Refine reader-facing scope wording (3/3)

data/two_evidence_line_result_summary.json

@@ -1,6 +1,6 @@
 {
-  "generated_at_utc": "2026-06-21T11:49:06+00:00",
-  "interpretation_rule": "Use the 1-episode line for task construction and reproducibility claims. Use the 128-episode line for same-split metadata/raw baselines, Qwen3-Omni v6 LoRA diagnostics, and Cosmos3 diagnostics.",
+  "generated_at_utc": "2026-06-22T09:56:30+00:00",
+  "interpretation_rule": "Read the 1-episode line as the inspectable task lab. Read the 128-episode line as the selected comparison surface for metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super, and Cosmos3-Nano.",
   "lines": [
     {
       "artifact_entry_points": [
@@ -10,7 +10,7 @@
         "results/episode_task_suite/feature_manifest.json",
         "docs/single_episode_explorer.html"
       ],
-      "claim_boundary": "Supports task construction, file inspection, local reproducibility, and controlled single-episode baseline claims.",
+      "best_read_as": "Inspect the raw sample, understand file organization, reproduce the 20 task targets, and compare Minimal vs Neural MLP behavior inside one episode.",
       "data_unit": "One public Xperience-10M sample episode",
       "direct_scored_method_task_count": 40,
       "id": "single_public_sample_episode",
@@ -45,13 +45,13 @@
           }
         }
       ],
-      "not_for": "Do not use this line as evidence of multi-episode generalization.",
       "primary_use": "Inspect raw files, understand each task, rerun local baselines, and debug task quality.",
       "primary_visuals": [
         "docs/assets/charts/two_evidence_line_map.svg",
         "docs/assets/charts/single_episode_task_model_radar.svg"
       ],
       "proxy_scored_method_task_count": 0,
+      "read_separately_from": "The selected-128 comparison rows and broader held-out model behavior.",
       "result_statement": "40/40 direct scores from Minimal and Neural MLP heads on the same 20 task contracts.",
       "scored_method_task_count": 40,
       "short_label": "Line 1",
@@ -66,7 +66,7 @@
         "docs/data/qwen3_omni_run_lineage.json",
         "docs/data/task_method_20_gap_audit.json"
       ],
-      "claim_boundary": "Supports same-split metadata/raw baseline comparison, Qwen3-Omni v6 diagnostics, Cosmos3 diagnostics, and scale-up planning on public-safe processed artifacts.",
+      "best_read_as": "Compare same-split metadata/raw baselines, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano while keeping the 6 compact-proxy cells visible.",
       "data_unit": "Selected held-out 96/16/16 split with public-safe processed features linked to official gated episode paths",
       "direct_scored_method_task_count": 134,
       "id": "selected_128_episode_surface",
@@ -170,7 +170,6 @@
           }
         }
       ],
-      "not_for": "Do not read compact-proxy cells as direct raw-target measurements.",
       "primary_use": "Compare same-split metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window while keeping evidence type explicit.",
       "primary_visuals": [
         "docs/assets/charts/two_evidence_line_map.svg",
@@ -178,6 +177,7 @@
         "docs/assets/charts/unified_task_model_radar.svg"
       ],
       "proxy_scored_method_task_count": 6,
+      "read_separately_from": "Direct raw-target interpretation for the proxy-marked cells.",
       "result_statement": "140/140 selected-128 scores across seven methods: 134 direct scores plus 6 documented compact-proxy scores.",
       "scored_method_task_count": 140,
       "short_label": "Line 2",
@@ -337,7 +337,7 @@
     "selected_128_episode_surface": "Use for held-out comparison, metadata/raw-feature baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, Cosmos3-Nano Future Window, and scale-up decisions.",
     "single_public_sample_episode": "Use for task construction, raw-file inspection, local reproducibility, and controlled Minimal-vs-Neural baseline behavior."
   },
-  "reader_summary": "The suite has two public evidence lines. Line 1 is the fully inspectable one-episode task lab. Line 2 is the 128-episode comparison surface for aligned baselines, the Qwen3-Omni series, and the Cosmos3 series. Do not mix the two when reading scores.",
+  "reader_summary": "The suite has two public reading lanes. Line 1 is the fully inspectable one-episode task lab. Line 2 is the 128-episode comparison surface for aligned baselines, the Qwen3-Omni series, and the Cosmos3 series. Compare scores within the same lane first.",
   "reading_order": [
     {
       "reason": "Line 1 answers task-lab and reproducibility questions; line 2 answers selected-128 comparison questions.",

