_PLAN.md`, `docs/data/foundation_model_plan.json`, `research_roadmap_interactive.json` |

RESEARCH_TAKEAWAYS.md

@@ -31,7 +31,7 @@ Current scope: This benchmark defines the task contract; cross-episode generaliz
 
 ### Chronological splits expose action-class shift
 
-Earlier all-feature action classifiers reach high macro-F1 on their local split, but the 12-task chronological action/subtask heads are much harder because later held-out windows include unseen labels.
+Earlier all-feature action classifiers reach high macro-F1 on their local split, but the core chronological action/subtask heads are much harder because later held-out windows include unseen labels.
 
 | Metric | Value |
 | --- | ---: |
@@ -80,7 +80,7 @@ Current scope: The current reconstruction task predicts feature vectors; depth,
 
 ### Audio helps some tasks and hurts others on the public sample
 
-Audio improves the primary metric on 6 of 12 tasks, while raw log-mel replacement improves over the current handcrafted block on 6 of 12 tasks. The largest current-audio gain appears in feature reconstruction, not in action classification.
+Audio improves the primary metric on 6 of the original task contracts, while raw log-mel replacement improves over the current handcrafted block on 6 of those contracts. The largest current-audio gain appears in feature reconstruction, not in action classification.
 
 | Metric | Value |
 | --- | ---: |

XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md

@@ -88,7 +88,7 @@ Recommended scale:
 - frozen or lightly trainable video/audio encoders at first,
 - 0.3B-1B temporal fusion model,
 - all available sensor modalities represented as tokens,
-- evaluation on the existing 12-task suite plus future-state/retrieval probes.
+- evaluation on the unified 20-task suite, the 180-result matrix, and future-state/retrieval probes.
 
 ### Stage 2: 10K Episode Domain Model
 
@@ -159,7 +159,7 @@ The model should not be judged only by training loss. Evaluation should include:
 
 The current public project is the harness for this future model:
 
-- the 12-task suite defines concrete input/output contracts,
+- the unified 20-task suite defines concrete input/output contracts,
 - minimal and neural baselines provide initial supervised targets,
 - audio/modality diagnostics show which signals contribute,
 - Qwen3-Omni LoRA provides the first trainable multi-episode adapter path,

data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
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-  "generated_at_utc": "2026-06-20T20:49:41+00:00",
+  "generated_at_utc": "2026-06-20T21:45:55+00:00",
   "hf_root": "hf_publish",
   "summary": {
-    "group_count": 1236,
+    "group_count": 1248,
     "failure_count": 0,
     "failures_by_surface": {}
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@@ -139,44 +139,44 @@
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data/project_status.json

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-      "readout": "Audio variants improve the primary metric on 6 of 12 task contracts in this single-episode setting."
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     "The Cosmos3-Nano future-window branch is verified as a compatibility adapter result, Cosmos3-Super Reasoner is verified as a base-weight evaluation, and Cosmos3-Super Forward-Dynamics LoRA is verified as the first fine-tuned Super adapter branch. Cosmos3-Super adapter weights belong in cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep; verified_public packages exclude safetensors.",
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     "Future model branches should be added through the backbone registry and verified package contract, not as one-off result folders with incompatible metrics or publication rules.",
     "The Xperience Embodied Foundation Model is a future native-pretraining goal, not a completed model or current benchmark."

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@@ -1005,8 +1005,8 @@
       "surface": "repo_hf",
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@@ -1149,7 +1149,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
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       "bytes": 8640,
-      "sha256": "445196830bb913bfa075ae4174e7b1f5b64f623cf13a2afde7513add9dbefc21"
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@@ -1216,7 +1216,7 @@
       "volatile": true,
       "shows": "Confirms the public original-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
       "exists": true,
-      "bytes": 45779,
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@@ -1228,7 +1228,7 @@
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@@ -1240,7 +1240,7 @@
       "volatile": true,
       "shows": "Machine-readable browser-level website check for the public static site.",
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@@ -1251,8 +1251,8 @@
       "surface": "repo_hf",
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-      "sha256": "670f31c75e9d641ef20f8ad761a63104e96fd631372c965c7f7746a692b4d514"
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@@ -1262,8 +1262,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the task-surface integrity report and fails if task cards expose raw artifact ids or lose the interactive player wiring.",
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@@ -1274,7 +1274,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
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-      "bytes": 181788,
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       "hash_policy": "existence_and_size_only"
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@@ -1296,8 +1296,8 @@
       "surface": "repo_hf",
       "shows": "Defines public reproduction commands, expected outputs, and non-reproducible scale-up boundaries.",
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-      "bytes": 10054,
-      "sha256": "d12c020fd00c6a7b907300c9c5f20d613a0f033e32f7a62cbed3f8dfbbe95216"
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@@ -1342,7 +1342,7 @@
       "volatile": true,
       "shows": "Separates setup paths from completed held-out-episode results.",
       "exists": true,
-      "bytes": 21630,
+      "bytes": 21313,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1354,7 +1354,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 1395239,
+      "bytes": 1407249,
       "hash_policy": "existence_and_size_only"
     },
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@@ -1399,8 +1399,8 @@
       "surface": "website_hf",
       "shows": "Mirrors task metrics for the static dashboard.",
       "exists": true,
-      "bytes": 27807,
-      "sha256": "3a6a5ee59562ae189844cb4ba26d6e261c2f73a8e54bb6e2fbc3e307c2d123fa"
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       "id": "feature_manifest",
@@ -1454,8 +1454,8 @@
       "surface": "repo_hf",
       "shows": "Maps the original tasks to the four Ropedia research directions as direct/proxy/diagnostic.",
       "exists": true,
-      "bytes": 19204,
-      "sha256": "59bece1a151d8475fde50396fd2e70ed4abcfec33f10e400ef165148fd6e7dde"
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       "id": "research_direction_extensions",
@@ -1520,8 +1520,8 @@
       "surface": "repo_hf",
       "shows": "Explains every task with case study, input, process modules, output, and limitation.",
       "exists": true,
-      "bytes": 15436,
-      "sha256": "de8607242ca8c5c1c632f7175eeffd0c009536eb050614bfae2ff561e14cd92d"
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     },
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       "id": "task_suite_infographic",
@@ -1531,8 +1531,8 @@
       "surface": "website_hf",
       "shows": "Presents the task suite and sample modality thumbnails with metrics generated from committed files.",
       "exists": true,
-      "bytes": 1591194,
-      "sha256": "95ab73e01cfba86538b63247869fae4091934ddedf9e22523ab4cead9c59086d"
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     {
       "id": "modality_atlas",
@@ -1564,8 +1564,8 @@
       "surface": "website_hf",
       "shows": "Shows the raw-episode to artifact pipeline with verified labels.",
       "exists": true,
-      "bytes": 704575,
-      "sha256": "c90723cc4b1bf5490269af2df594849030ae8d4cc8176e1d1eab96fabf9412f9"
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     {
       "id": "architecture_figure",
@@ -1575,8 +1575,8 @@
       "surface": "website_hf",
       "shows": "Shows the shared feature pipeline and minimal/neural head families.",
       "exists": true,
-      "bytes": 774391,
-      "sha256": "f08b03bc21e194efe382347d74cf89cd6ac65dede51889971dbfc2fb9d1de3c2"
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     },
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       "id": "qwen_data_access_status",
@@ -1663,8 +1663,8 @@
       "surface": "repo_hf",
       "shows": "Reader-facing comparison of the single-episode task suite, 128-episode aligned baselines, Qwen3-Omni packages, and Cosmos3 future-window branch.",
       "exists": true,
-      "bytes": 15999,
-      "sha256": "dd65ae9077acbce91870b182d701db367a9c79eb287aeee2a1e165ec4915e5f3"
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+      "sha256": "130578a51a77e2be0230da1288beee3528cff2c7a39830c91f0509682da4b404"
     },
     {
       "id": "omni_model_comparison_json",
@@ -1674,8 +1674,8 @@
       "surface": "repo_hf",
       "shows": "Machine-readable comparison of the current result versions, per-task aligned baselines, verified Qwen3 packages, and Cosmos3 package.",
       "exists": true,
-      "bytes": 81866,
-      "sha256": "dd7a599117defcc1fd783c3134b6b3fc92f2ec2190ea517624cb215b931bd87a"
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       "id": "cosmos3_nano_verified_summary",
@@ -1707,8 +1707,8 @@
       "surface": "repo_hf",
       "shows": "Makes the project externally citable.",
       "exists": true,
-      "bytes": 1431,
-      "sha256": "e84efc4c45ea168e1eb0f3db938077e04a9521c3abb8dc0cc1003e22958e0222"
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+      "sha256": "ae8a653734bd6063d8ccff324be361baa33d9bdadfa53cd9dc124cc15e561d46"
     },
     {
       "id": "license",

