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ARTIFACT_GUIDE.md

@@ -50,7 +50,7 @@ Xperience-native pretraining goal.
 | [`docs/data/brand_assets.json`](docs/data/brand_assets.json) | Machine-readable logo/brand manifest for the website, README, Hugging Face cards, favicon, app icon, and social preview. |
 | [`docs/assets/brand/xperience10m-logo-social-card.png`](docs/assets/brand/xperience10m-logo-social-card.png) | Project logo card used by README and Hugging Face cards. |
 | [`scripts/build_brand_assets.py`](scripts/build_brand_assets.py) | Regenerates deterministic logo derivatives, favicon variants, app icons, and the social card from the generated logo mark. |
-| [`docs/assets/task_suite_infographic.png`](docs/assets/task_suite_infographic.png) | Primary 12-task suite map with sample modality thumbnails. |
+| [`docs/assets/task_suite_infographic.png`](docs/assets/task_suite_infographic.png) | Primary task-suite map with sample modality thumbnails. |
 | [`docs/assets/pipeline_diagram.png`](docs/assets/pipeline_diagram.png) | Episode-to-task pipeline overview. |
 | [`docs/assets/task_architectures.png`](docs/assets/task_architectures.png) | Minimal and neural task-head architecture map. |
 
@@ -69,10 +69,13 @@ Xperience-native pretraining goal.
 
 | Artifact | What it shows |
 | --- | --- |
-| [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json) | The 12 task contracts, chronological split, and minimal/neural metrics. |
+| [`TASK_SUITE_20.md`](TASK_SUITE_20.md) | Reader-facing table for the unified 20-task suite. |
+| [`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_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_delta_summary.csv`](results/audio_ablation/audio_delta_summary.csv) | Compact per-task audio delta table for quick manual inspection. |

EVALUATION_PROTOCOL.md

@@ -43,41 +43,36 @@ Minimal heads are used first because they make task contracts easy to inspect.
 Neural MLP heads reuse the same windows, splits, and feature tensors; they
 are not foundation models.
 
-## Task Contracts
-
-| Task | Artifact id | Family | Unit | Input -> target | Primary metric | Minimal | Neural |
-| --- | --- | --- | --- | --- | --- | ---: | ---: |
-| Action Recognition | `timeline_action` | supervised classification | single window | current 20-frame all-feature window -> current action label | macro_f1 (higher better) | 0.0500 | 0.0148 |
-| Procedure Step Recognition | `timeline_subtask` | supervised classification | single window | current 20-frame all-feature window -> current subtask label | macro_f1 (higher better) | 0.0506 | 0.0281 |
-| Action Boundary Detection | `transition_detection` | temporal diagnostic | single window | current 20-frame all-feature window -> action boundary versus steady | macro_f1 (higher better) | 0.6118 | 0.5862 |
-| Next-Action Prediction | `next_action` | short-horizon prediction | single window | current 20-frame all-feature window at time t -> action label at t + 20 frames | macro_f1 (higher better) | 0.0593 | 0.0419 |
-| Hand Trajectory Forecasting | `hand_trajectory_forecast` | trajectory regression | single window | current all-feature window -> future left/right hand 3D joints for 10 frames | mpjpe (lower better) | 0.8647 | 0.1079 |
-| Contact State Prediction | `contact_prediction` | binary classification | single window | non-contact and non-caption feature blocks -> any body contact | macro_f1 (higher better) | 1.0000 | 1.0000 |
-| Object Relevance Prediction | `object_relevance` | multi-label classification | single window | non-caption feature blocks -> current relevant object set | micro_f1 (higher better) | 0.1803 | 0.1679 |
-| Language Grounding | `caption_grounding` | retrieval | caption query | caption object/interaction query plus candidate sensor windows -> matching time window | mrr (higher better) | 0.0160 | 0.0168 |
-| Cross-Modal Retrieval | `cross_modal_retrieval` | retrieval | sensor query | motion, IMU, and camera query features -> matching depth/video window | top5_accuracy (higher better) | 0.3678 | 0.1983 |
-| Cross-Modal Reconstruction | `modality_reconstruction` | cross-modal regression | single window | motion, IMU, and camera features -> depth/video feature vector | r2 (higher better) | -0.0153 | -0.0102 |
-| Temporal Order Verification | `temporal_order` | pairwise diagnostic | adjacent window pair | two adjacent windows -> correct versus reversed order | f1 (higher better) | 0.5400 | 0.8520 |
-| Multimodal Synchronization Detection | `misalignment_detection` | pairwise diagnostic | paired modality window | motion side plus visual/depth side -> aligned versus shifted by 8 windows | f1 (higher better) | 0.5052 | 0.7153 |
-
-## Tier-2 Extension Contracts
-
-The core 12-task suite remains the canonical benchmark. The Tier-2 layer
-adds sample-supported extension baselines using the same windows, feature
-manifest, chronological split, and minimal/neural head pattern. Regeneration
-requires the raw public-sample `annotation.hdf5` for interaction/object
-targets, but raw files are not redistributed.
-
-| Tier-2 task | Artifact id | Family | Input -> target | Primary metric | Minimal | Neural |
-| --- | --- | --- | --- | --- | ---: | ---: |
-| Long-Horizon Next-Action Forecasting | `long_horizon_next_action` | classification | Current 20-frame non-caption multimodal window. -> Action label five seconds later. | macro_f1 (higher better) | 0.0750 | 0.0655 |
-| Long-Horizon Next-Subtask Forecasting | `next_subtask_forecast` | classification | Current 20-frame non-caption multimodal window. -> Procedure subtask label five seconds later. | macro_f1 (higher better) | 0.0455 | 0.0507 |
-| Interaction Text Prediction | `interaction_text_prediction` | classification | Current 20-frame sensor window with caption-text features removed. -> Raw annotation interaction phrase for the same window. | macro_f1 (higher better) | 0.0444 | 0.0381 |
-| Action-Object Relation Prediction | `action_object_relation` | classification | Current 20-frame sensor window with caption-text features removed. -> Joint action plus active object-set relation. | macro_f1 (higher better) | 0.0000 | 0.0000 |
-| Future Object-Set Forecasting | `object_set_forecast` | multi_label | Current 20-frame sensor window with caption-text features removed. -> Object set active five seconds later. | micro_f1 (higher better) | 0.1694 | 0.1972 |
-| IMU-to-Hand Pose Reconstruction | `imu_to_hand_pose` | regression | Current IMU acceleration/gyroscope feature block only. -> Current left/right hand joint feature blocks. | mae (lower better) | 0.0420 | 0.0426 |
-| Camera-View Synchronization Retrieval | `camera_view_sync_retrieval` | retrieval | Fisheye camera-1 feature query projected into fisheye camera-3 feature space. -> The synchronized held-out camera-3 window. | mrr (higher better) | 0.4943 | 0.2409 |
-| Time-to-Next-Transition Regression | `time_to_transition` | regression | Current 20-frame non-caption multimodal window. -> Frames until the next action-label boundary, capped at 200 frames. | mae (lower better) | 10.5374 | 10.5545 |
+## Unified 20-Task Contracts
+
+Tasks 1-12 are the original public-sample task contracts. Tasks 13-20
+are additional sample-supported contracts attached to the same 20-frame
+window, feature, chronological split, leakage-control, and minimal/neural
+baseline setup. Historical `tier2_task_suite` paths are retained only as
+stable artifact locations for tasks 13-20.
+
+| # | Task | Artifact id | Origin | Family | Unit | Input -> target | Primary metric | Minimal | Neural |
+| ---: | --- | --- | --- | --- | --- | --- | --- | ---: | ---: |
+| 1 | Action Recognition | `timeline_action` | original | supervised classification | single window | current 20-frame all-feature window -> current action label | macro_f1 (higher better) | 0.0500 | 0.0148 |
+| 2 | Procedure Step Recognition | `timeline_subtask` | original | supervised classification | single window | current 20-frame all-feature window -> current subtask label | macro_f1 (higher better) | 0.0506 | 0.0281 |
+| 3 | Action Boundary Detection | `transition_detection` | original | temporal diagnostic | single window | current 20-frame all-feature window -> action boundary versus steady | macro_f1 (higher better) | 0.6118 | 0.5862 |
+| 4 | Next-Action Prediction | `next_action` | original | short-horizon prediction | single window | current 20-frame all-feature window at time t -> action label at t + 20 frames | macro_f1 (higher better) | 0.0593 | 0.0419 |
+| 5 | Hand Trajectory Forecasting | `hand_trajectory_forecast` | original | trajectory regression | single window | current all-feature window -> future left/right hand 3D joints for 10 frames | mpjpe (lower better) | 0.8647 | 0.1079 |
+| 6 | Contact State Prediction | `contact_prediction` | original | binary classification | single window | non-contact and non-caption feature blocks -> any body contact | macro_f1 (higher better) | 1.0000 | 1.0000 |
+| 7 | Object Relevance Prediction | `object_relevance` | original | multi-label classification | single window | non-caption feature blocks -> current relevant object set | micro_f1 (higher better) | 0.1803 | 0.1679 |
+| 8 | Language Grounding | `caption_grounding` | original | retrieval | caption query | caption object/interaction query plus candidate sensor windows -> matching time window | mrr (higher better) | 0.0160 | 0.0168 |
+| 9 | Cross-Modal Retrieval | `cross_modal_retrieval` | original | retrieval | sensor query | motion, IMU, and camera query features -> matching depth/video window | top5_accuracy (higher better) | 0.3678 | 0.1983 |
+| 10 | Cross-Modal Reconstruction | `modality_reconstruction` | original | cross-modal regression | single window | motion, IMU, and camera features -> depth/video feature vector | r2 (higher better) | -0.0153 | -0.0102 |
+| 11 | Temporal Order Verification | `temporal_order` | original | pairwise diagnostic | adjacent window pair | two adjacent windows -> correct versus reversed order | f1 (higher better) | 0.5400 | 0.8520 |
+| 12 | Multimodal Synchronization Detection | `misalignment_detection` | original | pairwise diagnostic | paired modality window | motion side plus visual/depth side -> aligned versus shifted by 8 windows | f1 (higher better) | 0.5052 | 0.7153 |
+| 13 | Long-Horizon Next-Action Forecasting | `long_horizon_next_action` | additional | classification | single aligned window | Current 20-frame non-caption multimodal window. -> Action label five seconds later. | macro_f1 (higher better) | 0.0750 | 0.0655 |
+| 14 | Long-Horizon Next-Subtask Forecasting | `next_subtask_forecast` | additional | classification | single aligned window | Current 20-frame non-caption multimodal window. -> Procedure subtask label five seconds later. | macro_f1 (higher better) | 0.0455 | 0.0507 |
+| 15 | Interaction Text Prediction | `interaction_text_prediction` | additional | classification | single aligned window | Current 20-frame sensor window with caption-text features removed. -> Raw annotation interaction phrase for the same window. | macro_f1 (higher better) | 0.0444 | 0.0381 |
+| 16 | Action-Object Relation Prediction | `action_object_relation` | additional | classification | single aligned window | Current 20-frame sensor window with caption-text features removed. -> Joint action plus active object-set relation. | macro_f1 (higher better) | 0.0000 | 0.0000 |
+| 17 | Future Object-Set Forecasting | `object_set_forecast` | additional | multi_label | single aligned window | Current 20-frame sensor window with caption-text features removed. -> Object set active five seconds later. | micro_f1 (higher better) | 0.1694 | 0.1972 |
+| 18 | IMU-to-Hand Pose Reconstruction | `imu_to_hand_pose` | additional | regression | single aligned window | Current IMU acceleration/gyroscope feature block only. -> Current left/right hand joint feature blocks. | mae (lower better) | 0.0420 | 0.0426 |
+| 19 | Camera-View Synchronization Retrieval | `camera_view_sync_retrieval` | additional | retrieval | held-out query window | Fisheye camera-1 feature query projected into fisheye camera-3 feature space. -> The synchronized held-out camera-3 window. | mrr (higher better) | 0.4943 | 0.2409 |
+| 20 | Time-to-Next-Transition Regression | `time_to_transition` | additional | regression | single aligned window | Current 20-frame non-caption multimodal window. -> Frames until the next action-label boundary, capped at 200 frames. | mae (lower better) | 10.5374 | 10.5545 |
 
 ## Leakage Controls
 

FIGURE_INDEX.md

@@ -14,10 +14,10 @@ Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience
 | Project logo mark | `docs/assets/brand/xperience10m-logo-mark-512.png` | 512 x 512 | `scripts/build_brand_assets.py` | Primary X-shaped multimodal camera mark used for the website header, README, HF cards, and brand identity. |
 | Project logo social card | `docs/assets/brand/xperience10m-logo-social-card.png` | 1200 x 630 | `scripts/build_brand_assets.py` | Large preview image for README, Hugging Face cards, and Open Graph/Twitter social sharing. |
 | Project favicon | `docs/assets/brand/xperience10m-logo-favicon-64.png` | 64 x 64 | `scripts/build_brand_assets.py` | Small dark-tile logo for browser tabs and compact navigation. |
-| 12-task suite infographic | `docs/assets/task_suite_infographic.png` | 1800 x 6600 | `scripts/render_task_suite_infographic.py` | Primary visual map of the task suite, verified metrics, and sample modalities. |
+| Original task-suite infographic | `docs/assets/task_suite_infographic.png` | 1800 x 6600 | `scripts/render_task_suite_infographic.py` | Primary visual map of the original task families, verified metrics, and sample modalities; the unified public suite is now documented as 20 tasks. |
 | Episode-to-task pipeline diagram | `docs/assets/pipeline_diagram.png` | 1800 x 1120 | `scripts/generate_visualizations.py` | End-to-end data processing and evaluation pipeline overview. |
 | Qwen3-Omni LoRA training pipeline | `docs/assets/qwen3_omni_lora_pipeline.png` | 1536 x 1024 | `docs/assets/qwen3_omni_lora_pipeline.prompt.md` | Detailed raw-data-to-adapter flow for staged Xperience-10M Qwen3-Omni LoRA training. |
-| Minimal and neural task architecture map | `docs/assets/task_architectures.png` | 1800 x 2450 | `scripts/render_overview_figures.py` | All 12 task heads and shared feature contracts. |
+| Minimal and neural task architecture map | `docs/assets/task_architectures.png` | 1800 x 2450 | `scripts/render_overview_figures.py` | Minimal and neural heads for the original task contracts and shared feature contracts. |
 | Video modality thumbnail | `docs/assets/modalities/video.jpg` | 880 x 520 | `scripts/export_modality_atlas_assets.py` | Derived thumbnail for synchronized camera streams. |
 | Audio modality thumbnail | `docs/assets/modalities/audio.png` | 880 x 520 | `scripts/export_modality_atlas_assets.py` | Derived waveform thumbnail for the MP4 AAC stream. |
 | Depth modality thumbnail | `docs/assets/modalities/depth.jpg` | 880 x 520 | `scripts/export_modality_atlas_assets.py` | Derived depth and confidence thumbnail. |
@@ -30,7 +30,7 @@ Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience
 | Minimal-vs-neural task score chart | `docs/assets/charts/episode_task_scores_minimal_vs_neural.svg` | 1100 x 964 | `scripts/generate_visualizations.py` | Side-by-side baseline comparison over the same window contracts. |
 | Research direction coverage chart | `docs/assets/charts/research_direction_coverage.svg` | 1180 x 700 | `scripts/generate_visualizations.py` | Four-track coverage map for Ropedia research directions. |
 | Research direction extension chart | `docs/assets/charts/research_direction_extension_tasks.svg` | 1420 x 920 | `scripts/generate_visualizations.py` | Four coded extension probes, one per Ropedia research direction. |
-| Tier-2 extension task suite chart | `docs/assets/charts/tier2_task_suite.svg` | 1440 x 832 | `scripts/tier2_task_suite.py` | Eight sample-supported Tier-2 extension tasks with aligned minimal and neural baseline metrics. |
+| Tasks 13-20 baseline chart | `docs/assets/charts/tier2_task_suite.svg` | 1440 x 832 | `scripts/tier2_task_suite.py` | Eight additional sample-supported tasks in the unified 20-task suite with aligned minimal and neural baseline metrics. |
 | Feature block chart | `docs/assets/charts/feature_blocks.svg` | 1100 x 760 | `scripts/generate_visualizations.py` | Feature allocation by modality block. |
 | Minimal task score chart | `docs/assets/charts/episode_task_scores.svg` | 1100 x 556 | `scripts/generate_visualizations.py` | Minimal baseline metric snapshot across the task suite. |
 | Cross-modal retrieval chart | `docs/assets/charts/cross_modal_retrieval.svg` | 1100 x 284 | `scripts/generate_visualizations.py` | Retrieval behavior chart for the cross-modal task. |

PROJECT_BRIEF.md

@@ -19,7 +19,7 @@ results, and see what remains before multi-episode model-quality claims.
 | Capability | Evidence in this project |
 | --- | --- |
 | Data understanding | `feature_manifest.json`, `available_modalities.json`, modality atlas, episode-window HF viewer |
-| Task design | 12 core task contracts, eight Tier-2 extension baselines, task cards, case-study walkthroughs, and four research-direction extension probes |
+| Task design | 20 unified task contracts, task cards, case-study walkthroughs, and four research-direction extension probes |
 | Evaluation rigor | chronological split, per-task metrics, predictions, confusion matrices, leakage notes, and generated takeaways |
 | Scale-up planning | Final verified 96/16/16 Qwen3-Omni diagnostic result, same-split 128-episode baseline alignment, Cosmos3-Nano compatibility branch, and policy-model candidates after action-space conversion |
 
@@ -29,8 +29,8 @@ results, and see what remains before multi-episode model-quality claims.
 | --- | --- |
 | Data unit | 1 public sample episode, 5,821 frames, 1,161 synchronized 20-frame windows |
 | Modalities | Video-derived features, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived features |
-| Task suite | 12 embodied-AI core task contracts plus eight Tier-2 extensions with inputs, targets, metrics, predictions, and setup alignment |
-| Models | Minimal linear/ridge/logistic baselines plus compact PyTorch MLP heads for the same 12 core tasks and Tier-2 extensions |
+| Task suite | 20 embodied-AI task contracts with inputs, targets, metrics, predictions, and setup alignment |
+| Models | Minimal linear/ridge/logistic baselines plus compact PyTorch MLP heads for the unified 20-task public-sample suite |
 | Research map | Four Ropedia research directions with direct, proxy, diagnostic, and extension-task coverage |
 | Scale-up path | A selected 96/16/16 Qwen3-Omni LoRA final diagnostic result is verified; strict-JSON validity meets target, while weak action/subtask metrics guide the next error-analysis pass |
 
@@ -42,7 +42,7 @@ results, and see what remains before multi-episode model-quality claims.
 3. Open `EVALUATION_PROTOCOL.md` before comparing task scores.
 4. Use `RESEARCH_TAKEAWAYS.md` for the current metric interpretation.
 5. Inspect `results/episode_task_suite/feature_manifest.json` to understand one model input.
-6. Use `docs/data/tier2_task_suite.json` to compare the 12 core tasks with the eight sample-supported extension tasks.
+6. Use `TASK_SUITE_20.md` and `docs/data/task_suite_20.json` to read the unified 20-task suite; the historical `docs/data/tier2_task_suite.json` path stores the tasks 13-20 result bundle.
 7. Use `docs/data/omni_finetune_verified_result.json` for the current multi-episode Qwen3-Omni pilot result.
 
 ## What This Enables

PROJECT_README.md

@@ -41,7 +41,7 @@ embodied-AI research infrastructure:
 | Capability | What this project shows |
 | --- | --- |
 | Multimodal data understanding | Parses the public sample into synchronized windows across video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals |
-| Task design | Defines 12 human-readable tasks as the core benchmark, eight Tier-2 extension baselines, and four direction-extension probes with inputs, outputs, process modules, metrics, and case-study walkthroughs |
+| Task design | Defines 20 human-readable tasks in one unified public-sample suite, plus four direction-extension probes with inputs, outputs, process modules, metrics, and case-study walkthroughs |
 | Model and evaluation discipline | Runs minimal and compact neural baselines, records predictions/metrics, keeps chronological split boundaries explicit, and separates sample evidence from held-out claims |
 | Scale-up planning | Connects the public-sample pipeline to 32/128-episode held-out pilots, Qwen3-Omni LoRA, Cosmos-style world-model branches, policy-model branches, and the future Xperience-native foundation-model pretraining goal |
 
@@ -50,7 +50,7 @@ embodied-AI research infrastructure:
 For a first pass, use [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md) or the
 machine-readable [`docs/data/project_brief.json`](docs/data/project_brief.json).
 They give the project shape in one page: what exists now, what the public
-sample can support, where the 12 tasks and baselines live, and how the verified
+sample can support, where the 20 tasks and baselines live, and how the verified
 128-episode baseline, Qwen3-Omni, Cosmos3-Nano, and Cosmos3-Super branches
 should be compared.
 
@@ -58,7 +58,7 @@ should be compared.
 | --- | --- |
 | Understand the whole project quickly | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md) |
 | See the visual research dashboard | [GitHub Pages dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) |
-| Navigate the 12 core tasks, Tier-2 tasks, four tracks, and scale-up plan | [Interactive research roadmap](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/research_roadmap.html), [`docs/data/research_roadmap_interactive.json`](docs/data/research_roadmap_interactive.json), [`docs/data/tier2_task_suite.json`](docs/data/tier2_task_suite.json) |
+| Navigate the unified 20 tasks, four tracks, and scale-up plan | [Interactive research roadmap](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/research_roadmap.html), [`TASK_SUITE_20.md`](TASK_SUITE_20.md), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json), [`docs/data/research_roadmap_interactive.json`](docs/data/research_roadmap_interactive.json) |
 | Compare current task metrics | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`docs/data/summary_metrics.json`](docs/data/summary_metrics.json) |
 | Compare possible foundation backbones | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json) |
 | Understand the future native pretraining goal | [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) |
@@ -81,7 +81,7 @@ Public release checks are exposed as JSON for mirrors and dashboards:
 | --- | --- |
 | Dataset slice | One public Xperience-10M sample episode, 5,821 frames, 1,161 windows, and an 8,546-dimensional representation |
 | Modalities | Video, audio, depth, camera pose/SLAM, hand/body mocap, IMU, calibration, and language annotations |
-| Task suite | 12 human-readable tasks form the core embodied-AI benchmark; eight Tier-2 extension baselines reuse the same public-sample windows, split discipline, and minimal/neural head pattern |
+| Task suite | 20 human-readable tasks form one embodied-AI public-sample suite; tasks 1-12 are the original contracts and tasks 13-20 reuse the same windows, split discipline, and minimal/neural head pattern |
 | Baselines | Minimal linear/ridge/logistic heads plus compact PyTorch MLP task heads over the same chronological split; companion simple/NN metadata baselines are also aligned to the selected 128-episode 96/16/16 split |
 | Research directions | Task mapping and extension probes for human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling |
 | Scale-up path | The selected-episode Qwen3-Omni LoRA final diagnostic result is verified on the 96/16/16 split; same-split simple/NN metadata baselines now cover the 12 task ids as a companion comparison. 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. Cosmos3 now 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. |
@@ -99,13 +99,13 @@ Current contributions:
 
 - manifested sliding-window features over the currently extracted modalities,
 - motion-only and current all-feature baseline models,
-- 12 end-to-end episode-level core tasks,
-- eight Tier-2 extension tasks aligned to the same 20-frame windows and chronological split,
-- lightweight neural MLP heads for the same 12 task contracts,
+- 20 end-to-end episode-level task contracts,
+- tasks 13-20 aligned to the same 20-frame windows and chronological split as tasks 1-12,
+- lightweight neural MLP heads for the same task contracts,
 - a generated four-direction research taxonomy matching the Ropedia job tracks,
 - four additional direction-extension probes with minimal and neural baselines,
 - human-readable research task cards and an interactive scrub/play walkthrough storyboard for every task,
-- an interactive research roadmap connecting 12 tasks, four research tracks, current sample evidence, the Qwen3-Omni scale-up path, and foundation-model branch selection,
+- an interactive research roadmap connecting 20 tasks, four research tracks, current sample evidence, the Qwen3-Omni scale-up path, and foundation-model branch selection,
 - a next-milestone track for Qwen3-Omni fine-tuning, Cosmos 3 world modeling, and sensor-bridge evaluation,
 - a future pretraining plan for an Xperience Embodied Foundation Model over the full corpus after smaller multi-episode stages prove value,
 - metrics, predictions, model weights, manifests, charts, and a two-level
@@ -119,7 +119,7 @@ This project is best read as a staged embodied-AI research study:
 | Layer | Current scope | Where to start |
 | --- | --- | --- |
 | Data understanding | One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned windows, and an 8,546-dimensional multimodal representation. | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md), [`PROJECT_STATUS.md`](PROJECT_STATUS.md) |
-| Task suite | Twelve human-readable core tasks cover action, procedure, contact, object, language, retrieval, reconstruction, order, and synchronization; eight Tier-2 extensions add long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing. | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json), [`results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md`](results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md) |
+| Task suite | Twenty human-readable tasks cover action, procedure, contact, object, language, retrieval, reconstruction, order, synchronization, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing. Tasks 13-20 live under the historical `tier2_task_suite` artifact path for link stability, but they are part of the same suite. | [`TASK_SUITE_20.md`](TASK_SUITE_20.md), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json), [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json), [`results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md`](results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md) |
 | Baselines | Minimal heads and compact PyTorch MLP heads provide a first controlled comparison on the same chronological split; the selected 128-episode setup also has same-split simple/NN metadata baselines for JSON-supported tasks. | [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/), [`results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md`](results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md) |
 | Diagnostics | Audio contribution, modality ablations, timeline overlays, object labels, and alignment stress tests show which signals are useful and which tasks remain hard. | [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md), [`docs/single_episode_explorer.html`](docs/single_episode_explorer.html) |
 | Scale-up | The selected 128-episode Qwen3-Omni LoRA diagnostic path now has a latest verified v6 held-out package: 96/16/16 selected episodes, 34,269 exported windows, 4,032 held-out test predictions, and public-safe metrics/predictions. v6 improves action macro-F1/contact accuracy versus v5, while v5 remains the pinned prior release row because it is stronger on several other metrics. Same-split simple/NN metadata baselines are published for the 12 task ids. Cosmos3-Nano has a verified future-window compatibility package. Cosmos3-Super now has two verified branches: a 448-window base-weight JSON-task Reasoner evaluation and a fine-tuned forward-dynamics LoRA package over camera-pose proxy targets with 2,848 train rows, 512 val rows, and 448 test rows. The 128-episode enhancement pack records the no-new-episode path: dense-window sizing, hierarchical action/subtask targets, task bottlenecks, and experiment cards for the next Qwen/Cosmos/policy pushes without overwriting existing results. | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`TASK_SUITE_ENHANCEMENT_128.md`](TASK_SUITE_ENHANCEMENT_128.md), [`docs/data/task_suite_enhancement_128.json`](docs/data/task_suite_enhancement_128.json), [`docs/data/omni_model_comparison.json`](docs/data/omni_model_comparison.json), [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json), [`docs/data/qwen3_v5_v6_comparison.json`](docs/data/qwen3_v5_v6_comparison.json), [`results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md`](results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md), [`results/omni_finetune/OMNI_MODEL_COMPARISON.md`](results/omni_finetune/OMNI_MODEL_COMPARISON.md), [`results/omni_finetune/verified_public/`](results/omni_finetune/verified_public/), [`results/omni_finetune/task_suite_enhancement_128_v1_20260608/`](results/omni_finetune/task_suite_enhancement_128_v1_20260608/) |
@@ -134,7 +134,8 @@ and [`docs/data/source_alignment_audit.json`](docs/data/source_alignment_audit.j
 The official gated `ropedia-ai/xperience-10m` card reports `31.9 TB` on the
 live HF surface and an `about-1PB` full-scale storage statement; the committed
 API-listing snapshot records `12,103 episode folders` as upstream `metadata only`,
-not a local raw-data inventory. The public sample remains
+not a local raw-data inventory. In other words, those episode folders are
+upstream listing metadata only for this project. The public sample remains
 `ropedia-ai/xperience-10m-sample` under `cc-by-nc-4.0`, with the `HOMIE Toolkit`
 and `Rerun 0.29.0` noted as source tooling. The official responsible-use note
 that the data is `limited in diversity` is preserved.
@@ -149,7 +150,7 @@ They give the current research state in one compact table:
 | Area | Current decision |
 | --- | --- |
 | Public-sample pipeline | Verified on one public sample episode: 5,821 frames, 1,161 windows, 8,546 dimensions |
-| 12-task suite | Verified minimal baselines with committed metrics, predictions, and manifests |
+| 20-task suite | Verified minimal baselines with committed metrics, predictions, and manifests |
 | Neural heads | Verified compact PyTorch MLP heads over the same task contracts and chronological splits |
 | Dataset context | Official Xperience-10M links, sample-vs-gated-data boundary, modality coverage, and redistribution policy are documented |
 | Evaluation protocol | Verified generated protocol for windowing, split policy, leakage controls, and per-task metrics |
@@ -163,14 +164,14 @@ If you are reading the project cold, open these in order:
 
 | Step | Question | Primary artifacts | What should be true |
 | --- | --- | --- | --- |
-| 1 | What is this project? | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md), [`PROJECT_STATUS.md`](PROJECT_STATUS.md), [dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) | A public-sample Xperience-10M research project with 12 tasks, baselines, and a scale-up plan. |
+| 1 | What is this project? | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md), [`PROJECT_STATUS.md`](PROJECT_STATUS.md), [dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) | A public-sample Xperience-10M research project with 20 tasks, baselines, and a scale-up plan. |
 | 2 | What data is used? | [`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md), [official HF dataset](https://huggingface.co/datasets/ropedia-ai/xperience-10m), [sample HF dataset](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) | The implemented suite uses one public sample episode; the gated dataset is reserved for selected multi-episode training. |
 | 3 | What does one model input contain? | [`windows.csv`](results/episode_task_suite/windows.csv), [`feature_manifest.json`](results/episode_task_suite/feature_manifest.json), [`available_modalities.json`](results/episode_task_suite/available_modalities.json) | Each window is an aligned multimodal unit with video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals. |
-| 4 | What are the 12 tasks? | [`results/episode_task_suite/task_walkthroughs/`](results/episode_task_suite/task_walkthroughs/), [`docs/data/task_walkthroughs.json`](docs/data/task_walkthroughs.json) | Every task has a human-readable name, case study, input, process modules, output, metric, and limitation. |
+| 4 | What are the 20 tasks? | [`TASK_SUITE_20.md`](TASK_SUITE_20.md), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json), [`results/episode_task_suite/task_walkthroughs/`](results/episode_task_suite/task_walkthroughs/), [`docs/data/task_walkthroughs.json`](docs/data/task_walkthroughs.json) | Every task has a human-readable name, input, output, metric, baseline scores, and an explicit artifact path. |
 | 5 | How are tasks evaluated? | [`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md), [`docs/data/evaluation_protocol.json`](docs/data/evaluation_protocol.json) | The window unit, chronological split, leakage controls, task metrics, and current limitations are explicit. |
 | 6 | What do the current results mean? | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`docs/data/research_takeaways.json`](docs/data/research_takeaways.json), [`docs/data/summary_metrics.json`](docs/data/summary_metrics.json) | Current metrics describe sample-level task behavior and identify which signals need larger held-out experiments. |
 | 7 | Which models are implemented? | [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json), [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/), [HF baseline repo](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) | Each task has minimal and neural-head evidence over the same feature windows. |
-| 8 | What research directions does this support? | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`docs/data/research_directions.json`](docs/data/research_directions.json), [`docs/data/research_direction_extensions.json`](docs/data/research_direction_extensions.json), [`docs/data/tier2_task_suite.json`](docs/data/tier2_task_suite.json) | The core and Tier-2 tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling. |
+| 8 | What research directions does this support? | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`docs/data/research_directions.json`](docs/data/research_directions.json), [`docs/data/research_direction_extensions.json`](docs/data/research_direction_extensions.json), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json) | The unified tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling. |
 | 9 | Which foundation model comes next? | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json), [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) | Qwen3-Omni is the first held-out LoRA baseline; Cosmos 3 is now represented by Nano future-window compatibility and Super forward-dynamics LoRA; policy models wait for robot-compatible action targets; Xperience-native pretraining is the full-corpus future goal. |
 | 10 | How can the 128-episode suite be pushed without more data? | [`TASK_SUITE_ENHANCEMENT_128.md`](TASK_SUITE_ENHANCEMENT_128.md), [`docs/data/task_suite_enhancement_128.json`](docs/data/task_suite_enhancement_128.json) | The enhancement pack proposes dense windows, hierarchical action/subtask labels, raw-feature shard priorities, and `multiscale_20s10_40s20_80s40` as the next export target. |
 | 11 | How do I reproduce it? | [`REPRODUCIBILITY.md`](REPRODUCIBILITY.md), [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) | Public commands and expected outputs are documented for the sample-episode task suite. |
@@ -214,7 +215,7 @@ robotics, spatial intelligence, and world modeling. The public
 repo provides the sample episode used for the implemented task suite here.
 