data/two_evidence_lines.json

@@ -1,8 +1,8 @@
 {
   "status": "current",
   "updated_utc": "2026-06-21T00:00:00Z",
-  "interpretation_rule": "Use the 1-episode line for task construction and reproducibility claims. Use the 128-episode line for same-split metadata/raw baselines, Qwen3-Omni v6 LoRA diagnostics, and Cosmos3 diagnostics.",
-  "reader_summary": "The suite has two public evidence lines. Line 1 is the fully inspectable one-episode task lab. Line 2 is the 128-episode comparison surface for aligned baselines, the Qwen3-Omni series, and the Cosmos3 series. Do not mix the two when reading scores.",
+  "interpretation_rule": "Read the 1-episode line as the inspectable task lab. Read the 128-episode line as the selected comparison surface for metadata/raw baselines, Qwen3-Omni v6 LoRA, Cosmos3-Super, and Cosmos3-Nano.",
+  "reader_summary": "The suite has two public reading lanes. Line 1 is the fully inspectable one-episode task lab. Line 2 is the 128-episode comparison surface for aligned baselines, the Qwen3-Omni series, and the Cosmos3 series. Compare scores within the same lane first.",
   "score_formula": "2 single-episode methods x 20 tasks = 40 records; 7 selected-128 methods x 20 tasks = 140 records; total public matrix = 180/180 scored records.",
   "lines": [
     {
@@ -11,8 +11,8 @@
       "short_label": "Line 1",
       "data_unit": "One public Xperience-10M sample episode",
       "result_statement": "40/40 direct scores from Minimal and Neural MLP heads on the same 20 task contracts.",
-      "claim_boundary": "Supports task construction, file inspection, local reproducibility, and controlled single-episode baseline claims.",
-      "not_for": "Do not use this line as evidence of multi-episode generalization.",
+      "best_read_as": "Inspect the raw sample, understand file organization, reproduce the 20 task targets, and compare Minimal vs Neural MLP behavior inside one episode.",
+      "read_separately_from": "The selected-128 comparison rows and broader held-out model behavior.",
       "frames": 5821,
       "windows": 1161,
       "window_definition": "20-frame aligned windows with 5-frame stride",
@@ -45,8 +45,8 @@
       "short_label": "Line 2",
       "data_unit": "Selected held-out 96/16/16 split with public-safe processed features linked to official gated episode paths",
       "result_statement": "140/140 selected-128 scores across seven methods: 134 direct scores plus 6 documented compact-proxy scores.",
-      "claim_boundary": "Supports same-split metadata/raw baseline comparison, Qwen3-Omni v6 diagnostics, Cosmos3 diagnostics, and scale-up planning on public-safe processed artifacts.",
-      "not_for": "Do not read compact-proxy cells as direct raw-target measurements.",
+      "best_read_as": "Compare same-split metadata/raw baselines, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano while keeping the 6 compact-proxy cells visible.",
+      "read_separately_from": "Direct raw-target interpretation for the proxy-marked cells.",
       "episodes": 128,
       "split": {
         "train": 96,

