Add files using upload-large-folder tool

data/artifact_index.json

@@ -1,6 +1,6 @@
 {
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-  "generated_at_utc": "2026-06-20T20:48:09+00:00",
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@@ -81,8 +81,8 @@
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@@ -983,8 +983,8 @@
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       "shows": "Confirms the public original-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
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       "shows": "Machine-readable browser-level website check for the public static site.",
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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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@@ -1342,7 +1342,7 @@
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@@ -1354,7 +1354,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 1395239,
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       "hash_policy": "existence_and_size_only"
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@@ -1399,8 +1399,8 @@
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       "shows": "Mirrors task metrics for the static dashboard.",
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@@ -1454,8 +1454,8 @@
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       "shows": "Maps the original tasks to the four Ropedia research directions as direct/proxy/diagnostic.",
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@@ -1531,8 +1531,8 @@
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@@ -1564,8 +1564,8 @@
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       "shows": "Shows the raw-episode to artifact pipeline with verified labels.",
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@@ -1575,8 +1575,8 @@
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       "shows": "Shows the shared feature pipeline and minimal/neural head families.",
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       "id": "qwen_data_access_status",
@@ -1663,8 +1663,8 @@
       "surface": "repo_hf",
       "shows": "Reader-facing comparison of the single-episode task suite, 128-episode aligned baselines, Qwen3-Omni packages, and Cosmos3 future-window branch.",
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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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       "id": "cosmos3_nano_verified_summary",
@@ -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/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 @@
 {
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-  "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 @@
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           "exists": true,
-          "bytes": 1922,
-          "sha256": "be747c041579fa1b1131ad1f8935217c607d7307843c88fef5e656cd74a74471"
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           "path": "hf_artifacts:RENDERED_SITE_CHECK.md",
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           "path": "hf_model:RENDERED_SITE_CHECK.md",
           "exists": true,
-          "bytes": 1922,
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@@ -30700,27 +30986,27 @@
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         "exists": true,
-        "bytes": 15276,
-        "sha256": "5004dea8de01cde4b4dfccd301fc826ed00b209ba1793e113a317402c3230173"
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+          "bytes": 15275,
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         "hf_artifacts": {
           "path": "hf_artifacts:RESEARCH_ROADMAP.md",
           "exists": true,
-          "bytes": 15276,
-          "sha256": "5004dea8de01cde4b4dfccd301fc826ed00b209ba1793e113a317402c3230173"
+          "bytes": 15275,
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           "path": "hf_model:RESEARCH_ROADMAP.md",
           "exists": true,
-          "bytes": 15276,
-          "sha256": "5004dea8de01cde4b4dfccd301fc826ed00b209ba1793e113a317402c3230173"
+          "bytes": 15275,
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@@ -30731,27 +31017,27 @@
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-        "bytes": 14819,
-        "sha256": "6c99463c3569b88f8e45ffd9f606f56689ad6b5a091f6080c30cb328e4f9c0e8"
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-          "bytes": 14819,
-          "sha256": "6c99463c3569b88f8e45ffd9f606f56689ad6b5a091f6080c30cb328e4f9c0e8"
+          "bytes": 14845,
+          "sha256": "128daeed89b672d89b4c422956bf4900ffe8efd54356ec657fb9cf6dcb880ba5"
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           "path": "hf_artifacts:PROJECT_STATUS.md",
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-          "bytes": 14819,
-          "sha256": "6c99463c3569b88f8e45ffd9f606f56689ad6b5a091f6080c30cb328e4f9c0e8"
+          "bytes": 14845,
+          "sha256": "128daeed89b672d89b4c422956bf4900ffe8efd54356ec657fb9cf6dcb880ba5"
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         "hf_model": {
           "path": "hf_model:PROJECT_STATUS.md",
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-          "bytes": 14819,
-          "sha256": "6c99463c3569b88f8e45ffd9f606f56689ad6b5a091f6080c30cb328e4f9c0e8"
+          "bytes": 14845,
+          "sha256": "128daeed89b672d89b4c422956bf4900ffe8efd54356ec657fb9cf6dcb880ba5"
         }
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@@ -30762,27 +31048,27 @@
       "local": {
         "path": "repo:REPRODUCIBILITY.md",
         "exists": true,
-        "bytes": 10054,
-        "sha256": "d12c020fd00c6a7b907300c9c5f20d613a0f033e32f7a62cbed3f8dfbbe95216"
+        "bytes": 10056,
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-          "bytes": 10054,
-          "sha256": "d12c020fd00c6a7b907300c9c5f20d613a0f033e32f7a62cbed3f8dfbbe95216"
+          "bytes": 10056,
+          "sha256": "007423c363cfa0af9f62a1c953a0babbd43183f854a6dddae0f28ff9180c3555"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:REPRODUCIBILITY.md",
           "exists": true,
-          "bytes": 10054,
-          "sha256": "d12c020fd00c6a7b907300c9c5f20d613a0f033e32f7a62cbed3f8dfbbe95216"
+          "bytes": 10056,
+          "sha256": "007423c363cfa0af9f62a1c953a0babbd43183f854a6dddae0f28ff9180c3555"
         },
         "hf_model": {
           "path": "hf_model:REPRODUCIBILITY.md",
           "exists": true,
-          "bytes": 10054,
-          "sha256": "d12c020fd00c6a7b907300c9c5f20d613a0f033e32f7a62cbed3f8dfbbe95216"
+          "bytes": 10056,
+          "sha256": "007423c363cfa0af9f62a1c953a0babbd43183f854a6dddae0f28ff9180c3555"
         }
       },
       "failures": []
@@ -30973,33 +31259,64 @@
       },
       "failures": []
     },
+    {
+      "name": "docs/EVIDENCE_CONTRACT.md",
+      "status": "pass",
+      "local": {
+        "path": "repo:EVIDENCE_CONTRACT.md",
+        "exists": true,
+        "bytes": 11440,
+        "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
+      },
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+          "bytes": 11440,
+          "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:EVIDENCE_CONTRACT.md",
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+          "bytes": 11440,
+          "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
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+          "path": "hf_model:EVIDENCE_CONTRACT.md",
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+          "sha256": "dc76c11a3a09dabc9f54772751d64e3a7cd3a20b10bd63fa6a99a33ec4617406"
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     {
       "name": "docs/RESEARCH_TAKEAWAYS.md",
       "status": "pass",
       "local": {
         "path": "repo:RESEARCH_TAKEAWAYS.md",
         "exists": true,
-        "bytes": 5149,
-        "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
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-          "bytes": 5149,
-          "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
+          "bytes": 5172,
+          "sha256": "39978c1e30b6aa76c5fd2684e9a1111ec2e813423feaff6053084b0335968db8"
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         "hf_artifacts": {
           "path": "hf_artifacts:RESEARCH_TAKEAWAYS.md",
           "exists": true,
-          "bytes": 5149,
-          "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
+          "bytes": 5172,
+          "sha256": "39978c1e30b6aa76c5fd2684e9a1111ec2e813423feaff6053084b0335968db8"
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           "path": "hf_model:RESEARCH_TAKEAWAYS.md",
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-          "bytes": 5149,
-          "sha256": "a2ab81a52a825b4f1dae59023cfe905a63128384f892dcc8e91c4c4351500aef"
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       "failures": []
@@ -31035,6 +31352,37 @@
       },
       "failures": []
     },
+    {
+      "name": "docs/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+      "status": "pass",
+      "local": {
+        "path": "repo:XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+        "exists": true,
+        "bytes": 9212,
+        "sha256": "ca5505af54aba88d1eb7355317261183a2a4e6226553316a2f934fbb25d31fb0"
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+          "bytes": 9212,
+          "sha256": "ca5505af54aba88d1eb7355317261183a2a4e6226553316a2f934fbb25d31fb0"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md",
+          "exists": true,
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+          "bytes": 9212,
+          "sha256": "ca5505af54aba88d1eb7355317261183a2a4e6226553316a2f934fbb25d31fb0"
+        }
+      },
+      "failures": []
+    },
     {
       "name": "docs/XPERIENCE10M_DATASET_CARD_ALIGNMENT.md",
       "status": "pass",