 This project keeps those layers separate: the public sample supports the
-current 12-task study, while the gated full dataset is used only for the
+current 20-task study, while the gated full dataset is used only for the
 selected multi-episode Qwen3-Omni pilot. Raw Xperience-10M MP4/HDF5/RRD files
 are not redistributed in this repo or in the Hugging Face mirrors.
 
@@ -229,6 +230,7 @@ The current verified public-sample subset is:
 
 Detailed dataset notes are available in
 [`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md)
+and [`docs/data/xperience10m_dataset_card_alignment.json`](docs/data/xperience10m_dataset_card_alignment.json)
 for readers who need the full upstream-card and access-term context. The
 practical boundary is simple: current task-suite results come from the public
 sample, and the first multi-episode Qwen3-Omni diagnostic pilot is verified but
@@ -294,7 +296,7 @@ The code files are MIT-licensed. Raw Xperience-10M data is not redistributed
 here, and dataset use remains governed by the official Ropedia/Xperience-10M
 terms. See [`LICENSE`](LICENSE) and [`DATA_NOTICE.md`](DATA_NOTICE.md).
 
-![Ropedia Xperience-10M 12-task infographic](docs/assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl)
+![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.
@@ -305,6 +307,11 @@ with [`scripts/render_task_suite_infographic.py`](scripts/render_task_suite_info
 so the published PNG is a presentation graphic with verified labels and metrics,
 not a hallucinated metric sheet.
 
+The complete unified task list is now documented in [`TASK_SUITE_20.md`](TASK_SUITE_20.md)
+and [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json). Tasks 13-20
+also have a compact chart and result bundle under the historical
+`tier2_task_suite` path for stable public links.
+
 The website also includes a responsive native modality atlas backed by
 [`docs/data/modality_atlas.json`](docs/data/modality_atlas.json) and
 [`docs/assets/modalities/`](docs/assets/modalities/). Those assets are small
@@ -314,7 +321,7 @@ derived thumbnails from the public sample, not raw Xperience-10M files.
 
 ![Qwen3-Omni LoRA training pipeline](docs/assets/qwen3_omni_lora_pipeline.png?v=qwen3-lora-v1)
 
-![Minimal and neural 12-task model architectures](docs/assets/task_architectures.png?v=xperience10m-nn)
+![Minimal and neural task model architectures](docs/assets/task_architectures.png?v=xperience10m-nn)
 
 The pipeline and architecture figures use the same pattern: text-free visual
 backgrounds carry the composition, while
@@ -333,11 +340,12 @@ over many episodes and split train/test by held-out episode.
 scripts/
   train_min_action_model.py         # motion/IMU baseline
   train_all_modalities_model.py     # current all-feature lightweight baseline
-  episode_task_suite.py             # 12 end-to-end task definitions
-  neural_task_models.py             # optional PyTorch MLP heads for all 12 tasks
-  research_direction_taxonomy.py    # maps 12 tasks to the four research tracks
+  episode_task_suite.py             # original end-to-end task definitions
+  neural_task_models.py             # optional PyTorch MLP heads for task contracts
+  research_direction_taxonomy.py    # maps original tasks to the four research tracks
   research_direction_extension_tasks.py # one extra data-backed probe per track
-  tier2_task_suite.py              # eight sample-supported Tier-2 extension baselines
+  tier2_task_suite.py              # historical-name builder for tasks 13-20
+  build_unified_task_suite.py       # builds TASK_SUITE_20.md and task_suite_20.json
   task_walkthroughs.py              # human-readable task-card and walkthrough-storyboard metadata
   generate_visualizations.py        # refreshes SVG charts + summary JSON
   render_task_suite_infographic.py  # renders the task-suite presentation PNG
@@ -364,18 +372,19 @@ results/
   min_subtask_model/                # motion-only subtask baseline artifacts
   min_all_modalities_action_model/  # current all-feature action artifacts
   min_all_modalities_subtask_model/ # current all-feature subtask artifacts
-  episode_task_suite/               # 12-task suite metrics and predictions
+  episode_task_suite/               # task-suite metrics and predictions
     neural_mlp/                     # optional neural baseline artifacts per task
     research_directions/            # four-track taxonomy, CSV, and summary
     research_direction_extensions/  # four extra direction probes + predictions
-    tier2_task_suite/               # eight Tier-2 extension baseline tasks + predictions
-    task_walkthroughs/              # case-study walkthroughs for all 12 tasks
+    tier2_task_suite/               # tasks 13-20 baseline tasks + predictions; historical path
+    task_walkthroughs/              # case-study walkthroughs for original tasks
   omni_exploration/                 # ModelScope readiness-check artifacts
 
 docs/
   index.html                        # GitHub Pages dashboard
   data/additional_development_directions.json # concrete non-backbone project directions
   data/summary_metrics.json         # website-readable metrics bundle
+  data/task_suite_20.json           # unified 20-task suite bundle
   data/evidence_contract.json       # machine-readable project scope
   data/artifact_index.json          # compact project-artifact catalog
   data/live_publication_status.json # live GitHub/HF publication verification
@@ -387,15 +396,15 @@ docs/
   data/research_roadmap.json        # multi-episode and omni-model roadmap
   data/research_directions.json     # four-track website data bundle
   data/research_direction_extensions.json # four extra probe data bundle
-  data/tier2_task_suite.json       # eight Tier-2 extension baseline bundle
+  data/tier2_task_suite.json       # tasks 13-20 baseline bundle; historical path
   data/task_walkthroughs.json       # human-readable task-card and walkthrough-storyboard data
   data/modality_atlas.json          # responsive modality-card data
   assets/brand/*.png                # project logo, favicon, social card
-  assets/task_suite_infographic.png # 12-task presentation graphic
+  assets/task_suite_infographic.png # task-suite presentation graphic
   assets/modalities/                # public-sample derived modality thumbnails
   assets/pipeline_diagram.png       # verified episode pipeline graphic
   assets/qwen3_omni_lora_pipeline.png # Qwen3-Omni LoRA training-flow figure
-  assets/task_architectures.png     # verified 12-task minimal architecture map
+  assets/task_architectures.png     # verified task-head architecture map
   assets/charts/*.svg               # regenerated visualizations
 
 notes/
@@ -497,7 +506,7 @@ cd ropedia-xperience-10m-task-suite
 python scripts/episode_task_suite.py --workspace /path/to/workspace
 ```
 
-Run the same 12-task suite with lightweight neural heads:
+Run the original task definitions with lightweight neural heads:
 
 ```bash
 pip install torch
@@ -506,6 +515,14 @@ python scripts/episode_task_suite.py \
   --include-neural
 ```
 
+Then rebuild the unified 20-task index after tasks 13-20 are generated:
+
+```bash
+python scripts/tier2_task_suite.py --workspace /path/to/workspace
+python scripts/build_unified_task_suite.py
+python scripts/build_evaluation_protocol.py
+```
+
 Run the smaller baselines:
 
 ```bash
@@ -870,7 +887,7 @@ and [`docs/data/additional_development_directions.json`](docs/data/additional_de
 
 ## Four Research Directions
 
-The 12 tasks are now organized against the four Ropedia research directions in
+The original task contracts are organized against the four Ropedia research directions in
 a generated artifact, not only in prose:
 
 - [`research_direction_taxonomy.json`](results/episode_task_suite/research_directions/research_direction_taxonomy.json)
@@ -896,12 +913,12 @@ Current direction-level coverage:
 
 The important interpretation is that all four directions can be **started** from
 the Xperience-10M sample modalities, but only direction C is strongly represented
-by the current 12-task suite. Directions A, B, and D need additional targets and
+by the original task suite. Directions A, B, and D need additional targets and
 multi-episode training before they become full research deliverables.
 
 ## Four Direction-Extension Probes
 
-Beyond the original 12 core tasks, the repo now includes one extra data-backed
+Beyond the original task contracts, the repo now includes one extra data-backed
 probe for each research direction. These probes are computed from the same
 `shared_windows.npz`, `windows.csv`, and `feature_manifest.json` artifacts, so
 the reported numbers are computed from sample-derived features and saved metric artifacts.
@@ -931,29 +948,38 @@ still single-episode extension baselines. Full research conclusions still requir
 multi-episode training, held-out episode evaluation, and stronger task-specific
 models.
 
-## Tier-2 Extension Task Suite
+## Unified 20-Task Suite
+
+The sample task surface is now presented as 20 tasks in one suite. Tasks 1-12
+are the original public-sample contracts; tasks 13-20 add long-horizon
+forecasting, interaction text, action-object binding, object-set forecasting,
+IMU-to-hand reconstruction, camera synchronization, and transition timing while
+keeping the same 20-frame window unit, 5-frame stride, chronological split, and
+minimal/neural comparison style.
 
-The sample can support more than the original 12 task contracts. The Tier-2
-runner adds eight extra baselines while keeping the same 20-frame window unit,
-5-frame stride, chronological split, and minimal/neural comparison style.
+The historical `tier2_task_suite` file and directory names remain only for
+stable artifact links. They should be read as the result bundle for tasks
+13-20, not as a separate benchmark tier.
 
+- [`TASK_SUITE_20.md`](TASK_SUITE_20.md)
+- [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json)
 - [`TIER2_TASK_BASELINES.md`](results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md)
 - [`tier2_task_suite_results.json`](results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json)
 - [`docs/data/tier2_task_suite.json`](docs/data/tier2_task_suite.json)
 - [`tier2_task_suite.svg`](docs/assets/charts/tier2_task_suite.svg)
 
-![Tier-2 extension task suite](docs/assets/charts/tier2_task_suite.svg)
-
-| Tier-2 task | Input | Output | Minimal | Neural MLP | Meaning |
-| --- | --- | --- | ---: | ---: | --- |
-| Long-Horizon Next-Action Forecasting | current non-caption multimodal window | action label five seconds later | `0.0750` macro-F1 | `0.0655` macro-F1 | Tests procedure context beyond the one-second next-action task. |
-| Long-Horizon Next-Subtask Forecasting | current non-caption multimodal window | subtask five seconds later | `0.0455` macro-F1 | `0.0507` macro-F1 | Moves anticipation from low-level action to high-level procedure state. |
-| Interaction Text Prediction | current sensor window without caption text | raw interaction phrase | `0.0444` macro-F1 | `0.0381` macro-F1 | Uses the original annotation interaction text instead of only hashed features. |
-| Action-Object Relation Prediction | current sensor window without caption text | joint action plus object-set label | `0.0000` macro-F1 | `0.0000` macro-F1 | Exposes a hard binding target for action-object reasoning. |
-| Future Object-Set Forecasting | current sensor window without caption text | object set five seconds later | `0.1694` micro-F1 | `0.1972` micro-F1 | Predicts which objects become relevant soon. |
-| IMU-to-Hand Pose Reconstruction | IMU feature block only | current left/right hand joints | `0.0420` MAE | `0.0426` MAE | Tests inertial-to-hand sensor bridging. |
-| Camera-View Synchronization Retrieval | fisheye camera-1 query | synchronized fisheye camera-3 window | `0.4943` MRR | `0.2409` MRR | Stress-tests multi-camera temporal alignment. |
-| Time-to-Next-Transition Regression | current non-caption multimodal window | capped frames until next action boundary | `10.5374` MAE frames | `10.5545` MAE frames | Converts boundary detection into continuous timing. |
+![Tasks 13-20 baseline chart](docs/assets/charts/tier2_task_suite.svg)
+
+| # | Task | Input | Output | Minimal | Neural MLP | Meaning |
+| ---: | --- | --- | --- | ---: | ---: | --- |
+| 13 | Long-Horizon Next-Action Forecasting | current non-caption multimodal window | action label five seconds later | `0.0750` macro-F1 | `0.0655` macro-F1 | Tests procedure context beyond the one-second next-action task. |
+| 14 | Long-Horizon Next-Subtask Forecasting | current non-caption multimodal window | subtask five seconds later | `0.0455` macro-F1 | `0.0507` macro-F1 | Moves anticipation from low-level action to high-level procedure state. |
+| 15 | Interaction Text Prediction | current sensor window without caption text | raw interaction phrase | `0.0444` macro-F1 | `0.0381` macro-F1 | Uses the original annotation interaction text instead of only hashed features. |
+| 16 | Action-Object Relation Prediction | current sensor window without caption text | joint action plus object-set label | `0.0000` macro-F1 | `0.0000` macro-F1 | Exposes a hard binding target for action-object reasoning. |
+| 17 | Future Object-Set Forecasting | current sensor window without caption text | object set five seconds later | `0.1694` micro-F1 | `0.1972` micro-F1 | Predicts which objects become relevant soon. |
+| 18 | IMU-to-Hand Pose Reconstruction | IMU feature block only | current left/right hand joints | `0.0420` MAE | `0.0426` MAE | Tests inertial-to-hand sensor bridging. |
+| 19 | Camera-View Synchronization Retrieval | fisheye camera-1 query | synchronized fisheye camera-3 window | `0.4943` MRR | `0.2409` MRR | Stress-tests multi-camera temporal alignment. |
+| 20 | Time-to-Next-Transition Regression | current non-caption multimodal window | capped frames until next action boundary | `10.5374` MAE frames | `10.5545` MAE frames | Converts boundary detection into continuous timing. |
 
 Run:
 

PROJECT_STATUS.md

@@ -20,8 +20,7 @@ The current no-new-episode enhancement layer records how to push the selected
 | Area | Current state | Evidence | Research readout |
 | --- | --- | --- | --- |
 | Public-sample pipeline | Verified | `results/episode_task_suite/summary_report.json`, `results/episode_task_suite/windows.csv`, `results/episode_task_suite/feature_manifest.json` | One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned 20-frame windows, and an 8,546-dimensional current feature contract. |
-| Task suite | Verified | `scripts/episode_task_suite.py`, `results/episode_task_suite/`, `docs/data/summary_metrics.json` | All 12 task contracts have committed metrics, predictions, and minimal baseline outputs. |
-| Tier-2 extension suite | Verified | `scripts/tier2_task_suite.py`, `results/episode_task_suite/tier2_task_suite/`, `docs/data/tier2_task_suite.json` | Eight extra sample-supported tasks reuse the same 20-frame windows, 5-frame stride, chronological split, and minimal/neural head pattern: long-horizon action/subtask forecasting, interaction text, action-object relation, future object set, IMU-to-hand pose, camera-view sync, and time-to-transition regression. |
+| 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. |
 | 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. |
 | 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. |
@@ -57,10 +56,11 @@ The current no-new-episode enhancement layer records how to push the selected
    a new Qwen, Cosmos-style, or VLA/policy branch.
 7. Inspect `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` for the
    long-term full-corpus pretraining goal.
-8. Inspect `docs/data/summary_metrics.json` and
-   `results/episode_task_suite/neural_mlp/` to check the 12-task outputs.
+8. Inspect `TASK_SUITE_20.md`, `docs/data/task_suite_20.json`,
+   `docs/data/summary_metrics.json`, and
+   `results/episode_task_suite/neural_mlp/` to check the unified 20-task outputs.
 9. Inspect `docs/data/tier2_task_suite.json` and
-   `results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md` to compare the eight Tier-2 extension tasks with the core 12.
+   `results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md` only as the historical artifact path for tasks 13-20.
 10. Inspect `results/audio_ablation/AUDIO_ABLATION_SUMMARY.md` before judging
    whether audio helps the current task suite.
 11. Inspect `EVALUATION_PROTOCOL.md` before judging task metrics or leakage

README.md

@@ -53,6 +53,15 @@ The public-sample task layer now includes eight Tier-2 extension baselines in
 5-frame stride, feature manifest, chronological split, and minimal/neural head
 pattern as the core 12 tasks.
 
+## Unified 20-Task Suite
+
+The public-sample task surface is now one unified 20-task suite in
+`TASK_SUITE_20.md` and `docs/data/task_suite_20.json`. Tasks 1-12 are the
+original sample tasks; tasks 13-20 reuse the same 20-frame windows, 5-frame
+stride, feature manifest, chronological split, and minimal/neural head pattern.
+The historical `tier2_task_suite` path is retained only for stable artifact
+links to tasks 13-20.
+
 ## Dataset Boundary
 
