Add files using upload-large-folder tool

data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-20T20:48:09+00:00",
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@@ -81,8 +81,8 @@
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@@ -1274,7 +1274,7 @@
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       "shows": "Records the last live GitHub/HF URL verification after upload.",
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@@ -1354,7 +1354,7 @@
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       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
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       "hash_policy": "existence_and_size_only"
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@@ -1399,8 +1399,8 @@
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@@ -1454,8 +1454,8 @@
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@@ -1531,8 +1531,8 @@
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@@ -1564,8 +1564,8 @@
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@@ -1575,8 +1575,8 @@
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@@ -1663,8 +1663,8 @@
       "surface": "repo_hf",
       "shows": "Reader-facing comparison of the single-episode task suite, 128-episode aligned baselines, Qwen3-Omni packages, and Cosmos3 future-window branch.",
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     {
       "id": "omni_model_comparison_json",
@@ -1674,8 +1674,8 @@
       "surface": "repo_hf",
       "shows": "Machine-readable comparison of the current result versions, per-task aligned baselines, verified Qwen3 packages, and Cosmos3 package.",
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-      "bytes": 81866,
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@@ -1707,8 +1707,8 @@
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       "id": "license",

data/omni_model_comparison.json

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

data/project_status.json

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

data/public_surface_qa.json

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

data/publication_audit.json

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

data/quality_gates.json

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

data/rendered_site_check.json

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

data/research_directions.json

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

data/research_takeaways.json

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

data/scope_claims_audit.json

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

data/source_alignment_audit.json

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

data/summary_metrics.json

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

data/task_surface_integrity.json

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

data/website_integrity.json

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

scripts/build_rendered_site_check.py

@@ -14,6 +14,7 @@ ROOT = Path(__file__).resolve().parents[1]
 DEFAULT_INPUT = Path("/tmp/xperience_rendered_site_observations.json")
 OUTPUT_JSON = ROOT / "docs/data/rendered_site_check.json"
 OUTPUT_MD = ROOT / "RENDERED_SITE_CHECK.md"
+TASK_MATRIX_JSON = ROOT / "docs/data/task_method_20_result_matrix.json"
 
 
 def load_json(path: Path) -> dict[str, Any]:
@@ -33,6 +34,10 @@ def check(name: str, passed: bool, reason: str, **detail: Any) -> dict[str, Any]
 
 def build_report(observations: dict[str, Any]) -> dict[str, Any]:
     viewport = observations.get("viewport") or {}
+    task_matrix = load_json(TASK_MATRIX_JSON) if TASK_MATRIX_JSON.exists() else {}
+    matrix_task_count = int(task_matrix.get("task_count", 0) or 0)
+    matrix_record_count = int(task_matrix.get("method_task_record_count", 0) or 0)
+    matrix_scored_count = int(task_matrix.get("scored_method_task_count", 0) or 0)
     checks = [
         check(
             "page_identity",
@@ -67,10 +72,18 @@ def build_report(observations: dict[str, Any]) -> dict[str, Any]:
         check(
             "task_and_modality_cards_render",
             observations.get("taskCardCount") == 12 and observations.get("atlasCardCount") == 7,
-            "The rendered task and modality sections should expose all 12 task cards and seven modality cards.",
+            "The rendered walkthrough should expose the original core task cards and seven modality cards.",
             task_card_count=observations.get("taskCardCount"),
             atlas_card_count=observations.get("atlasCardCount"),
         ),
+        check(
+            "unified_20_task_matrix_available",
+            matrix_task_count == 20 and matrix_record_count == 180 and matrix_scored_count == 180,
+            "The rendered site data bundle should include the unified 20-task / 180-result matrix.",
+            matrix_task_count=matrix_task_count,
+            matrix_record_count=matrix_record_count,
+            matrix_scored_count=matrix_scored_count,
+        ),
         check(
             "walkthrough_deep_link",
             observations.get("visibleWalkthrough") is True

scripts/generate_visualizations.py

@@ -25,6 +25,7 @@ RESULTS = ROOT / "results"
 DOCS = ROOT / "docs"
 ASSETS = DOCS / "assets"
 CHARTS = ASSETS / "charts"
+TASK_SUITE_20_PATH = DOCS / "data" / "task_suite_20.json"
 