docs/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."

docs/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",

docs/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"
       ]

results/omni_finetune/OMNI_MODEL_COMPARISON.md

@@ -1,6 +1,6 @@
 # Omni Model Comparison
 
-Generated: `2026-06-18T12:52:47+00:00`
+Generated: `2026-06-20T21:27:21+00:00`
 
 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.
 
@@ -8,13 +8,13 @@ Compare only rows with the same scope and target. Single-episode raw-feature met
 
 | version | status | scope | source |
 | --- | --- | --- | --- |
-| Single-Episode Public-Sample Task Suite | verified | one public Xperience-10M sample episode | `results/episode_task_suite/summary_report.json` |
+| Single-Episode Public-Sample 20-Task Suite | verified | one public Xperience-10M sample episode | `results/episode_task_suite/summary_report.json` |
 | 128-Episode Aligned Simple/NN Baselines | pass | selected 128-episode 96/16/16 split | `results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md` |
 | 128-Episode Foundation-Model Branches | partial_verified | selected 128-episode split and compatible derived windows | `results/omni_finetune/verified_public/` |
 
 Read the three rows this way:
 
-- 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.
 
@@ -34,7 +34,7 @@ This is the cleanest 1-episode versus 128-episode grouping for the same simple/N
 
 | scope | status | run | counts | metrics | source |
 | --- | --- | --- | --- | --- | --- |
-| 1 episode | verified | Single-Episode Public-Sample Task Suite | 1 episodes, 1161 windows/samples |  | `results/episode_task_suite/summary_report.json` |
+| 1 episode | verified | Single-Episode Public-Sample 20-Task Suite | 1 episodes, 1161 windows/samples |  | `results/episode_task_suite/summary_report.json` |
 | 128 episode | pass | 128-Episode Aligned Simple/NN Baselines | 3808 windows/samples |  | `results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md` |
 