 This artifact bundle contains derived artifacts only. It does not redistribute

REPRODUCIBILITY.md

@@ -10,8 +10,7 @@ outside the current public data scope.
 | --- | --- | --- |
 | Sample download | Yes, from `ropedia-ai/xperience-10m-sample` or ModelScope sample mirror | Sample card lists `cc-by-nc-4.0`; raw data is not redistributed in this repo. |
 | Minimal baselines | Yes | One public sample episode, chronological split. |
-| 12-task suite | Yes | Uses the current 8,546-d synchronized multimodal feature contract. |
-| Tier-2 extension suite | Yes, when `annotation.hdf5` is present and `h5py` or HOMIE Toolkit is available | Adds eight sample-supported extension baselines aligned to the same windows and split. |
+| Unified 20-task suite | Yes; tasks 13-20 require `annotation.hdf5` plus `h5py` or HOMIE Toolkit for regeneration | Uses the current 8,546-d synchronized multimodal feature contract, the same 20-frame windows, and the same chronological split. |
 | Neural MLP heads | Yes, when `torch` is installed | Compact task heads only, not a foundation model. |
 | Website figures and charts | Yes | Generated from committed metrics and sample thumbnails. |
 | Public bundle contents | Yes | Covers public repo and prepared HF bundles. |
@@ -77,6 +76,7 @@ python scripts/episode_task_suite.py \
 python scripts/research_direction_taxonomy.py
 python scripts/research_direction_extension_tasks.py
 python scripts/tier2_task_suite.py
+python scripts/build_unified_task_suite.py
 python scripts/task_walkthroughs.py
 python scripts/validate_source_alignment.py
 python scripts/build_evaluation_protocol.py
@@ -94,10 +94,11 @@ python scripts/validate_mirror_parity.py
 python scripts/validate_publication_package.py
 ```
 
-`scripts/tier2_task_suite.py` can use HOMIE Toolkit when present, or a direct
-`h5py` fallback for the public sample's caption JSON. It reads the local raw
-`annotation.hdf5` only to regenerate interaction/object targets; the raw HDF5
-is still ignored by git and excluded from public bundles.
+`scripts/tier2_task_suite.py` has a historical file name, but it now regenerates
+tasks 13-20 for the unified 20-task suite. It can use HOMIE Toolkit when
+present, or a direct `h5py` fallback for the public sample's caption JSON. It
+reads the local raw `annotation.hdf5` only to regenerate interaction/object
+targets; the raw HDF5 is still ignored by git and excluded from public bundles.
 
 ## Owner-Side Staged Qwen3-Omni v6 Reproduction
 
@@ -157,7 +158,7 @@ Verified staged-GPU smoke evidence from 2026-06-14:
 | Command group | Expected artifacts |
 | --- | --- |
 | Minimal baselines | `results/min_action_model/`, `results/min_all_modalities_action_model/`, metrics and model weights |
-| 12-task suite | `results/episode_task_suite/summary_report.json`, per-task `metrics.json`, predictions, confusion matrices |
+| Unified 20-task suite | `TASK_SUITE_20.md`, `docs/data/task_suite_20.json`, `results/episode_task_suite/summary_report.json`, per-task `metrics.json`, predictions, confusion matrices, and the tasks 13-20 historical `tier2_task_suite` result bundle |
 | Neural heads | `results/episode_task_suite/neural_mlp/**/metrics.json`, histories, model checkpoints |
 | Research directions | `results/episode_task_suite/research_directions/`, `docs/data/research_directions.json` |
 | Direction probes | `results/episode_task_suite/research_direction_extensions/`, `docs/data/research_direction_extensions.json` |
@@ -176,9 +177,10 @@ 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 12-task suite from the local
-public sample. The regenerated metrics matched the committed artifacts after
-float normalization.
+minimal baselines, all-modality baselines, and the original 12 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.
 
 Evidence:
 

TASK_SUITE_20.md

@@ -0,0 +1,49 @@
+# Unified 20-Task Suite
+
+The public Xperience-10M sample task surface is one unified set of 20 tasks.
+Tasks 1-12 are the original public-sample tasks. Tasks 13-20 are additional
+sample-supported tasks attached to the same window, split, feature, baseline,
+and leakage-control contract.
+
+Historical artifact paths containing `tier2_task_suite` are kept for stable
+links, but they should be read as the result directory for tasks 13-20, not
+as a separate benchmark tier.
+
+## Shared Setup
+
+- Episode scope: `1` public sample episode.
+- Frames/windows: `5,821` frames and `1,161` aligned windows.
+- Windowing: `20` frames per window, stride `5` frames.
+- Feature vector: `8,546` dimensions from the shared feature manifest.
+- Split: chronological 70/30 train/test by time within the sample episode.
+- Baselines: minimal interpretable heads and compact neural MLP heads.
+- Raw data: MP4/HDF5/RRD files are not redistributed.
+
+## Task Table
+
+| # | Task | Artifact id | Origin | Input -> output | Primary metric | Minimal | Neural |
+| ---: | --- | --- | --- | --- | --- | ---: | ---: |
+| 1 | Action Recognition | `timeline_action` | original task | 20-frame multimodal window -> current action class | macro-F1 (higher better) | 0.0500 | 0.0148 |
+| 2 | Procedure Step Recognition | `timeline_subtask` | original task | 20-frame multimodal window -> current procedure step | macro-F1 (higher better) | 0.0506 | 0.0281 |
+| 3 | Action Boundary Detection | `transition_detection` | original task | current window with boundary target -> boundary or steady | macro-F1 (higher better) | 0.6118 | 0.5862 |
+| 4 | Next-Action Prediction | `next_action` | original task | current window at time t -> action at t+20 frames | macro-F1 (higher better) | 0.0593 | 0.0419 |
+| 5 | Hand Trajectory Forecasting | `hand_trajectory_forecast` | original task | current multimodal window -> future hand-joint trajectory | MPJPE (lower better) | 0.8647 | 0.1079 |
+| 6 | Contact State Prediction | `contact_prediction` | original task | non-contact, non-caption features -> contact or no contact | macro-F1 (higher better) | 1.0000 | 1.0000 |
+| 7 | Object Relevance Prediction | `object_relevance` | original task | non-caption multimodal features -> relevant object set | micro-F1 (higher better) | 0.1803 | 0.1679 |
+| 8 | Language Grounding | `caption_grounding` | original task | text-like query and candidate windows -> ranked matching moments | MRR (higher better) | 0.0160 | 0.0168 |
+| 9 | Cross-Modal Retrieval | `cross_modal_retrieval` | original task | motion/IMU/pose query; depth/video candidates -> ranked visual windows | MRR (higher better) | 0.2693 | 0.1300 |
+| 10 | Cross-Modal Reconstruction | `modality_reconstruction` | original task | motion, IMU, and camera/pose features -> reconstructed depth/video vector | R2 (higher better) | -0.0153 | -0.0102 |
+| 11 | Temporal Order Verification | `temporal_order` | original task | two adjacent windows plus difference vector -> correct or reversed | F1 (higher better) | 0.5400 | 0.8520 |
+| 12 | Multimodal Synchronization Detection | `misalignment_detection` | original task | motion-side and visual/depth-side feature groups -> aligned or shifted | F1 (higher better) | 0.5052 | 0.7153 |
+| 13 | Long-Horizon Next-Action Forecasting | `long_horizon_next_action` | additional task | Current 20-frame non-caption multimodal window. -> Action label five seconds later. | macro-F1 (higher better) | 0.0750 | 0.0655 |
+| 14 | Long-Horizon Next-Subtask Forecasting | `next_subtask_forecast` | additional task | Current 20-frame non-caption multimodal window. -> Procedure subtask label five seconds later. | macro-F1 (higher better) | 0.0455 | 0.0507 |
+| 15 | Interaction Text Prediction | `interaction_text_prediction` | additional task | Current 20-frame sensor window with caption-text features removed. -> Raw annotation interaction phrase for the same window. | macro-F1 (higher better) | 0.0444 | 0.0381 |
+| 16 | Action-Object Relation Prediction | `action_object_relation` | additional task | Current 20-frame sensor window with caption-text features removed. -> Joint action plus active object-set relation. | macro-F1 (higher better) | 0.0000 | 0.0000 |
+| 17 | Future Object-Set Forecasting | `object_set_forecast` | additional task | Current 20-frame sensor window with caption-text features removed. -> Object set active five seconds later. | micro-F1 (higher better) | 0.1694 | 0.1972 |
+| 18 | IMU-to-Hand Pose Reconstruction | `imu_to_hand_pose` | additional task | Current IMU acceleration/gyroscope feature block only. -> Current left/right hand joint feature blocks. | MAE (lower better) | 0.0420 | 0.0426 |
+| 19 | Camera-View Synchronization Retrieval | `camera_view_sync_retrieval` | additional task | Fisheye camera-1 feature query projected into fisheye camera-3 feature space. -> The synchronized held-out camera-3 window. | MRR (higher better) | 0.4943 | 0.2409 |
+| 20 | Time-to-Next-Transition Regression | `time_to_transition` | additional task | Current 20-frame non-caption multimodal window. -> Frames until the next action-label boundary, capped at 200 frames. | MAE frames (lower better) | 10.5374 | 10.5545 |
+
+## Machine-Readable Copy
+
+The JSON mirror is `docs/data/task_suite_20.json`.

data/artifact_index.json

@@ -1,8 +1,8 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-15T17:23:58+00:00",
+  "generated_at_utc": "2026-06-16T04:56:20+00:00",
   "status": "pass",
-  "artifact_count": 169,
+  "artifact_count": 172,
   "missing": [],
   "by_kind": {
     "project_path": 14,
@@ -12,10 +12,10 @@
     "reproducibility": 4,
     "project_scope": 1,
     "source_alignment": 5,
-    "evaluation_protocol": 4,
+    "evaluation_protocol": 6,
+    "website_data": 5,
     "result_interpretation": 5,
     "metrics_source": 27,
-    "website_data": 4,
     "visual_evidence": 7,
     "dataset_context": 1,
     "quality_gate": 12,
@@ -44,8 +44,8 @@
       "surface": "repo_hf",
       "shows": "Gives first-pass readers a concise project shape before the detailed artifact trail.",
       "exists": true,
-      "bytes": 4048,
-      "sha256": "ac0a5129c5d22fe66505568539451960fb02c398296dde96d34df34ae7a2e80a"
+      "bytes": 4047,
+      "sha256": "b92d363caa8cc149a22647c6b8c340e86e850847fb1e1173a0245139850c87c7"
     },
     {
       "id": "project_brief_json",
@@ -55,8 +55,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable first-reader project brief for the website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 4030,
-      "sha256": "63a4ef388904bc9c7c6002f4db5cb5983d4e33de92383961be96c15435626117"
+      "bytes": 4019,
+      "sha256": "9521556a750941a0f9ee8e9541903acbb0fbec2501fd05ed4e7a017fc18cf794"
     },
     {
       "id": "project_status",
@@ -66,8 +66,8 @@
       "surface": "repo_hf",
       "shows": "Gives a compact current-state table for first-pass readers.",
       "exists": true,
-      "bytes": 14406,
-      "sha256": "859c0ef0f305e0a0ae9cb897fac90e4ade18fb0dd63b835a9b1c3a0e57385cf6"
+      "bytes": 14218,
+      "sha256": "079868a224fd516aa4d90a545f21f6281f3b270ea98bd23d4a1fbdb08b722a20"
     },
     {
       "id": "project_status_json",
@@ -77,8 +77,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable copy of the current project status for website and HF mirrors.",
       "exists": true,
-      "bytes": 24109,
-      "sha256": "e52aa0913478e1e447aaec539c1ceed7c96c3219786653c59e5d4f62d1293ce9"
+      "bytes": 22080,
+      "sha256": "bd57e2701085294a8ff32640c3b6012753826ef90e0bd500d111501aa88e08f4"
     },
     {
       "id": "research_roadmap",
@@ -407,8 +407,8 @@
       "surface": "website_hf",
       "shows": "Gives a short project path with scope status and public surfaces.",
       "exists": true,
-      "bytes": 10597,
-      "sha256": "a64b7c033c54879e0183e7ec794d3197fb483024c25947759287fcd4b7e0fec1"
+      "bytes": 10009,
+      "sha256": "e0f8bd65cd15b0fe68c8079045b4c72552daaf644c35b8a7a68426250a4aa441"
     },
     {
       "id": "artifact_guide",
@@ -418,8 +418,8 @@
       "surface": "repo_hf",
       "shows": "Gives the human-readable map from project scope to data, tasks, platform mirrors, and scale-up status.",
       "exists": true,
-      "bytes": 19677,
-      "sha256": "3a1fc1bcd83432be538bff201f55b2b6051d26cc7230deb380f2c76bca8b2c6d"
+      "bytes": 20096,
+      "sha256": "f4450170d655366ad7c1073a8468d61152ea27eea46f89b9e16e141e04bdfcf7"
     },
     {
       "id": "official_dataset_card_alignment",
@@ -463,7 +463,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "58546132bb14cb9e4fb12f4cfb3ca821e273664e25ec60bbde5a60174c3ad663"
+      "sha256": "c5401a313bcc68152e590cf06fa7c5219d42617566dff9cf82cc80905a4c288e"
     },
     {
       "id": "source_alignment_validator",
@@ -517,8 +517,8 @@
       "surface": "repo_hf",
       "shows": "Defines the window unit, chronological split, task metrics, leakage controls, and current limitations.",
       "exists": true,
-      "bytes": 8820,
-      "sha256": "9ade39fbb29f9d7bad609ea61697c52bc4928e6ca092f7ac5b0831192908b645"
+      "bytes": 9195,
+      "sha256": "ca60203b000726bb8e8cb3a2dc37e7e0821f7e7013a3437a1612f670269cc1df"
     },
     {
       "id": "evaluation_protocol_json",
@@ -528,8 +528,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable protocol generated from committed task metrics for website and HF mirrors.",
       "exists": true,
-      "bytes": 21815,
-      "sha256": "4aefd9340935eb5390b3f55040914ee2d2c27e1a2bcbb4e92070d1a89f00e126"
+      "bytes": 24020,
+      "sha256": "df8da790182374a64b7e5aed94f9cc945fdecef11faf37b707220bc91c1ac9a7"
     },
     {
       "id": "evaluation_protocol_builder",
@@ -539,8 +539,41 @@
       "surface": "repo_hf",
       "shows": "Regenerates the protocol from committed summary metrics and task artifacts.",
       "exists": true,
-      "bytes": 20657,
-      "sha256": "8d54f6b5e4bb66e22f863e5c9c9f7442c5ad3fd677f4c0202a37b0c987daa250"
+      "bytes": 19994,
+      "sha256": "153d1e82fa34f3e22fd1a37f3bf012f8a0eae98002278435991d973b2053d31e"
+    },
+    {
+      "id": "task_suite_20",
+      "title": "Unified 20-task suite",
+      "path": "TASK_SUITE_20.md",
+      "kind": "evaluation_protocol",
+      "surface": "repo_hf",
+      "shows": "Reader-facing table for the single unified public-sample task suite: tasks 1-12 plus tasks 13-20 under the same window, split, feature, and baseline contract.",
+      "exists": true,
+      "bytes": 5243,
+      "sha256": "57c5c742a43d37c2d17a2ee365374273f5d25f973b46b945972a3cfffd42aef7"
+    },
+    {
+      "id": "task_suite_20_json",
+      "title": "Unified 20-task suite JSON",
+      "path": "docs/data/task_suite_20.json",
+      "kind": "website_data",
+      "surface": "website_hf",
+      "shows": "Machine-readable unified 20-task index for the website, Hugging Face mirrors, and live verification.",
+      "exists": true,
+      "bytes": 34648,
+      "sha256": "3849e69949d65d1cf6c08bc77fc398c7c33b62b6cf2d0212cb8695fb976c3731"
+    },
+    {
+      "id": "task_suite_20_builder",
+      "title": "Unified 20-task suite builder",
+      "path": "scripts/build_unified_task_suite.py",
+      "kind": "evaluation_protocol",
+      "surface": "repo_hf",
+      "shows": "Regenerates the unified 20-task JSON and Markdown from the original 12-task metrics plus the tasks 13-20 result bundle.",
+      "exists": true,
+      "bytes": 12322,
+      "sha256": "ad2ae2f918fb8362e47d271ae8f1c1f3807b009a7241fa7df94f84fbddccffe8"
     },
     {
       "id": "research_takeaways",
@@ -581,7 +614,7 @@
       "path": "scripts/audio_ablation_and_raw_upgrade.py",
       "kind": "result_interpretation",
       "surface": "repo_hf",
-      "shows": "Measures audio contribution variants across all 12 task contracts.",
+      "shows": "Measures audio contribution variants across the original task contracts.",
       "exists": true,
       "bytes": 43144,
       "sha256": "f7e3a38ec906dac7ca55b13c49720bd41ed89a1fd994c7d54730a4de5dfd1b59"
@@ -625,7 +658,7 @@
       "path": "docs/assets/charts/audio_ablation_delta.svg",
       "kind": "visual_evidence",
       "surface": "website_hf",
-      "shows": "Bar chart of measured current-audio primary-metric deltas across the 12 tasks.",
+      "shows": "Bar chart of measured current-audio primary-metric deltas across the original tasks.",
       "exists": true,
       "bytes": 4146,
       "sha256": "187dbabe01f9ff18841ff61a1e7fbf85bebdd188cc0f248bb5090d64528e7568"
@@ -638,8 +671,8 @@
       "surface": "repo_hf",
       "shows": "Catalogs public figures, charts, modality thumbnails, dimensions, hashes, roles, and source scripts.",
       "exists": true,
-      "bytes": 5172,
-      "sha256": "7f0516ffebe03ddd778fc03e3bc1e00e3f28ab32833da060efd95cb66ac5fb30"
+      "bytes": 5301,
+      "sha256": "5cf29b921e910de49fba8692603ef22d559d243fc1c6b84503c0a9336d3eeeea"
     },
     {
       "id": "figure_index_json",
@@ -649,8 +682,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 14800,
-      "sha256": "4b1e2fe8760f4637c0bcaf29b095142212580ee14e4f1717928d8effbacb0f50"
+      "bytes": 14939,
+      "sha256": "fdf8d2d06d580908cc7fe70974cbd28c7490180cb314f5d532ba55985edcea6d"
     },
     {
       "id": "figure_index_builder",
@@ -660,8 +693,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
       "exists": true,
-      "bytes": 13795,
-      "sha256": "cac495f422d5e7c79512e21647ffd5cc71945568e70e4537fea3dbc344de1e64"
+      "bytes": 13934,
+      "sha256": "a10e699097c86b645ecb03e42ac4fc2409e10170431a5bccbc38abad403ef209"
     },
     {
       "id": "brand_assets_json",
@@ -726,8 +759,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
-      "bytes": 8097,
-      "sha256": "f3d60aa38872b87da270a42d3edb5d8e23fd8693c6955cbabddb8069f0d3a6ee"
+      "bytes": 8100,
+      "sha256": "f7d8517ba185cc99e0cde2db8412d7651bf906bdfc6ad2f881c34764b3f802fa"
     },
     {
       "id": "public_surface_qa",
@@ -749,7 +782,7 @@
       "volatile": true,
       "shows": "Machine-readable report for SEO/social metadata, accessible tab semantics, public links, project links, and clear project presentation.",
       "exists": true,
-      "bytes": 5659,
+      "bytes": 5698,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -760,8 +793,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the public presentation report before release.",
       "exists": true,
-      "bytes": 12200,
-      "sha256": "f540c839050c615eddf1500c95b2908e3f7a26eea99fb361e452c6dc330c6ad4"
+      "bytes": 12235,
+      "sha256": "fc5742789d882ceca8b7ce8a21ed401c2731fa22036c406847167199a724f09d"
     },
     {
       "id": "task_surface_integrity",
@@ -770,7 +803,7 @@
       "kind": "quality_gate",
       "surface": "website_hf",
       "volatile": true,
-      "shows": "Confirms the public 12-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
+      "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,
       "hash_policy": "existence_and_size_only"
@@ -830,7 +863,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
       "exists": true,
-      "bytes": 100705,
+      "bytes": 123218,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -841,8 +874,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 43271,
-      "sha256": "e2ef9d0537a928e3e17965c2c2df15b98f2def3420c063e934926e3a81dfd79c"
+      "bytes": 49719,
+      "sha256": "db6769f5ed43301f123950e0e2d3d1e8564115dbc6b43ea7a9080da4c9d4c3aa"
     },
     {
       "id": "reproducibility_contract",
@@ -852,8 +885,8 @@
       "surface": "repo_hf",
       "shows": "Defines public reproduction commands, expected outputs, and non-reproducible scale-up boundaries.",
       "exists": true,
-      "bytes": 9541,
-      "sha256": "8d83629254468923008716faedb037443065d7b03ccb7dd6bb038a48ffb835b6"
+      "bytes": 9878,
+      "sha256": "3d0525785f9a1eaad399aeed249fc899d0b141efd778d0d2f3f229dd9b6594a3"
     },
     {
       "id": "reproducibility_matrix",
@@ -863,8 +896,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable reproduction steps with expected artifacts and public boundaries.",
       "exists": true,
-      "bytes": 6545,
-      "sha256": "0ed658ddd3bda61342561473ba8f26d220c57431f118334aa12554ab10919462"
+      "bytes": 6673,
+      "sha256": "585c4e7c67b0bfca80628267b71c7fd5f0ac0c534bdc964c6509d8c53a0e0e83"
     },
     {
       "id": "artifact_index_builder",
@@ -874,8 +907,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 45466,
-      "sha256": "92579ef0fbd923fbefe9a287805e553b3ec657e225c24cdffb699573de196311"
+      "bytes": 46648,
+      "sha256": "86feb8e47b9766762cd683c94bac88a195e36c877e0a470604ff708da738fd00"
     },
     {
       "id": "publication_audit",
@@ -886,7 +919,7 @@
       "volatile": true,
       "shows": "Confirms public bundles exclude raw data, caches, heavy archives, and credential text.",
       "exists": true,
-      "bytes": 7591,
+      "bytes": 7712,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -910,7 +943,7 @@
       "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": 627841,
+      "bytes": 631418,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -922,7 +955,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
-      "bytes": 17710,
+      "bytes": 17815,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -933,12 +966,12 @@
       "surface": "website_hf",
       "shows": "Lists public URLs, upstream sources, and machine-readable project metadata.",
       "exists": true,
-      "bytes": 5436,
-      "sha256": "d8f186f3c6ef824057982fa3d1cd64e7a93e89f2a961d8c10520f0334d0b7cfa"
+      "bytes": 5672,
+      "sha256": "53584e1428f275dceee875aec6a2c0d36517e6dd2d368caee409504217f9aa75"
     },
     {
       "id": "task_summary",
-      "title": "12-task summary report",
+      "title": "Original task summary report",
       "path": "results/episode_task_suite/summary_report.json",
       "kind": "metrics_source",
       "surface": "repo_hf",
@@ -997,7 +1030,7 @@
       "path": "results/episode_task_suite/neural_mlp",
       "kind": "result_directory",
       "surface": "repo_hf_model",
-      "shows": "Stores matching PyTorch MLP results for the 12 task contracts.",
+      "shows": "Stores matching PyTorch MLP results for the original task contracts.",
       "exists": true,
       "file_count": 60,
       "bytes": 90609517
@@ -1008,7 +1041,7 @@
       "path": "results/episode_task_suite/research_directions/research_direction_taxonomy.json",
       "kind": "taxonomy",
       "surface": "repo_hf",
-      "shows": "Maps the 12 tasks to the four Ropedia research directions as direct/proxy/diagnostic.",
+      "shows": "Maps the original tasks to the four Ropedia research directions as direct/proxy/diagnostic.",
       "exists": true,
       "bytes": 19204,
       "sha256": "59bece1a151d8475fde50396fd2e70ed4abcfec33f10e400ef165148fd6e7dde"
@@ -1026,47 +1059,47 @@
     },
     {
       "id": "tier2_task_suite",
-      "title": "Tier-2 extension task suite",
+      "title": "Tasks 13-20 result bundle",
       "path": "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
       "kind": "metrics_source",
       "surface": "repo_hf",
-      "shows": "Stores eight extra sample-supported task contracts with minimal and neural baselines aligned to the core 12-task window/split setup.",
+      "shows": "Stores the historical result bundle for unified tasks 13-20 with minimal and neural baselines aligned to the same 20-task window/split setup.",
       "exists": true,
-      "bytes": 33010,
-      "sha256": "aea51b1e9561c2186ee2b0ae4d42ea07745fdd205de44bdbca2fb73a7de54fca"
+      "bytes": 33402,
+      "sha256": "d4c47e7ae318d7e287b22f8b3293ff19ea573cf3513cb042b8cc2b7cee4bc355"
     },
     {
       "id": "tier2_task_suite_json",
-      "title": "Tier-2 extension task suite JSON",
+      "title": "Tasks 13-20 result JSON",
       "path": "docs/data/tier2_task_suite.json",
       "kind": "website_data",
       "surface": "website_hf",
-      "shows": "Machine-readable Tier-2 task definitions, setup alignment, metrics, and public source paths.",
+      "shows": "Machine-readable tasks 13-20 definitions, setup alignment, metrics, and public source paths; the file name is historical.",
       "exists": true,
-      "bytes": 33010,
-      "sha256": "aea51b1e9561c2186ee2b0ae4d42ea07745fdd205de44bdbca2fb73a7de54fca"
+      "bytes": 33402,
+      "sha256": "d4c47e7ae318d7e287b22f8b3293ff19ea573cf3513cb042b8cc2b7cee4bc355"
     },
     {
       "id": "tier2_task_suite_chart",
-      "title": "Tier-2 task suite chart",
+      "title": "Tasks 13-20 chart",
       "path": "docs/assets/charts/tier2_task_suite.svg",
       "kind": "generated_figure",
       "surface": "website_hf",
-      "shows": "Visual summary of the eight Tier-2 extension baseline metrics.",
+      "shows": "Visual summary of the eight additional task baseline metrics in the unified 20-task suite.",
       "exists": true,
-      "bytes": 5420,
-      "sha256": "1fecf2df972a6c47681ca1fada967e1c222ff04f4ff2b80e19ffc73e19b0448c"
+      "bytes": 5437,
+      "sha256": "3e35e476f559cd6188e5417e4d28c25efc130abafc9cab2d941bc77d559177a1"
     },
     {
       "id": "tier2_task_suite_builder",
-      "title": "Tier-2 task suite builder",
+      "title": "Tasks 13-20 builder",
       "path": "scripts/tier2_task_suite.py",
       "kind": "evaluation_protocol",
       "surface": "repo_hf",
-      "shows": "Regenerates the Tier-2 baselines from shared windows plus the local public-sample annotation HDF5.",
+      "shows": "Regenerates tasks 13-20 from shared windows plus the local public-sample annotation HDF5; the script name is historical.",
       "exists": true,
-      "bytes": 46485,
-      "sha256": "9bc95ec338e6f9d1ffcd1b919f53d7c35e1185b534e901cc1ee59a0691c8c1ce"
+      "bytes": 47071,
+      "sha256": "3143c711af1edc20efd390a6470941c9e087677c6259486d0bdf1b0e06a543e5"
     },
     {
       "id": "task_walkthroughs",
@@ -1081,14 +1114,14 @@
     },
     {
       "id": "task_suite_infographic",
-      "title": "12-task suite infographic",
+      "title": "Original task-suite infographic",
       "path": "docs/assets/task_suite_infographic.png",
       "kind": "generated_figure",
       "surface": "website_hf",
       "shows": "Presents the task suite and sample modality thumbnails with metrics generated from committed files.",
       "exists": true,
-      "bytes": 1588641,
-      "sha256": "1275e2adaef920ecde7c29dc62c8d79d4f13475a0c09bc3baa693f47cdec2e1f"
+      "bytes": 2627286,
+      "sha256": "664c44bec150c4e857cae95d798e379f8a051067863bab1e51ec06d113a34fe4"
     },
     {
       "id": "modality_atlas",
@@ -1195,7 +1228,7 @@
       "path": "results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md",
       "kind": "scaleup_status",
       "surface": "repo_hf",
-      "shows": "Summarizes same-split simple and neural metadata baselines for the 12 task ids, with unsupported markers for tasks that need missing raw 128 feature blocks.",
+      "shows": "Summarizes same-split simple and neural metadata baselines for the 12 original task ids, with unsupported markers for tasks that need missing raw 128 feature blocks.",
       "exists": true,
       "bytes": 2238,
       "sha256": "c70440aa502ec569a840159ab7e05b8e7d4ed70e0091ad9a4b2fb3fb0d3803c1"

data/evaluation_protocol.json

@@ -2,12 +2,13 @@
   "title": "Ropedia Xperience-10M Task Suite Evaluation Protocol",
   "status": "pass",
   "version": "2026-06-01",
-  "generated_at_utc": "2026-06-15T17:23:57+00:00",
+  "generated_at_utc": "2026-06-16T04:47:57+00:00",
   "source_files": [
     "docs/data/summary_metrics.json",
     "results/episode_task_suite/summary_report.json",
     "results/episode_task_suite/windows.csv",
     "results/episode_task_suite/feature_manifest.json",
+    "docs/data/task_suite_20.json",
     "docs/data/tier2_task_suite.json",
     "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json"
   ],
@@ -22,18 +23,14 @@
     "audio_featurized": true,
     "raw_data_redistributed": false
   },
-  "task_tiers": {
-    "core_12": {
-      "status": "canonical_public_sample_suite",
-      "task_count": 12,
-      "results": "docs/data/summary_metrics.json"
-    },
-    "tier2_extension": {
-      "status": "generated_extension_baselines",
-      "task_count": 8,
-      "results": "docs/data/tier2_task_suite.json",
-      "combined_task_count": 20
-    }
+  "task_suite": {
+    "status": "unified_public_sample_suite",
+    "task_count": 20,
+    "original_public_sample_tasks": 12,
+    "additional_public_sample_tasks": 8,
+    "unified_results": "docs/data/task_suite_20.json",
+    "legacy_additional_task_result_path": "docs/data/tier2_task_suite.json",
+    "legacy_path_note": "The tier2_task_suite path is retained for stable links only; tasks 13-20 are presented as part of the same 20-task suite."
   },
   "split_policy": {
     "name": "single_episode_chronological",
@@ -85,6 +82,7 @@
     {
       "task": "timeline_action",
       "task_display_name": "Action Recognition",
+      "origin": "original_public_sample_tasks",
       "family": "supervised classification",
       "unit": "single window",
       "input": "current 20-frame all-feature window",
@@ -100,11 +98,14 @@
       "minimal_primary_metric": 0.05,
       "neural_primary_metric": 0.014814814814814814,
       "minimal_metric_source": "results/episode_task_suite/timeline_action/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json",
+      "task_number": 1,
+      "suite_label": "Task 01"
     },
     {
       "task": "timeline_subtask",
       "task_display_name": "Procedure Step Recognition",
+      "origin": "original_public_sample_tasks",
       "family": "supervised classification",
       "unit": "single window",
       "input": "current 20-frame all-feature window",
@@ -120,11 +121,14 @@
       "minimal_primary_metric": 0.05056355513846935,
       "neural_primary_metric": 0.02810810810810811,
       "minimal_metric_source": "results/episode_task_suite/timeline_subtask/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json",
+      "task_number": 2,
+      "suite_label": "Task 02"
     },
     {
       "task": "transition_detection",
       "task_display_name": "Action Boundary Detection",
+      "origin": "original_public_sample_tasks",
       "family": "temporal diagnostic",
       "unit": "single window",
       "input": "current 20-frame all-feature window",
@@ -140,11 +144,14 @@
       "minimal_primary_metric": 0.6118237590630229,
       "neural_primary_metric": 0.5862068965517241,
       "minimal_metric_source": "results/episode_task_suite/transition_detection/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json",
+      "task_number": 3,
+      "suite_label": "Task 03"
     },
     {
       "task": "next_action",
       "task_display_name": "Next-Action Prediction",
+      "origin": "original_public_sample_tasks",
       "family": "short-horizon prediction",
       "unit": "single window",
       "input": "current 20-frame all-feature window at time t",
@@ -160,11 +167,14 @@
       "minimal_primary_metric": 0.05925925925925927,
       "neural_primary_metric": 0.04186046511627907,
       "minimal_metric_source": "results/episode_task_suite/next_action/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/next_action/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/next_action/metrics.json",
+      "task_number": 4,
+      "suite_label": "Task 04"
     },
     {
       "task": "hand_trajectory_forecast",
       "task_display_name": "Hand Trajectory Forecasting",
+      "origin": "original_public_sample_tasks",
       "family": "trajectory regression",
       "unit": "single window",
       "input": "current all-feature window",
@@ -180,11 +190,14 @@
       "minimal_primary_metric": 0.8646570444107056,
       "neural_primary_metric": 0.10785018652677536,
       "minimal_metric_source": "results/episode_task_suite/hand_trajectory_forecast/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json",
+      "task_number": 5,
+      "suite_label": "Task 05"
     },
     {
       "task": "contact_prediction",
       "task_display_name": "Contact State Prediction",
+      "origin": "original_public_sample_tasks",
       "family": "binary classification",
       "unit": "single window",
       "input": "non-contact and non-caption feature blocks",
@@ -200,11 +213,14 @@
       "minimal_primary_metric": 1.0,
       "neural_primary_metric": 1.0,
       "minimal_metric_source": "results/episode_task_suite/contact_prediction/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json",
+      "task_number": 6,
+      "suite_label": "Task 06"
     },
     {
       "task": "object_relevance",
       "task_display_name": "Object Relevance Prediction",
+      "origin": "original_public_sample_tasks",
       "family": "multi-label classification",
       "unit": "single window",
       "input": "non-caption feature blocks",
@@ -220,11 +236,14 @@
       "minimal_primary_metric": 0.18034382095361662,
       "neural_primary_metric": 0.1679279279279279,
       "minimal_metric_source": "results/episode_task_suite/object_relevance/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json",
+      "task_number": 7,
+      "suite_label": "Task 07"
     },
     {
       "task": "caption_grounding",
       "task_display_name": "Language Grounding",
+      "origin": "original_public_sample_tasks",
       "family": "retrieval",
       "unit": "caption query",
       "input": "caption object/interaction query plus candidate sensor windows",
@@ -240,11 +259,14 @@
       "minimal_primary_metric": 0.016023479050338015,
       "neural_primary_metric": 0.01684125567132316,
       "minimal_metric_source": "results/episode_task_suite/caption_grounding/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json",
+      "task_number": 8,
+      "suite_label": "Task 08"
     },
     {
       "task": "cross_modal_retrieval",
       "task_display_name": "Cross-Modal Retrieval",
+      "origin": "original_public_sample_tasks",
       "family": "retrieval",
       "unit": "sensor query",
       "input": "motion, IMU, and camera query features",
@@ -260,11 +282,14 @@
       "minimal_primary_metric": 0.367816091954023,
       "neural_primary_metric": 0.19827586206896552,
       "minimal_metric_source": "results/episode_task_suite/cross_modal_retrieval/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json",
+      "task_number": 9,
+      "suite_label": "Task 09"
     },
     {
       "task": "modality_reconstruction",
       "task_display_name": "Cross-Modal Reconstruction",
+      "origin": "original_public_sample_tasks",
       "family": "cross-modal regression",
       "unit": "single window",
       "input": "motion, IMU, and camera features",
@@ -279,11 +304,14 @@
       "minimal_primary_metric": -0.015271898913936655,
       "neural_primary_metric": -0.010171410134180991,
       "minimal_metric_source": "results/episode_task_suite/modality_reconstruction/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json",
+      "task_number": 10,
+      "suite_label": "Task 10"
     },
     {
       "task": "temporal_order",
       "task_display_name": "Temporal Order Verification",
+      "origin": "original_public_sample_tasks",
       "family": "pairwise diagnostic",
       "unit": "adjacent window pair",
       "input": "two adjacent windows",
@@ -299,11 +327,14 @@
       "minimal_primary_metric": 0.5399515738498789,
       "neural_primary_metric": 0.8520179372197308,
       "minimal_metric_source": "results/episode_task_suite/temporal_order/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json"
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json",
+      "task_number": 11,
+      "suite_label": "Task 11"
     },
     {
       "task": "misalignment_detection",
       "task_display_name": "Multimodal Synchronization Detection",
+      "origin": "original_public_sample_tasks",
       "family": "pairwise diagnostic",
       "unit": "paired modality window",
       "input": "motion side plus visual/depth side",
@@ -319,13 +350,14 @@
       "minimal_primary_metric": 0.5051698670605613,
       "neural_primary_metric": 0.7152682255845944,
       "minimal_metric_source": "results/episode_task_suite/misalignment_detection/metrics.json",
-      "neural_metric_source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json"
-    }
-  ],
-  "tier2_task_protocols": [
+      "neural_metric_source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json",
+      "task_number": 12,
+      "suite_label": "Task 12"
+    },
     {
       "task": "long_horizon_next_action",
       "task_display_name": "Long-Horizon Next-Action Forecasting",
+      "origin": "additional_public_sample_tasks",
       "family": "classification",
       "unit": "single aligned window",
       "input": "Current 20-frame non-caption multimodal window.",
@@ -336,11 +368,14 @@
       "neural_primary_metric": 0.06545454545454546,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json",
-      "meaning": "Tests whether the current state carries enough procedure context to forecast beyond the one-second core next-action task."
+      "meaning": "Tests whether the current state carries enough procedure context to forecast beyond the one-second core next-action task.",
+      "task_number": 13,
+      "suite_label": "Task 13"
     },
     {
       "task": "next_subtask_forecast",
       "task_display_name": "Long-Horizon Next-Subtask Forecasting",
+      "origin": "additional_public_sample_tasks",
       "family": "classification",
       "unit": "single aligned window",
       "input": "Current 20-frame non-caption multimodal window.",
@@ -351,11 +386,14 @@
       "neural_primary_metric": 0.050724637681159424,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json",
-      "meaning": "Moves from immediate action anticipation to higher-level procedure-state prediction."
+      "meaning": "Moves from immediate action anticipation to higher-level procedure-state prediction.",
+      "task_number": 14,
+      "suite_label": "Task 14"
     },
     {
       "task": "interaction_text_prediction",
       "task_display_name": "Interaction Text Prediction",
+      "origin": "additional_public_sample_tasks",
       "family": "classification",
       "unit": "single aligned window",
       "input": "Current 20-frame sensor window with caption-text features removed.",
@@ -366,11 +404,14 @@
       "neural_primary_metric": 0.0380952380952381,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json",
-      "meaning": "Uses the raw caption JSON interaction field as a language target instead of only the hashed text feature."
+      "meaning": "Uses the raw caption JSON interaction field as a language target instead of only the hashed text feature.",
+      "task_number": 15,
+      "suite_label": "Task 15"
     },
     {
       "task": "action_object_relation",
       "task_display_name": "Action-Object Relation Prediction",
+      "origin": "additional_public_sample_tasks",
       "family": "classification",
       "unit": "single aligned window",
       "input": "Current 20-frame sensor window with caption-text features removed.",
@@ -381,11 +422,14 @@
       "neural_primary_metric": 0.0,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json",
-      "meaning": "Evaluates whether a model can bind what action is happening to which objects are involved."
+      "meaning": "Evaluates whether a model can bind what action is happening to which objects are involved.",
+      "task_number": 16,
+      "suite_label": "Task 16"
     },
     {
       "task": "object_set_forecast",
       "task_display_name": "Future Object-Set Forecasting",
+      "origin": "additional_public_sample_tasks",
       "family": "multi_label",
       "unit": "single aligned window",
       "input": "Current 20-frame sensor window with caption-text features removed.",
@@ -396,11 +440,14 @@
       "neural_primary_metric": 0.19718309859154928,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json",
-      "meaning": "Predicts which objects will become relevant soon, not only which objects are relevant now."
