Add files using upload-large-folder tool

.gitattributes

@@ -67,3 +67,4 @@ results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/interaction_t
 results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/interaction_text_prediction/confusion_matrix.csv filter=lfs diff=lfs merge=lfs -text
 results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/neural_mlp/interaction_text_prediction/confusion_matrix.csv filter=lfs diff=lfs merge=lfs -text
 results/omni_finetune/xperience10m_cosmos3_super_retrieval_task_probes_a100_textonly_prompatch_v2_20260620/modality_reconstruction/predictions.jsonl filter=lfs diff=lfs merge=lfs -text
+viewer/selected128_windows.jsonl filter=lfs diff=lfs merge=lfs -text

PROJECT_README.md

@@ -137,7 +137,7 @@ The public suite is organized around two evidence lines. Keep them separate when
     </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.</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>

README.md

@@ -12,6 +12,10 @@ configs:
     data_files:
       - split: public_sample
         path: viewer/episode_windows.parquet
+  - config_name: selected_128_windows
+    data_files:
+      - split: selected_128
+        path: viewer/selected128_windows.parquet
 ---
 
 # Ropedia Xperience-10M Task Suite Artifacts
@@ -108,6 +112,27 @@ Hugging Face mirror carries the same translated README files so readers can
 move between GitHub, the dashboard, the Space, the artifact dataset, and model
 cards without losing the evidence trail.
 
+## 128-Episode Source and Feature Index
+
+The selected 128-episode split is linked back to the official gated
+`ropedia-ai/xperience-10m` episode tree in
+`XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md` and
+`docs/data/xperience10m_128_episode_feature_index.json`. The public mirrors
+carry only public-safe processed artifacts: selection files, inspected
+manifests, dense multiscale window rows, metadata feature matrices, and result
+summaries.
+
+
+
+
+
+
+The Hugging Face artifact dataset exposes the 34,269 selected-128 exported
+windows as a separate viewer config, `selected_128_windows`, with split
+`selected_128` at `viewer/selected128_windows.parquet`. The one-sample episode
+viewer remains separate as `episode_sample/public_sample`; do not concatenate
+the two evidence lines when reading scores or dataset rows.
+
 ## Dataset Boundary
 
 This artifact bundle contains derived artifacts only. It does not redistribute

data/mirror_parity.json

@@ -1,6 +1,6 @@
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@@ -2540,44 +2540,44 @@
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@@ -5552,21 +5552,21 @@
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@@ -5977,21 +5977,21 @@
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@@ -6127,21 +6127,21 @@
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@@ -6331,39 +6331,39 @@
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data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-21T16:19:32+00:00",
+  "generated_at_utc": "2026-06-21T16:45:16+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
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@@ -18,7 +18,7 @@
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           "status": "pass",
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+          "generated_at_utc": "2026-06-21T16:42:18+00:00"
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@@ -43,12 +43,12 @@
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           "status": "pass",
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@@ -109,7 +109,7 @@
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data/publication_audit.json

@@ -1,6 +1,6 @@
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@@ -210,7 +210,7 @@
       "surface": "hf_artifact_bundle",
       "path": "README.md",
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-      "required_marker_count": 9,
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@@ -257,7 +257,7 @@
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-      "file_count": 4528,
+      "file_count": 4530,
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data/website_integrity.json

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@@ -81,7 +81,7 @@
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@@ -2540,44 +2540,44 @@
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@@ -5552,21 +5552,21 @@
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@@ -5977,21 +5977,21 @@
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@@ -6127,21 +6127,21 @@
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@@ -6177,21 +6177,21 @@
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@@ -6331,39 +6331,39 @@
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docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
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@@ -18,7 +18,7 @@
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@@ -43,12 +43,12 @@
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@@ -109,7 +109,7 @@
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-        "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts": 11,
+        "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts": 15,
         "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines": 14,
         "https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results": 6,
         "https://huggingface.co/datasets/ropedia-ai/xperience-10m": 38,