 ### Qwen3-Omni LoRA

scripts/audio_ablation_and_raw_upgrade.py

@@ -856,7 +856,7 @@ def write_delta_chart(path: Path, summary: dict) -> None:
     lines = [
         f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
         '<rect width="100%" height="100%" fill="#07110d"/>',
-        '<text x="36" y="42" fill="#e6f7ea" font-family="Arial, sans-serif" font-size="28" font-weight="700">Measured Audio Delta Across 12 Xperience-10M Tasks</text>',
+        '<text x="36" y="42" fill="#e6f7ea" font-family="Arial, sans-serif" font-size="28" font-weight="700">Measured Audio Delta Across Original Xperience-10M Task Contracts</text>',
         '<text x="36" y="70" fill="#a7b8ab" font-family="Arial, sans-serif" font-size="15">Positive means audio improved the task primary metric on the single public sample split.</text>',
         f'<line x1="{mid}" y1="92" x2="{mid}" y2="{height - 24}" stroke="#5b6f61" stroke-width="1"/>',
     ]

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/build_research_takeaways.py

@@ -78,7 +78,7 @@ def build_payload() -> dict:
             "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 "
+                "local split, but the core chronological action/subtask heads are "
                 "much harder because later held-out windows include unseen labels."
             ),
             "evidence": [
@@ -143,8 +143,8 @@ def build_payload() -> dict:
                 "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. "
+                    "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": [

scripts/export_modality_atlas_assets.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """Export standalone modality thumbnails and a website data manifest.
 
-The large 12-task infographic embeds modality thumbnails as data URIs. This
+The large 20-task infographic embeds modality thumbnails as data URIs. This
 script writes those same sample-derived thumbnails as first-class public assets
 so the website can present a responsive, readable modality atlas on small
 screens without redistributing raw videos or annotations.

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/render_task_suite_infographic.py

@@ -1,10 +1,10 @@
 #!/usr/bin/env python3
 """
-Render a polished Ropedia Xperience-10M 12-task infographic.
+Render a polished Ropedia Xperience-10M 20-task infographic.
 
-The task names, inputs, and metrics are read from
-results/episode_task_suite/summary_report.json. The output is a deterministic
-PNG rendered from HTML/CSS so the labels stay legible and inspectable.
+The task names, inputs, and metrics are read from docs/data/task_suite_20.json.
+The output is a deterministic PNG rendered from HTML/CSS so the labels stay
+legible and inspectable.
 """
 
 from __future__ import annotations
@@ -23,60 +23,76 @@ from task_display import task_display_name
 
 
 ROOT = Path(__file__).resolve().parents[1]
-SUMMARY_PATH = ROOT / "results/episode_task_suite/summary_report.json"
+SUMMARY_PATH = ROOT / "docs/data/task_suite_20.json"
 DEFAULT_BASE = ROOT / "docs/assets/task_suite_infographic_base.png"
 DEFAULT_SAMPLE_DIR = ROOT.parent / "data/sample/xperience-10m-sample"
 DROPBOX_SAMPLE_DIR = Path.home() / "Library/CloudStorage/Dropbox/Ropedia/data/sample/xperience-10m-sample"
 DEFAULT_OUTPUT = ROOT / "docs/assets/task_suite_infographic.png"
 CANVAS_WIDTH = 1800
-CANVAS_HEIGHT = 6600
+CANVAS_HEIGHT = 7600
 THUMB_WIDTH = 880
 THUMB_HEIGHT = 520
 
 
 GROUPS = [
     {
-        "name": "Label + State",
+        "name": "Action + Procedure",
         "tone": "teal",
         "color": "#9bdfff",
         "soft": "#071d20",
         "tasks": [
             ("timeline_action", "supervised"),
             ("timeline_subtask", "supervised"),
+            ("transition_detection", "diagnostic"),
             ("next_action", "supervised"),
         ],
     },
     {
-        "name": "Prediction + Reconstruction",
+        "name": "Motion + Objects",
         "tone": "blue",
         "color": "#ccffa0",
         "soft": "#10210a",
         "tasks": [
             ("hand_trajectory_forecast", "forecast"),
-            ("modality_reconstruction", "forecast"),
             ("contact_prediction", "supervised"),
+            ("object_relevance", "supervised"),
+            ("caption_grounding", "retrieval"),
         ],
     },
     {
-        "name": "Grounding + Retrieval",
+        "name": "Retrieval + Alignment",
         "tone": "amber",
         "color": "#7ae5c3",
         "soft": "#092019",
         "tasks": [
-            ("caption_grounding", "retrieval"),
             ("cross_modal_retrieval", "retrieval"),
-            ("object_relevance", "supervised"),
+            ("modality_reconstruction", "forecast"),
+            ("temporal_order", "diagnostic"),
+            ("misalignment_detection", "diagnostic"),
         ],
     },
     {
-        "name": "Temporal Diagnostics",
-        "tone": "red",
+        "name": "Long-Horizon Semantics",
+        "tone": "green",
         "color": "#d8f4a5",
         "soft": "#1b210d",
         "tasks": [
-            ("transition_detection", "diagnostic"),
-            ("temporal_order", "diagnostic"),
-            ("misalignment_detection", "diagnostic"),
+            ("long_horizon_next_action", "forecast"),
+            ("next_subtask_forecast", "forecast"),
+            ("interaction_text_prediction", "language"),
+            ("action_object_relation", "relation"),
+        ],
+    },
+    {
+        "name": "Future Sets + Sensors",
+        "tone": "red",
+        "color": "#b7ff91",
+        "soft": "#1b210d",
+        "tasks": [
+            ("object_set_forecast", "multi-label"),
+            ("imu_to_hand_pose", "regression"),
+            ("camera_view_sync_retrieval", "retrieval"),
+            ("time_to_transition", "regression"),
         ],
     },
 ]
@@ -470,6 +486,10 @@ def fmt(value: float) -> str:
 