+      "meaning": "Predicts which objects will become relevant soon, not only which objects are relevant now.",
+      "task_number": 17,
+      "suite_label": "Task 17"
     },
     {
       "task": "imu_to_hand_pose",
       "task_display_name": "IMU-to-Hand Pose Reconstruction",
+      "origin": "additional_public_sample_tasks",
       "family": "regression",
       "unit": "single aligned window",
       "input": "Current IMU acceleration/gyroscope feature block only.",
@@ -411,11 +458,14 @@
       "neural_primary_metric": 0.042562149465084076,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json",
-      "meaning": "A sensor-bridge probe for how much hand configuration can be recovered from inertial motion alone."
+      "meaning": "A sensor-bridge probe for how much hand configuration can be recovered from inertial motion alone.",
+      "task_number": 18,
+      "suite_label": "Task 18"
     },
     {
       "task": "camera_view_sync_retrieval",
       "task_display_name": "Camera-View Synchronization Retrieval",
+      "origin": "additional_public_sample_tasks",
       "family": "retrieval",
       "unit": "held-out query window",
       "input": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.",
@@ -426,11 +476,14 @@
       "neural_primary_metric": 0.24086658656597137,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json",
-      "meaning": "Stress-tests multi-camera time alignment beyond the core cross-modal retrieval task."
+      "meaning": "Stress-tests multi-camera time alignment beyond the core cross-modal retrieval task.",
+      "task_number": 19,
+      "suite_label": "Task 19"
     },
     {
       "task": "time_to_transition",
       "task_display_name": "Time-to-Next-Transition Regression",
+      "origin": "additional_public_sample_tasks",
       "family": "regression",
       "unit": "single aligned window",
       "input": "Current 20-frame non-caption multimodal window.",
@@ -441,7 +494,9 @@
       "neural_primary_metric": 10.55449390411377,
       "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json",
       "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json",
-      "meaning": "Turns boundary detection into a continuous timing estimate for procedural control."
+      "meaning": "Turns boundary detection into a continuous timing estimate for procedural control.",
+      "task_number": 20,
+      "suite_label": "Task 20"
     }
   ],
   "global_leakage_controls": [

data/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-15T17:23:38+00:00",
+  "generated_at_utc": "2026-06-16T04:56:21+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
   "figure_count": 23,
   "figures": [
@@ -58,14 +58,14 @@
     },
     {
       "id": "task_suite_infographic",
-      "title": "12-task suite infographic",
+      "title": "Original task-suite infographic",
       "path": "docs/assets/task_suite_infographic.png",
-      "role": "Primary visual map of the task suite, verified metrics, and sample modalities.",
+      "role": "Primary visual map of the original task families, verified metrics, and sample modalities; the unified public suite is now documented as 20 tasks.",
       "source_script": "scripts/render_task_suite_infographic.py",
       "surface": "README, website, HF Space, artifact dataset, model card",
       "exists": true,
-      "bytes": 1588641,
-      "sha256": "1275e2adaef920ecde7c29dc62c8d79d4f13475a0c09bc3baa693f47cdec2e1f",
+      "bytes": 2627286,
+      "sha256": "664c44bec150c4e857cae95d798e379f8a051067863bab1e51ec06d113a34fe4",
       "dimensions": {
         "format": "PNG",
         "width": 1800,
@@ -111,7 +111,7 @@
       "id": "task_architectures",
       "title": "Minimal and neural task architecture map",
       "path": "docs/assets/task_architectures.png",
-      "role": "All 12 task heads and shared feature contracts.",
+      "role": "Minimal and neural heads for the original task contracts and shared feature contracts.",
       "source_script": "scripts/render_overview_figures.py",
       "surface": "README, website, HF artifact dataset, model card",
       "exists": true,
@@ -335,14 +335,14 @@
     },
     {
       "id": "tier2_task_suite_chart",
-      "title": "Tier-2 extension task suite chart",
+      "title": "Tasks 13-20 baseline chart",
       "path": "docs/assets/charts/tier2_task_suite.svg",
-      "role": "Eight sample-supported Tier-2 extension tasks with aligned minimal and neural baseline metrics.",
+      "role": "Eight additional sample-supported tasks in the unified 20-task suite with aligned minimal and neural baseline metrics.",
       "source_script": "scripts/tier2_task_suite.py",
-      "surface": "website extensions, README, HF mirrors",
+      "surface": "website unified task section, README, HF mirrors",
       "exists": true,
-      "bytes": 5420,
-      "sha256": "1fecf2df972a6c47681ca1fada967e1c222ff04f4ff2b80e19ffc73e19b0448c",
+      "bytes": 5437,
+      "sha256": "3e35e476f559cd6188e5417e4d28c25efc130abafc9cab2d941bc77d559177a1",
       "dimensions": {
         "format": "SVG",
         "width": 1440,

data/project_brief.json

@@ -9,7 +9,7 @@
     },
     {
       "capability": "Task design",
-      "evidence": "12 core task contracts, eight Tier-2 extension baselines, task cards, case-study walkthroughs, and four research-direction extension probes"
+      "evidence": "20 unified task contracts, task cards, case-study walkthroughs, and four research-direction extension probes"
     },
     {
       "capability": "Evaluation rigor",
@@ -31,11 +31,11 @@
     },
     {
       "layer": "Task suite",
-      "status": "12 embodied-AI core task contracts plus eight Tier-2 extension baselines with inputs, targets, metrics, predictions, and setup alignment"
+      "status": "20 embodied-AI task contracts with inputs, targets, metrics, predictions, and setup alignment"
     },
     {
       "layer": "Models",
-      "status": "Minimal linear/ridge/logistic baselines plus compact PyTorch MLP heads for the same 12 core tasks and Tier-2 extensions"
+      "status": "Minimal linear/ridge/logistic baselines plus compact PyTorch MLP heads for the unified 20-task public-sample suite"
     },
     {
       "layer": "Research map",
@@ -52,7 +52,7 @@
     "Open EVALUATION_PROTOCOL.md before comparing task scores.",
     "Use RESEARCH_TAKEAWAYS.md for the current metric interpretation.",
     "Inspect results/episode_task_suite/feature_manifest.json to understand one model input.",
-    "Use docs/data/tier2_task_suite.json to compare the 12 core tasks with the eight sample-supported Tier-2 tasks.",
+    "Use TASK_SUITE_20.md and docs/data/task_suite_20.json to read the unified 20-task suite; the historical docs/data/tier2_task_suite.json path stores the tasks 13-20 result bundle.",
     "Use docs/data/omni_finetune_verified_result.json for the current multi-episode Qwen3-Omni pilot result."
   ],
   "scope_boundary": "The public sample is enough to build and verify task definitions, feature contracts, metrics, visualization, and baseline code. The final multi-episode Qwen3-Omni diagnostic result verifies the training loop and strict-JSON output reliability, but does not yet show strong action/subtask model quality.",

data/project_manifest.json

@@ -10,8 +10,6 @@
     "episode_count_verified": 1,
     "window_count_verified": 1161,
     "feature_dim_verified": 8546,
-    "core_task_count": 12,
-    "tier2_extension_task_count": 8,
     "audio_featurized": true,
     "qwen3_omni_32_episode_claim": false,
     "qwen3_omni_verified_diagnostic_pilot": true,
@@ -23,7 +21,11 @@
     "qwen3_omni_held_out_test_windows": 448,
     "qwen3_omni_json_validity_rate": 0.9977678571428571,
     "qwen3_omni_json_quality_target_met": true,
-    "qwen3_omni_lora_adapter_repo": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep"
+    "qwen3_omni_lora_adapter_repo": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep",
+    "task_count": 20,
+    "original_public_sample_task_count": 12,
+    "additional_public_sample_task_count": 8,
+    "legacy_tasks_13_to_20_result_path": "docs/data/tier2_task_suite.json"
   },
   "public_surfaces": {
     "github_repo": "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite",
@@ -90,8 +92,10 @@
     "modality_atlas": "docs/data/modality_atlas.json",
     "research_directions": "docs/data/research_directions.json",
     "research_direction_extensions": "docs/data/research_direction_extensions.json",
-    "tier2_task_suite": "docs/data/tier2_task_suite.json",
-    "task_walkthroughs": "docs/data/task_walkthroughs.json"
+    "task_walkthroughs": "docs/data/task_walkthroughs.json",
+    "task_suite_20": "TASK_SUITE_20.md",
+    "task_suite_20_json": "docs/data/task_suite_20.json",
+    "tasks_13_to_20_result_bundle": "docs/data/tier2_task_suite.json"
   },
   "citation_files": {
     "citation_cff": "CITATION.cff",

data/project_packet.json

@@ -1,167 +1,172 @@
 {
-    "title": "Ropedia Xperience-10M Task Suite Project Packet",
-    "version": "2026-06-14",
-    "scope_status": {
-        "validated_data": "one public Xperience-10M sample episode",
-        "aligned_frames": 5821,
-        "sliding_windows": 1161,
-        "current_feature_dimensions": 8546,
-        "core_task_count": 12,
-        "neural_head_count": 12,
-        "direction_extension_probe_count": 4,
-        "raw_xperience10m_data_in_repo": false,
-        "audio_feature_status": "Audio is one of the synchronized source modalities in the current task representation.",
-        "qwen3_omni_32_episode_claim": false,
-        "qwen3_omni_status": "The selected 96/16/16 Qwen3-Omni v6 diagnostic branch is verified, meets the strict-JSON target, improves action macro-F1/contact accuracy versus v5, and still has weak action/subtask metrics that guide the next error-analysis pass.",
-        "cosmos3_super_forward_dynamics_lora_status": "The first Cosmos3-Super fine-tuned adapter branch is verified as a forward-dynamics LoRA over camera-pose proxy targets; it reports loss metrics, not JSON action-label accuracy.",
-        "task_suite_enhancement_128_status": "Current no-new-episode enhancement pack recommends multiscale_20s10_40s20_80s40, hierarchical action/subtask targets, label-normalized scoring, and raw-feature shards before adding more episodes."
+  "title": "Ropedia Xperience-10M Task Suite Project Packet",
+  "version": "2026-06-14",
+  "scope_status": {
+    "validated_data": "one public Xperience-10M sample episode",
+    "aligned_frames": 5821,
+    "sliding_windows": 1161,
+    "current_feature_dimensions": 8546,
+    "neural_head_count": 12,
+    "direction_extension_probe_count": 4,
+    "raw_xperience10m_data_in_repo": false,
+    "audio_feature_status": "Audio is one of the synchronized source modalities in the current task representation.",
+    "qwen3_omni_32_episode_claim": false,
+    "qwen3_omni_status": "The selected 96/16/16 Qwen3-Omni v6 diagnostic branch is verified, meets the strict-JSON target, improves action macro-F1/contact accuracy versus v5, and still has weak action/subtask metrics that guide the next error-analysis pass.",
+    "cosmos3_super_forward_dynamics_lora_status": "The first Cosmos3-Super fine-tuned adapter branch is verified as a forward-dynamics LoRA over camera-pose proxy targets; it reports loss metrics, not JSON action-label accuracy.",
+    "task_suite_enhancement_128_status": "Current no-new-episode enhancement pack recommends multiscale_20s10_40s20_80s40, hierarchical action/subtask targets, label-normalized scoring, and raw-feature shards before adding more episodes.",
+    "task_count": 20,
+    "original_public_sample_task_count": 12,
+    "additional_public_sample_task_count": 8,
+    "legacy_tasks_13_to_20_result_path": "docs/data/tier2_task_suite.json"
+  },
+  "reading_path": [
+    {
+      "step": 1,
+      "question": "What is the current project scope?",
+      "primary_artifacts": [
+        "PROJECT_STATUS.md",
+        "docs/data/project_status.json",
+        "RESEARCH_ROADMAP.md",
+        "docs/data/research_roadmap.json",
+        "EVIDENCE_CONTRACT.md",
+        "ARTIFACT_GUIDE.md",
+        "EVALUATION_PROTOCOL.md",
+        "FIGURE_INDEX.md",
+        "SOURCE_ALIGNMENT_AUDIT.md",
+        "XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
+        "docs/data/evidence_contract.json",
+        "docs/data/artifact_index.json",
+        "docs/data/brand_assets.json",
+        "docs/data/evaluation_protocol.json",
+        "docs/data/figure_index.json",
+        "docs/data/source_alignment_audit.json",
+        "docs/data/xperience10m_dataset_card_alignment.json",
+        "docs/data/mirror_parity.json",
+        "docs/data/publication_audit.json",
+        "docs/data/scope_claims_audit.json",
+        "docs/data/website_integrity.json",
+        "TASK_SUITE_20.md",
+        "docs/data/task_suite_20.json"
+      ],
+      "readout": "The project status table and roadmap give the compact current-state summary. Single-episode task engineering, metrics, visualizations, public website integrity, mirror parity, same-split 128-episode baselines, the final selected-episode Qwen3-Omni diagnostic result, the Cosmos3-Nano compatibility package, the Cosmos3-Super base-weight Reasoner evaluation, and the Cosmos3-Super Forward-Dynamics LoRA package are implemented; stronger action/subtask quality and policy-compatible action targets remain follow-ups."
     },
-    "reading_path": [
-        {
-            "step": 1,
-            "question": "What is the current project scope?",
-            "primary_artifacts": [
-                "PROJECT_STATUS.md",
-                "docs/data/project_status.json",
-                "RESEARCH_ROADMAP.md",
-                "docs/data/research_roadmap.json",
-                "EVIDENCE_CONTRACT.md",
-                "ARTIFACT_GUIDE.md",
-                "EVALUATION_PROTOCOL.md",
-                "FIGURE_INDEX.md",
-                "SOURCE_ALIGNMENT_AUDIT.md",
-                "XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
-                "docs/data/evidence_contract.json",
-                "docs/data/artifact_index.json",
-                "docs/data/brand_assets.json",
-                "docs/data/evaluation_protocol.json",
-                "docs/data/figure_index.json",
-                "docs/data/source_alignment_audit.json",
-                "docs/data/xperience10m_dataset_card_alignment.json",
-                "docs/data/mirror_parity.json",
-                "docs/data/publication_audit.json",
-                "docs/data/scope_claims_audit.json",
-                "docs/data/website_integrity.json"
-            ],
-            "readout": "The project status table and roadmap give the compact current-state summary. Single-episode task engineering, metrics, visualizations, public website integrity, mirror parity, same-split 128-episode baselines, the final selected-episode Qwen3-Omni diagnostic result, the Cosmos3-Nano compatibility package, the Cosmos3-Super base-weight Reasoner evaluation, and the Cosmos3-Super Forward-Dynamics LoRA package are implemented; stronger action/subtask quality and policy-compatible action targets remain follow-ups."
-        },
-        {
-            "step": 2,
-            "question": "What do the official Xperience-10M dataset and sample cards say?",
-            "primary_artifacts": [
-                "XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
-                "docs/data/xperience10m_dataset_card_alignment.json",
-                "https://huggingface.co/datasets/ropedia-ai/xperience-10m",
-                "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample"
-            ],
-            "readout": "The full upstream dataset is a manually gated large-scale 4D multimodal egocentric source. The public sample card records the sample license, HOMIE Toolkit path, and Rerun 0.29.0 visualization path. This repo validates one public sample episode and lists the current project coverage."
-        },
-        {
-            "step": 3,
-            "question": "Are source facts consistently presented?",
-            "primary_artifacts": [
-                "SOURCE_ALIGNMENT_AUDIT.md",
-                "docs/data/source_alignment_audit.json",
-                "scripts/validate_source_alignment.py"
-            ],
-            "readout": "The source-alignment report checks full-dataset metadata, API-listing notes, public sample license/tooling, and project coverage across repo docs, website, and HF cards."
-        },
-        {
-            "step": 4,
-            "question": "How exactly are the tasks evaluated?",
-            "primary_artifacts": [
-                "EVALUATION_PROTOCOL.md",
-                "docs/data/evaluation_protocol.json",
-                "scripts/build_evaluation_protocol.py"
-            ],
-            "readout": "The protocol fixes the 20-frame window unit, chronological split, train-only normalization, leakage controls, per-task input/target/metric contracts, and current limitations."
-        },
-        {
-            "step": 5,
-            "question": "How can the public pipeline be reproduced?",
-            "primary_artifacts": [
-                "REPRODUCIBILITY.md",
-                "docs/data/reproducibility_matrix.json",
-                "notes/reproducibility_audit.md"
-            ],
-            "readout": "The public sample pipeline has explicit commands, expected outputs, and a prior exact-match reproduction check over the committed metrics."
-        },
-        {
-            "step": 6,
-            "question": "What is inside one model input?",
-            "primary_artifacts": [
-                "results/episode_task_suite/windows.csv",
-                "results/episode_task_suite/feature_manifest.json",
-                "results/episode_task_suite/available_modalities.json",
-                "docs/data/modality_atlas.json"
-            ],
-            "readout": "The current model input is an 8,546-dimensional aligned multimodal window, and the readable atlas shows each public-sample modality without raw data redistribution."
-        },
-        {
-            "step": 7,
-            "question": "Do the task metrics have committed evidence?",
-            "primary_artifacts": [
-                "results/episode_task_suite/summary_report.json",
-                "results/episode_task_suite/neural_mlp/",
-                "docs/data/summary_metrics.json"
-            ],
-            "readout": "Each of the 12 tasks has minimal-head metrics and a matching neural MLP result over the same window contracts."
-        },
-        {
-            "step": 8,
-            "question": "What is the scale-up path?",
-            "primary_artifacts": [
-                "RESEARCH_ROADMAP.md",
-                "docs/data/research_roadmap.json",
-                "results/omni_finetune/DATA_ACCESS_STATUS.md",
-                "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
-                "scripts/omni/discover_xperience10m_sources.py",
-                "docs/data/omni_finetune_verified_result.json"
-            ],
-            "readout": "The selected-episode held-out Qwen3-Omni v6 diagnostic branch is verified and JSON-format reliability meets the 98% target. The same public comparison also includes the verified 128-episode baselines, Cosmos3-Nano compatibility result, Cosmos3-Super Reasoner evaluation, and Cosmos3-Super Forward-Dynamics LoRA package. The next milestone is action/subtask error analysis and stronger model-quality runs on the same split."
-        },
-        {
-            "step": 9,
-            "question": "How can the current 128 episodes be pushed harder?",
-            "primary_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",
-                "results/omni_finetune/task_suite_enhancement_128_v1_20260608/dense_window_scenarios.csv"
-            ],
-            "readout": "The current selected split can be expanded with dense and multiscale windows without adding episodes. The recommended export target is multiscale_20s10_40s20_80s40, followed by hierarchical action/subtask targets and raw-feature shards for unsupported tasks."
-        }
-    ],
-    "project_status": "PROJECT_STATUS.md",
-    "project_status_json": "docs/data/project_status.json",
-    "research_roadmap": "RESEARCH_ROADMAP.md",
-    "research_roadmap_json": "docs/data/research_roadmap.json",
-    "evaluation_protocol": "EVALUATION_PROTOCOL.md",
-    "evaluation_protocol_json": "docs/data/evaluation_protocol.json",
-    "source_alignment_audit": "SOURCE_ALIGNMENT_AUDIT.md",
-    "source_alignment_audit_json": "docs/data/source_alignment_audit.json",
-    "artifact_guide": "ARTIFACT_GUIDE.md",
-    "artifact_index": "docs/data/artifact_index.json",
-    "brand_assets": "docs/data/brand_assets.json",
-    "figure_index": "FIGURE_INDEX.md",
-    "figure_index_json": "docs/data/figure_index.json",
-    "reproducibility_matrix": "docs/data/reproducibility_matrix.json",
-    "public_surfaces": {
-        "github_repo": "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite",
-        "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/",
-        "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite",
-        "hf_static_app": "https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/",
-        "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts",
-        "hf_model_baselines": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines"
+    {
+      "step": 2,
+      "question": "What do the official Xperience-10M dataset and sample cards say?",
+      "primary_artifacts": [
+        "XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
+        "docs/data/xperience10m_dataset_card_alignment.json",
+        "https://huggingface.co/datasets/ropedia-ai/xperience-10m",
+        "https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample"
+      ],
+      "readout": "The full upstream dataset is a manually gated large-scale 4D multimodal egocentric source. The public sample card records the sample license, HOMIE Toolkit path, and Rerun 0.29.0 visualization path. This repo validates one public sample episode and lists the current project coverage."
     },
-    "current_reading_notes": [
-        "The latest cross-episode Qwen3-Omni v6 diagnostic branch is verified, but strong model quality is not yet shown; action/subtask metrics remain weak and v5 remains stronger on several non-contact metrics.",
-        "The current 128-episode suite has a no-new-episode enhancement plan: multiscale_20s10_40s20_80s40 windows, hierarchical labels, label-normalized scoring, and raw-feature shard export.",
-        "Cosmos3-Super Forward-Dynamics LoRA is verified as a loss-based world-model adapter branch, not as JSON action-token prediction.",
-        "Older Qwen3-Omni setup artifacts are separate from the verified selected-episode diagnostic package.",
-        "Feature-vector reconstruction is separate from pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
-        "Raw Xperience-10M data is not redistributed in this repo."
-    ],
-    "task_suite_enhancement_128": "TASK_SUITE_ENHANCEMENT_128.md",
-    "task_suite_enhancement_128_json": "docs/data/task_suite_enhancement_128.json"
+    {
+      "step": 3,
+      "question": "Are source facts consistently presented?",
+      "primary_artifacts": [
+        "SOURCE_ALIGNMENT_AUDIT.md",
+        "docs/data/source_alignment_audit.json",
+        "scripts/validate_source_alignment.py"
+      ],
+      "readout": "The source-alignment report checks full-dataset metadata, API-listing notes, public sample license/tooling, and project coverage across repo docs, website, and HF cards."
+    },
+    {
+      "step": 4,
+      "question": "How exactly are the tasks evaluated?",
+      "primary_artifacts": [
+        "EVALUATION_PROTOCOL.md",
+        "docs/data/evaluation_protocol.json",
+        "scripts/build_evaluation_protocol.py"
+      ],
+      "readout": "The protocol fixes the 20-frame window unit, chronological split, train-only normalization, leakage controls, per-task input/target/metric contracts, and current limitations."
+    },
+    {
+      "step": 5,
+      "question": "How can the public pipeline be reproduced?",
+      "primary_artifacts": [
+        "REPRODUCIBILITY.md",
+        "docs/data/reproducibility_matrix.json",
+        "notes/reproducibility_audit.md"
+      ],
+      "readout": "The public sample pipeline has explicit commands, expected outputs, and a prior exact-match reproduction check over the committed metrics."
+    },
+    {
+      "step": 6,
+      "question": "What is inside one model input?",
+      "primary_artifacts": [
+        "results/episode_task_suite/windows.csv",
+        "results/episode_task_suite/feature_manifest.json",
+        "results/episode_task_suite/available_modalities.json",
+        "docs/data/modality_atlas.json"
+      ],
+      "readout": "The current model input is an 8,546-dimensional aligned multimodal window, and the readable atlas shows each public-sample modality without raw data redistribution."
+    },
+    {
+      "step": 7,
+      "question": "Do the task metrics have committed evidence?",
+      "primary_artifacts": [
+        "results/episode_task_suite/summary_report.json",
+        "results/episode_task_suite/neural_mlp/",
+        "docs/data/summary_metrics.json"
+      ],
+      "readout": "The unified suite has 20 task contracts; tasks 1-12 have walkthroughs and neural MLP heads, and tasks 13-20 have aligned minimal/neural result bundles under the historical tier2_task_suite path."
+    },
+    {
+      "step": 8,
+      "question": "What is the scale-up path?",
+      "primary_artifacts": [
+        "RESEARCH_ROADMAP.md",
+        "docs/data/research_roadmap.json",
+        "results/omni_finetune/DATA_ACCESS_STATUS.md",
+        "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
+        "scripts/omni/discover_xperience10m_sources.py",
+        "docs/data/omni_finetune_verified_result.json"
+      ],
+      "readout": "The selected-episode held-out Qwen3-Omni v6 diagnostic branch is verified and JSON-format reliability meets the 98% target. The same public comparison also includes the verified 128-episode baselines, Cosmos3-Nano compatibility result, Cosmos3-Super Reasoner evaluation, and Cosmos3-Super Forward-Dynamics LoRA package. The next milestone is action/subtask error analysis and stronger model-quality runs on the same split."
+    },
+    {
+      "step": 9,
+      "question": "How can the current 128 episodes be pushed harder?",
+      "primary_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",
+        "results/omni_finetune/task_suite_enhancement_128_v1_20260608/dense_window_scenarios.csv"
+      ],
+      "readout": "The current selected split can be expanded with dense and multiscale windows without adding episodes. The recommended export target is multiscale_20s10_40s20_80s40, followed by hierarchical action/subtask targets and raw-feature shards for unsupported tasks."
+    }
+  ],
+  "project_status": "PROJECT_STATUS.md",
+  "project_status_json": "docs/data/project_status.json",
+  "research_roadmap": "RESEARCH_ROADMAP.md",
+  "research_roadmap_json": "docs/data/research_roadmap.json",
+  "evaluation_protocol": "EVALUATION_PROTOCOL.md",
+  "evaluation_protocol_json": "docs/data/evaluation_protocol.json",
+  "source_alignment_audit": "SOURCE_ALIGNMENT_AUDIT.md",
+  "source_alignment_audit_json": "docs/data/source_alignment_audit.json",
+  "artifact_guide": "ARTIFACT_GUIDE.md",
+  "artifact_index": "docs/data/artifact_index.json",
+  "brand_assets": "docs/data/brand_assets.json",
+  "figure_index": "FIGURE_INDEX.md",
+  "figure_index_json": "docs/data/figure_index.json",
+  "reproducibility_matrix": "docs/data/reproducibility_matrix.json",
+  "public_surfaces": {
+    "github_repo": "https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite",
+    "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/",
+    "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite",
+    "hf_static_app": "https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/",
+    "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts",
+    "hf_model_baselines": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines"
+  },
+  "current_reading_notes": [
+    "The latest cross-episode Qwen3-Omni v6 diagnostic branch is verified, but strong model quality is not yet shown; action/subtask metrics remain weak and v5 remains stronger on several non-contact metrics.",
+    "The current 128-episode suite has a no-new-episode enhancement plan: multiscale_20s10_40s20_80s40 windows, hierarchical labels, label-normalized scoring, and raw-feature shard export.",
+    "Cosmos3-Super Forward-Dynamics LoRA is verified as a loss-based world-model adapter branch, not as JSON action-token prediction.",
+    "Older Qwen3-Omni setup artifacts are separate from the verified selected-episode diagnostic package.",
+    "Feature-vector reconstruction is separate from pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
+    "Raw Xperience-10M data is not redistributed in this repo."
+  ],
+  "task_suite_enhancement_128": "TASK_SUITE_ENHANCEMENT_128.md",
+  "task_suite_enhancement_128_json": "docs/data/task_suite_enhancement_128.json"
 }

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-15T18:10:15+00:00",
+  "generated_at_utc": "2026-06-16T04:56:20+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,7 +18,7 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-15T17:52:49+00:00"
+          "generated_at_utc": "2026-06-16T04:49:24+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-15T17:24:15+00:00"
+          "generated_at_utc": "2026-06-16T04:49:21+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-15T17:24:15+00:00"
+          "generated_at_utc": "2026-06-16T04:50:14+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-15T17:24:25+00:00"
+          "generated_at_utc": "2026-06-16T04:49:25+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-15T17:52:57+00:00"
+          "generated_at_utc": "2026-06-16T04:51:36+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-15T18:10:05+00:00"
+          "generated_at_utc": "2026-06-16T04:51:56+00:00"
         }
       },
       "failures": {}
@@ -97,7 +97,7 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 16,
         "Xperience-10M": 149,
-        "12-task": 32,
+        "20-task": 20,
         "Qwen3-Omni": 143,
         "128-episode pilot": 1
       }
@@ -129,7 +129,8 @@
         "data/public_surface_qa.json": 6,
         "data/research_roadmap.json": 15,
         "data/task_suite_enhancement_128.json": 28,
-        "data/tier2_task_suite.json": 23
+        "data/task_suite_20.json": 42,
+        "data/tier2_task_suite.json": 11
       }
     },
     {

data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-15T18:10:15+00:00",
+  "generated_at_utc": "2026-06-16T04:56:20+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": [
     {
@@ -58,7 +58,7 @@
       "command": "python scripts/validate_task_surface.py",
       "report": "docs/data/task_surface_integrity.json",
       "blocks_if": "Task cards expose raw artifact ids, human-readable task names drift, modality thumbnails are missing, or the interactive task player is not wired to the generated JSON.",
-      "shows": "The public task cards and walkthrough/player stay aligned with generated 12-task metadata.",
+      "shows": "The public task cards and walkthrough/player stay aligned with generated task-suite metadata.",
       "current_report": {
         "exists": true,
         "status": "pass"

data/reproducibility_matrix.json

@@ -36,10 +36,10 @@
       "boundary": "single-episode chronological split"
     },
     {
-      "id": "twelve_task_suite",
+      "id": "original_task_suite",
       "status": "reproducible",
       "command": "python scripts/episode_task_suite.py --workspace $WORKSPACE --include-neural",
-      "expected": "12 task metrics, predictions, manifests, and neural_mlp task-head artifacts",
+      "expected": "original task metrics, predictions, manifests, and neural_mlp task-head artifacts",
       "boundary": "8,546-dimensional multimodal window contract"
     },
     {
@@ -50,11 +50,11 @@
       "boundary": "single-episode probes, not full research-direction solutions"
     },
     {
-      "id": "tier2_task_suite",
+      "id": "tasks_13_to_20_and_unified_index",
       "status": "reproducible",
-      "command": "python scripts/tier2_task_suite.py",
-      "expected": "eight Tier-2 task metrics, prediction/rank artifacts, docs/data/tier2_task_suite.json, and docs/assets/charts/tier2_task_suite.svg",
-      "boundary": "requires local public-sample annotation.hdf5 plus HOMIE Toolkit or h5py; raw HDF5 and MP4 files are not redistributed"
+      "command": "python scripts/tier2_task_suite.py && python scripts/build_unified_task_suite.py",
+      "expected": "tasks 13-20 metrics, prediction/rank artifacts, TASK_SUITE_20.md, docs/data/task_suite_20.json, docs/data/tier2_task_suite.json, and docs/assets/charts/tier2_task_suite.svg",
+      "boundary": "requires local public-sample annotation.hdf5 plus HOMIE Toolkit or h5py for tasks 13-20; raw HDF5 and MP4 files are not redistributed"
     },
     {
       "id": "source_alignment_audit",

data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-15T17:24:25+00:00",
+  "generated_at_utc": "2026-06-16T04:56:59+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-15T17:24:15+00:00",
+  "generated_at_utc": "2026-06-16T04:56:56+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_suite_20.json

@@ -0,0 +1,723 @@
+{
+  "title": "Ropedia Xperience-10M Unified 20-Task Suite",
+  "status": "pass",
+  "generated_at_utc": "2026-06-16T04:47:33+00:00",
+  "task_count": 20,
+  "task_count_breakdown": {
+    "original_public_sample_tasks": 12,
+    "additional_public_sample_tasks": 8,
+    "total_unified_tasks": 20
+  },
+  "unification_policy": {
+    "public_framing": "The suite is presented as one 20-task benchmark surface. Tasks 1-12 are the original public-sample tasks; tasks 13-20 are additional sample-supported tasks that use the same window/split/baseline contract.",
+    "legacy_path_note": "The directory and file name tier2_task_suite are retained only for backward-compatible artifact links; they are not a separate public benchmark tier."
+  },
+  "dataset_scope": {
+    "sample_episode_count": 1,
+    "annotation": "data/sample/xperience-10m-sample/annotation.hdf5",
+    "num_frames": 5821,
+    "num_windows": 1161,
+    "feature_dim": 8546,
+    "window_frames": 20,
+    "stride_frames": 5,
+    "split_policy": "single_episode_chronological_70_30",
+    "raw_hdf5_required_for_tasks_13_20_regeneration": true,
+    "raw_data_redistributed": false
+  },
+  "setup_alignment": {
+    "same_window_unit": "20-frame aligned windows",
+    "same_stride": "5 frames",
+    "same_feature_manifest": "results/episode_task_suite/feature_manifest.json",
+    "same_shared_tensor": "results/episode_task_suite/shared_windows.npz",
+    "same_split": "chronological 70/30 train/test split within the public sample episode",
+    "same_baseline_pattern": "minimal interpretable heads plus compact neural MLP heads",
+    "same_leakage_policy": "Target-side future, contact, object, caption, relation, and interaction signals are excluded from inputs unless language is explicitly the query."
+  },
+  "source_files": [
+    "docs/data/summary_metrics.json",
+    "docs/data/task_walkthroughs.json",
+    "docs/data/tier2_task_suite.json",
+    "results/episode_task_suite/summary_report.json",
+    "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+    "results/episode_task_suite/windows.csv",
+    "results/episode_task_suite/feature_manifest.json"
+  ],
+  "tasks": [
+    {
+      "task_id": "timeline_action",
+      "task_display_name": "Action Recognition",
+      "research_name": "Egocentric Action Recognition",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "supervised",
+      "architecture_family": "multiclass classifier",
+      "primary_direction": "C. Egocentric Vision & Interaction",
+      "input": "One 20-frame window represented by the current feature vector: video/audio/depth summaries, pose, SLAM/camera pose, motion capture, IMU, calibration, and language-derived context.",
+      "input_short": "20-frame multimodal window",
+      "process": "window features -> action label builder -> classifier",
+      "output": "A single action class for the current window.",
+      "output_short": "current action class",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.05,
+      "neural_primary_metric": 0.014814814814814814,
+      "counts": {
+        "num_windows": 1144,
+        "num_eval_windows": 343,
+        "num_train_windows": 801,
+        "num_test_windows": 343,
+        "num_classes": 18
+      },
+      "meaning": "Recognize the current manipulation action from synchronized visual, motion, inertial, pose, and annotation context.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/timeline_action.md",
+        "minimal_metrics": "results/episode_task_suite/timeline_action/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json"
+      },
+      "task_number": 1,
+      "suite_label": "Task 01"
+    },
+    {
+      "task_id": "timeline_subtask",
+      "task_display_name": "Procedure Step Recognition",
+      "research_name": "Temporal Subtask Recognition",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "supervised",
+      "architecture_family": "multiclass classifier",
+      "primary_direction": "C. Egocentric Vision & Interaction",
+      "input": "The same all-modality window vector used by action recognition.",
+      "input_short": "20-frame multimodal window",
+      "process": "window features -> subtask label builder -> classifier",
+      "output": "A single subtask label for the current window.",
+      "output_short": "current procedure step",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.05056355513846935,
+      "neural_primary_metric": 0.02810810810810811,
+      "counts": {
+        "num_windows": 1147,
+        "num_eval_windows": 344,
+        "num_train_windows": 803,
+        "num_test_windows": 344,
+        "num_classes": 14
+      },
+      "meaning": "Recognize the broader activity stage so fine actions become a readable procedure timeline.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/timeline_subtask.md",
+        "minimal_metrics": "results/episode_task_suite/timeline_subtask/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json"
+      },
+      "task_number": 2,
+      "suite_label": "Task 02"
+    },
+    {
+      "task_id": "transition_detection",
+      "task_display_name": "Action Boundary Detection",
+      "research_name": "Temporal Action Segmentation",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "diagnostic",
+      "architecture_family": "binary classifier",
+      "primary_direction": "C. Egocentric Vision & Interaction",
+      "input": "One all-modality window vector plus labels derived from action-change timestamps.",
+      "input_short": "current window with boundary target",
+      "process": "action changes -> boundary labels -> binary classifier",
+      "output": "A binary label: boundary or steady.",
+      "output_short": "boundary or steady",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.6118237590630229,
+      "neural_primary_metric": 0.5862068965517241,
+      "counts": {
+        "num_windows": 1161,
+        "num_eval_windows": 348,
+        "num_train_windows": 813,
+        "num_test_windows": 348,
+        "num_classes": 2
+      },
+      "meaning": "Detect the local moment where the episode changes from one action segment to the next.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/transition_detection.md",
+        "minimal_metrics": "results/episode_task_suite/transition_detection/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json"