 OMNI_RELAY = {
     "status": "verified_full_128_episode_diagnostic_result",
@@ -100,7 +101,7 @@ def svg_feature_blocks(path: Path, feature_manifest: list[dict]) -> None:
 def svg_pipeline_diagram(path: Path, summary: dict) -> None:
     path.parent.mkdir(parents=True, exist_ok=True)
     suite = summary["suite"]
-    task_count = len(suite["tasks"])
+    task_count = int(summary.get("unified_task_count") or len(suite["tasks"]))
     width, height = 1400, 760
     boxes = [
         (60, 110, 250, 132, "1. Raw public sample", [
@@ -131,7 +132,7 @@ def svg_pipeline_diagram(path: Path, summary: dict) -> None:
             "metrics and predictions",
         ], "#9bdfff"),
         (520, 380, 360, 168, "6. Ropedia Xperience-10M suite", [
-            f"{task_count} supervised/self-supervised tasks",
+            f"{task_count} unified task contracts",
             "chronological split",
             "retrieval, forecast, alignment",
             "per-task artifacts",
@@ -150,7 +151,7 @@ def svg_pipeline_diagram(path: Path, summary: dict) -> None:
         '<rect x="0" y="0" width="1400" height="760" fill="url(#dotgrid)" opacity="0.55"/>',
         '<circle cx="1120" cy="132" r="170" fill="#ccffa0" opacity="0.10"/>',
         '<text x="60" y="58" font-family="Inter Tight, Arial, sans-serif" font-size="32" font-weight="800" fill="#f4f8ef">Verified Ropedia Xperience-10M Pipeline</text>',
-        '<text x="60" y="88" font-family="Space Grotesk, Arial, sans-serif" font-size="16" fill="#a5afa2">Generated from committed scripts and metrics with traceable stage labels.</text>',
+        '<text x="60" y="88" font-family="Space Grotesk, Arial, sans-serif" font-size="16" fill="#a5afa2">Generated from committed scripts, the unified 20-task index, and traceable metrics.</text>',
     ]
     arrows = [
         (310, 176, 365, 176),
@@ -381,8 +382,8 @@ def svg_task_architectures(path: Path, summary: dict) -> None:
         '<rect width="100%" height="100%" fill="#020502"/>',
         '<rect width="100%" height="100%" fill="url(#dotgrid2)" opacity="0.58"/>',
         '<circle cx="1190" cy="150" r="210" fill="#ccffa0" opacity="0.08"/>',
-        '<text x="60" y="56" font-family="Inter Tight, Arial, sans-serif" font-size="34" font-weight="800" fill="#f4f8ef">Minimal Architectures for 12 Ropedia Xperience-10M Tasks</text>',
-        '<text x="60" y="88" font-family="Space Grotesk, Arial, sans-serif" font-size="16" fill="#a5afa2">Generated from scripts/episode_task_suite.py semantics and committed summary metrics. These are minimal baselines, not deep foundation models.</text>',
+        '<text x="60" y="56" font-family="Inter Tight, Arial, sans-serif" font-size="34" font-weight="800" fill="#f4f8ef">Core Architecture Families in the 20-Task Xperience-10M Suite</text>',
+        '<text x="60" y="88" font-family="Space Grotesk, Arial, sans-serif" font-size="16" fill="#a5afa2">Generated from the original task-head semantics and unified 20-task release metadata. These are baselines, not deep foundation models.</text>',
     ]
 