 
 def metric_for(task_name: str, metrics: dict) -> tuple[str, str]:
+    if "minimal_primary_metric" in metrics:
+        label = metrics.get("metric_name") or metrics.get("metric_key") or "score"
+        value = metrics.get("minimal_primary_metric")
+        return str(label), "n/a" if value is None else fmt(value)
     if task_name == "hand_trajectory_forecast":
         return "MPJPE", fmt(metrics["mpjpe"])
     if task_name == "cross_modal_retrieval":
@@ -490,6 +510,10 @@ def metric_for(task_name: str, metrics: dict) -> tuple[str, str]:
 
 
 def short_io(task_name: str, metrics: dict) -> str:
+    if metrics.get("input_short") or metrics.get("output_short"):
+        left = metrics.get("input_short") or "input"
+        right = metrics.get("output_short") or "target"
+        return f"{left} -> {right}"
     custom = {
         "timeline_action": "all featurized modalities -> action label",
         "timeline_subtask": "all featurized modalities -> subtask label",
@@ -510,7 +534,16 @@ def short_io(task_name: str, metrics: dict) -> str:
 def task_card(task_name: str, kind: str, metrics: dict, group: dict, index: int, neural_metrics: dict | None = None) -> str:
     label, value = metric_for(task_name, metrics)
     neural_html = ""
-    if neural_metrics and "error" not in neural_metrics:
+    if "neural_primary_metric" in metrics and metrics.get("neural_primary_metric") is not None:
+        neural_label = metrics.get("metric_name") or metrics.get("metric_key") or "score"
+        neural_value = fmt(metrics["neural_primary_metric"])
+        neural_html = f"""
+        <div class="metric neural">
+          <span>NN {html.escape(str(neural_label))}</span>
+          <strong>{html.escape(neural_value)}</strong>
+        </div>
+        """
+    elif neural_metrics and "error" not in neural_metrics:
         neural_label, neural_value = metric_for(task_name, neural_metrics)
         neural_html = f"""
         <div class="metric neural">
@@ -525,7 +558,7 @@ def task_card(task_name: str, kind: str, metrics: dict, group: dict, index: int,
           <span class="index">{index:02d}</span>
           <span class="kind">{html.escape(kind)}</span>
         </div>
-        <h3>{html.escape(task_display_name(task_name))}</h3>
+        <h3>{html.escape(metrics.get("task_display_name") or task_display_name(task_name))}</h3>
         <p>{html.escape(io)}</p>
         <div class="metric">
           <span>min {html.escape(label)}</span>
@@ -565,17 +598,38 @@ def modality_card(name: str, modality_type: str, sample_text: str, feature_text:
 