docs/data/omni_model_comparison.json

@@ -1,19 +1,19 @@
 {
   "title": "Ropedia Xperience-10M Current Result Versions and Model Groups",
-  "generated_at_utc": "2026-06-18T12:52:47+00:00",
+  "generated_at_utc": "2026-06-20T21:27:21+00:00",
   "status": "pass",
   "version_count": 3,
   "model_group_count": 5,
   "comparison_rule": "Compare only rows with the same scope and target. Single-episode raw-feature metrics, 128-episode metadata baselines, Qwen3 structured JSON metrics, and the two Cosmos3 targets answer different questions: Nano future-window retrieval versus Super structured JSON Reasoner evaluation.",
   "version_reading_notes": [
-    "Version 1 is the public-sample 12-task harness with minimal and neural heads.",
+    "Version 1 is the public-sample 20-task surface: original core heads, tasks 13-20, and the 180-row method-task matrix.",
     "Version 2 is the selected 128-episode same-split simple/NN baseline alignment.",
     "Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, Cosmos3-Super Reasoner is a base-weight JSON-task evaluation, and Cosmos3-Super Forward-Dynamics LoRA is the first Super fine-tuned adapter branch."
   ],
   "versions": [
     {
       "id": "v1_single_episode_public_sample",
-      "title": "Single-Episode Public-Sample Task Suite",
+      "title": "Single-Episode Public-Sample 20-Task Suite",
       "status": "verified",
       "scope": "one public Xperience-10M sample episode",
       "source": "results/episode_task_suite/summary_report.json",
@@ -23,7 +23,9 @@
         "windows": 1161,
         "frames": 5821,
         "feature_dim": 8546,
-        "task_count": 12,
+        "core_task_count": 12,
+        "unified_task_count": 20,
+        "method_task_record_count": 180,
         "neural_task_count": 12
       },
       "models": [
@@ -152,7 +154,7 @@
           "neural_primary_score": 0.5862068965517241
         }
       ],
-      "interpretation": "This layer verifies the 12 task contracts and raw multimodal feature pipeline on the public sample. It is not a cross-episode benchmark."
+      "interpretation": "This layer verifies the original core task contracts, raw multimodal feature pipeline, and unified 20-task public result surface. It is not a cross-episode benchmark."
     },
     {
       "id": "v2_multi_episode_128_aligned_metadata_baselines",
@@ -824,7 +826,7 @@
       "one_episode_runs": [
         {
           "id": "task_heads_single_episode_public_sample",
-          "title": "Single-Episode Public-Sample Task Suite",
+          "title": "Single-Episode Public-Sample 20-Task Suite",
           "scope": "one public Xperience-10M sample episode",
           "status": "verified",
           "source": "results/episode_task_suite/summary_report.json",
@@ -834,7 +836,9 @@
             "windows": 1161,
             "frames": 5821,
             "feature_dim": 8546,
-            "task_count": 12,
+            "core_task_count": 12,
+            "unified_task_count": 20,
+            "method_task_record_count": 180,
             "neural_task_count": 12
           },
           "weights": "baseline model files in the baseline model repo; no foundation-model weights",

docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T20:48:08+00:00",
+  "generated_at_utc": "2026-06-20T21:45:18+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,37 +18,37 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T20:41:45+00:00"
+          "generated_at_utc": "2026-06-20T21:41:51+00:00"
         },
         "rendered_site_check": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-03T01:38:26+00:00"
+          "generated_at_utc": "2026-06-20T21:34:06+00:00"
         },
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:55:17+00:00"
+          "generated_at_utc": "2026-06-20T21:41:50+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:55:18+00:00"
+          "generated_at_utc": "2026-06-20T21:42:21+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:55:26+00:00"
+          "generated_at_utc": "2026-06-20T21:41:53+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T20:42:41+00:00"
+          "generated_at_utc": "2026-06-20T21:44:46+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T20:47:51+00:00"
+          "generated_at_utc": "2026-06-20T21:43:29+00:00"
         }
       },
       "failures": {}
@@ -97,8 +97,8 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 19,
         "Xperience-10M": 165,
-        "20-task": 61,
-        "Qwen3-Omni": 154,
+        "20-task": 69,
+        "Qwen3-Omni": 153,
         "128-episode pilot": 1
       }
     },

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T20:48:18+00:00",
+  "generated_at_utc": "2026-06-20T21:45:18+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

docs/data/rendered_site_check.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Rendered Website Check",
   "status": "pass",
-  "generated_at_utc": "2026-06-03T01:38:26+00:00",
+  "generated_at_utc": "2026-06-20T21:34:06+00:00",
   "flow": "load current docs server -> open #walkthroughs deep link -> click Next -> click Process story chapter",
   "checked_at_local": "2026-06-03T01:32:46.099Z",
   "screenshot_path": "/tmp/xperience_site_walkthrough_fresh.png",
@@ -71,10 +71,18 @@
     {
       "name": "task_and_modality_cards_render",
       "status": "pass",
-      "reason": "The rendered task and modality sections should expose all 12 task cards and seven modality cards.",
+      "reason": "The rendered walkthrough should expose the original core task cards and seven modality cards.",
       "task_card_count": 12,
       "atlas_card_count": 7
     },
+    {
+      "name": "unified_20_task_matrix_available",
+      "status": "pass",
+      "reason": "The rendered site data bundle should include the unified 20-task / 180-result matrix.",
+      "matrix_task_count": 20,
+      "matrix_record_count": 180,
+      "matrix_scored_count": 180
+    },
     {
       "name": "walkthrough_deep_link",
       "status": "pass",