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T16:23:07+00:00",
+  "generated_at_utc": "2026-06-21T16:50:47+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -210,7 +210,7 @@
       "surface": "hf_artifact_bundle",
       "path": "README.md",
       "exists": true,
-      "required_marker_count": 9,
+      "required_marker_count": 11,
       "missing_markers": [],
       "status": "pass"
     },
@@ -257,7 +257,7 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 4528,
+      "file_count": 4530,
       "text_file_count": 1286,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",

docs/data/website_integrity.json

@@ -1,12 +1,12 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T16:07:45+00:00",
+  "generated_at_utc": "2026-06-21T16:42:18+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
     "local_references": 259,
-    "external_reference_count": 159,
+    "external_reference_count": 160,
     "json_files": 55,
     "image_assets_referenced": 28,
     "failure_count": 0
@@ -81,7 +81,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 124064,
-      "evidence_index": 171101
+      "evidence_index": 171265
     },
     {
       "name": "project_status_links_json",
@@ -160,8 +160,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 124064,
-      "protocol_index": 167281,
-      "evidence_index": 171101
+      "protocol_index": 167445,
+      "evidence_index": 171265
     },
     {
       "name": "evaluation_protocol_links_json",

docs/index.html

@@ -4307,7 +4307,7 @@
               <td>140/140 selected-128 scores: 134 direct + 6 compact-proxy.</td>
               <td>Same-split comparison, Qwen3-Omni v6 LoRA diagnostics, Cosmos3-Super/Cosmos3-Nano diagnostics, and scale-up decisions.</td>
               <td>Reading compact-proxy cells as direct raw-target measurements.</td>
-              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
+              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/viewer/selected_128_windows/selected_128">HF selected-128 windows</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
             </tr>
           </tbody>
         </table>

index.html

@@ -4307,7 +4307,7 @@
               <td>140/140 selected-128 scores: 134 direct + 6 compact-proxy.</td>
               <td>Same-split comparison, Qwen3-Omni v6 LoRA diagnostics, Cosmos3-Super/Cosmos3-Nano diagnostics, and scale-up decisions.</td>
               <td>Reading compact-proxy cells as direct raw-target measurements.</td>
-              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
+              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/viewer/selected_128_windows/selected_128">HF selected-128 windows</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
             </tr>
           </tbody>
         </table>

scripts/build_multilingual_public_readmes.py

@@ -169,7 +169,7 @@ The public suite is organized around two evidence lines. Keep them separate when
     </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.</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>

scripts/publish_hf_bundles.py

@@ -96,6 +96,10 @@ ARTIFACT_VIEWER_CONFIG = """configs:
     data_files:
       - split: public_sample
         path: viewer/episode_windows.parquet
+  - config_name: selected_128_windows
+    data_files:
+      - split: selected_128
+        path: viewer/selected128_windows.parquet
 """
 ENHANCEMENT_MARKER = "docs/data/task_suite_enhancement_128.json"
 ENHANCEMENT_CARD_BLOCK = """
@@ -199,8 +203,101 @@ def modality_summary(modality_atlas: dict) -> str:
     return "|".join(names)
 