 
 def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None) -> str:
-    suite = summary["tasks"]
-    neural_suite = summary.get("neural_tasks", {})
+    if isinstance(summary.get("tasks"), list):
+        task_rows = summary["tasks"]
+        suite = {task["task_id"]: task for task in task_rows}
+        neural_suite = {}
+        dataset_scope = summary.get("dataset_scope", {})
+        num_frames = int(dataset_scope.get("num_frames", 0))
+        num_windows = int(dataset_scope.get("num_windows", 0))
+        feature_dim = int(dataset_scope.get("feature_dim", 0))
+        window_frames = int(dataset_scope.get("window_frames", 20))
+        stride_frames = int(dataset_scope.get("stride_frames", 5))
+        task_count = int(summary.get("task_count", len(suite)))
+        scored_records = 180
+    else:
+        suite = summary["tasks"]
+        neural_suite = summary.get("neural_tasks", {})
+        num_frames = int(summary["num_frames"])
+        num_windows = int(summary["num_windows"])
+        feature_dim = int(summary["feature_dim"])
+        window_frames = int(summary.get("window_frames", 20))
+        stride_frames = int(summary.get("stride_frames", 5))
+        task_count = len(suite)
+        scored_records = len(suite) + len(neural_suite)
     thumbnails = load_sample_thumbnails(sample_dir)
     base_layer = ""
     if base_image is not None and base_image.exists():
         base_layer = f'<div class="image-background" style="background-image:url(\'{base_image.resolve().as_uri()}\');"></div>'
     stats = [
-        (f"{summary['num_frames']:,}", "frames"),
-        (f"{summary['num_windows']:,}", "windows"),
-        (f"{summary['feature_dim']:,}", "features"),
-        (f"{len(suite)}+{len(neural_suite)}", "min + NN tasks"),
+        (f"{num_frames:,}", "frames"),
+        (f"{num_windows:,}", "windows"),
+        (f"{feature_dim:,}", "features"),
+        (f"{task_count}", "unified tasks"),
+        (f"{scored_records}", "method-task results"),
         ("70/30", "chronological split"),
     ]
     stats_html = "".join(
@@ -611,7 +665,7 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
 <head>
   <meta charset="utf-8">
   <meta name="viewport" content="width={CANVAS_WIDTH}, initial-scale=1">
-  <title>Xperience-10M 12-Task Episode Suite Infographic</title>
+  <title>Xperience-10M 20-Task Episode Suite Infographic</title>
   <style>
     * {{ box-sizing: border-box; }}
     html,
@@ -1014,14 +1068,14 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
   </style>
 </head>
 <body>
-  <main class="canvas" aria-label="Ropedia Xperience-10M 12-task suite infographic">
+  <main class="canvas" aria-label="Ropedia Xperience-10M unified 20-task infographic">
     {base_layer}
     <div class="content">
     <header class="header">
       <div>
-        <div class="kicker">verified single-episode task suite</div>
-        <h1>Ropedia Xperience-10M 12-task suite</h1>
-        <p class="subtitle">A clean map from synchronized multimodal windows to 12 research task heads, comparing minimal heads with neural MLP results. Next milestone: Qwen3-Omni fine-tuning with sensor-bridge evaluation.</p>
+        <div class="kicker">verified unified 20-task release</div>
+        <h1>Ropedia Xperience-10M task map</h1>
+        <p class="subtitle">A clean map from synchronized multimodal windows to 20 task contracts, comparing minimal heads, neural MLP heads, and the public 180-result matrix.</p>
       </div>
       <div class="stats">{stats_html}</div>
     </header>
@@ -1029,16 +1083,16 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
     <section class="shared-band" aria-label="shared processing contract">
       <div class="step"><strong>raw public episode</strong><span>video, audio, depth, pose, mocap, IMU, language</span></div>
       <div class="arrow">-></div>
-      <div class="step"><strong>20-frame windows</strong><span>stride 5, chronological order</span></div>
+      <div class="step"><strong>{window_frames}-frame windows</strong><span>stride {stride_frames}, chronological order</span></div>
       <div class="arrow">-></div>
-      <div class="step"><strong>{summary['feature_dim']:,}-d vector</strong><span>current manifest includes audio features</span></div>
+      <div class="step"><strong>{feature_dim:,}-d vector</strong><span>current manifest includes audio features</span></div>
       <div class="arrow">-></div>
-      <div class="step"><strong>12 minimal + NN heads</strong><span>softmax/ridge/logistic plus PyTorch MLP</span></div>
+      <div class="step"><strong>20 task contracts</strong><span>minimal/NN baselines plus model-branch results</span></div>
     </section>
 
     <div class="section-label">
-      <span>12 task families</span>
-      <span>Every task below has a minimal baseline and a neural MLP head over the same aligned window contract, making the suite easy to compare, extend, and scale to held-out episodes.</span>
+      <span>20 task contracts</span>
+      <span>Every task below is part of the unified public-sample suite. Tasks 1-12 are the original contracts; tasks 13-20 use the same window/split discipline and are scored in the 180-result matrix.</span>
     </div>
     <section class="families">{''.join(families)}</section>
 

scripts/research_direction_taxonomy.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-"""Organize the 12 Xperience-10M tasks into the four Ropedia research tracks.
+"""Organize the 20 Xperience-10M tasks into the four Ropedia research tracks.
 
 The script is intentionally deterministic: it reads the committed task metrics,
 adds a manually curated taxonomy, and writes machine-readable artifacts used by the
@@ -25,6 +25,7 @@ DOCS_DATA = ROOT / "docs" / "data"
 CHARTS = ROOT / "docs" / "assets" / "charts"
 
 SUMMARY_REPORT = RESULTS / "summary_report.json"
+TASK_SUITE_20 = DOCS_DATA / "task_suite_20.json"
 
 
 DIRECTIONS: OrderedDict[str, dict[str, Any]] = OrderedDict(
@@ -69,7 +70,7 @@ DIRECTIONS: OrderedDict[str, dict[str, Any]] = OrderedDict(
                 "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.",
@@ -255,6 +256,110 @@ TASK_TAXONOMY: OrderedDict[str, dict[str, Any]] = OrderedDict(
                 "current_limit": "Synthetic shifts diagnose alignment but do not solve calibration or mapping.",
             },
         ),
+        (
+            "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.",
+            },
+        ),
+        (
+            "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.",
+            },
+        ),
+        (
+            "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.",
+            },
+        ),
+        (
+            "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.",
+            },
+        ),
+        (
+            "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.",
+            },
+        ),
+        (
+            "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.",
+            },
+        ),
+        (
+            "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.",
+            },
+        ),
+        (
+            "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.",
+            },
+        ),
     ]
 )
 