docs/data/research_directions.json

@@ -1,5 +1,5 @@
 {
-  "source": "results/episode_task_suite/summary_report.json",
+  "source": "docs/data/task_suite_20.json plus results/episode_task_suite/summary_report.json",
   "dataset_scope": {
     "sample_episode_count": 1,
     "num_frames": 5821,
@@ -11,6 +11,7 @@
     "minimal": "Interpretable softmax, logistic, ridge, and retrieval heads over the 8,546-d window feature vector.",
     "neural_mlp": "Small PyTorch MLP classifiers/regressors using the same features, splits, and task contracts."
   },
+  "task_count": 20,
   "directions": {
     "A": {
       "id": "human_motion",
@@ -28,19 +29,23 @@
         "timeline_action",
         "hand_trajectory_forecast",
         "contact_prediction",
-        "object_relevance"
+        "object_relevance",
+        "interaction_text_prediction",
+        "imu_to_hand_pose"
       ],
       "task_display_names": [
         "Action Recognition",
         "Hand Trajectory Forecasting",
         "Contact State Prediction",
-        "Object Relevance Prediction"
+        "Object Relevance Prediction",
+        "Interaction Text Prediction",
+        "IMU-to-Hand Pose Reconstruction"
       ],
       "counts": {
-        "direct": 2,
-        "proxy": 2,
+        "direct": 3,
+        "proxy": 3,
         "diagnostic": 0,
-        "total_links": 4
+        "total_links": 6
       }
     },
     "B": {
@@ -58,18 +63,22 @@
       "tasks": [
         "cross_modal_retrieval",
         "modality_reconstruction",
-        "misalignment_detection"
+        "misalignment_detection",
+        "imu_to_hand_pose",
+        "camera_view_sync_retrieval"
       ],
       "task_display_names": [
         "Cross-Modal Retrieval",
         "Cross-Modal Reconstruction",
-        "Multimodal Synchronization Detection"
+        "Multimodal Synchronization Detection",
+        "IMU-to-Hand Pose Reconstruction",
+        "Camera-View Synchronization Retrieval"
       ],
       "counts": {
-        "direct": 0,
-        "proxy": 2,
+        "direct": 1,
+        "proxy": 3,
         "diagnostic": 1,
-        "total_links": 3
+        "total_links": 5
       }
     },
     "C": {
@@ -78,7 +87,7 @@
       "focus": "Egocentric action and intention understanding, hand-object interaction, gaze/attention modeling, task structure modeling.",
       "preferred_background": "Video understanding, action recognition, or egocentric vision.",
       "current_status": "strongest implemented track",
-      "current_readout": "Most of the 12 tasks directly target egocentric action, task state, interaction, grounding, and alignment.",
+      "current_readout": "The unified 20-task suite directly targets egocentric action, task state, interaction, grounding, forecasting, and alignment.",
       "next_steps": [
         "Move from single-episode chronological splits to held-out-episode splits.",
         "Use audio together with stronger multimodal backbones for action, intent, and grounding.",
@@ -95,7 +104,13 @@
         "caption_grounding",
         "cross_modal_retrieval",
         "temporal_order",
-        "misalignment_detection"
+        "misalignment_detection",
+        "long_horizon_next_action",
+        "next_subtask_forecast",
+        "interaction_text_prediction",
+        "action_object_relation",
+        "object_set_forecast",
+        "time_to_transition"
       ],
       "task_display_names": [
         "Action Recognition",
@@ -108,13 +123,19 @@
         "Language Grounding",
         "Cross-Modal Retrieval",
         "Temporal Order Verification",
-        "Multimodal Synchronization Detection"
+        "Multimodal Synchronization Detection",
+        "Long-Horizon Next-Action Forecasting",
+        "Long-Horizon Next-Subtask Forecasting",
+        "Interaction Text Prediction",
+        "Action-Object Relation Prediction",
+        "Future Object-Set Forecasting",
+        "Time-to-Next-Transition Regression"
       ],
       "counts": {
-        "direct": 6,
-        "proxy": 2,
-        "diagnostic": 3,
-        "total_links": 11
+        "direct": 10,
+        "proxy": 3,
+        "diagnostic": 4,
+        "total_links": 17
       }
     },
     "D": {
@@ -138,7 +159,13 @@
         "cross_modal_retrieval",
         "modality_reconstruction",
         "temporal_order",
-        "misalignment_detection"
+        "misalignment_detection",
+        "long_horizon_next_action",
+        "next_subtask_forecast",
+        "action_object_relation",
+        "object_set_forecast",
+        "camera_view_sync_retrieval",
+        "time_to_transition"
       ],
       "task_display_names": [
         "Procedure Step Recognition",
@@ -149,13 +176,19 @@
         "Cross-Modal Retrieval",
         "Cross-Modal Reconstruction",
         "Temporal Order Verification",
-        "Multimodal Synchronization Detection"
+        "Multimodal Synchronization Detection",
+        "Long-Horizon Next-Action Forecasting",
+        "Long-Horizon Next-Subtask Forecasting",
+        "Action-Object Relation Prediction",
+        "Future Object-Set Forecasting",
+        "Camera-View Synchronization Retrieval",
+        "Time-to-Next-Transition Regression"
       ],
       "counts": {
-        "direct": 0,
-        "proxy": 6,
-        "diagnostic": 3,
-        "total_links": 9
+        "direct": 1,
+        "proxy": 10,
+        "diagnostic": 4,
+        "total_links": 15
       }
     }
   },
@@ -438,6 +471,190 @@
         "neural_mlp": 0.7152682255845944,
         "better_baseline": "neural_mlp"
       }
+    },
+    "long_horizon_next_action": {
+      "name": "Long-horizon next-action forecasting",
+      "family": "classification",
+      "input": "current and historical windows",
+      "output": "future action label",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Extends short-horizon intention prediction into longer activity futures, a key egocentric and world-model signal.",
+      "current_limit": "Evaluated from sample-supported future labels, not full open-world action generation.",
+      "display_name": "Long-Horizon Next-Action Forecasting",
+      "artifact_id": "long_horizon_next_action",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.07499999999999998,
+        "neural_mlp": 0.06545454545454546,
+        "better_baseline": "minimal"
+      }
+    },
+    "next_subtask_forecast": {
+      "name": "Long-horizon next-subtask forecasting",
+      "family": "classification",
+      "input": "current and historical windows",
+      "output": "future procedure-step label",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Measures whether the model can anticipate the next procedural phase rather than only the current frame state.",
+      "current_limit": "Subtask labels are constrained to the available annotation vocabulary.",
+      "display_name": "Long-Horizon Next-Subtask Forecasting",
+      "artifact_id": "next_subtask_forecast",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.04545454545454545,
+        "neural_mlp": 0.050724637681159424,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "interaction_text_prediction": {
+      "name": "Interaction text prediction",
+      "family": "classification",
+      "input": "window features without target text leakage",
+      "output": "natural-language interaction class",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "A": "proxy"
+      },
+      "why": "Connects egocentric observations to the natural-language interaction semantics carried by the annotation.",
+      "current_limit": "Public derived features retain hashed text targets; raw full text requires the official annotation source.",
+      "display_name": "Interaction Text Prediction",
+      "artifact_id": "interaction_text_prediction",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.04444444444444444,
+        "neural_mlp": 0.0380952380952381,
+        "better_baseline": "minimal"
+      }
+    },
+    "action_object_relation": {
+      "name": "Action-object relation prediction",
+      "family": "classification",
+      "input": "window features with target-side relation leakage excluded",
+      "output": "action-object relation class",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Tests whether action recognition and object state are connected as a relational interaction representation.",
+      "current_limit": "Relation labels are derived from the public-sample annotation scope.",
+      "display_name": "Action-Object Relation Prediction",
+      "artifact_id": "action_object_relation",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.0,
+        "neural_mlp": 0.0,
+        "better_baseline": "tie"
+      }
+    },
+    "object_set_forecast": {
+      "name": "Future object-set forecasting",
+      "family": "multi-label",
+      "input": "current and historical windows",
+      "output": "future object set",
+      "primary_direction": "D",
+      "direction_roles": {
+        "D": "direct",
+        "C": "proxy"
+      },
+      "why": "Asks whether the current scene state supports predicting which objects will matter later.",
+      "current_limit": "This is a set-level proxy, not a persistent 3D scene graph.",
+      "display_name": "Future Object-Set Forecasting",
+      "artifact_id": "object_set_forecast",
+      "metric": {
+        "key": "micro_f1",
+        "name": "micro-F1",
+        "direction": "higher",
+        "minimal": 0.16939890710382516,
+        "neural_mlp": 0.19718309859154928,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "imu_to_hand_pose": {
+      "name": "IMU-to-hand pose reconstruction",
+      "family": "regression",
+      "input": "IMU and motion context",
+      "output": "hand pose target",
+      "primary_direction": "A",
+      "direction_roles": {
+        "A": "direct",
+        "B": "proxy"
+      },
+      "why": "Measures human-motion reconstruction from wearable and motion cues.",
+      "current_limit": "Pose reconstruction is window-level and does not yet fit a full parametric hand/body model.",
+      "display_name": "IMU-to-Hand Pose Reconstruction",
+      "artifact_id": "imu_to_hand_pose",
+      "metric": {
+        "key": "mae",
+        "name": "MAE",
+        "direction": "lower",
+        "minimal": 0.042049407958984375,
+        "neural_mlp": 0.042562149465084076,
+        "better_baseline": "minimal"
+      }
+    },
+    "camera_view_sync_retrieval": {
+      "name": "Camera-view synchronization retrieval",
+      "family": "retrieval",
+      "input": "one camera-view/window query",
+      "output": "matching synchronized view",
+      "primary_direction": "B",
+      "direction_roles": {
+        "B": "direct",
+        "D": "proxy"
+      },
+      "why": "Tests whether synchronized multi-view structure is recoverable across camera streams.",
+      "current_limit": "Retrieval checks view consistency but does not reconstruct geometry by itself.",
+      "display_name": "Camera-View Synchronization Retrieval",
+      "artifact_id": "camera_view_sync_retrieval",
+      "metric": {
+        "key": "mrr",
+        "name": "MRR",
+        "direction": "higher",
+        "minimal": 0.4943004846572876,
+        "neural_mlp": 0.24086658656597137,
+        "better_baseline": "minimal"
+      }
+    },
+    "time_to_transition": {
+      "name": "Time-to-next-transition regression",
+      "family": "regression",
+      "input": "current temporal window state",
+      "output": "frames/time until the next transition",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "diagnostic",
+        "D": "diagnostic"
+      },
+      "why": "Measures temporal boundary awareness as a continuous timing target.",
+      "current_limit": "Regression is local to the annotated public sample timeline.",
+      "display_name": "Time-to-Next-Transition Regression",
+      "artifact_id": "time_to_transition",
+      "metric": {
+        "key": "mae",
+        "name": "MAE frames",
+        "direction": "lower",
+        "minimal": 10.53735637664795,
+        "neural_mlp": 10.55449390411377,
+        "better_baseline": "minimal"
+      }
     }
   }
 }

docs/data/research_takeaways.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Research Takeaways",
   "status": "pass",
-  "generated_at_utc": "2026-06-06T23:26:13+00:00",
+  "generated_at_utc": "2026-06-20T21:27:21+00:00",
   "source_files": [
     "docs/data/summary_metrics.json",
     "results/episode_task_suite/summary_report.json",
@@ -42,7 +42,7 @@
     {
       "id": "chronological_split_exposes_class_shift",
       "title": "Chronological splits expose action-class shift",
-      "readout": "Earlier all-feature action classifiers reach high macro-F1 on their local split, but the 12-task chronological action/subtask heads are much harder because later held-out windows include unseen labels.",
+      "readout": "Earlier all-feature action classifiers reach high macro-F1 on their local split, but the core chronological action/subtask heads are much harder because later held-out windows include unseen labels.",
       "evidence": [
         {
           "label": "all_feature_action_macro_f1",
@@ -133,7 +133,7 @@
     {
       "id": "audio_contribution_is_task_specific",
       "title": "Audio helps some tasks and hurts others on the public sample",
-      "readout": "Audio improves the primary metric on 6 of 12 tasks, while raw log-mel replacement improves over the current handcrafted block on 6 of 12 tasks. The largest current-audio gain appears in feature reconstruction, not in action classification.",
+      "readout": "Audio improves the primary metric on 6 of the original task contracts, while raw log-mel replacement improves over the current handcrafted block on 6 of those contracts. The largest current-audio gain appears in feature reconstruction, not in action classification.",
       "evidence": [
         {
           "label": "tasks_where_current_audio_improves",

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:18+00:00",
+  "generated_at_utc": "2026-06-20T21:42:21+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/summary_metrics.json