     setup = [
@@ -465,6 +466,7 @@ def collect_summary() -> dict:
     min_action = read_json(RESULTS / "min_action_model/metrics.json")
     min_subtask = read_json(RESULTS / "min_subtask_model/metrics.json")
     suite = read_json(RESULTS / "episode_task_suite/summary_report.json")
+    unified = read_json(TASK_SUITE_20_PATH) if TASK_SUITE_20_PATH.exists() else {}
     manifest = read_json(RESULTS / "episode_task_suite/feature_manifest.json")
     public_manifest = [
         {**block, "name": display_feature_name(block["name"])}
@@ -479,6 +481,7 @@ def collect_summary() -> dict:
             "all_modalities_subtask": all_subtask,
         },
         "suite": suite,
+        "unified_task_count": unified.get("task_count", len(suite.get("tasks", {}))),
         "feature_manifest": public_manifest,
     }
 

scripts/omni/build_omni_model_comparison.py

@@ -106,7 +106,7 @@ def single_episode_summary() -> dict[str, Any]:
         )
     return {
         "id": "v1_single_episode_public_sample",
-        "title": "Single-Episode Public-Sample Task Suite",
+        "title": "Single-Episode Public-Sample 20-Task Suite",
         "status": "verified",
         "scope": "one public Xperience-10M sample episode",
         "source": rel(path),
@@ -116,14 +116,17 @@ def single_episode_summary() -> dict[str, Any]:
             "windows": summary.get("num_windows"),
             "frames": summary.get("num_frames"),
             "feature_dim": summary.get("feature_dim"),
-            "task_count": len(tasks),
+            "core_task_count": len(tasks),
+            "unified_task_count": 20,
+            "method_task_record_count": 180,
             "neural_task_count": len(neural),
         },
         "models": ["minimal task heads", "compact neural MLP task heads"],
         "task_metrics": task_rows,
         "interpretation": (
-            "This layer verifies the 12 task contracts and raw multimodal feature "
-            "pipeline on the public sample. It is not a cross-episode benchmark."
+            "This layer verifies the original core task contracts, raw multimodal "
+            "feature pipeline, and unified 20-task public result surface. It is "
+            "not a cross-episode benchmark."
         ),
     }
 
@@ -752,7 +755,7 @@ def build_report() -> dict[str, Any]:
             "versus Super structured JSON Reasoner evaluation."
         ),
         "version_reading_notes": [
-            "Version 1 is the public-sample 12-task harness with minimal and neural heads.",
+            "Version 1 is the public-sample 20-task surface: original core heads, tasks 13-20, and the 180-row method-task matrix.",
             "Version 2 is the selected 128-episode same-split simple/NN baseline alignment.",
             "Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, Cosmos3-Super Reasoner is a base-weight JSON-task evaluation, and Cosmos3-Super Forward-Dynamics LoRA is the first Super fine-tuned adapter branch.",
         ],

scripts/render_overview_figures.py

@@ -94,7 +94,7 @@ def arrow() -> str:
 