data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T08:57:54+00:00",
+  "generated_at_utc": "2026-06-22T10:09:34+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 151055,
-      "evidence_index": 202390
+      "overview_index": 151125,
+      "evidence_index": 202548
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 151055,
-      "protocol_index": 198578,
-      "evidence_index": 202390
+      "overview_index": 151125,
+      "protocol_index": 198738,
+      "evidence_index": 202548
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -315,7 +315,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 124471,
+      "bytes": 124475,
       "top_level_type": "dict"
     },
     {
@@ -335,7 +335,7 @@
     },
     {
       "path": "data/evaluation_protocol.json",
-      "bytes": 24267,
+      "bytes": 24281,
       "top_level_type": "dict"
     },
     {
@@ -350,12 +350,12 @@
     },
     {
       "path": "data/foundation_model_plan.json",
-      "bytes": 13925,
+      "bytes": 13918,
       "top_level_type": "dict"
     },
     {
       "path": "data/glossary.json",
-      "bytes": 18754,
+      "bytes": 18767,
       "top_level_type": "dict"
     },
     {
@@ -470,12 +470,12 @@
     },
     {
       "path": "data/research_roadmap.json",
-      "bytes": 14129,
+      "bytes": 14143,
       "top_level_type": "dict"
     },
     {
       "path": "data/research_roadmap_interactive.json",
-      "bytes": 191028,
+      "bytes": 191136,
       "top_level_type": "dict"
     },
     {
@@ -550,7 +550,7 @@
     },
     {
       "path": "data/three_foundation_pipelines.json",
-      "bytes": 14465,
+      "bytes": 14557,
       "top_level_type": "dict"
     },
     {
@@ -560,12 +560,12 @@
     },
     {
       "path": "data/two_evidence_line_result_summary.json",
-      "bytes": 17416,
+      "bytes": 17451,
       "top_level_type": "dict"
     },
     {
       "path": "data/two_evidence_lines.json",
-      "bytes": 7351,
+      "bytes": 7386,
       "top_level_type": "dict"
     },
     {
@@ -788,140 +788,140 @@
     {
       "path": "assets/task-icons/01_timeline_action.svg",
       "exists": true,
-      "bytes": 1486,
+      "bytes": 1481,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/02_timeline_subtask.svg",
       "exists": true,
-      "bytes": 1513,
+      "bytes": 1508,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/03_transition_detection.svg",
       "exists": true,
-      "bytes": 1531,
+      "bytes": 1526,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/04_next_action.svg",
       "exists": true,
-      "bytes": 1484,
+      "bytes": 1479,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/05_hand_trajectory_forecast.svg",
       "exists": true,
-      "bytes": 1552,
+      "bytes": 1547,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/06_contact_prediction.svg",
       "exists": true,
-      "bytes": 1540,
+      "bytes": 1535,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/07_object_relevance.svg",
       "exists": true,
-      "bytes": 1623,
+      "bytes": 1618,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/08_caption_grounding.svg",
       "exists": true,
-      "bytes": 1503,
+      "bytes": 1498,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/09_cross_modal_retrieval.svg",
       "exists": true,
-      "bytes": 1607,
+      "bytes": 1602,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/10_modality_reconstruction.svg",
       "exists": true,
-      "bytes": 1587,
+      "bytes": 1582,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/11_temporal_order.svg",
       "exists": true,
-      "bytes": 1543,
+      "bytes": 1538,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/12_misalignment_detection.svg",
       "exists": true,
-      "bytes": 1582,
+      "bytes": 1577,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/13_long_horizon_next_action.svg",
       "exists": true,
-      "bytes": 1595,
+      "bytes": 1590,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/14_next_subtask_forecast.svg",
       "exists": true,
-      "bytes": 1625,
+      "bytes": 1620,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/15_interaction_text_prediction.svg",
       "exists": true,
-      "bytes": 1578,
+      "bytes": 1573,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/16_action_object_relation.svg",
       "exists": true,
-      "bytes": 1574,
+      "bytes": 1569,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/17_object_set_forecast.svg",
       "exists": true,
-      "bytes": 1688,
+      "bytes": 1683,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/18_imu_to_hand_pose.svg",
       "exists": true,
-      "bytes": 1566,
+      "bytes": 1561,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/19_camera_view_sync_retrieval.svg",
       "exists": true,
-      "bytes": 1645,
+      "bytes": 1640,
       "format": "SVG",
       "has_viewbox": true
     },
     {
       "path": "assets/task-icons/20_time_to_transition.svg",
       "exists": true,
-      "bytes": 1526,
+      "bytes": 1521,
       "format": "SVG",
       "has_viewbox": true
     },