@@ -14,7 +14,7 @@
       "visualization.rrd"
     ],
     "access_status": "The gated Xperience-10M dataset is available for selected multi-episode pilot preparation.",
-    "current_scope": "The selected-episode Qwen3-Omni v6 diagnostic branch is verified on the 96/16/16 split and meets the 98% target for JSON validity; action/subtask quality remains weak, so it is a structured-task baseline rather than a strong model-quality claim. v6 improves action macro-F1 and contact accuracy versus v5, while v5 remains stronger on JSON validity, subtask, next-action, transition, and object metrics. Cosmos3-Nano future-window compatibility and Cosmos3-Super Forward-Dynamics LoRA are also verified as separate world-model diagnostics with different metrics."
+    "current_scope": "The selected-episode Qwen3-Omni diagnostic pilot is verified on the 96/16/16 split and now meets the 98% target for JSON validity; action/subtask quality remains weak, so current results are diagnostic baselines, not strong model-quality claims."
   },
   "models": {
     "motion_action": {
@@ -699,6 +699,7 @@
       "misalignment_detection": "Multimodal Synchronization Detection"
     }
   },
+  "unified_task_count": 20,
   "feature_manifest": [
     {
       "name": "hand left joints",

docs/data/task_surface_integrity.json

@@ -1,9 +1,12 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:17+00:00",
+  "generated_at_utc": "2026-06-20T21:41:50+00:00",
   "summary": {
-    "task_count": 12,
-    "expected_task_count": 12,
+    "original_walkthrough_task_count": 12,
+    "expected_original_walkthrough_task_count": 12,
+    "unified_task_count": 20,
+    "method_task_record_count": 180,
+    "scored_method_task_count": 180,
     "task_family_counts": {
       "diagnostic": 3,
       "forecast": 2,
@@ -36,13 +39,13 @@
       "status": "pass"
     },
     {
-      "name": "exactly_12_tasks",
+      "name": "original_walkthrough_task_count",
       "status": "pass",
       "observed": 12,
       "expected": 12
     },
     {
-      "name": "expected_task_ids_present",
+      "name": "expected_original_walkthrough_task_ids_present",
       "status": "pass",
       "missing": [],
       "extra": []
@@ -1522,7 +1525,7 @@
       "expected": "### Multimodal Synchronization Detection (`misalignment_detection`)"
     },
     {
-      "name": "markdown_has_12_task_sections",
+      "name": "markdown_has_original_walkthrough_sections",
       "status": "pass",
       "observed": 12
     },
@@ -1656,6 +1659,17 @@
       "name": "extension_probe_uses_human_name:ego_motion_forecast",
       "status": "pass",
       "expected": "Short-Horizon Ego-Motion Forecasting"
+    },
+    {
+      "name": "unified_20_task_suite_present",
+      "status": "pass",
+      "task_count": 20
+    },
+    {
+      "name": "unified_180_result_matrix_present",
+      "status": "pass",
+      "method_task_record_count": 180,
+      "scored_method_task_count": 180
     }
   ],
   "failures": []

docs/data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T20:41:45+00:00",
+  "generated_at_utc": "2026-06-20T21:41:51+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -81,7 +81,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 95783,
-      "evidence_index": 132423
+      "evidence_index": 132453
     },
     {
       "name": "project_status_links_json",
@@ -160,8 +160,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 95783,
-      "protocol_index": 128604,
-      "evidence_index": 132423
+      "protocol_index": 128634,
+      "evidence_index": 132453
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -346,12 +346,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 181788,
+      "bytes": 184670,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1392513,
+      "bytes": 1407249,
       "top_level_type": "dict"
     },
     {
@@ -366,7 +366,7 @@
     },
     {
       "path": "data/omni_model_comparison.json",
-      "bytes": 81866,
+      "bytes": 82110,
       "top_level_type": "dict"
     },
     {
@@ -386,7 +386,7 @@
     },
     {
       "path": "data/project_status.json",
-      "bytes": 23041,
+      "bytes": 23057,
       "top_level_type": "dict"
     },
     {
@@ -401,12 +401,12 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 10502,
+      "bytes": 10662,
       "top_level_type": "dict"
     },
     {
       "path": "data/quality_gates.json",
-      "bytes": 8100,
+      "bytes": 8640,
       "top_level_type": "dict"
     },
     {
@@ -426,7 +426,7 @@
     },
     {
       "path": "data/rendered_site_check.json",
-      "bytes": 4032,
+      "bytes": 4318,
       "top_level_type": "dict"
     },
     {
@@ -441,7 +441,7 @@
     },
     {
       "path": "data/research_directions.json",
-      "bytes": 16694,
+      "bytes": 25046,
       "top_level_type": "dict"
     },
     {
@@ -456,12 +456,12 @@
     },
     {
       "path": "data/research_takeaways.json",
-      "bytes": 7139,
+      "bytes": 7162,
       "top_level_type": "dict"
     },
     {
       "path": "data/scope_claims_audit.json",
-      "bytes": 21630,
+      "bytes": 21313,
       "top_level_type": "dict"
     },
     {
@@ -481,7 +481,7 @@
     },
     {
       "path": "data/summary_metrics.json",
-      "bytes": 27807,
+      "bytes": 27518,
       "top_level_type": "dict"
     },
     {
@@ -511,7 +511,7 @@
     },
     {
       "path": "data/task_surface_integrity.json",
-      "bytes": 45779,
+      "bytes": 46246,
       "top_level_type": "dict"
     },
     {
@@ -536,7 +536,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 20022,
+      "bytes": 20141,
       "top_level_type": "dict"
     },
     {
@@ -618,7 +618,7 @@
     {
       "path": "assets/charts/research_direction_coverage.svg",
       "exists": true,
-      "bytes": 5078,
+      "bytes": 5347,
       "format": "SVG",
       "has_viewbox": true
     },
@@ -733,7 +733,7 @@
     {
       "path": "assets/pipeline_diagram.png",
       "exists": true,
-      "bytes": 704575,
+      "bytes": 711222,
       "width": 1800,
       "height": 1120,
       "format": "PNG"
@@ -749,7 +749,7 @@
     {
       "path": "assets/task_architectures.png",
       "exists": true,
-      "bytes": 774391,
+      "bytes": 757827,
       "width": 1800,
       "height": 2450,
       "format": "PNG"
@@ -757,9 +757,9 @@
     {
       "path": "assets/task_suite_infographic.png",
       "exists": true,
-      "bytes": 1591194,
+      "bytes": 1899884,
       "width": 1800,
-      "height": 6600,
+      "height": 7600,
       "format": "PNG"
     }
   ]

docs/index.html

@@ -3523,7 +3523,7 @@
           <article class="roadmap-card" data-status="verified_companion_result">
             <span class="roadmap-status">verified companion result</span>
             <h3>128-Episode Same-Split Simple/NN Baselines</h3>
-            <p>Align simple metadata/text baselines and neural MLP baselines to the same selected 96/16/16 split and the same 12 task ids used by the Qwen3-Omni pilot.</p>
+            <p>Align simple metadata/text baselines, raw-feature proxies, and neural MLP baselines to the same selected 96/16/16 split and the unified 20-task axes used by the public result matrix.</p>
             <div class="roadmap-meta">
               <strong>Entry</strong><p>Derived Qwen JSONL export for the selected 96/16/16 split.</p>
               <strong>Evidence</strong><p>Baseline alignment report, summary metrics, task metrics, and the 128-task baseline runner.</p>
@@ -3696,8 +3696,8 @@
           </article>
           <article class="evidence-card">
             <span class="status-pill">verified</span>
-            <h3>12 minimal heads + 12 neural MLP heads</h3>
-            <p>Every task has a minimal interpretable head and a matching neural MLP run over the same windows, splits, and task contract.</p>
+            <h3>20 task contracts + 180 public results</h3>
+            <p>The current release reports nine method families over the unified 20-task axes, with minimal, neural, 128-episode, Qwen3, Cosmos3, and proxy-scored rows kept source-linked.</p>
             <div class="evidence-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite">task artifacts</a>
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a>
@@ -3706,7 +3706,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>Audio contribution is measured task by task</h3>
-            <p>Audio variants improve the primary metric on 6 of 12 task contracts in this single-episode setting.</p>
+            <p>Audio variants improve the primary metric on 6 of the original task contracts in this single-episode setting.</p>
             <div class="evidence-links">
               <a href="data/audio_ablation_summary.json">audio summary</a>
               <a href="assets/charts/audio_ablation_delta.svg">delta chart</a>
@@ -4145,7 +4145,7 @@
           </article>
           <article class="artifact">
             <h3>Chronological split exposes class shift</h3>
-            <p>All-feature action reaches 0.9829 macro-F1 on its local split, while the 12-task chronological action head is 0.0500 macro-F1 with four unseen later action labels.</p>
+            <p>All-feature action reaches 0.9829 macro-F1 on its local split, while the chronological action head in the core task suite is 0.0500 macro-F1 with four unseen later action labels.</p>
             <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/RESEARCH_TAKEAWAYS.md">takeaways</a>
           </article>
           <article class="artifact">
@@ -4474,7 +4474,7 @@
           <img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart">
           <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart">
           <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart">
-          <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across 12 task contracts">
+          <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across original task contracts">
         </div>
         <p class="section-note"><a href="single_episode_explorer.html">Open the single-episode explorer</a> to inspect window-level labels, predictions, modality statistics, object labels, and diagnostic scores. The <a href="data/audio_ablation_summary.json">audio ablation summary</a> records the task-by-task audio contribution.</p>
       </div>