 def build_pipeline_html(summary: dict, base_path: Path) -> str:
     suite = summary["suite"]
-    task_count = len(suite["tasks"])
+    task_count = int(summary.get("unified_task_count") or len(suite["tasks"]))
     neural_count = len(suite.get("neural_tasks", {}))
     stage_rows = [
         [
@@ -141,7 +141,7 @@ def build_pipeline_html(summary: dict, base_path: Path) -> str:
             stage_card(
                 "06",
                 "Ropedia Xperience-10M suite",
-                [f"{task_count} minimal + {neural_count} neural results", "forecast, retrieval, alignment", "chronological evaluation"],
+                [f"{task_count} task contracts", "180 public result rows", "forecast, retrieval, alignment", "chronological evaluation"],
                 COLORS["teal"],
             ),
             arrow(),
@@ -366,7 +366,8 @@ def build_pipeline_html(summary: dict, base_path: Path) -> str:
           <div class="metric"><strong>{suite['num_frames']:,}</strong><span>frames</span></div>
           <div class="metric"><strong>{suite['num_windows']:,}</strong><span>windows</span></div>
           <div class="metric"><strong>{suite['feature_dim']:,}</strong><span>features</span></div>
-          <div class="metric"><strong>{task_count}+{neural_count}</strong><span>min + NN tasks</span></div>
+          <div class="metric"><strong>{task_count}</strong><span>unified task contracts</span></div>
+          <div class="metric"><strong>180</strong><span>public result rows</span></div>
         </div>
       </header>
       {rows_html}
@@ -408,6 +409,7 @@ def build_task_card(row: dict, color: str) -> str:
 def build_architecture_html(summary: dict, base_path: Path) -> str:
     suite = summary["suite"]
     neural_count = len(suite.get("neural_tasks", {}))
+    task_count = int(summary.get("unified_task_count") or len(suite["tasks"]))
     rows_by_task = {row["task"]: row for row in task_architecture_rows(summary)}
     group_html = []
     for title, color, task_names in TASK_GROUPS:
@@ -698,10 +700,10 @@ def build_architecture_html(summary: dict, base_path: Path) -> str:
       <header>
         <div>
           <div class="kicker">minimal + neural verified model architectures</div>
-          <h1>12 Ropedia Xperience-10M tasks, minimal and NN heads</h1>
-          <p class="subtitle">Each task uses the same aligned episode-window contract. The figure shows minimal heads beside neural MLP metrics; next milestone is Qwen3-Omni fine-tuning with sensor-bridge evaluation.</p>
+          <h1>Core architecture families for the 20-task suite</h1>
+          <p class="subtitle">The original core heads stay inspectable, and the unified release extends them into the 20-task / 180-result public matrix.</p>
         </div>
-        <div class="summary-pill"><strong>{len(suite['tasks'])}+{neural_count}</strong><span>min + NN tasks</span></div>
+        <div class="summary-pill"><strong>{task_count}</strong><span>unified tasks</span></div>
       </header>
       <section class="shared">
         <article><h2>Shared windows</h2><p>{suite['num_frames']:,} frames to {suite['num_windows']:,} windows over video, audio, depth, pose, mocap, inertial, and language features.</p></article>

scripts/validate_mirror_parity.py

@@ -99,6 +99,7 @@ ASSET_FILES = [
     "charts/episode128_task_model_radar.svg",
     "charts/tier2_task_suite.svg",
     "charts/unified_task_model_radar.svg",
+    "charts/research_direction_coverage.svg",
     "brand/xperience10m-logo-apple-touch.png",
     "brand/xperience10m-logo-favicon-32.png",
     "brand/xperience10m-logo-favicon-64.png",
@@ -108,7 +109,9 @@ ASSET_FILES = [
     "brand/xperience10m-logo-social-card.png",
     "task_suite_infographic.png",
     "pipeline_diagram.png",
+    "pipeline_diagram.svg",
     "task_architectures.png",
+    "task_architectures.svg",
     "foundation-pipelines/spatial-intelligence-pipeline.png",
     "foundation-pipelines/human-video-world-model-pipeline.png",
     "foundation-pipelines/vision-language-action-pipeline.png",
@@ -191,6 +194,13 @@ SCRIPT_FILES = [
     "build_single_episode_explorer.py",
     "build_task_method_20_gap_audit.py",
     "build_research_takeaways.py",
+    "export_modality_atlas_assets.py",
+    "generate_visualizations.py",
+    "render_overview_figures.py",
+    "render_task_suite_infographic.py",
+    "research_direction_taxonomy.py",
+    "task_display.py",
+    "task_walkthroughs.py",
     "build_unified_task_suite.py",
     "build_unified_task_model_radar.py",
     "single_episode_diagnostics.py",
@@ -321,8 +331,10 @@ DOC_FILES = [
     "TASK_METHOD_20_RESULT_MATRIX.md",
     "TASK_SUITE_20.md",
     "PUBLIC_SURFACE_QA.md",
+    "EVIDENCE_CONTRACT.md",
     "RESEARCH_TAKEAWAYS.md",
     "SOURCE_ALIGNMENT_AUDIT.md",
+    "XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
     "XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
 ]