@@ -272,6 +377,14 @@ METRIC_SPECS = {
     "modality_reconstruction": ("r2", "R2", "higher"),
     "temporal_order": ("f1", "F1", "higher"),
     "misalignment_detection": ("f1", "F1", "higher"),
+    "long_horizon_next_action": ("macro_f1", "macro-F1", "higher"),
+    "next_subtask_forecast": ("macro_f1", "macro-F1", "higher"),
+    "interaction_text_prediction": ("macro_f1", "macro-F1", "higher"),
+    "action_object_relation": ("macro_f1", "macro-F1", "higher"),
+    "object_set_forecast": ("micro_f1", "micro-F1", "higher"),
+    "imu_to_hand_pose": ("mae", "MAE", "lower"),
+    "camera_view_sync_retrieval": ("mrr", "MRR", "higher"),
+    "time_to_transition": ("mae", "MAE", "lower"),
 }
 
 
@@ -279,6 +392,17 @@ def load_summary() -> dict[str, Any]:
     return json.loads(SUMMARY_REPORT.read_text(encoding="utf-8"))
 
 
+def load_unified_tasks() -> dict[str, dict[str, Any]]:
+    if not TASK_SUITE_20.exists():
+        return {}
+    payload = json.loads(TASK_SUITE_20.read_text(encoding="utf-8"))
+    return {
+        task["task_id"]: task
+        for task in payload.get("tasks", [])
+        if isinstance(task, dict) and task.get("task_id")
+    }
+
+
 def metric_value(metrics: dict[str, Any] | None, task: str) -> float | None:
     if not metrics:
         return None
@@ -320,6 +444,7 @@ def baseline_readout(label: str) -> str:
 def build_taxonomy(summary: dict[str, Any]) -> dict[str, Any]:
     minimal_tasks = summary["tasks"]
     neural_tasks = summary.get("neural_tasks", {})
+    unified_tasks = load_unified_tasks()
 
     task_records: OrderedDict[str, dict[str, Any]] = OrderedDict()
     direction_counts = {
@@ -328,9 +453,19 @@ def build_taxonomy(summary: dict[str, Any]) -> dict[str, Any]:
     }
 
     for task, spec in TASK_TAXONOMY.items():
+        unified = unified_tasks.get(task, {})
         metric_key, metric_name, metric_direction = METRIC_SPECS[task]
-        minimal_metric = metric_value(minimal_tasks.get(task), task)
-        neural_metric = metric_value(neural_tasks.get(task), task)
+        metric_key = unified.get("metric_key") or metric_key
+        metric_name = unified.get("metric_name") or metric_name
+        metric_direction = unified.get("metric_direction") or metric_direction
+        if task in minimal_tasks:
+            minimal_metric = metric_value(minimal_tasks.get(task), task)
+            neural_metric = metric_value(neural_tasks.get(task), task)
+        else:
+            minimal = unified.get("minimal_primary_metric")
+            neural = unified.get("neural_primary_metric")
+            minimal_metric = float(minimal) if minimal is not None else None
+            neural_metric = float(neural) if neural is not None else None
         better = choose_better(task, minimal_metric, neural_metric)
 
         roles = spec["direction_roles"]
@@ -340,7 +475,7 @@ def build_taxonomy(summary: dict[str, Any]) -> dict[str, Any]:
 
         task_records[task] = {
             **spec,
-            "display_name": task_display_name(task),
+            "display_name": unified.get("task_display_name") or task_display_name(task),
             "artifact_id": task,
             "metric": {
                 "key": metric_key,
@@ -362,12 +497,12 @@ def build_taxonomy(summary: dict[str, Any]) -> dict[str, Any]:
         direction_records[code] = {
             **info,
             "tasks": linked_tasks,
-            "task_display_names": [task_display_name(task) for task in linked_tasks],
+            "task_display_names": [task_records[task]["display_name"] for task in linked_tasks],
             "counts": direction_counts[code],
         }
 
     return {
-        "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": summary.get("num_frames"),
@@ -379,6 +514,7 @@ def build_taxonomy(summary: dict[str, Any]) -> dict[str, Any]:
             "minimal": f"Interpretable softmax, logistic, ridge, and retrieval heads over the {summary.get('feature_dim'):,}-d window feature vector.",
             "neural_mlp": "Small PyTorch MLP classifiers/regressors using the same features, splits, and task contracts.",
         },
+        "task_count": len(task_records),
         "directions": direction_records,
         "tasks": task_records,
     }
@@ -433,7 +569,7 @@ def write_markdown(taxonomy: dict[str, Any]) -> None:
     lines = [
         "# Four-Direction Task Taxonomy",
         "",
-        "This file is generated by `scripts/research_direction_taxonomy.py` from the committed 12-task metrics.",
+        "This file is generated by `scripts/research_direction_taxonomy.py` from the unified 20-task index and committed metrics.",
         "It maps the current Xperience-10M sample tasks to the four Ropedia research directions and marks which parts require multi-episode evidence.",
         "",
         "## Baseline Families",
@@ -563,11 +699,11 @@ def write_svg(taxonomy: dict[str, Any]) -> None:
     svg = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}" role="img" aria-label="Xperience-10M task coverage across four research directions">
   <rect width="100%" height="100%" fill="#020502"/>
   <rect x="24" y="24" width="1132" height="652" rx="20" fill="#050905" stroke="#ccffa0" stroke-opacity="0.24"/>
-  {svg_text(margin, 64, "Xperience-10M 12-Task Suite: Four Research Directions", 30, 800)}
-  {svg_text(margin, 96, "One public sample episode, two baseline families, explicit direct/proxy/diagnostic coverage.", 16, 500, "#a5afa2")}
+  {svg_text(margin, 64, "Xperience-10M 20-Task Suite: Four Research Directions", 30, 800)}
+  {svg_text(margin, 96, "One public sample episode, two baseline families, model branches, and explicit direct/proxy/diagnostic coverage.", 16, 500, "#a5afa2")}
   {"".join(cards)}
   {"".join(legend)}
-  {svg_text(margin, 670, "Generated from results/episode_task_suite/summary_report.json and scripts/research_direction_taxonomy.py", 13, 500, "#a5afa2")}
+  {svg_text(margin, 670, "Generated from docs/data/task_suite_20.json, committed metrics, and scripts/research_direction_taxonomy.py", 13, 500, "#a5afa2")}
 </svg>
 """
     (CHARTS / "research_direction_coverage.svg").write_text(svg, encoding="utf-8")