docs/research_roadmap.html

@@ -4,7 +4,7 @@
   <meta charset="utf-8">
   <meta name="viewport" content="width=device-width, initial-scale=1">
   <title>Research Roadmap | Ropedia Xperience-10M</title>
-  <meta name="description" content="Interactive research roadmap connecting the Ropedia Xperience-10M 12-task suite, four research tracks, current sample results, Qwen3-Omni fine-tuning, and foundation-model branch selection.">
+  <meta name="description" content="Interactive research roadmap connecting the Ropedia Xperience-10M 20-task suite, four research tracks, current sample results, Qwen3-Omni fine-tuning, and foundation-model branch selection.">
   <meta name="theme-color" content="#020502">
   <link rel="icon" href="favicon.png" type="image/png" sizes="64x64">
   <link rel="preconnect" href="https://fonts.googleapis.com">
@@ -626,7 +626,7 @@
   <header class="hero">
     <div class="wrap hero-grid">
       <div>
-        <div class="eyebrow">12 tasks / 4 tracks / scale-up path</div>
+        <div class="eyebrow">20 tasks / 4 tracks / scale-up path</div>
         <h1>Interactive Research Roadmap.</h1>
         <p class="hero-copy">
           This page connects the current public-sample task lab to the four research
@@ -657,7 +657,7 @@
         <div class="route">
           <div class="route-step">
             <strong>01</strong>
-            <div><b>Public sample</b><span>12 task contracts and current baselines</span></div>
+            <div><b>Public sample</b><span>20 task contracts and current baselines</span></div>
             <em id="routeSample">1 episode</em>
           </div>
           <div class="route-step">

index.html

@@ -3523,7 +3523,7 @@
           <article class="roadmap-card" data-status="verified_companion_result">
             <span class="roadmap-status">verified companion result</span>
             <h3>128-Episode Same-Split Simple/NN Baselines</h3>
-            <p>Align simple metadata/text baselines and neural MLP baselines to the same selected 96/16/16 split and the same 12 task ids used by the Qwen3-Omni pilot.</p>
+            <p>Align simple metadata/text baselines, raw-feature proxies, and neural MLP baselines to the same selected 96/16/16 split and the unified 20-task axes used by the public result matrix.</p>
             <div class="roadmap-meta">
               <strong>Entry</strong><p>Derived Qwen JSONL export for the selected 96/16/16 split.</p>
               <strong>Evidence</strong><p>Baseline alignment report, summary metrics, task metrics, and the 128-task baseline runner.</p>
@@ -3696,8 +3696,8 @@
           </article>
           <article class="evidence-card">
             <span class="status-pill">verified</span>
-            <h3>12 minimal heads + 12 neural MLP heads</h3>
-            <p>Every task has a minimal interpretable head and a matching neural MLP run over the same windows, splits, and task contract.</p>
+            <h3>20 task contracts + 180 public results</h3>
+            <p>The current release reports nine method families over the unified 20-task axes, with minimal, neural, 128-episode, Qwen3, Cosmos3, and proxy-scored rows kept source-linked.</p>
             <div class="evidence-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite">task artifacts</a>
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a>
@@ -3706,7 +3706,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>Audio contribution is measured task by task</h3>
-            <p>Audio variants improve the primary metric on 6 of 12 task contracts in this single-episode setting.</p>
+            <p>Audio variants improve the primary metric on 6 of the original task contracts in this single-episode setting.</p>
             <div class="evidence-links">
               <a href="data/audio_ablation_summary.json">audio summary</a>
               <a href="assets/charts/audio_ablation_delta.svg">delta chart</a>
@@ -4145,7 +4145,7 @@
           </article>
           <article class="artifact">
             <h3>Chronological split exposes class shift</h3>
-            <p>All-feature action reaches 0.9829 macro-F1 on its local split, while the 12-task chronological action head is 0.0500 macro-F1 with four unseen later action labels.</p>
+            <p>All-feature action reaches 0.9829 macro-F1 on its local split, while the chronological action head in the core task suite is 0.0500 macro-F1 with four unseen later action labels.</p>
             <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/RESEARCH_TAKEAWAYS.md">takeaways</a>
           </article>
           <article class="artifact">
@@ -4474,7 +4474,7 @@
           <img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart">
           <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart">
           <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart">
-          <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across 12 task contracts">
+          <img class="chart" src="assets/charts/audio_ablation_delta.svg" alt="Measured audio delta chart across original task contracts">
         </div>
         <p class="section-note"><a href="single_episode_explorer.html">Open the single-episode explorer</a> to inspect window-level labels, predictions, modality statistics, object labels, and diagnostic scores. The <a href="data/audio_ablation_summary.json">audio ablation summary</a> records the task-by-task audio contribution.</p>
       </div>

metrics/artifact_index.json

@@ -1,6 +1,6 @@
 {
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metrics/mirror_parity.json

@@ -1,9 +1,9 @@
 {
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       "failures": []
@@ -30973,33 +31259,64 @@
       },
       "failures": []
     },
+    {
+      "name": "docs/EVIDENCE_CONTRACT.md",
+      "status": "pass",
+      "local": {
+        "path": "repo:EVIDENCE_CONTRACT.md",
+        "exists": true,
+        "bytes": 11440,
+        "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
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+          "path": "hf_space:EVIDENCE_CONTRACT.md",
+          "exists": true,
+          "bytes": 11440,
+          "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:EVIDENCE_CONTRACT.md",
+          "exists": true,
+          "bytes": 11440,
+          "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
+        },
+        "hf_model": {
+          "path": "hf_model:EVIDENCE_CONTRACT.md",
+          "exists": true,
+          "bytes": 11440,
+          "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
+        }
+      },
+      "failures": []
+    },
     {
       "name": "docs/RESEARCH_TAKEAWAYS.md",
       "status": "pass",
       "local": {
         "path": "repo:RESEARCH_TAKEAWAYS.md",
         "exists": true,
-        "bytes": 5149,
-        "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
+        "bytes": 5172,
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       "mirrors": {
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           "path": "hf_space:RESEARCH_TAKEAWAYS.md",
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-          "bytes": 5149,
-          "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
+          "bytes": 5172,
+          "sha256": "39978c1e30b6aa76c5fd2684e9a1111ec2e813423feaff6053084b0335968db8"
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         "hf_artifacts": {
           "path": "hf_artifacts:RESEARCH_TAKEAWAYS.md",
           "exists": true,
-          "bytes": 5149,
-          "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
+          "bytes": 5172,
+          "sha256": "39978c1e30b6aa76c5fd2684e9a1111ec2e813423feaff6053084b0335968db8"
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         "hf_model": {
           "path": "hf_model:RESEARCH_TAKEAWAYS.md",
           "exists": true,
-          "bytes": 5149,
-          "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
+          "bytes": 5172,
+          "sha256": "39978c1e30b6aa76c5fd2684e9a1111ec2e813423feaff6053084b0335968db8"
         }
       },
       "failures": []
@@ -31035,6 +31352,37 @@
       },
       "failures": []
     },
+    {
+      "name": "docs/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+      "status": "pass",
+      "local": {
+        "path": "repo:XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+        "exists": true,
+        "bytes": 9212,
+        "sha256": "ca5505af54aba88d1eb7355317261183a2a4e6226553316a2f934fbb25d31fb0"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+          "exists": true,
+          "bytes": 9212,
+          "sha256": "ca5505af54aba88d1eb7355317261183a2a4e6226553316a2f934fbb25d31fb0"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+          "exists": true,
+          "bytes": 9212,
+          "sha256": "ca5505af54aba88d1eb7355317261183a2a4e6226553316a2f934fbb25d31fb0"
+        },
+        "hf_model": {
+          "path": "hf_model:XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+          "exists": true,
+          "bytes": 9212,
+          "sha256": "ca5505af54aba88d1eb7355317261183a2a4e6226553316a2f934fbb25d31fb0"
+        }
+      },
+      "failures": []
+    },
     {
       "name": "docs/XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
       "status": "pass",

metrics/omni_model_comparison.json

@@ -1,19 +1,19 @@
 {
   "title": "Ropedia Xperience-10M Current Result Versions and Model Groups",
-  "generated_at_utc": "2026-06-18T12:52:47+00:00",
+  "generated_at_utc": "2026-06-20T21:27:21+00:00",
   "status": "pass",
   "version_count": 3,
   "model_group_count": 5,
   "comparison_rule": "Compare only rows with the same scope and target. Single-episode raw-feature metrics, 128-episode metadata baselines, Qwen3 structured JSON metrics, and the two Cosmos3 targets answer different questions: Nano future-window retrieval versus Super structured JSON Reasoner evaluation.",
   "version_reading_notes": [
-    "Version 1 is the public-sample 12-task harness with minimal and neural heads.",
+    "Version 1 is the public-sample 20-task surface: original core heads, tasks 13-20, and the 180-row method-task matrix.",
     "Version 2 is the selected 128-episode same-split simple/NN baseline alignment.",
     "Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, Cosmos3-Super Reasoner is a base-weight JSON-task evaluation, and Cosmos3-Super Forward-Dynamics LoRA is the first Super fine-tuned adapter branch."
   ],
   "versions": [
     {
       "id": "v1_single_episode_public_sample",
-      "title": "Single-Episode Public-Sample Task Suite",
+      "title": "Single-Episode Public-Sample 20-Task Suite",
       "status": "verified",
       "scope": "one public Xperience-10M sample episode",
       "source": "results/episode_task_suite/summary_report.json",
@@ -23,7 +23,9 @@
         "windows": 1161,
         "frames": 5821,
         "feature_dim": 8546,
-        "task_count": 12,
+        "core_task_count": 12,
+        "unified_task_count": 20,
+        "method_task_record_count": 180,
         "neural_task_count": 12
       },
       "models": [
@@ -152,7 +154,7 @@
           "neural_primary_score": 0.5862068965517241
         }
       ],
-      "interpretation": "This layer verifies the 12 task contracts and raw multimodal feature pipeline on the public sample. It is not a cross-episode benchmark."
+      "interpretation": "This layer verifies the original core task contracts, raw multimodal feature pipeline, and unified 20-task public result surface. It is not a cross-episode benchmark."
     },
     {
       "id": "v2_multi_episode_128_aligned_metadata_baselines",
@@ -824,7 +826,7 @@
       "one_episode_runs": [
         {
           "id": "task_heads_single_episode_public_sample",
-          "title": "Single-Episode Public-Sample Task Suite",
+          "title": "Single-Episode Public-Sample 20-Task Suite",
           "scope": "one public Xperience-10M sample episode",
           "status": "verified",
           "source": "results/episode_task_suite/summary_report.json",
@@ -834,7 +836,9 @@
             "windows": 1161,
             "frames": 5821,
             "feature_dim": 8546,
-            "task_count": 12,
+            "core_task_count": 12,
+            "unified_task_count": 20,
+            "method_task_record_count": 180,
             "neural_task_count": 12
           },
           "weights": "baseline model files in the baseline model repo; no foundation-model weights",