+      },
+      "task_number": 3,
+      "suite_label": "Task 03"
+    },
+    {
+      "task_id": "next_action",
+      "task_display_name": "Next-Action Prediction",
+      "research_name": "Short-Horizon Intention Prediction",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "supervised",
+      "architecture_family": "future-label classifier",
+      "primary_direction": "C. Egocentric Vision & Interaction",
+      "input": "The current all-modality window vector at time t.",
+      "input_short": "current window at time t",
+      "process": "current features -> future label shift -> classifier",
+      "output": "A single action class for t+20 frames.",
+      "output_short": "action at t+20 frames",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.05925925925925927,
+      "neural_primary_metric": 0.04186046511627907,
+      "counts": {
+        "num_windows": 1161,
+        "num_eval_windows": 348,
+        "num_train_windows": 813,
+        "num_test_windows": 348,
+        "num_classes": 18
+      },
+      "meaning": "Forecast the near-future action from the current observations only.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/next_action.md",
+        "minimal_metrics": "results/episode_task_suite/next_action/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/next_action/metrics.json"
+      },
+      "task_number": 4,
+      "suite_label": "Task 04"
+    },
+    {
+      "task_id": "hand_trajectory_forecast",
+      "task_display_name": "Hand Trajectory Forecasting",
+      "research_name": "3D Hand Motion Forecasting",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "forecast",
+      "architecture_family": "continuous regressor",
+      "primary_direction": "A. Human Modeling & Motion Understanding",
+      "input": "The current all-modality window vector at time t.",
+      "input_short": "current multimodal window",
+      "process": "current features -> future mocap target -> regression head",
+      "output": "A future trajectory vector for left and right hand joints.",
+      "output_short": "future hand-joint trajectory",
+      "metric_key": "mpjpe",
+      "metric_name": "MPJPE",
+      "metric_direction": "lower",
+      "minimal_primary_metric": 0.8646570444107056,
+      "neural_primary_metric": 0.10785018652677536,
+      "counts": {
+        "num_windows": 1159,
+        "num_train_windows": 811,
+        "num_test_windows": 348
+      },
+      "meaning": "Predict the future 3D left/right hand path from the current multimodal state.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/hand_trajectory_forecast.md",
+        "minimal_metrics": "results/episode_task_suite/hand_trajectory_forecast/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json"
+      },
+      "task_number": 5,
+      "suite_label": "Task 05"
+    },
+    {
+      "task_id": "contact_prediction",
+      "task_display_name": "Contact State Prediction",
+      "research_name": "Human-Object Contact Prediction",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "supervised",
+      "architecture_family": "binary classifier",
+      "primary_direction": "A. Human Modeling & Motion Understanding",
+      "input": "Non-contact and non-caption feature blocks, so the answer is not directly leaked from the target labels.",
+      "input_short": "non-contact, non-caption features",
+      "process": "feature filter -> contact target -> binary classifier",
+      "output": "A binary contact label.",
+      "output_short": "contact or no contact",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 1.0,
+      "neural_primary_metric": 1.0,
+      "counts": {
+        "num_windows": 1161,
+        "num_eval_windows": 348,
+        "num_train_windows": 813,
+        "num_test_windows": 348,
+        "num_classes": 1
+      },
+      "meaning": "Predict whether body or hand contact with the scene is occurring without leaking contact labels.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/contact_prediction.md",
+        "minimal_metrics": "results/episode_task_suite/contact_prediction/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json"
+      },
+      "task_number": 6,
+      "suite_label": "Task 06"
+    },
+    {
+      "task_id": "object_relevance",
+      "task_display_name": "Object Relevance Prediction",
+      "research_name": "Object-Centric Interaction Recognition",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "supervised",
+      "architecture_family": "multi-label classifier",
+      "primary_direction": "C. Egocentric Vision & Interaction",
+      "input": "Non-caption feature blocks, so the model must infer objects from sensors rather than copying the caption words.",
+      "input_short": "non-caption multimodal features",
+      "process": "object vocabulary -> multi-hot labels -> sigmoid heads",
+      "output": "A multi-label object set for the current window.",
+      "output_short": "relevant object set",
+      "metric_key": "micro_f1",
+      "metric_name": "micro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.18034382095361662,
+      "neural_primary_metric": 0.1679279279279279,
+      "counts": {
+        "num_windows": 1161,
+        "num_train_windows": 813,
+        "num_test_windows": 348
+      },
+      "meaning": "Infer which objects are relevant to the current manipulation window from non-caption features.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/object_relevance.md",
+        "minimal_metrics": "results/episode_task_suite/object_relevance/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json"
+      },
+      "task_number": 7,
+      "suite_label": "Task 07"
+    },
+    {
+      "task_id": "caption_grounding",
+      "task_display_name": "Language Grounding",
+      "research_name": "Language-to-Moment Grounding",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "retrieval",
+      "architecture_family": "retrieval ranker",
+      "primary_direction": "C. Egocentric Vision & Interaction",
+      "input": "Caption/object/interaction query features and a set of candidate sensor-window features.",
+      "input_short": "text-like query and candidate windows",
+      "process": "query features -> candidate index -> cosine ranker",
+      "output": "A ranked list of windows, with the correct matching window ideally near rank 1.",
+      "output_short": "ranked matching moments",
+      "metric_key": "mrr",
+      "metric_name": "MRR",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.016023479050338015,
+      "neural_primary_metric": 0.01684125567132316,
+      "counts": {
+        "num_queries": 348,
+        "num_train_windows": 813,
+        "num_test_windows": 348
+      },
+      "meaning": "Retrieve the matching time window for an annotation-derived text query.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/caption_grounding.md",
+        "minimal_metrics": "results/episode_task_suite/caption_grounding/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json"
+      },
+      "task_number": 8,
+      "suite_label": "Task 08"
+    },
+    {
+      "task_id": "cross_modal_retrieval",
+      "task_display_name": "Cross-Modal Retrieval",
+      "research_name": "Multimodal Representation Retrieval",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "retrieval",
+      "architecture_family": "two-tower retrieval head",
+      "primary_direction": "D. Scene Reconstruction & World Modeling",
+      "input": "Query side: motion, IMU, and camera/pose features. Candidate side: depth and video features.",
+      "input_short": "motion/IMU/pose query; depth/video candidates",
+      "process": "modality split -> projection -> nearest-neighbor ranker",
+      "output": "A ranked list of candidate depth/video windows.",
+      "output_short": "ranked visual windows",
+      "metric_key": "mrr",
+      "metric_name": "MRR",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.26925966892956127,
+      "neural_primary_metric": 0.1299971898648288,
+      "counts": {
+        "num_queries": 348,
+        "num_train_windows": 813,
+        "num_test_windows": 348
+      },
+      "meaning": "Use motion, IMU, and camera-pose signals to retrieve the matching depth/video window.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/cross_modal_retrieval.md",
+        "minimal_metrics": "results/episode_task_suite/cross_modal_retrieval/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json"
+      },
+      "task_number": 9,
+      "suite_label": "Task 09"
+    },
+    {
+      "task_id": "modality_reconstruction",
+      "task_display_name": "Cross-Modal Reconstruction",
+      "research_name": "Modality Feature Reconstruction",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "forecast",
+      "architecture_family": "feature regressor",
+      "primary_direction": "B. 3D/4D Reconstruction & Neural Rendering",
+      "input": "Motion, IMU, and camera/pose features as input; depth/video features as the regression target.",
+      "input_short": "motion, IMU, and camera/pose features",
+      "process": "source-target split -> scaler -> regression head",
+      "output": "A reconstructed depth/video feature vector.",
+      "output_short": "reconstructed depth/video vector",
+      "metric_key": "r2",
+      "metric_name": "R2",
+      "metric_direction": "higher",
+      "minimal_primary_metric": -0.015271898913936655,
+      "neural_primary_metric": -0.010171410134180991,
+      "counts": {
+        "num_train_windows": 813,
+        "num_test_windows": 348
+      },
+      "meaning": "Predict compressed depth/video feature vectors from motion, IMU, and camera-pose features.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/modality_reconstruction.md",
+        "minimal_metrics": "results/episode_task_suite/modality_reconstruction/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json"
+      },
+      "task_number": 10,
+      "suite_label": "Task 10"
+    },
+    {
+      "task_id": "temporal_order",
+      "task_display_name": "Temporal Order Verification",
+      "research_name": "Temporal Order Verification",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "diagnostic",
+      "architecture_family": "pairwise classifier",
+      "primary_direction": "D. Scene Reconstruction & World Modeling",
+      "input": "A pair of adjacent window vectors, plus their difference vector.",
+      "input_short": "two adjacent windows plus difference vector",
+      "process": "pair builder -> feature combiner -> binary classifier",
+      "output": "A binary label: correct order or reversed order.",
+      "output_short": "correct or reversed",
+      "metric_key": "f1",
+      "metric_name": "F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.5399515738498789,
+      "neural_primary_metric": 0.8520179372197308,
+      "counts": {
+        "num_samples": 2320,
+        "num_train_samples": 1624,
+        "num_test_samples": 696
+      },
+      "meaning": "Tell whether two neighboring windows are in chronological order or reversed.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/temporal_order.md",
+        "minimal_metrics": "results/episode_task_suite/temporal_order/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json"
+      },
+      "task_number": 11,
+      "suite_label": "Task 11"
+    },
+    {
+      "task_id": "misalignment_detection",
+      "task_display_name": "Multimodal Synchronization Detection",
+      "research_name": "Cross-Modal Misalignment Detection",
+      "origin": "original_public_sample_tasks",
+      "origin_count_label": "original task",
+      "family": "diagnostic",
+      "architecture_family": "pairwise classifier",
+      "primary_direction": "B. 3D/4D Reconstruction & Neural Rendering",
+      "input": "A motion-side feature group and a visual/depth-side feature group, either aligned or artificially shifted.",
+      "input_short": "motion-side and visual/depth-side feature groups",
+      "process": "aligned/shifted pairs -> feature combiner -> binary classifier",
+      "output": "A binary label: aligned or shifted.",
+      "output_short": "aligned or shifted",
+      "metric_key": "f1",
+      "metric_name": "F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.5051698670605613,
+      "neural_primary_metric": 0.7152682255845944,
+      "counts": {
+        "num_samples": 2306,
+        "num_train_samples": 1614,
+        "num_test_samples": 692
+      },
+      "meaning": "Detect whether motion and visual/depth streams have been artificially shifted out of sync.",
+      "artifact_sources": {
+        "walkthrough": "results/episode_task_suite/task_walkthroughs/misalignment_detection.md",
+        "minimal_metrics": "results/episode_task_suite/misalignment_detection/metrics.json",
+        "neural_metrics": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json"
+      },
+      "task_number": 12,
+      "suite_label": "Task 12"
+    },
+    {
+      "task_id": "long_horizon_next_action",
+      "task_display_name": "Long-Horizon Next-Action Forecasting",
+      "research_name": "Long-Horizon Next-Action Forecasting",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "classification",
+      "architecture_family": "minimal_softmax",
+      "primary_direction": "sample-supported extension",
+      "input": "Current 20-frame non-caption multimodal window.",
+      "input_short": "Current 20-frame non-caption multimodal window.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "Action label five seconds later.",
+      "output_short": "Action label five seconds later.",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.07499999999999998,
+      "neural_primary_metric": 0.06545454545454546,
+      "counts": {
+        "num_windows": 1073,
+        "num_eval_windows": 322,
+        "num_train_windows": 751,
+        "num_test_windows": 322,
+        "num_classes": 18
+      },
+      "meaning": "Tests whether the current state carries enough procedure context to forecast beyond the one-second core next-action task.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json"
+      },
+      "task_number": 13,
+      "suite_label": "Task 13"
+    },
+    {
+      "task_id": "next_subtask_forecast",
+      "task_display_name": "Long-Horizon Next-Subtask Forecasting",
+      "research_name": "Long-Horizon Next-Subtask Forecasting",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "classification",
+      "architecture_family": "minimal_softmax",
+      "primary_direction": "sample-supported extension",
+      "input": "Current 20-frame non-caption multimodal window.",
+      "input_short": "Current 20-frame non-caption multimodal window.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "Procedure subtask label five seconds later.",
+      "output_short": "Procedure subtask label five seconds later.",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.04545454545454545,
+      "neural_primary_metric": 0.050724637681159424,
+      "counts": {
+        "num_windows": 1141,
+        "num_eval_windows": 342,
+        "num_train_windows": 799,
+        "num_test_windows": 342,
+        "num_classes": 14
+      },
+      "meaning": "Moves from immediate action anticipation to higher-level procedure-state prediction.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json"
+      },
+      "task_number": 14,
+      "suite_label": "Task 14"
+    },
+    {
+      "task_id": "interaction_text_prediction",
+      "task_display_name": "Interaction Text Prediction",
+      "research_name": "Interaction Text Prediction",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "classification",
+      "architecture_family": "minimal_softmax",
+      "primary_direction": "sample-supported extension",
+      "input": "Current 20-frame sensor window with caption-text features removed.",
+      "input_short": "Current 20-frame sensor window with caption-text features removed.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "Raw annotation interaction phrase for the same window.",
+      "output_short": "Raw annotation interaction phrase for the same window.",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.04444444444444444,
+      "neural_primary_metric": 0.0380952380952381,
+      "counts": {
+        "num_windows": 192,
+        "num_eval_windows": 58,
+        "num_train_windows": 134,
+        "num_test_windows": 58,
+        "num_classes": 46
+      },
+      "meaning": "Uses the raw caption JSON interaction field as a language target instead of only the hashed text feature.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json"
+      },
+      "task_number": 15,
+      "suite_label": "Task 15"
+    },
+    {
+      "task_id": "action_object_relation",
+      "task_display_name": "Action-Object Relation Prediction",
+      "research_name": "Action-Object Relation Prediction",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "classification",
+      "architecture_family": "minimal_softmax",
+      "primary_direction": "sample-supported extension",
+      "input": "Current 20-frame sensor window with caption-text features removed.",
+      "input_short": "Current 20-frame sensor window with caption-text features removed.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "Joint action plus active object-set relation.",
+      "output_short": "Joint action plus active object-set relation.",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.0,
+      "neural_primary_metric": 0.0,
+      "counts": {
+        "num_windows": 178,
+        "num_eval_windows": 53,
+        "num_train_windows": 125,
+        "num_test_windows": 53,
+        "num_classes": 42
+      },
+      "meaning": "Evaluates whether a model can bind what action is happening to which objects are involved.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json"
+      },
+      "task_number": 16,
+      "suite_label": "Task 16"
+    },
+    {
+      "task_id": "object_set_forecast",
+      "task_display_name": "Future Object-Set Forecasting",
+      "research_name": "Future Object-Set Forecasting",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "multi_label",
+      "architecture_family": "minimal_ridge_multilabel",
+      "primary_direction": "sample-supported extension",
+      "input": "Current 20-frame sensor window with caption-text features removed.",
+      "input_short": "Current 20-frame sensor window with caption-text features removed.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "Object set active five seconds later.",
+      "output_short": "Object set active five seconds later.",
+      "metric_key": "micro_f1",
+      "metric_name": "micro-F1",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.16939890710382516,
+      "neural_primary_metric": 0.19718309859154928,
+      "counts": {
+        "num_windows": 188,
+        "num_train_windows": 132,
+        "num_test_windows": 56
+      },
+      "meaning": "Predicts which objects will become relevant soon, not only which objects are relevant now.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json"
+      },
+      "task_number": 17,
+      "suite_label": "Task 17"
+    },
+    {
+      "task_id": "imu_to_hand_pose",
+      "task_display_name": "IMU-to-Hand Pose Reconstruction",
+      "research_name": "IMU-to-Hand Pose Reconstruction",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "regression",
+      "architecture_family": "minimal_ridge_regression",
+      "primary_direction": "sample-supported extension",
+      "input": "Current IMU acceleration/gyroscope feature block only.",
+      "input_short": "Current IMU acceleration/gyroscope feature block only.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "Current left/right hand joint feature blocks.",
+      "output_short": "Current left/right hand joint feature blocks.",
+      "metric_key": "mae",
+      "metric_name": "MAE",
+      "metric_direction": "lower",
+      "minimal_primary_metric": 0.042049407958984375,
+      "neural_primary_metric": 0.042562149465084076,
+      "counts": {
+        "num_windows": 1161,
+        "num_train_windows": 813,
+        "num_test_windows": 348
+      },
+      "meaning": "A sensor-bridge probe for how much hand configuration can be recovered from inertial motion alone.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json"
+      },
+      "task_number": 18,
+      "suite_label": "Task 18"
+    },
+    {
+      "task_id": "camera_view_sync_retrieval",
+      "task_display_name": "Camera-View Synchronization Retrieval",
+      "research_name": "Camera-View Synchronization Retrieval",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "retrieval",
+      "architecture_family": "minimal_ridge_projection_cosine_retrieval",
+      "primary_direction": "sample-supported extension",
+      "input": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.",
+      "input_short": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "The synchronized held-out camera-3 window.",
+      "output_short": "The synchronized held-out camera-3 window.",
+      "metric_key": "mrr",
+      "metric_name": "MRR",
+      "metric_direction": "higher",
+      "minimal_primary_metric": 0.4943004846572876,
+      "neural_primary_metric": 0.24086658656597137,
+      "counts": {
+        "num_train_windows": 813,
+        "num_test_windows": 348
+      },
+      "meaning": "Stress-tests multi-camera time alignment beyond the core cross-modal retrieval task.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json"
+      },
+      "task_number": 19,
+      "suite_label": "Task 19"
+    },
+    {
+      "task_id": "time_to_transition",
+      "task_display_name": "Time-to-Next-Transition Regression",
+      "research_name": "Time-to-Next-Transition Regression",
+      "origin": "additional_public_sample_tasks",
+      "origin_count_label": "additional task",
+      "family": "regression",
+      "architecture_family": "minimal_ridge_regression",
+      "primary_direction": "sample-supported extension",
+      "input": "Current 20-frame non-caption multimodal window.",
+      "input_short": "Current 20-frame non-caption multimodal window.",
+      "process": "shared window features -> task-specific target builder -> minimal/neural head",
+      "output": "Frames until the next action-label boundary, capped at 200 frames.",
+      "output_short": "Frames until the next action-label boundary, capped at 200 frames.",
+      "metric_key": "mae",
+      "metric_name": "MAE frames",
+      "metric_direction": "lower",
+      "minimal_primary_metric": 10.53735637664795,
+      "neural_primary_metric": 10.55449390411377,
+      "counts": {
+        "num_windows": 1161,
+        "num_train_windows": 813,
+        "num_test_windows": 348
+      },
+      "meaning": "Turns boundary detection into a continuous timing estimate for procedural control.",
+      "artifact_sources": {
+        "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+        "minimal_metrics": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json",
+        "neural_metrics": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json"
+      },
+      "task_number": 20,
+      "suite_label": "Task 20"
+    }
+  ]
+}

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-15T17:24:15+00:00",
+  "generated_at_utc": "2026-06-16T04:56:56+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

data/tier2_task_suite.json

@@ -1,14 +1,15 @@
 {
-  "title": "Ropedia Xperience-10M Tier-2 Extension Task Suite",
+  "title": "Ropedia Xperience-10M Unified Tasks 13-20 Result Bundle",
   "status": "pass",
-  "generated_at_utc": "2026-06-15T17:19:10+00:00",
-  "tier": "tier2_extension",
-  "integrated_with_core_12": {
-    "core_task_count": 12,
-    "tier2_task_count": 8,
+  "generated_at_utc": "2026-06-16T04:47:52+00:00",
+  "suite_position": "tasks_13_to_20",
+  "legacy_path_note": "The tier2_task_suite file and directory names are retained for stable public links; these tasks are part of the unified 20-task suite, not a separate public tier.",
+  "integrated_with_tasks_1_to_12": {
+    "tasks_1_to_12_count": 12,
+    "additional_task_count": 8,
     "combined_task_count": 20,
-    "core_metrics": "docs/data/summary_metrics.json",
-    "core_protocol": "docs/data/evaluation_protocol.json"
+    "tasks_1_to_12_metrics": "docs/data/summary_metrics.json",
+    "unified_protocol": "docs/data/evaluation_protocol.json"
   },
   "dataset_scope": {
     "sample_episode_count": 1,
@@ -27,9 +28,9 @@
     "raw_data_redistributed": false
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   "setup_alignment": {
-    "same_window_unit_as_core_12": true,
-    "same_feature_manifest_as_core_12": "results/episode_task_suite/feature_manifest.json",
-    "same_shared_tensor_as_core_12": "results/episode_task_suite/shared_windows.npz",
+    "same_window_unit_as_tasks_1_to_12": true,
+    "same_feature_manifest_as_tasks_1_to_12": "results/episode_task_suite/feature_manifest.json",
+    "same_shared_tensor_as_tasks_1_to_12": "results/episode_task_suite/shared_windows.npz",
     "minimal_baselines": "softmax, ridge regression/projection, and ridge multilabel heads",
     "neural_baselines": "compact one-hidden-layer/two-layer PyTorch MLP heads with the same chronological split",
     "leakage_policy": "Caption-derived text features are removed whenever the target is a label, object, relation, interaction phrase, or future semantic state."
@@ -134,7 +135,7 @@
         "status": "pass",
         "task": "long_horizon_next_action",
         "task_display_name": "Long-Horizon Next-Action Forecasting",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_softmax",
         "input": "Current 20-frame non-caption multimodal window.",
         "split": "single_episode_chronological",
@@ -220,7 +221,7 @@
         "status": "pass",
         "task": "long_horizon_next_action",
         "task_display_name": "Long-Horizon Next-Action Forecasting",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp",
         "input": "Current 20-frame non-caption multimodal window.",
         "split": "single_episode_chronological",
@@ -275,7 +276,7 @@
         "status": "pass",
         "task": "next_subtask_forecast",
         "task_display_name": "Long-Horizon Next-Subtask Forecasting",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_softmax",
         "input": "Current 20-frame non-caption multimodal window.",
         "split": "single_episode_chronological",
@@ -360,7 +361,7 @@
         "status": "pass",
         "task": "next_subtask_forecast",
         "task_display_name": "Long-Horizon Next-Subtask Forecasting",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp",
         "input": "Current 20-frame non-caption multimodal window.",
         "split": "single_episode_chronological",
@@ -415,7 +416,7 @@
         "status": "pass",
         "task": "interaction_text_prediction",
         "task_display_name": "Interaction Text Prediction",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_softmax",
         "input": "Current 20-frame sensor window with caption-text features removed.",
         "split": "single_episode_chronological",
@@ -511,7 +512,7 @@
         "status": "pass",
         "task": "interaction_text_prediction",
         "task_display_name": "Interaction Text Prediction",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp",
         "input": "Current 20-frame sensor window with caption-text features removed.",
         "split": "single_episode_chronological",
@@ -566,7 +567,7 @@
         "status": "pass",
         "task": "action_object_relation",
         "task_display_name": "Action-Object Relation Prediction",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_softmax",
         "input": "Current 20-frame sensor window with caption-text features removed.",
         "split": "single_episode_chronological",
@@ -658,7 +659,7 @@
         "status": "pass",
         "task": "action_object_relation",
         "task_display_name": "Action-Object Relation Prediction",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp",
         "input": "Current 20-frame sensor window with caption-text features removed.",
         "split": "single_episode_chronological",
@@ -712,7 +713,7 @@
         "status": "pass",
         "task": "object_set_forecast",
         "task_display_name": "Future Object-Set Forecasting",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_ridge_multilabel",
         "input": "Current 20-frame sensor window with caption-text features removed.",
         "split": "single_episode_chronological",
@@ -746,7 +747,7 @@
         "status": "pass",
         "task": "object_set_forecast",
         "task_display_name": "Future Object-Set Forecasting",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp_multilabel",
         "input": "Current 20-frame sensor window with caption-text features removed.",
         "split": "single_episode_chronological",
@@ -794,7 +795,7 @@
         "status": "pass",
         "task": "imu_to_hand_pose",
         "task_display_name": "IMU-to-Hand Pose Reconstruction",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_ridge_regression",
         "input": "Current IMU acceleration/gyroscope feature block only.",
         "split": "single_episode_chronological",
@@ -813,7 +814,7 @@
         "status": "pass",
         "task": "imu_to_hand_pose",
         "task_display_name": "IMU-to-Hand Pose Reconstruction",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp_regression",
         "input": "Current IMU acceleration/gyroscope feature block only.",
         "split": "single_episode_chronological",
@@ -863,7 +864,7 @@
         "status": "pass",
         "task": "camera_view_sync_retrieval",
         "task_display_name": "Camera-View Synchronization Retrieval",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_ridge_projection_cosine_retrieval",
         "input": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.",
         "split": "single_episode_chronological",
@@ -884,7 +885,7 @@
         "status": "pass",
         "task": "camera_view_sync_retrieval",
         "task_display_name": "Camera-View Synchronization Retrieval",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp_projection_cosine_retrieval",
         "input": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.",
         "split": "single_episode_chronological",
@@ -933,7 +934,7 @@
         "status": "pass",
         "task": "time_to_transition",
         "task_display_name": "Time-to-Next-Transition Regression",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "minimal_ridge_regression",
         "input": "Current 20-frame non-caption multimodal window.",
         "split": "single_episode_chronological",
@@ -953,7 +954,7 @@
         "status": "pass",
         "task": "time_to_transition",
         "task_display_name": "Time-to-Next-Transition Regression",
-        "tier": "tier2_extension",
+        "suite_position": "tasks_13_to_20",
         "model_family": "neural_mlp_regression",
         "input": "Current 20-frame non-caption multimodal window.",
         "split": "single_episode_chronological",

data/website_integrity.json

@@ -1,13 +1,13 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-15T17:52:49+00:00",
+  "generated_at_utc": "2026-06-16T04:56:57+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 153,
+    "local_references": 154,
     "external_reference_count": 121,
-    "json_files": 40,
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     "image_assets_referenced": 22,
     "failure_count": 0
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@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 76656,
-      "evidence_index": 102436
+      "overview_index": 76641,
+      "evidence_index": 102471
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 76656,
-      "protocol_index": 98618,
-      "evidence_index": 102436
+      "overview_index": 76641,
+      "protocol_index": 98652,
+      "evidence_index": 102471
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     {
       "name": "evaluation_protocol_links_json",
@@ -178,9 +178,9 @@
     {
       "name": "suite_task_map_precedes_modality_atlas",
       "status": "pass",
-      "reason": "The Suite anchor should show the full 12-task map before the modality atlas.",
-      "first_marker_index": 419,
-      "second_marker_index": 710
+      "reason": "The Suite anchor should show the task-suite map before the modality atlas.",
+      "first_marker_index": 471,
+      "second_marker_index": 763
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     {
       "name": "suite_modality_atlas_contains_seven_cards",
@@ -277,7 +277,7 @@
     {
       "path": "index.html",
       "id_count": 90,
-      "reference_count": 129,
+      "reference_count": 130,
       "image_count": 25
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     {
@@ -301,7 +301,7 @@
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     {
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@@ -316,7 +316,7 @@
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     {
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@@ -326,7 +326,7 @@
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@@ -336,12 +336,12 @@
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-      "bytes": 115491,
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@@ -361,37 +361,37 @@
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-      "bytes": 10597,
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       "path": "data/project_status.json",
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     {
       "path": "data/public_surface_qa.json",
-      "bytes": 5659,
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       "top_level_type": "dict"
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     {
@@ -416,7 +416,7 @@
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       "top_level_type": "dict"
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     {
@@ -464,6 +464,11 @@
       "bytes": 27807,
       "top_level_type": "dict"
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+    {
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+      "bytes": 34648,
+      "top_level_type": "dict"
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       "bytes": 20181,
@@ -481,12 +486,12 @@
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+      "bytes": 33402,
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     {
       "path": "data/website_integrity.json",
-      "bytes": 17710,
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@@ -570,7 +575,7 @@
     {
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-      "bytes": 5420,
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@@ -657,7 +662,7 @@
     {
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-      "bytes": 1588641,
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docs/index.html

@@ -4,7 +4,7 @@
   <meta charset="utf-8">
   <meta name="viewport" content="width=device-width, initial-scale=1">
   <title>Ropedia Xperience-10M Task Suite</title>
-  <meta name="description" content="A research-development task lab for Ropedia Xperience-10M: multimodal sample exploration, 12 embodied-AI tasks, baseline models, and a multi-episode fine-tuning path.">
+  <meta name="description" content="A research-development task lab for Ropedia Xperience-10M: multimodal sample exploration, 20 embodied-AI tasks, baseline models, and a multi-episode fine-tuning path.">
   <meta name="theme-color" content="#020502">
   <meta name="robots" content="index, follow">
   <link rel="canonical" href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">
@@ -12,7 +12,7 @@
   <link rel="apple-touch-icon" href="apple-touch-icon.png">
   <link rel="manifest" href="site.webmanifest">
   <meta property="og:title" content="Ropedia Xperience-10M Task Suite">
-  <meta property="og:description" content="A Ropedia Xperience-10M research task lab with multimodal sample exploration, 12 task contracts, minimal and neural baselines, metrics, diagrams, and a scale-up plan.">
+  <meta property="og:description" content="A Ropedia Xperience-10M research task lab with multimodal sample exploration, 20 task contracts, minimal and neural baselines, metrics, diagrams, and a scale-up plan.">
   <meta property="og:type" content="website">
   <meta property="og:url" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">
   <meta property="og:image" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png?v=xperience10m-logo-v3">
@@ -20,7 +20,7 @@
   <meta property="og:image:height" content="630">
   <meta name="twitter:card" content="summary_large_image">
   <meta name="twitter:title" content="Ropedia Xperience-10M Task Suite">
-  <meta name="twitter:description" content="Xperience-10M research task lab with multimodal sample exploration, 12 task contracts, and minimal plus neural baselines.">
+  <meta name="twitter:description" content="Xperience-10M research task lab with multimodal sample exploration, 20 task contracts, and minimal plus neural baselines.">
   <meta name="twitter:image" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png?v=xperience10m-logo-v3">
   <script type="application/ld+json">
     {
@@ -2486,7 +2486,7 @@
         </p>
         <div class="hero-actions">
           <a class="button primary" href="research_roadmap.html">Open roadmap</a>
-          <a class="button" href="#suite">Inspect 12 core tasks</a>
+          <a class="button" href="#suite">Inspect 20 tasks</a>
           <a class="button" href="single_episode_explorer.html">Open explorer</a>
           <a class="button" href="https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/">Open HF app</a>
         </div>
@@ -2494,7 +2494,7 @@
           <div class="stat"><strong>5,821</strong><span>frames in sample episode</span></div>
           <div class="stat"><strong>1,161</strong><span>20-frame windows</span></div>
           <div class="stat"><strong>8,546</strong><span>feature dimensions</span></div>
-          <div class="stat"><strong>12+8</strong><span>core and Tier-2 task contracts</span></div>
+          <div class="stat"><strong>20</strong><span>unified task contracts</span></div>
         </div>
       </div>
       <div class="hero-panel" aria-label="Feature allocation summary">
@@ -2567,8 +2567,8 @@
               <strong>What is implemented</strong>
               <ul>
                 <li>1,161 aligned windows from one public sample episode</li>
-                <li>12 core task contracts with minimal and neural heads</li>
-                <li>8 Tier-2 extension baselines aligned to the same setup</li>
+                <li>20 unified task contracts with minimal and neural evidence</li>
+                <li>Tasks 13-20 aligned to the same setup as tasks 1-12</li>
                 <li>Four research-direction maps and extension probes</li>
               </ul>
             </article>
@@ -2608,11 +2608,11 @@
           <article class="snapshot-card">
             <span class="status-pill">featured</span>
             <h3>Interactive research roadmap</h3>
-            <p>Use this as the front door for the project: it links the 12 tasks, four research tracks, current sample evidence, and the multi-episode Qwen3-Omni scale-up path.</p>
+            <p>Use this as the front door for the project: it links the unified 20 tasks, four research tracks, current sample evidence, and the multi-episode Qwen3-Omni scale-up path.</p>
             <div class="snapshot-meta">
               <span>tracks <strong>4</strong></span>
-          <span>core tasks <strong>12</strong></span>
-          <span>Tier-2 tasks <strong>8</strong></span>
+          <span>tasks <strong>20</strong></span>
+          <span>tasks 13-20 <strong>aligned</strong></span>
               <span>roadmap phases <strong>5</strong></span>
             </div>
             <div class="snapshot-actions">
@@ -2633,11 +2633,11 @@
           <article class="snapshot-card">