scripts/task_display.py

@@ -18,6 +18,14 @@ TASK_DISPLAY_NAMES = {
     "modality_reconstruction": "Cross-Modal Reconstruction",
     "temporal_order": "Temporal Order Verification",
     "misalignment_detection": "Multimodal Synchronization Detection",
+    "long_horizon_next_action": "Long-Horizon Next-Action Forecasting",
+    "next_subtask_forecast": "Long-Horizon Next-Subtask Forecasting",
+    "interaction_text_prediction": "Interaction Text Prediction",
+    "action_object_relation": "Action-Object Relation Prediction",
+    "object_set_forecast": "Future Object-Set Forecasting",
+    "imu_to_hand_pose": "IMU-to-Hand Pose Reconstruction",
+    "camera_view_sync_retrieval": "Camera-View Synchronization Retrieval",
+    "time_to_transition": "Time-to-Next-Transition Regression",
 }
 
 

scripts/task_walkthroughs.py

@@ -455,7 +455,7 @@ def build_payload(summary: dict[str, Any]) -> dict[str, Any]:
 
 def write_markdown(payload: dict[str, Any]) -> None:
     lines = [
-        "# Junior-Friendly 12-Task Walkthroughs",
+        "# Junior-Friendly Original Task Walkthroughs",
         "",
         "This file explains every task in the Xperience-10M episode suite as an input -> process -> output pipeline.",
         "It is generated by `scripts/task_walkthroughs.py` from committed metrics plus hand-curated task explanations.",

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",
 ]
 

scripts/validate_task_surface.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-"""Validate the public 12-task card and walkthrough surface.
+"""Validate the public task card, walkthrough, and 20-task matrix surface.
 
 This gate is deliberately about presentation integrity, not model quality. The
 repo keeps snake_case artifact ids for reproducibility, but the public website
@@ -23,8 +23,27 @@ TASK_JSON = ROOT / "docs/data/task_walkthroughs.json"
 WEBSITE = ROOT / "docs/index.html"
 WALKTHROUGH_MD = ROOT / "results/episode_task_suite/task_walkthroughs/TASK_WALKTHROUGHS.md"
 OUTPUT = ROOT / "docs/data/task_surface_integrity.json"
-
-EXPECTED_TASKS = TASK_DISPLAY_NAMES
+TASK_SUITE_20 = ROOT / "docs/data/task_suite_20.json"
+TASK_MATRIX_20 = ROOT / "docs/data/task_method_20_result_matrix.json"
+
+ORIGINAL_WALKTHROUGH_TASK_IDS = (
+    "timeline_action",
+    "timeline_subtask",
+    "transition_detection",
+    "next_action",
+    "hand_trajectory_forecast",
+    "contact_prediction",
+    "object_relevance",
+    "caption_grounding",
+    "cross_modal_retrieval",
+    "modality_reconstruction",
+    "temporal_order",
+    "misalignment_detection",
+)
+EXPECTED_WALKTHROUGH_TASKS = {
+    task_id: TASK_DISPLAY_NAMES[task_id] for task_id in ORIGINAL_WALKTHROUGH_TASK_IDS
+}
+EXPECTED_UNIFIED_TASKS = TASK_DISPLAY_NAMES
 
 EXPECTED_EXTENSION_NAMES = {
     "body_motion_intensity": "Body and Hand Motion Intensity",
@@ -120,20 +139,20 @@ def validate_tasks(payload: dict[str, Any], failures: list[dict[str, Any]]) -> l
     task_ids = set(tasks)
     checks.append(
         check(
-            len(tasks) == len(EXPECTED_TASKS),
-            "exactly_12_tasks",
+            len(tasks) == len(EXPECTED_WALKTHROUGH_TASKS),
+            "original_walkthrough_task_count",
             failures,
             observed=len(tasks),
-            expected=len(EXPECTED_TASKS),
+            expected=len(EXPECTED_WALKTHROUGH_TASKS),
         )
     )
     checks.append(
         check(
-            task_ids == set(EXPECTED_TASKS),
-            "expected_task_ids_present",
+            task_ids == set(EXPECTED_WALKTHROUGH_TASKS),
+            "expected_original_walkthrough_task_ids_present",
             failures,
-            missing=sorted(set(EXPECTED_TASKS) - task_ids),
-            extra=sorted(task_ids - set(EXPECTED_TASKS)),
+            missing=sorted(set(EXPECTED_WALKTHROUGH_TASKS) - task_ids),
+            extra=sorted(task_ids - set(EXPECTED_WALKTHROUGH_TASKS)),
         )
     )
 