metrics/project_status.json

@@ -116,7 +116,7 @@
         "results/audio_ablation/",
         "docs/data/audio_ablation_summary.json"
       ],
-      "readout": "Audio variants improve the primary metric on 6 of 12 task contracts in this single-episode setting."
+      "readout": "Audio variants improve the primary metric on 6 of the original task contracts in this single-episode setting."
     },
     {
       "area": "Evaluation protocol",
@@ -353,7 +353,7 @@
     "The Cosmos3-Nano future-window branch is verified as a compatibility adapter result, Cosmos3-Super Reasoner is verified as a base-weight evaluation, and Cosmos3-Super Forward-Dynamics LoRA is verified as the first fine-tuned Super adapter branch. Cosmos3-Super adapter weights belong in cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep; verified_public packages exclude safetensors.",
     "The current reconstruction task reconstructs feature vectors, not pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Audio is one of the synchronized source modalities in the current task representation.",
-    "The audio ablation report compares audio/no-audio variants across all 12 task contracts in results/audio_ablation/.",
+    "The audio ablation report compares audio/no-audio variants across the original task contracts in results/audio_ablation/.",
     "Foundation-model selection is explicit: Qwen3-Omni is the structured JSON baseline, Cosmos 3 is the world-model branch with Nano compatibility and Super forward-dynamics LoRA results, and policy models such as OpenVLA/openpi/GR00T wait for robot-compatible action-target conversion.",
     "Future model branches should be added through the backbone registry and verified package contract, not as one-off result folders with incompatible metrics or publication rules.",
     "The Xperience Embodied Foundation Model is a future native-pretraining goal, not a completed model or current benchmark."

metrics/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T20:48:08+00:00",
+  "generated_at_utc": "2026-06-20T21:45:18+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,37 +18,37 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T20:41:45+00:00"
+          "generated_at_utc": "2026-06-20T21:41:51+00:00"
         },
         "rendered_site_check": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-03T01:38:26+00:00"
+          "generated_at_utc": "2026-06-20T21:34:06+00:00"
         },
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:55:17+00:00"
+          "generated_at_utc": "2026-06-20T21:41:50+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:55:18+00:00"
+          "generated_at_utc": "2026-06-20T21:42:21+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:55:26+00:00"
+          "generated_at_utc": "2026-06-20T21:41:53+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T20:42:41+00:00"
+          "generated_at_utc": "2026-06-20T21:44:46+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T20:47:51+00:00"
+          "generated_at_utc": "2026-06-20T21:43:29+00:00"
         }
       },
       "failures": {}
@@ -97,8 +97,8 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 19,
         "Xperience-10M": 165,
-        "20-task": 61,
-        "Qwen3-Omni": 154,
+        "20-task": 69,
+        "Qwen3-Omni": 153,
         "128-episode pilot": 1
       }
     },

metrics/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T20:49:00+00:00",
+  "generated_at_utc": "2026-06-20T21:44:46+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -240,8 +240,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 558,
-      "text_file_count": 411,
+      "file_count": 561,
+      "text_file_count": 414,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/predictions.jsonl",
         "bytes": 10221085
@@ -251,7 +251,7 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 4477,
+      "file_count": 4483,
       "text_file_count": 1271,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",
@@ -262,7 +262,7 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 5228,
+      "file_count": 5236,
       "text_file_count": 1440,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",

metrics/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T20:48:18+00:00",
+  "generated_at_utc": "2026-06-20T21:45:18+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

metrics/rendered_site_check.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Rendered Website Check",
   "status": "pass",
-  "generated_at_utc": "2026-06-03T01:38:26+00:00",
+  "generated_at_utc": "2026-06-20T21:34:06+00:00",
   "flow": "load current docs server -> open #walkthroughs deep link -> click Next -> click Process story chapter",
   "checked_at_local": "2026-06-03T01:32:46.099Z",
   "screenshot_path": "/tmp/xperience_site_walkthrough_fresh.png",
@@ -71,10 +71,18 @@
     {
       "name": "task_and_modality_cards_render",
       "status": "pass",
-      "reason": "The rendered task and modality sections should expose all 12 task cards and seven modality cards.",
+      "reason": "The rendered walkthrough should expose the original core task cards and seven modality cards.",
       "task_card_count": 12,
       "atlas_card_count": 7
     },
+    {
+      "name": "unified_20_task_matrix_available",
+      "status": "pass",
+      "reason": "The rendered site data bundle should include the unified 20-task / 180-result matrix.",
+      "matrix_task_count": 20,
+      "matrix_record_count": 180,
+      "matrix_scored_count": 180
+    },
     {
       "name": "walkthrough_deep_link",
       "status": "pass",