             <span class="status-pill">verified</span>
             <h3>Task suite and baseline heads</h3>
-            <p>Every core task has a minimal baseline and a compact PyTorch MLP head over the same windows, splits, and labels.</p>
+            <p>The unified task suite has minimal baseline evidence, and the original task cards plus tasks 13-20 share the same windows, splits, and label discipline.</p>
             <div class="snapshot-meta">
-              <span>core tasks <strong>12</strong></span>
-              <span>neural heads <strong>12</strong></span>
-              <span>Tier-2 extensions <strong>8</strong></span>
+              <span>tasks <strong>20</strong></span>
+              <span>original neural heads <strong>12</strong></span>
+              <span>tasks 13-20 <strong>8</strong></span>
             </div>
           </article>
           <article class="snapshot-card">
@@ -2655,7 +2655,7 @@
             <h3>Public research artifacts</h3>
             <p>Metrics, figures, walkthroughs, baseline weights, Qwen3-Omni results, and Cosmos3 public-safe packages are staged across GitHub, GitHub Pages, and Hugging Face.</p>
             <div class="snapshot-meta">
-              <span>tasks <strong>12</strong></span>
+              <span>tasks <strong>20</strong></span>
               <span>baselines <strong>minimal + neural</strong></span>
               <span>reader path <strong>tabs</strong></span>
             </div>
@@ -2844,9 +2844,9 @@
         <div class="artifact-grid">
           <article class="artifact primary-artifact"><div><h3>Data unit</h3><p>One 20-frame aligned window from the public sample episode, stride 5 frames, 1,161 windows total, represented by 8,546 synchronized multimodal dimensions.</p></div><a href="data/evaluation_protocol.json">evaluation protocol</a></article>
           <article class="artifact"><h3>Split policy</h3><p>Single-episode chronological 70/30 train/test split. This avoids random future-window mixing; cross-episode generalization is measured in the later multi-episode pilot.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVALUATION_PROTOCOL.md">protocol document</a></article>
-          <article class="artifact"><h3>Metric contract</h3><p>All 12 tasks list input, target, primary metric, minimal baseline score, and neural MLP score from committed result files.</p><a href="data/summary_metrics.json">summary metrics</a></article>
+          <article class="artifact"><h3>Metric contract</h3><p>All 20 tasks list input, target, primary metric, baseline score, and source artifact path in the unified suite file.</p><a href="data/task_suite_20.json">task_suite_20.json</a></article>
           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
-          <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across all 12 task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
+          <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across the original task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation branch selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 becomes the world-model branch with a camera-pose proxy forward-dynamics contract ready for trainer work, policy models wait for robot-compatible action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
           <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target; Cosmos3-Nano has a verified future-window compatibility package; and Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch. The next stage is action/subtask error analysis, stronger model-quality runs, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>128-Episode Task Suite Enhancement Pack</h3><p>Before adding episodes, the suite should try `multiscale_20s10_40s20_80s40`, hierarchical action/subtask targets, label-normalized scoring, and compact raw-feature shards for unsupported tasks.</p><a href="data/task_suite_enhancement_128.json">task_suite_enhancement_128.json</a></article>
@@ -2951,7 +2951,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>Figures are indexed</h3>
-            <p>The visual set includes the logo, modality atlas, 12-task suite figure, model-architecture figure, and Qwen3-Omni LoRA training-flow figure.</p>
+            <p>The visual set includes the logo, modality atlas, task-suite figure, model-architecture figure, tasks 13-20 chart, and Qwen3-Omni LoRA training-flow figure.</p>
             <div class="evidence-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FIGURE_INDEX.md">figure guide</a>
               <a href="assets/task_suite_infographic.png">task-suite figure</a>
@@ -2979,7 +2979,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>The dashboard is designed as the visual entry point</h3>
-            <p>Tabs organize the sample data, 12 tasks, model method, results, research directions, and next-stage resources.</p>
+            <p>Tabs organize the sample data, 20 tasks, model method, results, research directions, and next-stage resources.</p>
             <div class="evidence-links">
               <a href="#dataset-card">dataset</a>
               <a href="#tasks">tasks</a>
@@ -3070,7 +3070,7 @@
           </article>
         </div>
         <div class="boundary-strip">
-          <div class="boundary-item"><strong>Verified now</strong><span>One public episode, 5,821 frames, 1,161 aligned windows, 8,546 dimensions, 12 core tasks, 8 Tier-2 extension tasks, 12 neural core heads, and 4 direction-extension probes.</span></div>
+          <div class="boundary-item"><strong>Verified now</strong><span>One public episode, 5,821 frames, 1,161 aligned windows, 8,546 dimensions, 20 unified task contracts, 12 original neural heads, and 4 direction-extension probes.</span></div>
           <div class="boundary-item"><strong>Next: no-new-episode scale</strong><span>The selected 128-episode suite should next use dense/multiscale windows, hierarchical labels, and raw-feature shards before adding more episodes.</span></div>
           <div class="boundary-item"><strong>Next: error analysis</strong><span>The selected 128-episode Qwen3-Omni LoRA result has a final verified diagnostic package; JSON validity meets target, and the next pass should improve action/subtask quality.</span></div>
           <div class="boundary-item"><strong>Not redistributed</strong><span>Raw videos, raw annotations, full Qwen weights, and private gated Xperience-10M data are not included in the public repo or HF bundles.</span></div>
@@ -3086,13 +3086,13 @@
         </div>
         <div class="artifact-grid">
           <article class="artifact primary-artifact"><div><h3>Official dataset</h3><p>Xperience-10M is a gated large-scale egocentric multimodal dataset for embodied AI, robotics, spatial intelligence, and world modeling.</p></div><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official HF dataset</a></article>
-          <article class="artifact"><h3>Public sample</h3><p>The current task suite is built from one public sample episode, not from the entire gated dataset.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
+          <article class="artifact"><h3>Public sample</h3><p>The current unified 20-task suite is built from one public sample episode, not from the entire gated dataset.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
           <article class="artifact"><h3>Modalities</h3><p>The sample exposes synchronized video, audio, depth, pose/SLAM, motion capture, inertial signals, calibration, and language annotations.</p><a href="data/modality_atlas.json">modality atlas</a></article>
           <article class="artifact"><h3>Multi-episode pilot</h3><p>The selected 128-episode Qwen3-Omni LoRA v6 diagnostic branch is verified with 4,032 held-out test predictions and 99.90% JSON validity. Action/subtask metrics are still weak, so this remains a baseline for error analysis.</p><a href="https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep">LoRA adapter</a><a href="data/qwen3_v5_v6_comparison.json">v5/v6 comparison</a></article>
           <article class="artifact"><h3>Raw sample browser</h3><p>The Data tab now exposes the official public sample files directly, including playable MP4 video streams and the audio track embedded in fisheye_cam0.mp4.</p><a href="#raw-sample">open raw browser</a><a href="data/raw_sample_files.json">raw manifest</a></article>
           <article class="artifact"><h3>Data boundary</h3><p>Raw MP4, HDF5, RRD files are streamed from the official public sample source when opened here; private gated data and full Qwen weights are not redistributed in this project.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">data notice</a></article>
           <article class="artifact"><h3>Current project subset</h3><p>One public sample episode, 5,821 frames, 1,161 aligned windows, 8,546-dimensional task inputs, plus direct links to the official raw sample files.</p><a href="data/modality_atlas.json">modality atlas</a></article>
-          <article class="artifact"><h3>Covered now</h3><p>Action/subtask labels, next-action prediction, temporal diagnostics, hand trajectory, contact, object relevance, caption grounding, retrieval, reconstruction, and misalignment.</p><a href="data/summary_metrics.json">summary metrics</a></article>
+          <article class="artifact"><h3>Covered now</h3><p>Action/subtask labels, next-action prediction, temporal diagnostics, hand trajectory, contact, object relevance, caption grounding, retrieval, reconstruction, misalignment, long-horizon forecasting, interaction text, action-object relation, sensor bridging, camera sync, and transition timing.</p><a href="data/summary_metrics.json">summary metrics</a></article>
           <article class="artifact"><h3>Responsible use</h3><p>This project is for research exploration and excludes identity recognition, surveillance, biometric profiling, sensitive-attribute inference, and safety-critical deployment.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">use notes</a></article>
           <article class="artifact"><h3>Later milestones</h3><p>Full audio-visual learning, caption generation, depth-pixel prediction, SLAM estimation, neural rendering, policy learning, cross-episode generalization, held-out Qwen3-Omni evaluation, and future Xperience-native pretraining.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">native pretraining</a></article>
         </div>
@@ -3181,11 +3181,11 @@
     <section id="suite" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>Ropedia Xperience-10M 12-task suite.</h2>
-          <p>The task map connects synchronized multimodal windows to 12 research task heads, then the modality atlas shows the sample streams used to build those contracts.</p>
+          <h2>Ropedia Xperience-10M unified 20-task suite.</h2>
+          <p>The suite connects synchronized multimodal windows to 20 task contracts. The large map visualizes the original task families, while tasks 13-20 are listed as the aligned continuation under the same setup.</p>
         </div>
         <div class="figure-pan" id="task-suite-map">
-          <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl" alt="Infographic showing all 12 Ropedia Xperience-10M tasks with enlarged full-width modality cards">
+          <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl" alt="Infographic showing Ropedia Xperience-10M task families with enlarged full-width modality cards">
         </div>
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
@@ -3344,7 +3344,7 @@
     <section id="directions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>The 12 tasks organized into four research directions.</h2>
+          <h2>The original tasks organized into four research directions.</h2>
           <p>Each task is mapped as direct, proxy, or diagnostic evidence for the Ropedia research tracks. The mapping uses two current baselines: minimal interpretable heads and neural MLP heads over the same feature contract.</p>
         </div>
         <div class="direction-grid">
@@ -3373,7 +3373,7 @@
             <div class="direction-counts"><span><strong>0</strong>direct</span><span><strong>6</strong>proxy</span><span><strong>3</strong>diagnostic</span></div>
           </article>
         </div>
-        <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the 12 Xperience-10M tasks across four research directions">
+        <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the original Xperience-10M tasks across four research directions">
         <div class="baseline-strip">
           <div class="callout">
             <h3>Baseline 1: minimal heads</h3>
@@ -3390,62 +3390,62 @@
     <section id="extensions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>Tier-2 Extension Task Suite broadens the sample task space.</h2>
-          <p>The original four direction probes remain as focused examples. The new Tier-2 layer adds eight more sample-supported baselines using the same windows, feature manifest, chronological split, and minimal/neural head pattern as the core 12 tasks.</p>
+          <h2>Tasks 13-20 complete the unified 20-task suite.</h2>
+          <p>The original four direction probes remain as focused examples. Tasks 13-20 add eight sample-supported baselines using the same windows, feature manifest, chronological split, and minimal/neural head pattern as tasks 1-12.</p>
         </div>
-        <img class="chart" src="assets/charts/tier2_task_suite.svg?v=xperience10m-tier2" alt="Eight Xperience-10M Tier-2 extension tasks with minimal and neural metrics">
+        <img class="chart" src="assets/charts/tier2_task_suite.svg?v=xperience10m-tier2" alt="Eight Xperience-10M tasks 13-20 with minimal and neural metrics">
         <div class="extension-grid">
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / forecast</span>
+            <span class="status-pill">Task 13 / forecast</span>
             <h3>Long-Horizon Next-Action Forecasting</h3>
             <p><strong>Input:</strong> current non-caption multimodal window.</p>
             <p><strong>Output:</strong> action label five seconds later.</p>
             <div class="extension-metrics"><span><strong>0.0750</strong>minimal macro-F1</span><span><strong>0.0655</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / procedure</span>
+            <span class="status-pill">Task 14 / procedure</span>
             <h3>Long-Horizon Next-Subtask Forecasting</h3>
             <p><strong>Input:</strong> current non-caption multimodal window.</p>
             <p><strong>Output:</strong> procedure subtask five seconds later.</p>
             <div class="extension-metrics"><span><strong>0.0455</strong>minimal macro-F1</span><span><strong>0.0507</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / language</span>
+            <span class="status-pill">Task 15 / language</span>
             <h3>Interaction Text Prediction</h3>
             <p><strong>Input:</strong> current sensor window with caption features removed.</p>
             <p><strong>Output:</strong> raw annotation interaction phrase.</p>
             <div class="extension-metrics"><span><strong>0.0444</strong>minimal macro-F1</span><span><strong>0.0381</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / relation</span>
+            <span class="status-pill">Task 16 / relation</span>
             <h3>Action-Object Relation Prediction</h3>
             <p><strong>Input:</strong> current sensor window with caption features removed.</p>
             <p><strong>Output:</strong> joint action plus active object-set label.</p>
             <div class="extension-metrics"><span><strong>0.0000</strong>minimal macro-F1</span><span><strong>0.0000</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / objects</span>
+            <span class="status-pill">Task 17 / objects</span>
             <h3>Future Object-Set Forecasting</h3>
             <p><strong>Input:</strong> current sensor window with caption features removed.</p>
             <p><strong>Output:</strong> object set active five seconds later.</p>
             <div class="extension-metrics"><span><strong>0.1694</strong>minimal micro-F1</span><span><strong>0.1972</strong>neural micro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / sensor bridge</span>
+            <span class="status-pill">Task 18 / sensor bridge</span>
             <h3>IMU-to-Hand Pose Reconstruction</h3>
             <p><strong>Input:</strong> IMU acceleration and gyroscope features only.</p>
             <p><strong>Output:</strong> current left/right hand joint feature blocks.</p>
             <div class="extension-metrics"><span><strong>0.0420</strong>minimal MAE</span><span><strong>0.0426</strong>neural MAE</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / camera sync</span>
+            <span class="status-pill">Task 19 / camera sync</span>
             <h3>Camera-View Synchronization Retrieval</h3>
             <p><strong>Input:</strong> fisheye camera-1 feature query.</p>
             <p><strong>Output:</strong> synchronized fisheye camera-3 window rank.</p>
             <div class="extension-metrics"><span><strong>0.4943</strong>minimal MRR</span><span><strong>0.2409</strong>neural MRR</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / timing</span>
+            <span class="status-pill">Task 20 / timing</span>
             <h3>Time-to-Next-Transition Regression</h3>
             <p><strong>Input:</strong> current non-caption multimodal window.</p>
             <p><strong>Output:</strong> capped frames until the next action boundary.</p>
@@ -3454,13 +3454,13 @@
         </div>
         <div class="callout-row">
           <div class="callout">
-            <h3>Tier-2 artifact package</h3>
+            <h3>Tasks 13-20 artifact package</h3>
             <p>The eight-task package has JSON metrics, prediction/rank files, a Markdown summary, and a chart generated from the local public-sample annotation and committed shared-window tensor.</p>
-            <p><a href="data/tier2_task_suite.json">Open Tier-2 JSON</a></p>
+            <p><a href="data/task_suite_20.json">Open unified 20-task JSON</a> · <a href="data/tier2_task_suite.json">tasks 13-20 result JSON</a></p>
           </div>
           <div class="callout">
             <h3>Setup alignment</h3>
-            <p>Tier-2 uses the same 20-frame windows, 5-frame stride, 8,546-dimensional feature manifest, chronological split, and minimal/neural comparison pattern as the core 12 tasks.</p>
+            <p>Tasks 13-20 use the same 20-frame windows, 5-frame stride, 8,546-dimensional feature manifest, chronological split, and minimal/neural comparison pattern as tasks 1-12.</p>
           </div>
         </div>
         <img class="chart" src="assets/charts/research_direction_extension_tasks.svg?v=xperience10m-ext" alt="Four Xperience-10M research-direction extension probes with minimal and neural metrics">
@@ -3514,10 +3514,10 @@
     <section id="architectures" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>The 12 tasks share four head families.</h2>
+          <h2>The original task heads share four head families.</h2>
           <p>The diagram separates the shared episode-window representation from the task-specific heads, so the task contracts stay readable before scaling to larger models.</p>
         </div>
-        <img class="architecture-image" src="assets/task_architectures.png?v=xperience10m-nn" alt="Verified minimal and neural architecture diagram for all 12 Ropedia Xperience-10M tasks">
+        <img class="architecture-image" src="assets/task_architectures.png?v=xperience10m-nn" alt="Verified minimal and neural architecture diagram for Ropedia Xperience-10M task heads">
       </div>
     </section>
 
@@ -3579,7 +3579,7 @@
       <div class="wrap">
         <div class="section-head">
           <h2>Task cards and metrics.</h2>
-          <p>The 12 task cards use readable research names, representative modality thumbnails, explicit input-process-output contracts, and verified minimal versus neural scores from the committed result files.</p>
+          <p>The original task cards use readable research names, representative modality thumbnails, explicit input-process-output contracts, and verified minimal versus neural scores. The unified 20-task index adds tasks 13-20 in the same suite.</p>
         </div>
         <div class="task-toolbar" aria-label="Task filters">
           <button class="filter active" data-filter="all">All tasks</button>
@@ -3653,10 +3653,10 @@
               <article class="artifact primary-artifact"><div><h3>Task results</h3><p>Every task definition, split detail, feature dimension, and minimal/neural metric in one project output.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/summary_report.json">task results</a></article>
               <article class="artifact"><h3>Windows table</h3><p>Window start/end frames and aligned action/subtask labels for the public sample episode.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">window table</a></article>
               <article class="artifact"><h3>Feature inputs</h3><p>Source map for the current modality inputs used by the task suite.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">feature inputs</a></article>
-              <article class="artifact"><h3>Neural MLP task results</h3><p>Per-task PyTorch MLP metrics, predictions, histories, and checkpoints for the same 12 task contracts.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a></article>
-              <article class="artifact"><h3>Four-direction taxonomy</h3><p>Maps all 12 tasks to the four research tracks: human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_directions">research direction outputs</a></article>
+              <article class="artifact"><h3>Neural MLP task results</h3><p>Per-task PyTorch MLP metrics, predictions, histories, and checkpoints for the original task contracts, with tasks 13-20 published in the aligned result bundle.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a></article>
+              <article class="artifact"><h3>Four-direction taxonomy</h3><p>Maps the original tasks to the four research tracks: human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_directions">research direction outputs</a></article>
               <article class="artifact"><h3>Direction extension probes</h3><p>Four coded probes, one per research direction, with minimal and neural metrics plus prediction/rank CSVs.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_direction_extensions">extension probe outputs</a></article>
-              <article class="artifact"><h3>Task walkthroughs</h3><p>Case studies for all 12 tasks, including input, middle process modules, output, metric, limitation, and task-player data.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/task_walkthroughs">walkthrough outputs</a></article>
+              <article class="artifact"><h3>Task walkthroughs</h3><p>Case studies for the original tasks, including input, middle process modules, output, metric, limitation, and task-player data.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/task_walkthroughs">walkthrough outputs</a></article>
               <article class="artifact"><h3>Audio ablation and raw upgrade</h3><p>All 72 task/variant rows comparing current audio, no audio, raw audio, replacement, and combined-input settings.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/audio_ablation">audio ablation outputs</a></article>
               <article class="artifact"><h3>Single-episode explorer</h3><p>Interactive window-level view of labels, predictions, modality statistics, object labels, and diagnostics.</p><a href="single_episode_explorer.html">open explorer</a></article>
               <article class="artifact"><h3>Cross-modal retrieval</h3><p>The strongest self-supervised signal from the single episode.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/cross_modal_retrieval/metrics.json">retrieval metrics</a></article>
@@ -3705,7 +3705,7 @@
             </div>
             <div class="artifact-grid">
               <article class="artifact"><h3>Project brief</h3><p>The fastest written overview of the dataset sample, tasks, baselines, and scale-up plan.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_BRIEF.md">brief</a></article>
-              <article class="artifact"><h3>Task walkthroughs</h3><p>Human-readable case studies for all 12 tasks, including input, process modules, output, metric, and limitation.</p><a href="data/task_walkthroughs.json">walkthroughs</a></article>
+              <article class="artifact"><h3>Task walkthroughs</h3><p>Human-readable case studies for the original tasks, including input, process modules, output, metric, and limitation.</p><a href="data/task_walkthroughs.json">walkthroughs</a></article>
               <article class="artifact"><h3>Task results</h3><p>Minimal and neural-head metrics for the same sample windows and chronological split.</p><a href="data/summary_metrics.json">metrics</a></article>
               <article class="artifact"><h3>Visual figures</h3><p>Task-suite map, modality atlas, pipeline diagram, model architecture figure, and Qwen3-Omni LoRA training-flow figure.</p><a href="assets/task_suite_infographic.png">task-suite figure</a></article>
               <article class="artifact"><h3>Dataset notes</h3><p>Official dataset links, public sample source, modalities, access boundary, and current project subset.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE10M_DATASET_CARD_ALIGNMENT.md">dataset notes</a></article>
@@ -3713,7 +3713,7 @@
               <article class="artifact"><h3>Qwen3-Omni status</h3><p>Data requirements and evaluation boundary for the selected multi-episode LoRA pilot.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_ACCESS_STATUS.md">training status</a></article>
               <article class="artifact"><h3>Foundation-model plan</h3><p>Qwen3-Omni, Cosmos 3, GR00T, OpenVLA/openpi, Gemini Robotics, Octo, SmolVLA-style branches, and the Xperience-native pretraining goal by role.</p><a href="data/foundation_model_plan.json">model plan</a></article>
               <article class="artifact"><h3>Hub artifacts</h3><p>Derived CSV/JSON/Markdown/figure artifacts without redistributing raw Xperience-10M data.</p><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts">artifact dataset</a></article>
-              <article class="artifact"><h3>Baseline models</h3><p>Lightweight minimal and neural task-head model files for the 12 task contracts.</p><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines">model repo</a></article>
+              <article class="artifact"><h3>Baseline models</h3><p>Lightweight minimal and neural task-head model files for the task contracts.</p><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines">model repo</a></article>
             </div>
           </section>
         </div>
@@ -3745,12 +3745,12 @@
         </div>
         <div class="artifact-grid">
           <article class="artifact"><h3>Reproducibility guide</h3><p>Human-readable commands, expected artifacts, and current scope for the public single-episode pipeline.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/REPRODUCIBILITY.md">reproducibility guide</a></article>
-          <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, 12 tasks, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
+          <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, the unified 20-task suite, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
           <article class="artifact"><h3>Exact-match reproduction record</h3><p>The last metric rebuild reproduced the public-sample outputs from a fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproduction audit</a></article>
           <article class="artifact"><h3>Project dashboard</h3><p>The website organizes the dataset sample, tasks, methods, results, directions, and scale-up path in one tabbed reader flow.</p><a href="#artifacts">project materials</a></article>
           <article class="artifact"><h3>Multi-episode pilot status</h3><p>The comparison JSON now supports both the three-version reading and model-family grouping, with Qwen3 v5/v6 detail kept as a separate machine-readable audit.</p><a href="data/omni_model_comparison.json">comparison</a><a href="data/qwen3_v5_v6_comparison.json">Qwen v5/v6</a></article>
         </div>
-        <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the 12-task suite with neural heads, regenerate visualizations, then rebuild the supporting project reports.</p>
+        <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the task suite with neural heads, regenerate tasks 13-20, build the unified 20-task index, regenerate visualizations, then rebuild the supporting project reports.</p>
         <pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git
 python3.12 -m venv .venv
 source .venv/bin/activate
@@ -3767,7 +3767,10 @@ cd ropedia-xperience-10m-task-suite
 export WORKSPACE=/path/to/workspace
 python scripts/episode_task_suite.py --workspace "$WORKSPACE" --include-neural
 python scripts/research_direction_extension_tasks.py
+python scripts/tier2_task_suite.py --workspace "$WORKSPACE"
+python scripts/build_unified_task_suite.py
 python scripts/task_walkthroughs.py
+python scripts/build_evaluation_protocol.py
 python scripts/generate_visualizations.py
 python scripts/render_overview_figures.py
 python scripts/render_task_suite_infographic.py
@@ -3923,7 +3926,7 @@ python scripts/validate_publication_package.py</code></pre>
       { id: "reading-path", label: "Reading Path" },
       { id: "dataset-card", label: "Dataset Card" },
       { id: "raw-sample", label: "Raw Sample Browser" },
-      { id: "suite", label: "12-Task Map" },
+      { id: "suite", label: "20-Task Suite" },
       { id: "walkthroughs", label: "Interactive Walkthrough" },
       { id: "tasks", label: "Task Cards" },
       { id: "pipeline", label: "Pipeline" },
@@ -3934,7 +3937,7 @@ python scripts/validate_publication_package.py</code></pre>
       { id: "models", label: "Minimal Baselines" },
       { id: "neural", label: "Neural Heads" },
       { id: "directions", label: "Four Directions" },
-      { id: "extensions", label: "Extension Probes" },
+      { id: "extensions", label: "Tasks 13-20" },
       { id: "diagnostics", label: "Diagnostic Charts" },
       { id: "artifacts", label: "Research Artifacts" },
       { id: "evidence", label: "Research Progress" },

index.html

@@ -4,7 +4,7 @@
   <meta charset="utf-8">
   <meta name="viewport" content="width=device-width, initial-scale=1">
   <title>Ropedia Xperience-10M Task Suite</title>
-  <meta name="description" content="A research-development task lab for Ropedia Xperience-10M: multimodal sample exploration, 12 embodied-AI tasks, baseline models, and a multi-episode fine-tuning path.">
+  <meta name="description" content="A research-development task lab for Ropedia Xperience-10M: multimodal sample exploration, 20 embodied-AI tasks, baseline models, and a multi-episode fine-tuning path.">
   <meta name="theme-color" content="#020502">
   <meta name="robots" content="index, follow">
   <link rel="canonical" href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">
@@ -12,7 +12,7 @@
   <link rel="apple-touch-icon" href="apple-touch-icon.png">
   <link rel="manifest" href="site.webmanifest">
   <meta property="og:title" content="Ropedia Xperience-10M Task Suite">
-  <meta property="og:description" content="A Ropedia Xperience-10M research task lab with multimodal sample exploration, 12 task contracts, minimal and neural baselines, metrics, diagrams, and a scale-up plan.">
+  <meta property="og:description" content="A Ropedia Xperience-10M research task lab with multimodal sample exploration, 20 task contracts, minimal and neural baselines, metrics, diagrams, and a scale-up plan.">
   <meta property="og:type" content="website">
   <meta property="og:url" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">
   <meta property="og:image" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png?v=xperience10m-logo-v3">
@@ -20,7 +20,7 @@
   <meta property="og:image:height" content="630">
   <meta name="twitter:card" content="summary_large_image">
   <meta name="twitter:title" content="Ropedia Xperience-10M Task Suite">
-  <meta name="twitter:description" content="Xperience-10M research task lab with multimodal sample exploration, 12 task contracts, and minimal plus neural baselines.">
+  <meta name="twitter:description" content="Xperience-10M research task lab with multimodal sample exploration, 20 task contracts, and minimal plus neural baselines.">
   <meta name="twitter:image" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png?v=xperience10m-logo-v3">
   <script type="application/ld+json">
     {
@@ -2486,7 +2486,7 @@
         </p>
         <div class="hero-actions">
           <a class="button primary" href="research_roadmap.html">Open roadmap</a>
-          <a class="button" href="#suite">Inspect 12 core tasks</a>
+          <a class="button" href="#suite">Inspect 20 tasks</a>
           <a class="button" href="single_episode_explorer.html">Open explorer</a>
           <a class="button" href="https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/">Open HF app</a>
         </div>
@@ -2494,7 +2494,7 @@
           <div class="stat"><strong>5,821</strong><span>frames in sample episode</span></div>
           <div class="stat"><strong>1,161</strong><span>20-frame windows</span></div>
           <div class="stat"><strong>8,546</strong><span>feature dimensions</span></div>
-          <div class="stat"><strong>12+8</strong><span>core and Tier-2 task contracts</span></div>
+          <div class="stat"><strong>20</strong><span>unified task contracts</span></div>
         </div>
       </div>
       <div class="hero-panel" aria-label="Feature allocation summary">
@@ -2567,8 +2567,8 @@
               <strong>What is implemented</strong>
               <ul>
                 <li>1,161 aligned windows from one public sample episode</li>
-                <li>12 core task contracts with minimal and neural heads</li>
-                <li>8 Tier-2 extension baselines aligned to the same setup</li>
+                <li>20 unified task contracts with minimal and neural evidence</li>
+                <li>Tasks 13-20 aligned to the same setup as tasks 1-12</li>
                 <li>Four research-direction maps and extension probes</li>
               </ul>
             </article>
@@ -2608,11 +2608,11 @@
           <article class="snapshot-card">
             <span class="status-pill">featured</span>
             <h3>Interactive research roadmap</h3>
-            <p>Use this as the front door for the project: it links the 12 tasks, four research tracks, current sample evidence, and the multi-episode Qwen3-Omni scale-up path.</p>
+            <p>Use this as the front door for the project: it links the unified 20 tasks, four research tracks, current sample evidence, and the multi-episode Qwen3-Omni scale-up path.</p>
             <div class="snapshot-meta">
               <span>tracks <strong>4</strong></span>
-          <span>core tasks <strong>12</strong></span>
-          <span>Tier-2 tasks <strong>8</strong></span>
+          <span>tasks <strong>20</strong></span>
+          <span>tasks 13-20 <strong>aligned</strong></span>
               <span>roadmap phases <strong>5</strong></span>
             </div>
             <div class="snapshot-actions">
@@ -2633,11 +2633,11 @@
           <article class="snapshot-card">
             <span class="status-pill">verified</span>
             <h3>Task suite and baseline heads</h3>
-            <p>Every core task has a minimal baseline and a compact PyTorch MLP head over the same windows, splits, and labels.</p>
+            <p>The unified task suite has minimal baseline evidence, and the original task cards plus tasks 13-20 share the same windows, splits, and label discipline.</p>
             <div class="snapshot-meta">
-              <span>core tasks <strong>12</strong></span>
-              <span>neural heads <strong>12</strong></span>
-              <span>Tier-2 extensions <strong>8</strong></span>
+              <span>tasks <strong>20</strong></span>
+              <span>original neural heads <strong>12</strong></span>
+              <span>tasks 13-20 <strong>8</strong></span>
             </div>
           </article>
           <article class="snapshot-card">
@@ -2655,7 +2655,7 @@
             <h3>Public research artifacts</h3>
             <p>Metrics, figures, walkthroughs, baseline weights, Qwen3-Omni results, and Cosmos3 public-safe packages are staged across GitHub, GitHub Pages, and Hugging Face.</p>
             <div class="snapshot-meta">
-              <span>tasks <strong>12</strong></span>
+              <span>tasks <strong>20</strong></span>
               <span>baselines <strong>minimal + neural</strong></span>
               <span>reader path <strong>tabs</strong></span>
             </div>
@@ -2844,9 +2844,9 @@
         <div class="artifact-grid">
           <article class="artifact primary-artifact"><div><h3>Data unit</h3><p>One 20-frame aligned window from the public sample episode, stride 5 frames, 1,161 windows total, represented by 8,546 synchronized multimodal dimensions.</p></div><a href="data/evaluation_protocol.json">evaluation protocol</a></article>
           <article class="artifact"><h3>Split policy</h3><p>Single-episode chronological 70/30 train/test split. This avoids random future-window mixing; cross-episode generalization is measured in the later multi-episode pilot.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVALUATION_PROTOCOL.md">protocol document</a></article>
-          <article class="artifact"><h3>Metric contract</h3><p>All 12 tasks list input, target, primary metric, minimal baseline score, and neural MLP score from committed result files.</p><a href="data/summary_metrics.json">summary metrics</a></article>
+          <article class="artifact"><h3>Metric contract</h3><p>All 20 tasks list input, target, primary metric, baseline score, and source artifact path in the unified suite file.</p><a href="data/task_suite_20.json">task_suite_20.json</a></article>
           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
-          <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across all 12 task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
+          <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across the original task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation branch selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 becomes the world-model branch with a camera-pose proxy forward-dynamics contract ready for trainer work, policy models wait for robot-compatible action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
           <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target; Cosmos3-Nano has a verified future-window compatibility package; and Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch. The next stage is action/subtask error analysis, stronger model-quality runs, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>128-Episode Task Suite Enhancement Pack</h3><p>Before adding episodes, the suite should try `multiscale_20s10_40s20_80s40`, hierarchical action/subtask targets, label-normalized scoring, and compact raw-feature shards for unsupported tasks.</p><a href="data/task_suite_enhancement_128.json">task_suite_enhancement_128.json</a></article>
@@ -2951,7 +2951,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>Figures are indexed</h3>
-            <p>The visual set includes the logo, modality atlas, 12-task suite figure, model-architecture figure, and Qwen3-Omni LoRA training-flow figure.</p>
+            <p>The visual set includes the logo, modality atlas, task-suite figure, model-architecture figure, tasks 13-20 chart, and Qwen3-Omni LoRA training-flow figure.</p>