scripts/build_artifact_index.py

@@ -867,7 +867,7 @@ ARTIFACTS = [
         "path": "PUBLIC_READER_MAP.md",
         "kind": "project_path",
         "surface": "repo_hf",
-        "shows": "Provides the first-pass navigation layer for GitHub, GitHub Pages, Hugging Face mirrors, Qwen3-Omni/Cosmos3 repos, evidence lines, and claim boundaries.",
+        "shows": "Provides the first-pass navigation layer for GitHub, GitHub Pages, Hugging Face mirrors, Qwen3-Omni/Cosmos3 repos, evidence lines, and result-reading lanes.",
     },
     {
         "id": "public_reader_map_json",

scripts/build_evaluation_protocol.py

@@ -283,7 +283,7 @@ def build_payload() -> dict:
         "current_limitations": [
             "Cross-episode generalization for Qwen3-Omni has a first verified diagnostic pilot, but strong model quality is not yet shown.",
             "Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.",
-            "The final verified Qwen3-Omni diagnostic result meets the strict-JSON target, but action/subtask held-out quality remains weak and needs error analysis before larger model-quality claims.",
+            "The final verified Qwen3-Omni diagnostic result meets the strict-JSON target, but action/subtask held-out quality remains weak and needs error analysis before presenting stronger model-quality numbers.",
             "Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations.",
         ],
         "scale_up_gate": {

scripts/build_interactive_research_roadmap.py

@@ -234,7 +234,7 @@ def main() -> int:
         "three_foundation_pipelines": {
             "title": three_foundation_pipelines.get("title"),
             "status": three_foundation_pipelines.get("status"),
-            "claim_boundary": three_foundation_pipelines.get("claim_boundary"),
+            "reader_note": three_foundation_pipelines.get("reader_note"),
             "tracks": three_foundation_pipelines.get("tracks", []),
             "source_document": three_foundation_pipelines.get("source_document"),
         },

scripts/build_multilingual_public_readmes.py

@@ -155,8 +155,8 @@ The public suite is organized around two evidence lines. Keep them separate when
       <th width="20%">Line</th>
       <th width="24%">Data unit</th>
       <th width="22%">Score statement</th>
-      <th width="20%">Valid claim</th>
-      <th>Do not claim</th>
+      <th width="20%">Best use</th>
+      <th>Read separately from</th>
     </tr>
   </thead>
   <tbody>
@@ -164,15 +164,15 @@ The public suite is organized around two evidence lines. Keep them separate when
       <td><strong>1 sample episode</strong></td>
       <td>One public Xperience-10M sample episode: 5,821 frames, 1,161 aligned 20-frame windows, 8,546 feature dimensions.</td>
       <td>40/40 direct scores from Minimal and Neural MLP heads.</td>
-      <td>Task construction, file inspection, local reproducibility, and controlled single-episode baselines.</td>
-      <td>Multi-episode generalization.</td>
+      <td>Inspect the raw sample, understand file organization, reproduce the 20 task targets, and compare Minimal vs Neural MLP behavior inside one episode.</td>
+      <td>The selected-128 comparison rows and any broader held-out model behavior.</td>
     </tr>
     <tr>
       <td><strong>128 selected episodes</strong></td>
       <td>Selected held-out 96/16/16 split: 34,269 exported windows with public-safe processed features linked to official gated episode paths. The Hugging Face artifact dataset exposes these rows separately as <a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/viewer/selected_128_windows/selected_128"><code>selected_128_windows/selected_128</code></a>; it is not mixed with the one-sample <code>episode_sample/public_sample</code> viewer.</td>
       <td>140/140 selected-128 scores: 134 direct + 6 compact-proxy.</td>
-      <td>Same-split metadata/raw baseline comparison, Qwen3-Omni v6 diagnostics, Cosmos3 diagnostics, and scale-up planning.</td>
-      <td>Reading proxy cells as direct raw-target measurements.</td>
+      <td>Compare same-split metadata/raw baselines, Qwen3-Omni v6, Cosmos3-Super, and Cosmos3-Nano while keeping the 6 compact-proxy cells visible.</td>
+      <td>Direct raw-target measurements for the proxy-marked cells.</td>
     </tr>
   </tbody>
 </table>