metrics/research_directions.json

@@ -1,5 +1,5 @@
 {
-  "source": "results/episode_task_suite/summary_report.json",
+  "source": "docs/data/task_suite_20.json plus results/episode_task_suite/summary_report.json",
   "dataset_scope": {
     "sample_episode_count": 1,
     "num_frames": 5821,
@@ -11,6 +11,7 @@
     "minimal": "Interpretable softmax, logistic, ridge, and retrieval heads over the 8,546-d window feature vector.",
     "neural_mlp": "Small PyTorch MLP classifiers/regressors using the same features, splits, and task contracts."
   },
+  "task_count": 20,
   "directions": {
     "A": {
       "id": "human_motion",
@@ -28,19 +29,23 @@
         "timeline_action",
         "hand_trajectory_forecast",
         "contact_prediction",
-        "object_relevance"
+        "object_relevance",
+        "interaction_text_prediction",
+        "imu_to_hand_pose"
       ],
       "task_display_names": [
         "Action Recognition",
         "Hand Trajectory Forecasting",
         "Contact State Prediction",
-        "Object Relevance Prediction"
+        "Object Relevance Prediction",
+        "Interaction Text Prediction",
+        "IMU-to-Hand Pose Reconstruction"
       ],
       "counts": {
-        "direct": 2,
-        "proxy": 2,
+        "direct": 3,
+        "proxy": 3,
         "diagnostic": 0,
-        "total_links": 4
+        "total_links": 6
       }
     },
     "B": {
@@ -58,18 +63,22 @@
       "tasks": [
         "cross_modal_retrieval",
         "modality_reconstruction",
-        "misalignment_detection"
+        "misalignment_detection",
+        "imu_to_hand_pose",
+        "camera_view_sync_retrieval"
       ],
       "task_display_names": [
         "Cross-Modal Retrieval",
         "Cross-Modal Reconstruction",
-        "Multimodal Synchronization Detection"
+        "Multimodal Synchronization Detection",
+        "IMU-to-Hand Pose Reconstruction",
+        "Camera-View Synchronization Retrieval"
       ],
       "counts": {
-        "direct": 0,
-        "proxy": 2,
+        "direct": 1,
+        "proxy": 3,
         "diagnostic": 1,
-        "total_links": 3
+        "total_links": 5
       }
     },
     "C": {
@@ -78,7 +87,7 @@
       "focus": "Egocentric action and intention understanding, hand-object interaction, gaze/attention modeling, task structure modeling.",
       "preferred_background": "Video understanding, action recognition, or egocentric vision.",
       "current_status": "strongest implemented track",
-      "current_readout": "Most of the 12 tasks directly target egocentric action, task state, interaction, grounding, and alignment.",
+      "current_readout": "The unified 20-task suite directly targets egocentric action, task state, interaction, grounding, forecasting, and alignment.",
       "next_steps": [
         "Move from single-episode chronological splits to held-out-episode splits.",
         "Use audio together with stronger multimodal backbones for action, intent, and grounding.",
@@ -95,7 +104,13 @@
         "caption_grounding",
         "cross_modal_retrieval",
         "temporal_order",
-        "misalignment_detection"
+        "misalignment_detection",
+        "long_horizon_next_action",
+        "next_subtask_forecast",
+        "interaction_text_prediction",
+        "action_object_relation",
+        "object_set_forecast",
+        "time_to_transition"
       ],
       "task_display_names": [
         "Action Recognition",
@@ -108,13 +123,19 @@
         "Language Grounding",
         "Cross-Modal Retrieval",
         "Temporal Order Verification",
-        "Multimodal Synchronization Detection"
+        "Multimodal Synchronization Detection",
+        "Long-Horizon Next-Action Forecasting",
+        "Long-Horizon Next-Subtask Forecasting",
+        "Interaction Text Prediction",
+        "Action-Object Relation Prediction",
+        "Future Object-Set Forecasting",
+        "Time-to-Next-Transition Regression"
       ],
       "counts": {
-        "direct": 6,
-        "proxy": 2,
-        "diagnostic": 3,
-        "total_links": 11
+        "direct": 10,
+        "proxy": 3,
+        "diagnostic": 4,
+        "total_links": 17
       }
     },
     "D": {
@@ -138,7 +159,13 @@
         "cross_modal_retrieval",
         "modality_reconstruction",
         "temporal_order",
-        "misalignment_detection"
+        "misalignment_detection",
+        "long_horizon_next_action",
+        "next_subtask_forecast",
+        "action_object_relation",
+        "object_set_forecast",
+        "camera_view_sync_retrieval",
+        "time_to_transition"
       ],
       "task_display_names": [
         "Procedure Step Recognition",
@@ -149,13 +176,19 @@
         "Cross-Modal Retrieval",
         "Cross-Modal Reconstruction",
         "Temporal Order Verification",
-        "Multimodal Synchronization Detection"
+        "Multimodal Synchronization Detection",
+        "Long-Horizon Next-Action Forecasting",
+        "Long-Horizon Next-Subtask Forecasting",
+        "Action-Object Relation Prediction",
+        "Future Object-Set Forecasting",
+        "Camera-View Synchronization Retrieval",
+        "Time-to-Next-Transition Regression"
       ],
       "counts": {
-        "direct": 0,
-        "proxy": 6,
-        "diagnostic": 3,
-        "total_links": 9
+        "direct": 1,
+        "proxy": 10,
+        "diagnostic": 4,
+        "total_links": 15
       }
     }
   },
@@ -438,6 +471,190 @@
         "neural_mlp": 0.7152682255845944,
         "better_baseline": "neural_mlp"
       }
+    },
+    "long_horizon_next_action": {
+      "name": "Long-horizon next-action forecasting",
+      "family": "classification",
+      "input": "current and historical windows",
+      "output": "future action label",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Extends short-horizon intention prediction into longer activity futures, a key egocentric and world-model signal.",
+      "current_limit": "Evaluated from sample-supported future labels, not full open-world action generation.",
+      "display_name": "Long-Horizon Next-Action Forecasting",
+      "artifact_id": "long_horizon_next_action",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.07499999999999998,
+        "neural_mlp": 0.06545454545454546,
+        "better_baseline": "minimal"
+      }
+    },
+    "next_subtask_forecast": {
+      "name": "Long-horizon next-subtask forecasting",
+      "family": "classification",
+      "input": "current and historical windows",
+      "output": "future procedure-step label",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Measures whether the model can anticipate the next procedural phase rather than only the current frame state.",
+      "current_limit": "Subtask labels are constrained to the available annotation vocabulary.",
+      "display_name": "Long-Horizon Next-Subtask Forecasting",
+      "artifact_id": "next_subtask_forecast",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.04545454545454545,
+        "neural_mlp": 0.050724637681159424,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "interaction_text_prediction": {
+      "name": "Interaction text prediction",
+      "family": "classification",
+      "input": "window features without target text leakage",
+      "output": "natural-language interaction class",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "A": "proxy"
+      },
+      "why": "Connects egocentric observations to the natural-language interaction semantics carried by the annotation.",
+      "current_limit": "Public derived features retain hashed text targets; raw full text requires the official annotation source.",
+      "display_name": "Interaction Text Prediction",
+      "artifact_id": "interaction_text_prediction",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.04444444444444444,
+        "neural_mlp": 0.0380952380952381,
+        "better_baseline": "minimal"
+      }
+    },
+    "action_object_relation": {
+      "name": "Action-object relation prediction",
+      "family": "classification",
+      "input": "window features with target-side relation leakage excluded",
+      "output": "action-object relation class",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "direct",
+        "D": "proxy"
+      },
+      "why": "Tests whether action recognition and object state are connected as a relational interaction representation.",
+      "current_limit": "Relation labels are derived from the public-sample annotation scope.",
+      "display_name": "Action-Object Relation Prediction",
+      "artifact_id": "action_object_relation",
+      "metric": {
+        "key": "macro_f1",
+        "name": "macro-F1",
+        "direction": "higher",
+        "minimal": 0.0,
+        "neural_mlp": 0.0,
+        "better_baseline": "tie"
+      }
+    },
+    "object_set_forecast": {
+      "name": "Future object-set forecasting",
+      "family": "multi-label",
+      "input": "current and historical windows",
+      "output": "future object set",
+      "primary_direction": "D",
+      "direction_roles": {
+        "D": "direct",
+        "C": "proxy"
+      },
+      "why": "Asks whether the current scene state supports predicting which objects will matter later.",
+      "current_limit": "This is a set-level proxy, not a persistent 3D scene graph.",
+      "display_name": "Future Object-Set Forecasting",
+      "artifact_id": "object_set_forecast",
+      "metric": {
+        "key": "micro_f1",
+        "name": "micro-F1",
+        "direction": "higher",
+        "minimal": 0.16939890710382516,
+        "neural_mlp": 0.19718309859154928,
+        "better_baseline": "neural_mlp"
+      }
+    },
+    "imu_to_hand_pose": {
+      "name": "IMU-to-hand pose reconstruction",
+      "family": "regression",
+      "input": "IMU and motion context",
+      "output": "hand pose target",
+      "primary_direction": "A",
+      "direction_roles": {
+        "A": "direct",
+        "B": "proxy"
+      },
+      "why": "Measures human-motion reconstruction from wearable and motion cues.",
+      "current_limit": "Pose reconstruction is window-level and does not yet fit a full parametric hand/body model.",
+      "display_name": "IMU-to-Hand Pose Reconstruction",
+      "artifact_id": "imu_to_hand_pose",
+      "metric": {
+        "key": "mae",
+        "name": "MAE",
+        "direction": "lower",
+        "minimal": 0.042049407958984375,
+        "neural_mlp": 0.042562149465084076,
+        "better_baseline": "minimal"
+      }
+    },
+    "camera_view_sync_retrieval": {
+      "name": "Camera-view synchronization retrieval",
+      "family": "retrieval",
+      "input": "one camera-view/window query",
+      "output": "matching synchronized view",
+      "primary_direction": "B",
+      "direction_roles": {
+        "B": "direct",
+        "D": "proxy"
+      },
+      "why": "Tests whether synchronized multi-view structure is recoverable across camera streams.",
+      "current_limit": "Retrieval checks view consistency but does not reconstruct geometry by itself.",
+      "display_name": "Camera-View Synchronization Retrieval",
+      "artifact_id": "camera_view_sync_retrieval",
+      "metric": {
+        "key": "mrr",
+        "name": "MRR",
+        "direction": "higher",
+        "minimal": 0.4943004846572876,
+        "neural_mlp": 0.24086658656597137,
+        "better_baseline": "minimal"
+      }
+    },
+    "time_to_transition": {
+      "name": "Time-to-next-transition regression",
+      "family": "regression",
+      "input": "current temporal window state",
+      "output": "frames/time until the next transition",
+      "primary_direction": "C",
+      "direction_roles": {
+        "C": "diagnostic",
+        "D": "diagnostic"
+      },
+      "why": "Measures temporal boundary awareness as a continuous timing target.",
+      "current_limit": "Regression is local to the annotated public sample timeline.",
+      "display_name": "Time-to-Next-Transition Regression",
+      "artifact_id": "time_to_transition",
+      "metric": {
+        "key": "mae",
+        "name": "MAE frames",
+        "direction": "lower",
+        "minimal": 10.53735637664795,
+        "neural_mlp": 10.55449390411377,
+        "better_baseline": "minimal"
+      }
     }
   }
 }

metrics/research_takeaways.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Research Takeaways",
   "status": "pass",
-  "generated_at_utc": "2026-06-06T23:26:13+00:00",
+  "generated_at_utc": "2026-06-20T21:27:21+00:00",
   "source_files": [
     "docs/data/summary_metrics.json",
     "results/episode_task_suite/summary_report.json",
@@ -42,7 +42,7 @@
     {
       "id": "chronological_split_exposes_class_shift",
       "title": "Chronological splits expose action-class shift",
-      "readout": "Earlier all-feature action classifiers reach high macro-F1 on their local split, but the 12-task chronological action/subtask heads are much harder because later held-out windows include unseen labels.",
+      "readout": "Earlier all-feature action classifiers reach high macro-F1 on their local split, but the core chronological action/subtask heads are much harder because later held-out windows include unseen labels.",
       "evidence": [
         {
           "label": "all_feature_action_macro_f1",
@@ -133,7 +133,7 @@
     {
       "id": "audio_contribution_is_task_specific",
       "title": "Audio helps some tasks and hurts others on the public sample",
-      "readout": "Audio improves the primary metric on 6 of 12 tasks, while raw log-mel replacement improves over the current handcrafted block on 6 of 12 tasks. The largest current-audio gain appears in feature reconstruction, not in action classification.",
+      "readout": "Audio improves the primary metric on 6 of the original task contracts, while raw log-mel replacement improves over the current handcrafted block on 6 of those contracts. The largest current-audio gain appears in feature reconstruction, not in action classification.",
       "evidence": [
         {
           "label": "tasks_where_current_audio_improves",