             <div class="evidence-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FIGURE_INDEX.md">figure guide</a>
               <a href="assets/task_suite_infographic.png">task-suite figure</a>
@@ -2979,7 +2979,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>The dashboard is designed as the visual entry point</h3>
-            <p>Tabs organize the sample data, 12 tasks, model method, results, research directions, and next-stage resources.</p>
+            <p>Tabs organize the sample data, 20 tasks, model method, results, research directions, and next-stage resources.</p>
             <div class="evidence-links">
               <a href="#dataset-card">dataset</a>
               <a href="#tasks">tasks</a>
@@ -3070,7 +3070,7 @@
           </article>
         </div>
         <div class="boundary-strip">
-          <div class="boundary-item"><strong>Verified now</strong><span>One public episode, 5,821 frames, 1,161 aligned windows, 8,546 dimensions, 12 core tasks, 8 Tier-2 extension tasks, 12 neural core heads, and 4 direction-extension probes.</span></div>
+          <div class="boundary-item"><strong>Verified now</strong><span>One public episode, 5,821 frames, 1,161 aligned windows, 8,546 dimensions, 20 unified task contracts, 12 original neural heads, and 4 direction-extension probes.</span></div>
           <div class="boundary-item"><strong>Next: no-new-episode scale</strong><span>The selected 128-episode suite should next use dense/multiscale windows, hierarchical labels, and raw-feature shards before adding more episodes.</span></div>
           <div class="boundary-item"><strong>Next: error analysis</strong><span>The selected 128-episode Qwen3-Omni LoRA result has a final verified diagnostic package; JSON validity meets target, and the next pass should improve action/subtask quality.</span></div>
           <div class="boundary-item"><strong>Not redistributed</strong><span>Raw videos, raw annotations, full Qwen weights, and private gated Xperience-10M data are not included in the public repo or HF bundles.</span></div>
@@ -3086,13 +3086,13 @@
         </div>
         <div class="artifact-grid">
           <article class="artifact primary-artifact"><div><h3>Official dataset</h3><p>Xperience-10M is a gated large-scale egocentric multimodal dataset for embodied AI, robotics, spatial intelligence, and world modeling.</p></div><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official HF dataset</a></article>
-          <article class="artifact"><h3>Public sample</h3><p>The current task suite is built from one public sample episode, not from the entire gated dataset.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
+          <article class="artifact"><h3>Public sample</h3><p>The current unified 20-task suite is built from one public sample episode, not from the entire gated dataset.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article>
           <article class="artifact"><h3>Modalities</h3><p>The sample exposes synchronized video, audio, depth, pose/SLAM, motion capture, inertial signals, calibration, and language annotations.</p><a href="data/modality_atlas.json">modality atlas</a></article>
           <article class="artifact"><h3>Multi-episode pilot</h3><p>The selected 128-episode Qwen3-Omni LoRA v6 diagnostic branch is verified with 4,032 held-out test predictions and 99.90% JSON validity. Action/subtask metrics are still weak, so this remains a baseline for error analysis.</p><a href="https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep">LoRA adapter</a><a href="data/qwen3_v5_v6_comparison.json">v5/v6 comparison</a></article>
           <article class="artifact"><h3>Raw sample browser</h3><p>The Data tab now exposes the official public sample files directly, including playable MP4 video streams and the audio track embedded in fisheye_cam0.mp4.</p><a href="#raw-sample">open raw browser</a><a href="data/raw_sample_files.json">raw manifest</a></article>
           <article class="artifact"><h3>Data boundary</h3><p>Raw MP4, HDF5, RRD files are streamed from the official public sample source when opened here; private gated data and full Qwen weights are not redistributed in this project.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">data notice</a></article>
           <article class="artifact"><h3>Current project subset</h3><p>One public sample episode, 5,821 frames, 1,161 aligned windows, 8,546-dimensional task inputs, plus direct links to the official raw sample files.</p><a href="data/modality_atlas.json">modality atlas</a></article>
-          <article class="artifact"><h3>Covered now</h3><p>Action/subtask labels, next-action prediction, temporal diagnostics, hand trajectory, contact, object relevance, caption grounding, retrieval, reconstruction, and misalignment.</p><a href="data/summary_metrics.json">summary metrics</a></article>
+          <article class="artifact"><h3>Covered now</h3><p>Action/subtask labels, next-action prediction, temporal diagnostics, hand trajectory, contact, object relevance, caption grounding, retrieval, reconstruction, misalignment, long-horizon forecasting, interaction text, action-object relation, sensor bridging, camera sync, and transition timing.</p><a href="data/summary_metrics.json">summary metrics</a></article>
           <article class="artifact"><h3>Responsible use</h3><p>This project is for research exploration and excludes identity recognition, surveillance, biometric profiling, sensitive-attribute inference, and safety-critical deployment.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/DATA_NOTICE.md">use notes</a></article>
           <article class="artifact"><h3>Later milestones</h3><p>Full audio-visual learning, caption generation, depth-pixel prediction, SLAM estimation, neural rendering, policy learning, cross-episode generalization, held-out Qwen3-Omni evaluation, and future Xperience-native pretraining.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">native pretraining</a></article>
         </div>
@@ -3181,11 +3181,11 @@
     <section id="suite" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>Ropedia Xperience-10M 12-task suite.</h2>
-          <p>The task map connects synchronized multimodal windows to 12 research task heads, then the modality atlas shows the sample streams used to build those contracts.</p>
+          <h2>Ropedia Xperience-10M unified 20-task suite.</h2>
+          <p>The suite connects synchronized multimodal windows to 20 task contracts. The large map visualizes the original task families, while tasks 13-20 are listed as the aligned continuation under the same setup.</p>
         </div>
         <div class="figure-pan" id="task-suite-map">
-          <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl" alt="Infographic showing all 12 Ropedia Xperience-10M tasks with enlarged full-width modality cards">
+          <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl" alt="Infographic showing Ropedia Xperience-10M task families with enlarged full-width modality cards">
         </div>
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
@@ -3344,7 +3344,7 @@
     <section id="directions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>The 12 tasks organized into four research directions.</h2>
+          <h2>The original tasks organized into four research directions.</h2>
           <p>Each task is mapped as direct, proxy, or diagnostic evidence for the Ropedia research tracks. The mapping uses two current baselines: minimal interpretable heads and neural MLP heads over the same feature contract.</p>
         </div>
         <div class="direction-grid">
@@ -3373,7 +3373,7 @@
             <div class="direction-counts"><span><strong>0</strong>direct</span><span><strong>6</strong>proxy</span><span><strong>3</strong>diagnostic</span></div>
           </article>
         </div>
-        <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the 12 Xperience-10M tasks across four research directions">
+        <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the original Xperience-10M tasks across four research directions">
         <div class="baseline-strip">
           <div class="callout">
             <h3>Baseline 1: minimal heads</h3>
@@ -3390,62 +3390,62 @@
     <section id="extensions" data-project-tab="directions" role="tabpanel" aria-labelledby="tab-directions" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>Tier-2 Extension Task Suite broadens the sample task space.</h2>
-          <p>The original four direction probes remain as focused examples. The new Tier-2 layer adds eight more sample-supported baselines using the same windows, feature manifest, chronological split, and minimal/neural head pattern as the core 12 tasks.</p>
+          <h2>Tasks 13-20 complete the unified 20-task suite.</h2>
+          <p>The original four direction probes remain as focused examples. Tasks 13-20 add eight sample-supported baselines using the same windows, feature manifest, chronological split, and minimal/neural head pattern as tasks 1-12.</p>
         </div>
-        <img class="chart" src="assets/charts/tier2_task_suite.svg?v=xperience10m-tier2" alt="Eight Xperience-10M Tier-2 extension tasks with minimal and neural metrics">
+        <img class="chart" src="assets/charts/tier2_task_suite.svg?v=xperience10m-tier2" alt="Eight Xperience-10M tasks 13-20 with minimal and neural metrics">
         <div class="extension-grid">
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / forecast</span>
+            <span class="status-pill">Task 13 / forecast</span>
             <h3>Long-Horizon Next-Action Forecasting</h3>
             <p><strong>Input:</strong> current non-caption multimodal window.</p>
             <p><strong>Output:</strong> action label five seconds later.</p>
             <div class="extension-metrics"><span><strong>0.0750</strong>minimal macro-F1</span><span><strong>0.0655</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / procedure</span>
+            <span class="status-pill">Task 14 / procedure</span>
             <h3>Long-Horizon Next-Subtask Forecasting</h3>
             <p><strong>Input:</strong> current non-caption multimodal window.</p>
             <p><strong>Output:</strong> procedure subtask five seconds later.</p>
             <div class="extension-metrics"><span><strong>0.0455</strong>minimal macro-F1</span><span><strong>0.0507</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / language</span>
+            <span class="status-pill">Task 15 / language</span>
             <h3>Interaction Text Prediction</h3>
             <p><strong>Input:</strong> current sensor window with caption features removed.</p>
             <p><strong>Output:</strong> raw annotation interaction phrase.</p>
             <div class="extension-metrics"><span><strong>0.0444</strong>minimal macro-F1</span><span><strong>0.0381</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / relation</span>
+            <span class="status-pill">Task 16 / relation</span>
             <h3>Action-Object Relation Prediction</h3>
             <p><strong>Input:</strong> current sensor window with caption features removed.</p>
             <p><strong>Output:</strong> joint action plus active object-set label.</p>
             <div class="extension-metrics"><span><strong>0.0000</strong>minimal macro-F1</span><span><strong>0.0000</strong>neural macro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / objects</span>
+            <span class="status-pill">Task 17 / objects</span>
             <h3>Future Object-Set Forecasting</h3>
             <p><strong>Input:</strong> current sensor window with caption features removed.</p>
             <p><strong>Output:</strong> object set active five seconds later.</p>
             <div class="extension-metrics"><span><strong>0.1694</strong>minimal micro-F1</span><span><strong>0.1972</strong>neural micro-F1</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / sensor bridge</span>
+            <span class="status-pill">Task 18 / sensor bridge</span>
             <h3>IMU-to-Hand Pose Reconstruction</h3>
             <p><strong>Input:</strong> IMU acceleration and gyroscope features only.</p>
             <p><strong>Output:</strong> current left/right hand joint feature blocks.</p>
             <div class="extension-metrics"><span><strong>0.0420</strong>minimal MAE</span><span><strong>0.0426</strong>neural MAE</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / camera sync</span>
+            <span class="status-pill">Task 19 / camera sync</span>
             <h3>Camera-View Synchronization Retrieval</h3>
             <p><strong>Input:</strong> fisheye camera-1 feature query.</p>
             <p><strong>Output:</strong> synchronized fisheye camera-3 window rank.</p>
             <div class="extension-metrics"><span><strong>0.4943</strong>minimal MRR</span><span><strong>0.2409</strong>neural MRR</span></div>
           </article>
           <article class="extension-card">
-            <span class="status-pill">Tier-2 / timing</span>
+            <span class="status-pill">Task 20 / timing</span>
             <h3>Time-to-Next-Transition Regression</h3>
             <p><strong>Input:</strong> current non-caption multimodal window.</p>
             <p><strong>Output:</strong> capped frames until the next action boundary.</p>
@@ -3454,13 +3454,13 @@
         </div>
         <div class="callout-row">
           <div class="callout">
-            <h3>Tier-2 artifact package</h3>
+            <h3>Tasks 13-20 artifact package</h3>
             <p>The eight-task package has JSON metrics, prediction/rank files, a Markdown summary, and a chart generated from the local public-sample annotation and committed shared-window tensor.</p>
-            <p><a href="data/tier2_task_suite.json">Open Tier-2 JSON</a></p>
+            <p><a href="data/task_suite_20.json">Open unified 20-task JSON</a> · <a href="data/tier2_task_suite.json">tasks 13-20 result JSON</a></p>
           </div>
           <div class="callout">
             <h3>Setup alignment</h3>
-            <p>Tier-2 uses the same 20-frame windows, 5-frame stride, 8,546-dimensional feature manifest, chronological split, and minimal/neural comparison pattern as the core 12 tasks.</p>
+            <p>Tasks 13-20 use the same 20-frame windows, 5-frame stride, 8,546-dimensional feature manifest, chronological split, and minimal/neural comparison pattern as tasks 1-12.</p>
           </div>
         </div>
         <img class="chart" src="assets/charts/research_direction_extension_tasks.svg?v=xperience10m-ext" alt="Four Xperience-10M research-direction extension probes with minimal and neural metrics">
@@ -3514,10 +3514,10 @@
     <section id="architectures" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>The 12 tasks share four head families.</h2>
+          <h2>The original task heads share four head families.</h2>
           <p>The diagram separates the shared episode-window representation from the task-specific heads, so the task contracts stay readable before scaling to larger models.</p>
         </div>
-        <img class="architecture-image" src="assets/task_architectures.png?v=xperience10m-nn" alt="Verified minimal and neural architecture diagram for all 12 Ropedia Xperience-10M tasks">
+        <img class="architecture-image" src="assets/task_architectures.png?v=xperience10m-nn" alt="Verified minimal and neural architecture diagram for Ropedia Xperience-10M task heads">
       </div>
     </section>
 
@@ -3579,7 +3579,7 @@
       <div class="wrap">
         <div class="section-head">
           <h2>Task cards and metrics.</h2>
-          <p>The 12 task cards use readable research names, representative modality thumbnails, explicit input-process-output contracts, and verified minimal versus neural scores from the committed result files.</p>
+          <p>The original task cards use readable research names, representative modality thumbnails, explicit input-process-output contracts, and verified minimal versus neural scores. The unified 20-task index adds tasks 13-20 in the same suite.</p>
         </div>
         <div class="task-toolbar" aria-label="Task filters">
           <button class="filter active" data-filter="all">All tasks</button>
@@ -3653,10 +3653,10 @@
               <article class="artifact primary-artifact"><div><h3>Task results</h3><p>Every task definition, split detail, feature dimension, and minimal/neural metric in one project output.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/summary_report.json">task results</a></article>
               <article class="artifact"><h3>Windows table</h3><p>Window start/end frames and aligned action/subtask labels for the public sample episode.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">window table</a></article>
               <article class="artifact"><h3>Feature inputs</h3><p>Source map for the current modality inputs used by the task suite.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">feature inputs</a></article>
-              <article class="artifact"><h3>Neural MLP task results</h3><p>Per-task PyTorch MLP metrics, predictions, histories, and checkpoints for the same 12 task contracts.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a></article>
-              <article class="artifact"><h3>Four-direction taxonomy</h3><p>Maps all 12 tasks to the four research tracks: human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_directions">research direction outputs</a></article>
+              <article class="artifact"><h3>Neural MLP task results</h3><p>Per-task PyTorch MLP metrics, predictions, histories, and checkpoints for the original task contracts, with tasks 13-20 published in the aligned result bundle.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural MLP outputs</a></article>
+              <article class="artifact"><h3>Four-direction taxonomy</h3><p>Maps the original tasks to the four research tracks: human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_directions">research direction outputs</a></article>
               <article class="artifact"><h3>Direction extension probes</h3><p>Four coded probes, one per research direction, with minimal and neural metrics plus prediction/rank CSVs.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_direction_extensions">extension probe outputs</a></article>
-              <article class="artifact"><h3>Task walkthroughs</h3><p>Case studies for all 12 tasks, including input, middle process modules, output, metric, limitation, and task-player data.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/task_walkthroughs">walkthrough outputs</a></article>
+              <article class="artifact"><h3>Task walkthroughs</h3><p>Case studies for the original tasks, including input, middle process modules, output, metric, limitation, and task-player data.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/task_walkthroughs">walkthrough outputs</a></article>
               <article class="artifact"><h3>Audio ablation and raw upgrade</h3><p>All 72 task/variant rows comparing current audio, no audio, raw audio, replacement, and combined-input settings.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/audio_ablation">audio ablation outputs</a></article>
               <article class="artifact"><h3>Single-episode explorer</h3><p>Interactive window-level view of labels, predictions, modality statistics, object labels, and diagnostics.</p><a href="single_episode_explorer.html">open explorer</a></article>
               <article class="artifact"><h3>Cross-modal retrieval</h3><p>The strongest self-supervised signal from the single episode.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/cross_modal_retrieval/metrics.json">retrieval metrics</a></article>
@@ -3705,7 +3705,7 @@
             </div>
             <div class="artifact-grid">
               <article class="artifact"><h3>Project brief</h3><p>The fastest written overview of the dataset sample, tasks, baselines, and scale-up plan.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_BRIEF.md">brief</a></article>
-              <article class="artifact"><h3>Task walkthroughs</h3><p>Human-readable case studies for all 12 tasks, including input, process modules, output, metric, and limitation.</p><a href="data/task_walkthroughs.json">walkthroughs</a></article>
+              <article class="artifact"><h3>Task walkthroughs</h3><p>Human-readable case studies for the original tasks, including input, process modules, output, metric, and limitation.</p><a href="data/task_walkthroughs.json">walkthroughs</a></article>
               <article class="artifact"><h3>Task results</h3><p>Minimal and neural-head metrics for the same sample windows and chronological split.</p><a href="data/summary_metrics.json">metrics</a></article>
               <article class="artifact"><h3>Visual figures</h3><p>Task-suite map, modality atlas, pipeline diagram, model architecture figure, and Qwen3-Omni LoRA training-flow figure.</p><a href="assets/task_suite_infographic.png">task-suite figure</a></article>
               <article class="artifact"><h3>Dataset notes</h3><p>Official dataset links, public sample source, modalities, access boundary, and current project subset.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE10M_DATASET_CARD_ALIGNMENT.md">dataset notes</a></article>
@@ -3713,7 +3713,7 @@
               <article class="artifact"><h3>Qwen3-Omni status</h3><p>Data requirements and evaluation boundary for the selected multi-episode LoRA pilot.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_ACCESS_STATUS.md">training status</a></article>
               <article class="artifact"><h3>Foundation-model plan</h3><p>Qwen3-Omni, Cosmos 3, GR00T, OpenVLA/openpi, Gemini Robotics, Octo, SmolVLA-style branches, and the Xperience-native pretraining goal by role.</p><a href="data/foundation_model_plan.json">model plan</a></article>
               <article class="artifact"><h3>Hub artifacts</h3><p>Derived CSV/JSON/Markdown/figure artifacts without redistributing raw Xperience-10M data.</p><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts">artifact dataset</a></article>
-              <article class="artifact"><h3>Baseline models</h3><p>Lightweight minimal and neural task-head model files for the 12 task contracts.</p><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines">model repo</a></article>
+              <article class="artifact"><h3>Baseline models</h3><p>Lightweight minimal and neural task-head model files for the task contracts.</p><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines">model repo</a></article>
             </div>
           </section>
         </div>
@@ -3745,12 +3745,12 @@
         </div>
         <div class="artifact-grid">
           <article class="artifact"><h3>Reproducibility guide</h3><p>Human-readable commands, expected artifacts, and current scope for the public single-episode pipeline.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/REPRODUCIBILITY.md">reproducibility guide</a></article>
-          <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, 12 tasks, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
+          <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, the unified 20-task suite, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
           <article class="artifact"><h3>Exact-match reproduction record</h3><p>The last metric rebuild reproduced the public-sample outputs from a fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproduction audit</a></article>
           <article class="artifact"><h3>Project dashboard</h3><p>The website organizes the dataset sample, tasks, methods, results, directions, and scale-up path in one tabbed reader flow.</p><a href="#artifacts">project materials</a></article>
           <article class="artifact"><h3>Multi-episode pilot status</h3><p>The comparison JSON now supports both the three-version reading and model-family grouping, with Qwen3 v5/v6 detail kept as a separate machine-readable audit.</p><a href="data/omni_model_comparison.json">comparison</a><a href="data/qwen3_v5_v6_comparison.json">Qwen v5/v6</a></article>
         </div>
-        <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the 12-task suite with neural heads, regenerate visualizations, then rebuild the supporting project reports.</p>
+        <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the task suite with neural heads, regenerate tasks 13-20, build the unified 20-task index, regenerate visualizations, then rebuild the supporting project reports.</p>
         <pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git
 python3.12 -m venv .venv
 source .venv/bin/activate
@@ -3767,7 +3767,10 @@ cd ropedia-xperience-10m-task-suite
 export WORKSPACE=/path/to/workspace
 python scripts/episode_task_suite.py --workspace "$WORKSPACE" --include-neural
 python scripts/research_direction_extension_tasks.py
+python scripts/tier2_task_suite.py --workspace "$WORKSPACE"
+python scripts/build_unified_task_suite.py
 python scripts/task_walkthroughs.py
+python scripts/build_evaluation_protocol.py
 python scripts/generate_visualizations.py
 python scripts/render_overview_figures.py
 python scripts/render_task_suite_infographic.py
@@ -3923,7 +3926,7 @@ python scripts/validate_publication_package.py</code></pre>
       { id: "reading-path", label: "Reading Path" },
       { id: "dataset-card", label: "Dataset Card" },
       { id: "raw-sample", label: "Raw Sample Browser" },
-      { id: "suite", label: "12-Task Map" },
+      { id: "suite", label: "20-Task Suite" },
       { id: "walkthroughs", label: "Interactive Walkthrough" },
       { id: "tasks", label: "Task Cards" },
       { id: "pipeline", label: "Pipeline" },
@@ -3934,7 +3937,7 @@ python scripts/validate_publication_package.py</code></pre>
       { id: "models", label: "Minimal Baselines" },
       { id: "neural", label: "Neural Heads" },
       { id: "directions", label: "Four Directions" },
-      { id: "extensions", label: "Extension Probes" },
+      { id: "extensions", label: "Tasks 13-20" },
       { id: "diagnostics", label: "Diagnostic Charts" },
       { id: "artifacts", label: "Research Artifacts" },
       { id: "evidence", label: "Research Progress" },

results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json

@@ -8,7 +8,7 @@
   "status": "pass",
   "task": "next_subtask_forecast",
   "task_display_name": "Long-Horizon Next-Subtask Forecasting",
-  "tier": "tier2_extension",
+  "suite_position": "tasks_13_to_20",
   "model_family": "neural_mlp",
   "input": "Current 20-frame non-caption multimodal window.",
   "split": "single_episode_chronological",

scripts/build_artifact_index.py

@@ -385,6 +385,30 @@ ARTIFACTS = [
         "surface": "repo_hf",
         "shows": "Regenerates the protocol from committed summary metrics and task artifacts.",
     },
+    {
+        "id": "task_suite_20",
+        "title": "Unified 20-task suite",
+        "path": "TASK_SUITE_20.md",
+        "kind": "evaluation_protocol",
+        "surface": "repo_hf",
+        "shows": "Reader-facing table for the single unified public-sample task suite: tasks 1-12 plus tasks 13-20 under the same window, split, feature, and baseline contract.",
+    },
+    {
+        "id": "task_suite_20_json",
+        "title": "Unified 20-task suite JSON",
+        "path": "docs/data/task_suite_20.json",
+        "kind": "website_data",
+        "surface": "website_hf",
+        "shows": "Machine-readable unified 20-task index for the website, Hugging Face mirrors, and live verification.",
+    },
+    {
+        "id": "task_suite_20_builder",
+        "title": "Unified 20-task suite builder",
+        "path": "scripts/build_unified_task_suite.py",
+        "kind": "evaluation_protocol",
+        "surface": "repo_hf",
+        "shows": "Regenerates the unified 20-task JSON and Markdown from the original 12-task metrics plus the tasks 13-20 result bundle.",
+    },
     {
         "id": "research_takeaways",
         "title": "Research takeaways",
@@ -415,7 +439,7 @@ ARTIFACTS = [
         "path": "scripts/audio_ablation_and_raw_upgrade.py",
         "kind": "result_interpretation",
         "surface": "repo_hf",
-        "shows": "Measures audio contribution variants across all 12 task contracts.",
+        "shows": "Measures audio contribution variants across the original task contracts.",
     },
     {
         "id": "audio_ablation_summary",
@@ -447,7 +471,7 @@ ARTIFACTS = [
         "path": "docs/assets/charts/audio_ablation_delta.svg",
         "kind": "visual_evidence",
         "surface": "website_hf",
-        "shows": "Bar chart of measured current-audio primary-metric deltas across the 12 tasks.",
+        "shows": "Bar chart of measured current-audio primary-metric deltas across the original tasks.",
     },
     {
         "id": "figure_index",
@@ -553,7 +577,7 @@ ARTIFACTS = [
         "kind": "quality_gate",
         "surface": "website_hf",
         "volatile": True,
-        "shows": "Confirms the public 12-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
+        "shows": "Confirms the public original-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
     },
     {
         "id": "rendered_site_check",
@@ -676,7 +700,7 @@ ARTIFACTS = [
     },
     {
         "id": "task_summary",
-        "title": "12-task summary report",
+        "title": "Original task summary report",
         "path": "results/episode_task_suite/summary_report.json",
         "kind": "metrics_source",
         "surface": "repo_hf",
@@ -720,7 +744,7 @@ ARTIFACTS = [
         "path": "results/episode_task_suite/neural_mlp",
         "kind": "result_directory",
         "surface": "repo_hf_model",
-        "shows": "Stores matching PyTorch MLP results for the 12 task contracts.",
+        "shows": "Stores matching PyTorch MLP results for the original task contracts.",
     },
     {
         "id": "research_direction_taxonomy",
@@ -728,7 +752,7 @@ ARTIFACTS = [
         "path": "results/episode_task_suite/research_directions/research_direction_taxonomy.json",
         "kind": "taxonomy",
         "surface": "repo_hf",
-        "shows": "Maps the 12 tasks to the four Ropedia research directions as direct/proxy/diagnostic.",
+        "shows": "Maps the original tasks to the four Ropedia research directions as direct/proxy/diagnostic.",
     },
     {
         "id": "research_direction_extensions",
@@ -740,35 +764,35 @@ ARTIFACTS = [
     },
     {
         "id": "tier2_task_suite",
-        "title": "Tier-2 extension task suite",
+        "title": "Tasks 13-20 result bundle",
         "path": "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
         "kind": "metrics_source",
         "surface": "repo_hf",
-        "shows": "Stores eight extra sample-supported task contracts with minimal and neural baselines aligned to the core 12-task window/split setup.",
+        "shows": "Stores the historical result bundle for unified tasks 13-20 with minimal and neural baselines aligned to the same 20-task window/split setup.",
     },
     {
         "id": "tier2_task_suite_json",
-        "title": "Tier-2 extension task suite JSON",
+        "title": "Tasks 13-20 result JSON",
         "path": "docs/data/tier2_task_suite.json",
         "kind": "website_data",
         "surface": "website_hf",
-        "shows": "Machine-readable Tier-2 task definitions, setup alignment, metrics, and public source paths.",
+        "shows": "Machine-readable tasks 13-20 definitions, setup alignment, metrics, and public source paths; the file name is historical.",
     },
     {
         "id": "tier2_task_suite_chart",
-        "title": "Tier-2 task suite chart",
+        "title": "Tasks 13-20 chart",
         "path": "docs/assets/charts/tier2_task_suite.svg",
         "kind": "generated_figure",
         "surface": "website_hf",
-        "shows": "Visual summary of the eight Tier-2 extension baseline metrics.",
+        "shows": "Visual summary of the eight additional task baseline metrics in the unified 20-task suite.",
     },
     {
         "id": "tier2_task_suite_builder",
-        "title": "Tier-2 task suite builder",
+        "title": "Tasks 13-20 builder",
         "path": "scripts/tier2_task_suite.py",
         "kind": "evaluation_protocol",
         "surface": "repo_hf",
-        "shows": "Regenerates the Tier-2 baselines from shared windows plus the local public-sample annotation HDF5.",
+        "shows": "Regenerates tasks 13-20 from shared windows plus the local public-sample annotation HDF5; the script name is historical.",
     },
     {
         "id": "task_walkthroughs",
@@ -780,7 +804,7 @@ ARTIFACTS = [
     },
     {
         "id": "task_suite_infographic",
-        "title": "12-task suite infographic",
+        "title": "Original task-suite infographic",
         "path": "docs/assets/task_suite_infographic.png",
         "kind": "generated_figure",
         "surface": "website_hf",
@@ -864,7 +888,7 @@ ARTIFACTS = [
         "path": "results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md",
         "kind": "scaleup_status",
         "surface": "repo_hf",
-        "shows": "Summarizes same-split simple and neural metadata baselines for the 12 task ids, with unsupported markers for tasks that need missing raw 128 feature blocks.",
+        "shows": "Summarizes same-split simple and neural metadata baselines for the 12 original task ids, with unsupported markers for tasks that need missing raw 128 feature blocks.",
     },
     {
         "id": "multi_episode_128_baseline_summary",

scripts/build_evaluation_protocol.py

@@ -13,6 +13,7 @@ from task_display import task_display_name
 ROOT = Path(__file__).resolve().parents[1]
 SUMMARY_PATH = ROOT / "docs/data/summary_metrics.json"
 TIER2_PATH = ROOT / "docs/data/tier2_task_suite.json"
+TASK_SUITE_20_PATH = ROOT / "docs/data/task_suite_20.json"
 OUTPUT_JSON = ROOT / "docs/data/evaluation_protocol.json"
 OUTPUT_MD = ROOT / "EVALUATION_PROTOCOL.md"
 
@@ -163,6 +164,7 @@ def build_payload() -> dict:
             {
                 "task": task_name,
                 "task_display_name": task_display_name(task_name),
+                "origin": "original_public_sample_tasks",
                 **protocol,
                 "counts": count_record(minimal),
                 "minimal_primary_metric": metric_value(minimal, primary),
@@ -181,6 +183,7 @@ def build_payload() -> dict:
     if tier2:
         source_files.extend(
             [
+                "docs/data/task_suite_20.json",
                 "docs/data/tier2_task_suite.json",
                 "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
             ]
@@ -197,6 +200,7 @@ def build_payload() -> dict:
                 {
                     "task": task_name,
                     "task_display_name": spec.get("name", task_name),
+                    "origin": "additional_public_sample_tasks",
                     "family": spec.get("family"),
                     "unit": "single aligned window" if spec.get("family") != "retrieval" else "held-out query window",
                     "input": spec.get("input"),
@@ -211,6 +215,11 @@ def build_payload() -> dict:
                 }
             )
 
+    all_task_rows = task_rows + tier2_rows
+    for idx, row in enumerate(all_task_rows, start=1):
+        row["task_number"] = idx
+        row["suite_label"] = f"Task {idx:02d}"
+
     return {
         "title": "Ropedia Xperience-10M Task Suite Evaluation Protocol",
         "status": "pass",
@@ -228,18 +237,14 @@ def build_payload() -> dict:
             "audio_featurized": True,
             "raw_data_redistributed": False,
         },
-        "task_tiers": {
-            "core_12": {
-                "status": "canonical_public_sample_suite",
-                "task_count": len(task_rows),
-                "results": "docs/data/summary_metrics.json",
-            },
-            "tier2_extension": {
-                "status": "generated_extension_baselines" if tier2 else "not_generated",
-                "task_count": len(tier2_rows),
-                "results": "docs/data/tier2_task_suite.json",
-                "combined_task_count": len(task_rows) + len(tier2_rows),
-            },
+        "task_suite": {
+            "status": "unified_public_sample_suite",
+            "task_count": len(all_task_rows),
+            "original_public_sample_tasks": len(task_rows),
+            "additional_public_sample_tasks": len(tier2_rows),
+            "unified_results": "docs/data/task_suite_20.json" if TASK_SUITE_20_PATH.exists() else None,
+            "legacy_additional_task_result_path": "docs/data/tier2_task_suite.json",
+            "legacy_path_note": "The tier2_task_suite path is retained for stable links only; tasks 13-20 are presented as part of the same 20-task suite.",
         },
         "split_policy": {
             "name": "single_episode_chronological",
@@ -267,8 +272,7 @@ def build_payload() -> dict:
                 "config": suite.get("neural_model", {}),
             },
         ],
-        "task_protocols": task_rows,
-        "tier2_task_protocols": tier2_rows,
+        "task_protocols": all_task_rows,
         "global_leakage_controls": [
             "Use chronological train/test splits instead of random window shuffling.",
             "Fit scalers and learned projections on train windows only.",
@@ -301,8 +305,8 @@ def build_payload() -> dict:
 
 def markdown_table(rows: list[dict]) -> list[str]:
     lines = [
-        "| Task | Artifact id | Family | Unit | Input -> target | Primary metric | Minimal | Neural |",
-        "| --- | --- | --- | --- | --- | --- | ---: | ---: |",
+        "| # | Task | Artifact id | Origin | Family | Unit | Input -> target | Primary metric | Minimal | Neural |",
+        "| ---: | --- | --- | --- | --- | --- | --- | --- | ---: | ---: |",
     ]
     for row in rows:
         metric = row["primary_metric"]
@@ -312,9 +316,11 @@ def markdown_table(rows: list[dict]) -> list[str]:
         neural_text = "n/a" if neural is None else f"{neural:.4f}"
         direction = "higher better" if row["higher_is_better"] else "lower better"
         lines.append(
-            "| {task} | `{artifact}` | {family} | {unit} | {input} -> {target} | {metric} ({direction}) | {minimal} | {neural} |".format(
+            "| {number} | {task} | `{artifact}` | {origin} | {family} | {unit} | {input} -> {target} | {metric} ({direction}) | {minimal} | {neural} |".format(
+                number=row.get("task_number", ""),
                 task=row["task_display_name"],
                 artifact=row["task"],
+                origin="original" if row.get("origin") == "original_public_sample_tasks" else "additional",
                 family=row["family"],
                 unit=row["unit"],
                 input=row["input"],
@@ -328,34 +334,6 @@ def markdown_table(rows: list[dict]) -> list[str]:
     return lines
 
 
-def tier2_markdown_table(rows: list[dict]) -> list[str]:
-    lines = [
-        "| Tier-2 task | Artifact id | Family | Input -> target | Primary metric | Minimal | Neural |",
-        "| --- | --- | --- | --- | --- | ---: | ---: |",
-    ]
-    for row in rows:
-        metric = row["primary_metric"]
-        minimal = row["minimal_primary_metric"]
-        neural = row["neural_primary_metric"]
-        minimal_text = "n/a" if minimal is None else f"{minimal:.4f}"
-        neural_text = "n/a" if neural is None else f"{neural:.4f}"
-        direction = "higher better" if row["higher_is_better"] else "lower better"
-        lines.append(
-            "| {task} | `{artifact}` | {family} | {input} -> {target} | {metric} ({direction}) | {minimal} | {neural} |".format(
-                task=row["task_display_name"],
-                artifact=row["task"],
-                family=row["family"],
-                input=row["input"],
-                target=row["target"],
-                metric=metric,
-                direction=direction,