@@ -145,7 +164,7 @@ def validate_tasks(payload: dict[str, Any], failures: list[dict[str, Any]]) -> l
         checks.append(
             check(not missing_fields, f"{task_id}: required_fields", failures, missing=missing_fields)
         )
-        expected_name = EXPECTED_TASKS.get(task_id)
+        expected_name = EXPECTED_WALKTHROUGH_TASKS.get(task_id)
         checks.append(
             check(
                 task.get("display_name") == expected_name,
@@ -165,7 +184,11 @@ def validate_tasks(payload: dict[str, Any], failures: list[dict[str, Any]]) -> l
         )
         for field in DISPLAY_FIELDS:
             value = str(task.get(field, ""))
-            raw_hits = [hit for hit in RAW_ID_PATTERN.findall(value) if hit in EXPECTED_TASKS or hit in MODALITY_ASSETS]
+            raw_hits = [
+                hit
+                for hit in RAW_ID_PATTERN.findall(value)
+                if hit in EXPECTED_UNIFIED_TASKS or hit in MODALITY_ASSETS
+            ]
             checks.append(
                 check(
                     not raw_hits,
@@ -255,7 +278,7 @@ def validate_tasks(payload: dict[str, Any], failures: list[dict[str, Any]]) -> l
 
 def validate_markdown(source: str, tasks: dict[str, Any], failures: list[dict[str, Any]]) -> list[dict[str, Any]]:
     checks: list[dict[str, Any]] = []
-    for task_id, display_name in EXPECTED_TASKS.items():
+    for task_id, display_name in EXPECTED_WALKTHROUGH_TASKS.items():
         expected_heading = f"### {display_name} (`{task_id}`)"
         checks.append(
             check(
@@ -267,8 +290,8 @@ def validate_markdown(source: str, tasks: dict[str, Any], failures: list[dict[st
         )
     checks.append(
         check(
-            source.count("### ") == len(EXPECTED_TASKS),
-            "markdown_has_12_task_sections",
+            source.count("### ") == len(EXPECTED_WALKTHROUGH_TASKS),
+            "markdown_has_original_walkthrough_sections",
             failures,
             observed=source.count("### "),
         )
@@ -412,10 +435,30 @@ def build_report() -> dict[str, Any]:
     website_source = WEBSITE.read_text(encoding="utf-8")
     markdown_source = WALKTHROUGH_MD.read_text(encoding="utf-8")
     tasks = task_payload.get("tasks", {}) if isinstance(task_payload.get("tasks", {}), dict) else {}
+    task_suite_20 = load_json(TASK_SUITE_20) if TASK_SUITE_20.exists() else {}
+    task_matrix_20 = load_json(TASK_MATRIX_20) if TASK_MATRIX_20.exists() else {}
 
     checks.extend(validate_tasks(task_payload, failures))
     checks.extend(validate_markdown(markdown_source, tasks, failures))
     checks.extend(validate_website(website_source, failures))
+    checks.append(
+        check(
+            task_suite_20.get("task_count") == 20,
+            "unified_20_task_suite_present",
+            failures,
+            task_count=task_suite_20.get("task_count"),
+        )
+    )
+    checks.append(
+        check(
+            task_matrix_20.get("method_task_record_count") == 180
+            and task_matrix_20.get("scored_method_task_count") == 180,
+            "unified_180_result_matrix_present",
+            failures,
+            method_task_record_count=task_matrix_20.get("method_task_record_count"),
+            scored_method_task_count=task_matrix_20.get("scored_method_task_count"),
+        )
+    )
 
     task_families = {}
     task_modalities = {}
@@ -430,8 +473,11 @@ def build_report() -> dict[str, Any]:
         "status": "pass" if not failures else "fail",
         "generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
         "summary": {
-            "task_count": len(tasks),
-            "expected_task_count": len(EXPECTED_TASKS),
+            "original_walkthrough_task_count": len(tasks),
+            "expected_original_walkthrough_task_count": len(EXPECTED_WALKTHROUGH_TASKS),
+            "unified_task_count": task_suite_20.get("task_count"),
+            "method_task_record_count": task_matrix_20.get("method_task_record_count"),
+            "scored_method_task_count": task_matrix_20.get("scored_method_task_count"),
             "task_family_counts": dict(sorted(task_families.items())),
             "modality_usage_counts": dict(sorted(task_modalities.items())),
             "interactive_surface": "task cards plus scrub/play/chapter walkthrough storyboard",