metrics/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:26+00:00",
+  "generated_at_utc": "2026-06-20T21:41:53+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,
@@ -25,7 +25,7 @@
     {
       "name": "summary_metrics_preserves_verified_diagnostic_status",
       "status": "pass",
-      "detail": "The selected-episode Qwen3-Omni v6 diagnostic branch is verified on the 96/16/16 split and meets the 98% target for JSON validity; action/subtask quality remains weak, so it is a structured-task baseline rather than a strong model-quality claim. v6 improves action macro-F1 and contact accuracy versus v5, while v5 remains stronger on JSON validity, subtask, next-action, transition, and object metrics. Cosmos3-Nano future-window compatibility and Cosmos3-Super Forward-Dynamics LoRA are also verified as separate world-model diagnostics with different metrics.",
+      "detail": "The selected-episode Qwen3-Omni diagnostic pilot is verified on the 96/16/16 split and now meets the 98% target for JSON validity; action/subtask quality remains weak, so current results are diagnostic baselines, not strong model-quality claims.",
       "evidence": [
         "docs/data/summary_metrics.json"
       ]

metrics/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:18+00:00",
+  "generated_at_utc": "2026-06-20T21:42:21+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

metrics/summary_metrics.json

@@ -14,7 +14,7 @@
       "visualization.rrd"
     ],
     "access_status": "The gated Xperience-10M dataset is available for selected multi-episode pilot preparation.",
-    "current_scope": "The selected-episode Qwen3-Omni v6 diagnostic branch is verified on the 96/16/16 split and meets the 98% target for JSON validity; action/subtask quality remains weak, so it is a structured-task baseline rather than a strong model-quality claim. v6 improves action macro-F1 and contact accuracy versus v5, while v5 remains stronger on JSON validity, subtask, next-action, transition, and object metrics. Cosmos3-Nano future-window compatibility and Cosmos3-Super Forward-Dynamics LoRA are also verified as separate world-model diagnostics with different metrics."
+    "current_scope": "The selected-episode Qwen3-Omni diagnostic pilot is verified on the 96/16/16 split and now meets the 98% target for JSON validity; action/subtask quality remains weak, so current results are diagnostic baselines, not strong model-quality claims."
   },
   "models": {
     "motion_action": {
@@ -699,6 +699,7 @@
       "misalignment_detection": "Multimodal Synchronization Detection"
     }
   },
+  "unified_task_count": 20,
   "feature_manifest": [
     {
       "name": "hand left joints",

metrics/task_surface_integrity.json

@@ -1,9 +1,12 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:17+00:00",
+  "generated_at_utc": "2026-06-20T21:41:50+00:00",
   "summary": {
-    "task_count": 12,
-    "expected_task_count": 12,
+    "original_walkthrough_task_count": 12,
+    "expected_original_walkthrough_task_count": 12,
+    "unified_task_count": 20,
+    "method_task_record_count": 180,
+    "scored_method_task_count": 180,
     "task_family_counts": {
       "diagnostic": 3,
       "forecast": 2,
@@ -36,13 +39,13 @@
       "status": "pass"
     },
     {
-      "name": "exactly_12_tasks",
+      "name": "original_walkthrough_task_count",
       "status": "pass",
       "observed": 12,
       "expected": 12
     },
     {
-      "name": "expected_task_ids_present",
+      "name": "expected_original_walkthrough_task_ids_present",
       "status": "pass",
       "missing": [],
       "extra": []
@@ -1522,7 +1525,7 @@
       "expected": "### Multimodal Synchronization Detection (`misalignment_detection`)"
     },
     {
-      "name": "markdown_has_12_task_sections",
+      "name": "markdown_has_original_walkthrough_sections",
       "status": "pass",
       "observed": 12
     },
@@ -1656,6 +1659,17 @@
       "name": "extension_probe_uses_human_name:ego_motion_forecast",
       "status": "pass",
       "expected": "Short-Horizon Ego-Motion Forecasting"
+    },
+    {
+      "name": "unified_20_task_suite_present",
+      "status": "pass",
+      "task_count": 20
+    },
+    {
+      "name": "unified_180_result_matrix_present",
+      "status": "pass",
+      "method_task_record_count": 180,
+      "scored_method_task_count": 180
     }
   ],
   "failures": []

metrics/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T20:41:45+00:00",
+  "generated_at_utc": "2026-06-20T21:41:51+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -81,7 +81,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 95783,
-      "evidence_index": 132423
+      "evidence_index": 132453
     },
     {
       "name": "project_status_links_json",
@@ -160,8 +160,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 95783,
-      "protocol_index": 128604,
-      "evidence_index": 132423
+      "protocol_index": 128634,
+      "evidence_index": 132453
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -346,12 +346,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 181788,
+      "bytes": 184670,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1392513,
+      "bytes": 1407249,
       "top_level_type": "dict"
     },
     {
@@ -366,7 +366,7 @@
     },
     {
       "path": "data/omni_model_comparison.json",
-      "bytes": 81866,
+      "bytes": 82110,
       "top_level_type": "dict"
     },
     {
@@ -386,7 +386,7 @@
     },
     {
       "path": "data/project_status.json",
-      "bytes": 23041,
+      "bytes": 23057,
       "top_level_type": "dict"
     },
     {
@@ -401,12 +401,12 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 10502,
+      "bytes": 10662,
       "top_level_type": "dict"
     },
     {
       "path": "data/quality_gates.json",
-      "bytes": 8100,
+      "bytes": 8640,
       "top_level_type": "dict"
     },
     {
@@ -426,7 +426,7 @@
     },
     {
       "path": "data/rendered_site_check.json",
-      "bytes": 4032,
+      "bytes": 4318,
       "top_level_type": "dict"
     },
     {
@@ -441,7 +441,7 @@
     },
     {
       "path": "data/research_directions.json",
-      "bytes": 16694,
+      "bytes": 25046,
       "top_level_type": "dict"
     },
     {
@@ -456,12 +456,12 @@
     },
     {
       "path": "data/research_takeaways.json",
-      "bytes": 7139,
+      "bytes": 7162,
       "top_level_type": "dict"
     },
     {
       "path": "data/scope_claims_audit.json",
-      "bytes": 21630,
+      "bytes": 21313,
       "top_level_type": "dict"
     },
     {
@@ -481,7 +481,7 @@
     },
     {
       "path": "data/summary_metrics.json",
-      "bytes": 27807,
+      "bytes": 27518,
       "top_level_type": "dict"
     },
     {
@@ -511,7 +511,7 @@
     },
     {
       "path": "data/task_surface_integrity.json",
-      "bytes": 45779,
+      "bytes": 46246,
       "top_level_type": "dict"
     },
     {
@@ -536,7 +536,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 20022,
+      "bytes": 20141,
       "top_level_type": "dict"
     },
     {
@@ -618,7 +618,7 @@
     {
       "path": "assets/charts/research_direction_coverage.svg",
       "exists": true,
-      "bytes": 5078,
+      "bytes": 5347,
       "format": "SVG",
       "has_viewbox": true
     },
@@ -733,7 +733,7 @@
     {
       "path": "assets/pipeline_diagram.png",
       "exists": true,
-      "bytes": 704575,
+      "bytes": 711222,
       "width": 1800,
       "height": 1120,
       "format": "PNG"
@@ -749,7 +749,7 @@
     {
       "path": "assets/task_architectures.png",
       "exists": true,
-      "bytes": 774391,
+      "bytes": 757827,
       "width": 1800,
       "height": 2450,
       "format": "PNG"
@@ -757,9 +757,9 @@
     {
       "path": "assets/task_suite_infographic.png",
       "exists": true,
-      "bytes": 1591194,
+      "bytes": 1899884,
       "width": 1800,
-      "height": 6600,
+      "height": 7600,
       "format": "PNG"
     }
   ]

research_roadmap.html

@@ -4,7 +4,7 @@
   <meta charset="utf-8">
   <meta name="viewport" content="width=device-width, initial-scale=1">
   <title>Research Roadmap | Ropedia Xperience-10M</title>
-  <meta name="description" content="Interactive research roadmap connecting the Ropedia Xperience-10M 12-task suite, four research tracks, current sample results, Qwen3-Omni fine-tuning, and foundation-model branch selection.">
+  <meta name="description" content="Interactive research roadmap connecting the Ropedia Xperience-10M 20-task suite, four research tracks, current sample results, Qwen3-Omni fine-tuning, and foundation-model branch selection.">
   <meta name="theme-color" content="#020502">
   <link rel="icon" href="favicon.png" type="image/png" sizes="64x64">
   <link rel="preconnect" href="https://fonts.googleapis.com">
@@ -626,7 +626,7 @@
   <header class="hero">
     <div class="wrap hero-grid">
       <div>
-        <div class="eyebrow">12 tasks / 4 tracks / scale-up path</div>
+        <div class="eyebrow">20 tasks / 4 tracks / scale-up path</div>
         <h1>Interactive Research Roadmap.</h1>
         <p class="hero-copy">
           This page connects the current public-sample task lab to the four research
@@ -657,7 +657,7 @@
         <div class="route">
           <div class="route-step">
             <strong>01</strong>
-            <div><b>Public sample</b><span>12 task contracts and current baselines</span></div>
+            <div><b>Public sample</b><span>20 task contracts and current baselines</span></div>
             <em id="routeSample">1 episode</em>
           </div>
           <div class="route-step">