-                minimal=minimal_text,
-                neural=neural_text,
-            )
-        )
-    return lines
-
-
 def render_markdown(payload: dict) -> str:
     scope = payload["scope"]
     split = payload["split_policy"]
@@ -406,19 +384,15 @@ def render_markdown(payload: dict) -> str:
         "Neural MLP heads reuse the same windows, splits, and feature tensors; they",
         "are not foundation models.",
         "",
-        "## Task Contracts",
+        "## Unified 20-Task Contracts",
         "",
-        *markdown_table(payload["task_protocols"]),
+        "Tasks 1-12 are the original public-sample task contracts. Tasks 13-20",
+        "are additional sample-supported contracts attached to the same 20-frame",
+        "window, feature, chronological split, leakage-control, and minimal/neural",
+        "baseline setup. Historical `tier2_task_suite` paths are retained only as",
+        "stable artifact locations for tasks 13-20.",
         "",
-        "## Tier-2 Extension Contracts",
-        "",
-        "The core 12-task suite remains the canonical benchmark. The Tier-2 layer",
-        "adds sample-supported extension baselines using the same windows, feature",
-        "manifest, chronological split, and minimal/neural head pattern. Regeneration",
-        "requires the raw public-sample `annotation.hdf5` for interaction/object",
-        "targets, but raw files are not redistributed.",
-        "",
-        *tier2_markdown_table(payload.get("tier2_task_protocols", [])),
+        *markdown_table(payload["task_protocols"]),
         "",
         "## Leakage Controls",
         "",

scripts/build_figure_index.py

@@ -44,9 +44,9 @@ FIGURES = [
     },
     {
         "id": "task_suite_infographic",
-        "title": "12-task suite infographic",
+        "title": "Original task-suite infographic",
         "path": "docs/assets/task_suite_infographic.png",
-        "role": "Primary visual map of the task suite, verified metrics, and sample modalities.",
+        "role": "Primary visual map of the original task families, verified metrics, and sample modalities; the unified public suite is now documented as 20 tasks.",
         "source_script": "scripts/render_task_suite_infographic.py",
         "surface": "README, website, HF Space, artifact dataset, model card",
     },
@@ -70,7 +70,7 @@ FIGURES = [
         "id": "task_architectures",
         "title": "Minimal and neural task architecture map",
         "path": "docs/assets/task_architectures.png",
-        "role": "All 12 task heads and shared feature contracts.",
+        "role": "Minimal and neural heads for the original task contracts and shared feature contracts.",
         "source_script": "scripts/render_overview_figures.py",
         "surface": "README, website, HF artifact dataset, model card",
     },
@@ -172,11 +172,11 @@ FIGURES = [
     },
     {
         "id": "tier2_task_suite_chart",
-        "title": "Tier-2 extension task suite chart",
+        "title": "Tasks 13-20 baseline chart",
         "path": "docs/assets/charts/tier2_task_suite.svg",
-        "role": "Eight sample-supported Tier-2 extension tasks with aligned minimal and neural baseline metrics.",
+        "role": "Eight additional sample-supported tasks in the unified 20-task suite with aligned minimal and neural baseline metrics.",
         "source_script": "scripts/tier2_task_suite.py",
-        "surface": "website extensions, README, HF mirrors",
+        "surface": "website unified task section, README, HF mirrors",
     },
     {
         "id": "feature_blocks_chart",

scripts/build_public_surface_qa.py

@@ -152,7 +152,7 @@ def build_report() -> dict:
     naming_markers = [
         "Ropedia Xperience-10M Task Suite",
         "Xperience-10M",
-        "12-task",
+        "20-task",
         "Qwen3-Omni",
         "128-episode pilot",
     ]
@@ -174,6 +174,7 @@ def build_report() -> dict:
         "data/public_surface_qa.json",
         "data/research_roadmap.json",
         "data/task_suite_enhancement_128.json",
+        "data/task_suite_20.json",
         "data/tier2_task_suite.json",
     ]
 

scripts/build_quality_gates.py

@@ -57,7 +57,7 @@ GATES = [
         "command": "python scripts/validate_task_surface.py",
         "report": "docs/data/task_surface_integrity.json",
         "blocks_if": "Task cards expose raw artifact ids, human-readable task names drift, modality thumbnails are missing, or the interactive task player is not wired to the generated JSON.",
-        "shows": "The public task cards and walkthrough/player stay aligned with generated 12-task metadata.",
+        "shows": "The public task cards and walkthrough/player stay aligned with generated task-suite metadata.",
     },
     {
         "id": "evaluation_protocol",

scripts/build_unified_task_suite.py

@@ -0,0 +1,267 @@
+#!/usr/bin/env python3
+"""Build the unified 20-task public-sample task-suite index."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+
+ROOT = Path(__file__).resolve().parents[1]
+SUMMARY_PATH = ROOT / "docs/data/summary_metrics.json"
+WALKTHROUGHS_PATH = ROOT / "docs/data/task_walkthroughs.json"
+ADDITIONAL_TASKS_PATH = ROOT / "docs/data/tier2_task_suite.json"
+OUTPUT_JSON = ROOT / "docs/data/task_suite_20.json"
+OUTPUT_MD = ROOT / "TASK_SUITE_20.md"
+
+
+def read_json(path: Path) -> dict[str, Any]:
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def metric_value(metrics: dict[str, Any], metric_key: str | None) -> float | None:
+    if not metrics or not metric_key:
+        return None
+    if "primary_score" in metrics:
+        return metrics.get("primary_score")
+    return metrics.get(metric_key)
+
+
+def count_fields(metrics: dict[str, Any]) -> dict[str, Any]:
+    keys = [
+        "num_windows",
+        "num_samples",
+        "num_queries",
+        "num_eval_windows",
+        "num_train_windows",
+        "num_test_windows",
+        "num_train_samples",
+        "num_test_samples",
+        "num_classes",
+        "num_labels",
+    ]
+    return {key: metrics[key] for key in keys if key in metrics}
+
+
+def source_for(task_id: str, origin: str, neural: bool = False) -> str:
+    if origin == "original_public_sample_tasks":
+        prefix = "results/episode_task_suite/neural_mlp" if neural else "results/episode_task_suite"
+        return f"{prefix}/{task_id}/metrics.json"
+    prefix = "results/episode_task_suite/tier2_task_suite/neural_mlp" if neural else "results/episode_task_suite/tier2_task_suite"
+    return f"{prefix}/{task_id}/metrics.json"
+
+
+def build_core_tasks(summary: dict[str, Any], walkthroughs: dict[str, Any]) -> list[dict[str, Any]]:
+    suite = summary["suite"]
+    minimal_tasks = suite.get("tasks", {})
+    neural_tasks = suite.get("neural_tasks", {})
+    rows: list[dict[str, Any]] = []
+    for task_id, walkthrough in walkthroughs["tasks"].items():
+        metric = walkthrough.get("metric", {})
+        metric_key = metric.get("key")
+        minimal = minimal_tasks.get(task_id, {})
+        neural = neural_tasks.get(task_id, {})
+        rows.append(
+            {
+                "task_id": task_id,
+                "task_display_name": walkthrough.get("display_name") or walkthrough.get("research_name") or task_id,
+                "research_name": walkthrough.get("research_name"),
+                "origin": "original_public_sample_tasks",
+                "origin_count_label": "original task",
+                "family": walkthrough.get("task_family"),
+                "architecture_family": walkthrough.get("architecture_family"),
+                "primary_direction": walkthrough.get("primary_direction"),
+                "input": walkthrough.get("input"),
+                "input_short": walkthrough.get("input_short"),
+                "process": walkthrough.get("process_short"),
+                "output": walkthrough.get("output"),
+                "output_short": walkthrough.get("output_short"),
+                "metric_key": metric_key,
+                "metric_name": metric.get("name"),
+                "metric_direction": metric.get("direction"),
+                "minimal_primary_metric": metric_value(minimal, metric_key),
+                "neural_primary_metric": metric_value(neural, metric_key),
+                "counts": count_fields(minimal),
+                "meaning": walkthrough.get("card_blurb") or walkthrough.get("plain_goal"),
+                "artifact_sources": {
+                    "walkthrough": f"results/episode_task_suite/task_walkthroughs/{task_id}.md",
+                    "minimal_metrics": source_for(task_id, "original_public_sample_tasks", neural=False),
+                    "neural_metrics": source_for(task_id, "original_public_sample_tasks", neural=True),
+                },
+            }
+        )
+    return rows
+
+
+def build_additional_tasks(additional: dict[str, Any]) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for task_id, spec in additional.get("task_specs", {}).items():
+        result = additional.get("tasks", {}).get(task_id, {})
+        minimal = result.get("minimal") or {}
+        neural = result.get("neural_mlp") or {}
+        metric_key = spec.get("metric_key")
+        rows.append(
+            {
+                "task_id": task_id,
+                "task_display_name": spec.get("name", task_id.replace("_", " ").title()),
+                "research_name": spec.get("name", task_id.replace("_", " ").title()),
+                "origin": "additional_public_sample_tasks",
+                "origin_count_label": "additional task",
+                "family": spec.get("family"),
+                "architecture_family": minimal.get("model_family"),
+                "primary_direction": spec.get("research_direction", "sample-supported extension"),
+                "input": spec.get("input"),
+                "input_short": spec.get("input"),
+                "process": "shared window features -> task-specific target builder -> minimal/neural head",
+                "output": spec.get("target"),
+                "output_short": spec.get("target"),
+                "metric_key": metric_key,
+                "metric_name": spec.get("metric_name"),
+                "metric_direction": spec.get("metric_direction"),
+                "minimal_primary_metric": metric_value(minimal, metric_key),
+                "neural_primary_metric": metric_value(neural, metric_key),
+                "counts": count_fields(minimal),
+                "meaning": spec.get("meaning"),
+                "artifact_sources": {
+                    "legacy_result_directory": "results/episode_task_suite/tier2_task_suite/",
+                    "minimal_metrics": source_for(task_id, "additional_public_sample_tasks", neural=False),
+                    "neural_metrics": source_for(task_id, "additional_public_sample_tasks", neural=True),
+                },
+            }
+        )
+    return rows
+
+
+def build_payload() -> dict[str, Any]:
+    summary = read_json(SUMMARY_PATH)
+    walkthroughs = read_json(WALKTHROUGHS_PATH)
+    additional = read_json(ADDITIONAL_TASKS_PATH)
+    suite = summary["suite"]
+    tasks = build_core_tasks(summary, walkthroughs) + build_additional_tasks(additional)
+    for idx, row in enumerate(tasks, start=1):
+        row["task_number"] = idx
+        row["suite_label"] = f"Task {idx:02d}"
+
+    return {
+        "title": "Ropedia Xperience-10M Unified 20-Task Suite",
+        "status": "pass",
+        "generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+        "task_count": len(tasks),
+        "task_count_breakdown": {
+            "original_public_sample_tasks": 12,
+            "additional_public_sample_tasks": len(tasks) - 12,
+            "total_unified_tasks": len(tasks),
+        },
+        "unification_policy": {
+            "public_framing": "The suite is presented as one 20-task benchmark surface. Tasks 1-12 are the original public-sample tasks; tasks 13-20 are additional sample-supported tasks that use the same window/split/baseline contract.",
+            "legacy_path_note": "The directory and file name tier2_task_suite are retained only for backward-compatible artifact links; they are not a separate public benchmark tier.",
+        },
+        "dataset_scope": {
+            "sample_episode_count": 1,
+            "annotation": suite.get("annotation"),
+            "num_frames": suite.get("num_frames"),
+            "num_windows": suite.get("num_windows"),
+            "feature_dim": suite.get("feature_dim"),
+            "window_frames": suite.get("window_frames"),
+            "stride_frames": suite.get("stride_frames"),
+            "split_policy": "single_episode_chronological_70_30",
+            "raw_hdf5_required_for_tasks_13_20_regeneration": True,
+            "raw_data_redistributed": False,
+        },
+        "setup_alignment": {
+            "same_window_unit": "20-frame aligned windows",
+            "same_stride": "5 frames",
+            "same_feature_manifest": "results/episode_task_suite/feature_manifest.json",
+            "same_shared_tensor": "results/episode_task_suite/shared_windows.npz",
+            "same_split": "chronological 70/30 train/test split within the public sample episode",
+            "same_baseline_pattern": "minimal interpretable heads plus compact neural MLP heads",
+            "same_leakage_policy": "Target-side future, contact, object, caption, relation, and interaction signals are excluded from inputs unless language is explicitly the query.",
+        },
+        "source_files": [
+            "docs/data/summary_metrics.json",
+            "docs/data/task_walkthroughs.json",
+            "docs/data/tier2_task_suite.json",
+            "results/episode_task_suite/summary_report.json",
+            "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
+            "results/episode_task_suite/windows.csv",
+            "results/episode_task_suite/feature_manifest.json",
+        ],
+        "tasks": tasks,
+    }
+
+
+def fmt(value: float | None) -> str:
+    return "n/a" if value is None else f"{value:.4f}"
+
+
+def render_markdown(payload: dict[str, Any]) -> str:
+    scope = payload["dataset_scope"]
+    lines = [
+        "# Unified 20-Task Suite",
+        "",
+        "The public Xperience-10M sample task surface is one unified set of 20 tasks.",
+        "Tasks 1-12 are the original public-sample tasks. Tasks 13-20 are additional",
+        "sample-supported tasks attached to the same window, split, feature, baseline,",
+        "and leakage-control contract.",
+        "",
+        "Historical artifact paths containing `tier2_task_suite` are kept for stable",
+        "links, but they should be read as the result directory for tasks 13-20, not",
+        "as a separate benchmark tier.",
+        "",
+        "## Shared Setup",
+        "",
+        f"- Episode scope: `{scope['sample_episode_count']}` public sample episode.",
+        f"- Frames/windows: `{scope['num_frames']:,}` frames and `{scope['num_windows']:,}` aligned windows.",
+        f"- Windowing: `{scope['window_frames']}` frames per window, stride `{scope['stride_frames']}` frames.",
+        f"- Feature vector: `{scope['feature_dim']:,}` dimensions from the shared feature manifest.",
+        "- Split: chronological 70/30 train/test by time within the sample episode.",
+        "- Baselines: minimal interpretable heads and compact neural MLP heads.",
+        "- Raw data: MP4/HDF5/RRD files are not redistributed.",
+        "",
+        "## Task Table",
+        "",
+        "| # | Task | Artifact id | Origin | Input -> output | Primary metric | Minimal | Neural |",
+        "| ---: | --- | --- | --- | --- | --- | ---: | ---: |",
+    ]
+    for row in payload["tasks"]:
+        metric_direction = "higher better" if row.get("metric_direction") == "higher" else "lower better"
+        lines.append(
+            "| {num} | {name} | `{task_id}` | {origin} | {inp} -> {out} | {metric} ({direction}) | {minimal} | {neural} |".format(
+                num=row["task_number"],
+                name=row["task_display_name"],
+                task_id=row["task_id"],
+                origin=row["origin_count_label"],
+                inp=row.get("input_short") or row.get("input"),
+                out=row.get("output_short") or row.get("output"),
+                metric=row.get("metric_name") or row.get("metric_key"),
+                direction=metric_direction,
+                minimal=fmt(row.get("minimal_primary_metric")),
+                neural=fmt(row.get("neural_primary_metric")),
+            )
+        )
+    lines.extend(
+        [
+            "",
+            "## Machine-Readable Copy",
+            "",
+            "The JSON mirror is `docs/data/task_suite_20.json`.",
+            "",
+        ]
+    )
+    return "\n".join(lines)
+
+
+def main() -> int:
+    payload = build_payload()
+    OUTPUT_JSON.parent.mkdir(parents=True, exist_ok=True)
+    OUTPUT_JSON.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+    OUTPUT_MD.write_text(render_markdown(payload), encoding="utf-8")
+    print(f"PASS: wrote {OUTPUT_JSON}")
+    print(f"PASS: wrote {OUTPUT_MD}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/sync_hf_publish_mirrors.py

@@ -37,15 +37,16 @@ and `docs/data/task_suite_enhancement_128.json`. It recommends
 label-normalized scoring, and compact raw-feature shards before adding more
 episodes.
 """
-TIER2_MARKER = "docs/data/tier2_task_suite.json"
+TIER2_MARKER = "docs/data/task_suite_20.json"
 TIER2_CARD_BLOCK = """
-## Tier-2 Extension Baselines
-
-The public-sample task layer now includes eight Tier-2 extension baselines in
-`results/episode_task_suite/tier2_task_suite/` and
-`docs/data/tier2_task_suite.json`. They reuse the same 20-frame windows,
-5-frame stride, feature manifest, chronological split, and minimal/neural head
-pattern as the core 12 tasks.
+## Unified 20-Task Suite
+
+The public-sample task surface is now one unified 20-task suite in
+`TASK_SUITE_20.md` and `docs/data/task_suite_20.json`. Tasks 1-12 are the
+original sample tasks; tasks 13-20 reuse the same 20-frame windows, 5-frame
+stride, feature manifest, chronological split, and minimal/neural head pattern.
+The historical `tier2_task_suite` path is retained only for stable artifact
+links to tasks 13-20.
 """
 QWEN_COMPARISON_MARKER = "docs/data/qwen3_v5_v6_comparison.json"
 QWEN_COMPARISON_ROW = (

scripts/tier2_task_suite.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python3
-"""Tier-2 extension baselines for the Xperience-10M public sample.
+"""Additional tasks 13-20 for the Xperience-10M public sample.
 
-The core benchmark remains the 12-task suite in ``episode_task_suite.py``.  This
-runner adds a second, explicitly marked layer of tasks that the same public
-sample can support after the raw ``annotation.hdf5`` is available locally.
+The public benchmark is presented as one 20-task suite. The output path keeps
+its historical ``tier2_task_suite`` name for backwards-compatible public links,
+but the generated tasks are tasks 13-20 in the unified suite.
 """
 
 from __future__ import annotations
@@ -158,7 +158,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--output-dir", type=Path, default=OUT_DIR)
     parser.add_argument("--train-fraction", type=float, default=0.70)
     parser.add_argument("--stride-frames", type=int, default=5)
-    parser.add_argument("--future-windows", type=int, default=20, help="Tier-2 long-horizon offset in 5-frame windows.")
+    parser.add_argument("--future-windows", type=int, default=20, help="Long-horizon offset in 5-frame windows for task 13.")
     parser.add_argument("--transition-cap-frames", type=int, default=200)
     parser.add_argument("--epochs", type=int, default=220)
     parser.add_argument("--learning-rate", type=float, default=0.12)
@@ -259,12 +259,12 @@ def ensure_frame_info(frame_info: dict[int, dict[str, Any]], frame: int) -> dict
 
 
 def load_annotation_direct(annotation: Path) -> dict[str, Any]:
-    """Load just the caption fields needed for Tier-2 tasks without HOMIE."""
+    """Load just the caption fields needed for tasks 13-20 without HOMIE."""
     try:
         import h5py
     except ImportError as exc:
         raise RuntimeError(
-            "Tier-2 regeneration needs HOMIE-toolkit or h5py. Use the project Ropedia virtualenv if system Python lacks h5py."
+            "Regenerating tasks 13-20 needs HOMIE-toolkit or h5py. Use the project Ropedia virtualenv if system Python lacks h5py."
         ) from exc
 
     with h5py.File(annotation, "r") as handle:
@@ -425,7 +425,7 @@ def softmax_classification(
             "status": "pass",
             "task": task_id,
             "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-            "tier": "tier2_extension",
+            "suite_position": "tasks_13_to_20",
             "model_family": "minimal_softmax",
             "input": input_description,
             "split": "single_episode_chronological",
@@ -475,7 +475,7 @@ def softmax_classification(
                 "status": "pass",
                 "task": task_id,
                 "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-                "tier": "tier2_extension",
+                "suite_position": "tasks_13_to_20",
                 "model_family": "neural_mlp",
                 "input": input_description,
                 "split": "single_episode_chronological",
@@ -589,7 +589,7 @@ def object_set_forecast(
             "status": "pass",
             "task": task_id,
             "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-            "tier": "tier2_extension",
+            "suite_position": "tasks_13_to_20",
             "model_family": "minimal_ridge_multilabel",
             "input": TIER2_TASK_SPECS[task_id]["input"],
             "split": "single_episode_chronological",
@@ -638,7 +638,7 @@ def object_set_forecast(
                 "status": "pass",
                 "task": task_id,
                 "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-                "tier": "tier2_extension",
+                "suite_position": "tasks_13_to_20",
                 "model_family": "neural_mlp_multilabel",
                 "input": TIER2_TASK_SPECS[task_id]["input"],
                 "split": "single_episode_chronological",
@@ -677,7 +677,7 @@ def regression_task(
             "status": "pass",
             "task": task_id,
             "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-            "tier": "tier2_extension",
+            "suite_position": "tasks_13_to_20",
             "model_family": "minimal_ridge_regression",
             "input": TIER2_TASK_SPECS[task_id]["input"],
             "split": "single_episode_chronological",
@@ -723,7 +723,7 @@ def regression_task(
                 "status": "pass",
                 "task": task_id,
                 "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-                "tier": "tier2_extension",
+                "suite_position": "tasks_13_to_20",
                 "model_family": "neural_mlp_regression",
                 "input": TIER2_TASK_SPECS[task_id]["input"],
                 "split": "single_episode_chronological",
@@ -760,7 +760,7 @@ def retrieval_task(
             "status": "pass",
             "task": task_id,
             "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-            "tier": "tier2_extension",
+            "suite_position": "tasks_13_to_20",
             "model_family": "minimal_ridge_projection_cosine_retrieval",
             "input": TIER2_TASK_SPECS[task_id]["input"],
             "split": "single_episode_chronological",
@@ -791,7 +791,7 @@ def retrieval_task(
                 "status": "pass",
                 "task": task_id,
                 "task_display_name": TIER2_TASK_SPECS[task_id]["name"],
-                "tier": "tier2_extension",
+                "suite_position": "tasks_13_to_20",
                 "model_family": "neural_mlp_projection_cosine_retrieval",
                 "input": TIER2_TASK_SPECS[task_id]["input"],
                 "split": "single_episode_chronological",
@@ -919,16 +919,17 @@ def build_payload(args: argparse.Namespace) -> dict[str, Any]:
     )
 
     payload = {
-        "title": "Ropedia Xperience-10M Tier-2 Extension Task Suite",
+        "title": "Ropedia Xperience-10M Unified Tasks 13-20 Result Bundle",
         "status": "pass",
         "generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
-        "tier": "tier2_extension",
-        "integrated_with_core_12": {
-            "core_task_count": 12,
-            "tier2_task_count": len(TIER2_TASK_SPECS),
+        "suite_position": "tasks_13_to_20",
+        "legacy_path_note": "The tier2_task_suite file and directory names are retained for stable public links; these tasks are part of the unified 20-task suite, not a separate public tier.",
+        "integrated_with_tasks_1_to_12": {
+            "tasks_1_to_12_count": 12,
+            "additional_task_count": len(TIER2_TASK_SPECS),
             "combined_task_count": 12 + len(TIER2_TASK_SPECS),
-            "core_metrics": "docs/data/summary_metrics.json",
-            "core_protocol": "docs/data/evaluation_protocol.json",
+            "tasks_1_to_12_metrics": "docs/data/summary_metrics.json",
+            "unified_protocol": "docs/data/evaluation_protocol.json",
         },
         "dataset_scope": {
             "sample_episode_count": 1,
@@ -947,9 +948,9 @@ def build_payload(args: argparse.Namespace) -> dict[str, Any]:
             "raw_data_redistributed": False,
         },
         "setup_alignment": {
-            "same_window_unit_as_core_12": True,
-            "same_feature_manifest_as_core_12": "results/episode_task_suite/feature_manifest.json",
-            "same_shared_tensor_as_core_12": "results/episode_task_suite/shared_windows.npz",
+            "same_window_unit_as_tasks_1_to_12": True,
+            "same_feature_manifest_as_tasks_1_to_12": "results/episode_task_suite/feature_manifest.json",
+            "same_shared_tensor_as_tasks_1_to_12": "results/episode_task_suite/shared_windows.npz",
             "minimal_baselines": "softmax, ridge regression/projection, and ridge multilabel heads",
             "neural_baselines": "compact one-hidden-layer/two-layer PyTorch MLP heads with the same chronological split",
             "leakage_policy": "Caption-derived text features are removed whenever the target is a label, object, relation, interaction phrase, or future semantic state.",
@@ -974,35 +975,37 @@ def format_metric(value: float | None, metric_key: str) -> str:
 
 def write_markdown(payload: dict[str, Any], output_dir: Path) -> None:
     lines = [
-        "# Tier-2 Extension Task Baselines",
+        "# Tasks 13-20 Baselines",
         "",
-        "These tasks extend the original 12-task suite without changing the core benchmark. They reuse the same 20-frame windows, 5-frame stride, shared feature tensor, chronological split, and minimal/neural baseline discipline.",
+        "These eight tasks are part of the unified 20-task public-sample suite. They reuse the same 20-frame windows, 5-frame stride, shared feature tensor, chronological split, and minimal/neural baseline discipline as tasks 1-12.",
+        "",
+        "The file and directory names still contain `tier2_task_suite` for backwards-compatible public links, but this is not a separate benchmark tier.",
         "",
         "## Setup Alignment",
         "",
-        f"- Core tasks: `{payload['integrated_with_core_12']['core_task_count']}`",
-        f"- Tier-2 tasks: `{payload['integrated_with_core_12']['tier2_task_count']}`",
-        f"- Combined task contracts: `{payload['integrated_with_core_12']['combined_task_count']}`",
+        f"- Tasks 1-12: `{payload['integrated_with_tasks_1_to_12']['tasks_1_to_12_count']}`",
+        f"- Tasks 13-20: `{payload['integrated_with_tasks_1_to_12']['additional_task_count']}`",
+        f"- Unified task contracts: `{payload['integrated_with_tasks_1_to_12']['combined_task_count']}`",
         f"- Long-horizon offset: `{payload['dataset_scope']['future_horizon_frames']}` frames, about `{payload['dataset_scope']['future_horizon_seconds_at_20fps']:.1f}` seconds at 20 FPS",
         "- Raw public-sample HDF5 is required to regenerate the interaction/object targets; raw media/HDF5 files are not redistributed.",
         "",
         "## Results",
         "",
-        "| Tier-2 task | Input | Output | Minimal | Neural MLP | Meaning |",
-        "| --- | --- | --- | ---: | ---: | --- |",
+        "| # | Task | Input | Output | Minimal | Neural MLP | Meaning |",
+        "| ---: | --- | --- | --- | ---: | ---: | --- |",
     ]
-    for task_id, spec in payload["task_specs"].items():
+    for offset, (task_id, spec) in enumerate(payload["task_specs"].items(), start=13):
         result = payload["tasks"][task_id]
         key = spec["metric_key"]
         lines.append(
-            f"| {spec['name']} | {spec['input']} | {spec['target']} | {format_metric(metric_value(task_id, result.get('minimal')), key)} {spec['metric_name']} | {format_metric(metric_value(task_id, result.get('neural_mlp')), key)} {spec['metric_name']} | {spec['meaning']} |"
+            f"| {offset} | {spec['name']} | {spec['input']} | {spec['target']} | {format_metric(metric_value(task_id, result.get('minimal')), key)} {spec['metric_name']} | {format_metric(metric_value(task_id, result.get('neural_mlp')), key)} {spec['metric_name']} | {spec['meaning']} |"
         )
     lines.extend(
         [
             "",
             "## Interpretation Boundary",
             "",
-            "Tier-2 results are sample-level extension baselines. They prove that the public sample can support richer task contracts, but they do not prove cross-episode model quality.",
+            "Tasks 13-20 are sample-level baselines in the same unified public-sample suite. They prove that the sample can support richer task contracts, but they do not prove cross-episode model quality.",
             "",
         ]
     )
@@ -1019,8 +1022,8 @@ def write_svg(payload: dict[str, Any]) -> None:
         f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
         '<rect width="100%" height="100%" fill="#020502"/>',
         '<rect x="32" y="32" width="1376" height="{}" rx="12" fill="#071207" stroke="#ccffa0" stroke-opacity="0.22"/>'.format(height - 64),
-        '<text x="72" y="82" fill="#f4f8ef" font-size="32" font-weight="760">Ropedia Xperience-10M Tier-2 extension baselines</text>',
-        '<text x="72" y="112" fill="#a5afa2" font-size="16">Eight extra task contracts aligned with the same 20-frame window, 5-frame stride, and chronological split as the core 12 tasks.</text>',
+        '<text x="72" y="82" fill="#f4f8ef" font-size="32" font-weight="760">Ropedia Xperience-10M tasks 13-20 baselines</text>',
+        '<text x="72" y="112" fill="#a5afa2" font-size="16">Eight additional task contracts in the same unified 20-task suite and aligned with the same 20-frame window, 5-frame stride, and chronological split.</text>',
     ]
     colors = ["#ccffa0", "#7ae5c3", "#9bdfff", "#d8f4a5"]
     for idx, (task_id, spec) in enumerate(TIER2_TASK_SPECS.items()):

scripts/validate_mirror_parity.py

@@ -53,6 +53,7 @@ DATA_FILES = [
     "single_episode_explorer.json",
     "source_alignment_audit.json",
     "summary_metrics.json",
+    "task_suite_20.json",
     "task_suite_enhancement_128.json",
     "task_surface_integrity.json",
     "task_walkthroughs.json",
@@ -119,6 +120,7 @@ SCRIPT_FILES = [
     "build_interactive_research_roadmap.py",
     "build_single_episode_explorer.py",
     "build_research_takeaways.py",
+    "build_unified_task_suite.py",
     "single_episode_diagnostics.py",
     "verify_live_publication.py",
     "validate_mirror_parity.py",
@@ -230,6 +232,7 @@ DOC_FILES = [
     "PROJECT_STATUS.md",
     "REPRODUCIBILITY.md",
     "TASK_SUITE_ENHANCEMENT_128.md",
+    "TASK_SUITE_20.md",
     "PUBLIC_SURFACE_QA.md",
     "RESEARCH_TAKEAWAYS.md",
     "SOURCE_ALIGNMENT_AUDIT.md",
@@ -296,7 +299,10 @@ def verified_public_result_files() -> list[str]:
 
 
 def tier2_result_files() -> list[str]:
-    """Return every generated public-safe Tier-2 baseline artifact."""
+    """Return every generated public-safe artifact for tasks 13-20.
+
+    The directory name is historical and kept for stable public links.
+    """
 
     tier2_root = ROOT / "results/episode_task_suite/tier2_task_suite"
     if not tier2_root.exists():

scripts/validate_publication_package.py

@@ -75,7 +75,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "Dataset Context",
             "Qwen3-Omni",
             "Cosmos 3",
-            "12 human-readable tasks",
+            "20 human-readable tasks",
             "interactive scrub/play walkthrough storyboard",
         ],
     },
@@ -91,7 +91,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "Dataset Context",
             "Qwen3-Omni",
             "Cosmos 3",
-            "12 human-readable tasks",
+            "20 human-readable tasks",
             "interactive scrub/play walkthrough storyboard",
         ],
     },
@@ -120,7 +120,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "Dataset Context",
             "Qwen3-Omni",
             "Cosmos 3",
-            "12 human-readable tasks",
+            "20 human-readable tasks",
             "interactive scrub/play walkthrough storyboard",
         ],
     },
@@ -136,7 +136,7 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "Dataset Context",
             "Qwen3-Omni",
             "Cosmos 3",
-            "12 human-readable tasks",
+            "20 human-readable tasks",
             "interactive scrub/play walkthrough storyboard",
         ],
     },
@@ -268,6 +268,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "PUBLIC_SURFACE_QA.md",
         "RENDERED_SITE_CHECK.md",
         "EVALUATION_PROTOCOL.md",
+        "TASK_SUITE_20.md",
         "FIGURE_INDEX.md",
         "SOURCE_ALIGNMENT_AUDIT.md",
         "XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
@@ -307,6 +308,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "docs/data/task_surface_integrity.json",
         "docs/data/website_integrity.json",
         "docs/data/summary_metrics.json",
+        "docs/data/task_suite_20.json",
         "docs/data/task_suite_enhancement_128.json",
         "docs/assets/modalities/video.jpg",
         "docs/assets/modalities/audio.png",
@@ -335,6 +337,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "scripts/build_artifact_index.py",
         "scripts/build_brand_assets.py",
         "scripts/build_evaluation_protocol.py",
+        "scripts/build_unified_task_suite.py",
         "scripts/build_figure_index.py",
         "scripts/build_quality_gates.py",
         "scripts/build_public_surface_qa.py",

scripts/validate_website_integrity.py

@@ -380,7 +380,7 @@ def validate(docs_root: Path, site_base: str) -> dict:
             "suite_task_map_precedes_modality_atlas",
             '<div class="figure-pan" id="task-suite-map">',
             '<div class="modality-atlas-panel"',
-            "The Suite anchor should show the full 12-task map before the modality atlas.",
+            "The Suite anchor should show the task-suite map before the modality atlas.",
         ),
         (
             "suite_modality_atlas_contains_seven_cards",

scripts/verify_live_publication.py

@@ -64,9 +64,20 @@ HASH_GROUPS = [
             "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/quality_gates.json",
         },
     },
+    {
+        "id": "task_suite_20_json",
+        "title": "Unified 20-task suite JSON",
+        "local_path": "docs/data/task_suite_20.json",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/data/task_suite_20.json",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/raw/main/data/task_suite_20.json",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/data/task_suite_20.json",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/task_suite_20.json",
+        },
+    },
     {
         "id": "tier2_task_suite_json",
-        "title": "Tier-2 task-suite JSON",
+        "title": "Tasks 13-20 result JSON",
         "local_path": "docs/data/tier2_task_suite.json",
         "urls": {
             "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/data/tier2_task_suite.json",
@@ -77,7 +88,7 @@ HASH_GROUPS = [
     },
     {
         "id": "tier2_task_suite_chart",
-        "title": "Tier-2 task-suite chart",
+        "title": "Tasks 13-20 chart",
         "local_path": "docs/assets/charts/tier2_task_suite.svg",
         "urls": {
             "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/charts/tier2_task_suite.svg",
@@ -88,7 +99,7 @@ HASH_GROUPS = [
     },
     {
         "id": "tier2_result_summary",
-        "title": "Tier-2 result summary",
+        "title": "Tasks 13-20 result summary",
         "local_path": "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
         "urls": {
             "github_raw": "https://raw.githubusercontent.com/ChaoYue0307/ropedia-xperience-10m-task-suite/main/results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json",
@@ -99,7 +110,7 @@ HASH_GROUPS = [
     },
     {
         "id": "tier2_baseline_report",
-        "title": "Tier-2 baseline report",
+        "title": "Tasks 13-20 baseline report",
         "local_path": "results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md",
         "urls": {
             "github_raw": "https://raw.githubusercontent.com/ChaoYue0307/ropedia-xperience-10m-task-suite/main/results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md",
@@ -110,7 +121,7 @@ HASH_GROUPS = [
     },
     {
         "id": "tier2_minimal_model_npz",
-        "title": "Tier-2 minimal model NPZ",
+        "title": "Tasks 13-20 minimal model NPZ",
         "local_path": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/model.npz",
         "urls": {
             "github_raw": "https://raw.githubusercontent.com/ChaoYue0307/ropedia-xperience-10m-task-suite/main/results/episode_task_suite/tier2_task_suite/long_horizon_next_action/model.npz",
@@ -425,8 +436,10 @@ MARKER_CHECKS = [
             "ropedia-qwen3-omni-lora-128ep",
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",
             "Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch",
+            "task_suite_20.json",
+            "Unified 20-Task Suite",
             "tier2_task_suite.json",
-            "Tier-2 Extension Task Suite",
+            "Tasks 13-20",
             "Long-Horizon Next-Action Forecasting",
         ],
         "forbidden": [
@@ -462,8 +475,10 @@ MARKER_CHECKS = [
             "ropedia-qwen3-omni-lora-128ep",
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",
             "Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch",
+            "task_suite_20.json",
+            "Unified 20-Task Suite",
             "tier2_task_suite.json",
-            "Tier-2 Extension Task Suite",
+            "Tasks 13-20",
             "Long-Horizon Next-Action Forecasting",
         ],
         "forbidden": [
@@ -485,8 +500,10 @@ MARKER_CHECKS = [
             "Cosmos3-Super",
             "ropedia-qwen3-omni-lora-128ep",
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",
+            "docs/data/task_suite_20.json",
+            "Unified 20-Task Suite",
             "docs/data/tier2_task_suite.json",
-            "Tier-2 Extension Baselines",
+            "Tasks 13-20",
         ],
         "forbidden": ["xperience10m-" + "taskfirst-v10"],
     },
@@ -539,8 +556,10 @@ MARKER_CHECKS = [
             "Cosmos3-Super",
             "ropedia-qwen3-omni-lora-128ep",
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",
+            "docs/data/task_suite_20.json",
+            "Unified 20-Task Suite",
             "docs/data/tier2_task_suite.json",
-            "Tier-2 Extension Task Suite",
+            "Tasks 13-20",
         ],
         "forbidden": ["xperience10m-" + "taskfirst-v10"],
     },