 
+def parse_multiscale_window_id(window_id: str) -> dict[str, int | str]:
+    prefix = window_id.split(":", 1)[0]
+    parts = prefix.split("_")
+    parsed: dict[str, int | str] = {
+        "window_scale": parts[0] if parts else "",
+        "window_frames": 0,
+        "stride_frames": 0,
+    }
+    for part in parts:
+        if part.endswith("f") and part[:-1].isdigit():
+            parsed["window_frames"] = int(part[:-1])
+        elif part.startswith("stride") and part.removeprefix("stride").isdigit():
+            parsed["stride_frames"] = int(part.removeprefix("stride"))
+    return parsed
+
+
+def selected128_episode_key_to_path(episode_key: str) -> str:
+    if "__" not in episode_key:
+        return episode_key.replace("__", "/")
+    session_id, ep_id = episode_key.split("__", 1)
+    return f"{session_id}/{ep_id}"
+
+
+def write_selected128_viewer_table(artifact_root: Path, viewer_dir: Path) -> None:
+    """Expose selected-128 exported windows as a separate HF dataset config."""
+    windows_path = artifact_root / "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/windows.csv"
+    feature_index_path = artifact_root / "docs/data/xperience10m_128_episode_feature_index.json"
+    feature_index = load_json(feature_index_path)
+    selection_summary = feature_index.get("selection_summary", {})
+    processed_summary = feature_index.get("processed_summary", {})
+    qwen_export = processed_summary.get("qwen_v6_multiscale_export", {})
+    selected_source_episode_count = int(selection_summary.get("selected_episode_count", 128) or 128)
+    expected_rows = int(qwen_export.get("num_samples", 34269) or 34269)
+    expected_window_episode_count = int(qwen_export.get("num_episodes", 119) or 119)
+
+    rows = []
+    with windows_path.open("r", encoding="utf-8", newline="") as handle:
+        for row in csv.DictReader(handle):
+            parsed = parse_multiscale_window_id(row["id"])
+            start_frame = int(row["start_frame"])
+            end_frame = int(row["end_frame"])
+            row_id = row["id"]
+            episode_id = row["episode_id"]
+            rows.append(
+                {
+                    "evidence_line": "selected_128_episodes",
+                    "source_dataset_repo": "ropedia-ai/xperience-10m",
+                    "source_access": "gated_upstream_not_redistributed",
+                    "source_episode_id": episode_id,
+                    "official_episode_path": selected128_episode_key_to_path(episode_id),
+                    "window_id": row_id,
+                    "window_scale": parsed["window_scale"],
+                    "window_frames": parsed["window_frames"],
+                    "stride_frames": parsed["stride_frames"],
+                    "split": row["split"],
+                    "start_frame": start_frame,
+                    "end_frame": end_frame,
+                    "center_frame": (start_frame + end_frame) // 2,
+                    "main_task": row["main_task"],
+                    "selected_source_episode_count": selected_source_episode_count,
+                    "exported_window_episode_count": expected_window_episode_count,
+                    "exported_window_count": expected_rows,
+                    "split_policy": "selected 96/16/16 episode split",
+                    "feature_index": "docs/data/xperience10m_128_episode_feature_index.json",
+                    "source_window_table": windows_path.relative_to(artifact_root).as_posix(),
+                    "raw_data_included": False,
+                }
+            )
+
+    if len(rows) != expected_rows:
+        raise RuntimeError(f"Expected {expected_rows} selected-128 rows, found {len(rows)} in {windows_path}")
+    episode_count = len({row["source_episode_id"] for row in rows})
+    if episode_count != expected_window_episode_count:
+        raise RuntimeError(f"Expected {expected_window_episode_count} exported-window episodes, found {episode_count}")
+    if selected_source_episode_count < episode_count:
+        raise RuntimeError(
+            f"Selected source episode count {selected_source_episode_count} is smaller than exported episode count {episode_count}"
+        )
+
+    jsonl_path = viewer_dir / "selected128_windows.jsonl"
+    jsonl_path.write_text(
+        "\n".join(json.dumps(row, ensure_ascii=True) for row in rows) + "\n",
+        encoding="utf-8",
+    )
+    try:
+        import pandas as pd
+
+        parquet_path = viewer_dir / "selected128_windows.parquet"
+        pd.DataFrame(rows).to_parquet(parquet_path, index=False)
+    except ImportError:
+        print("pandas/pyarrow unavailable; wrote selected-128 JSONL viewer fallback only")
+
+
 def ensure_artifact_dataset_viewer_config(hf_root: Path) -> None:
-    """Expose the public sample episode as HF-viewable window rows."""
+    """Expose public sample and selected-128 windows as separate HF-viewable tables."""
     artifact_root = hf_root / "artifacts"
     readme_path = artifact_root / "README.md"
     viewer_dir = artifact_root / "viewer"
@@ -272,6 +369,7 @@ def ensure_artifact_dataset_viewer_config(hf_root: Path) -> None:
         pd.DataFrame(rows).to_parquet(parquet_path, index=False)
     except ImportError:
         print("pandas/pyarrow unavailable; wrote JSONL viewer fallback only")
+    write_selected128_viewer_table(artifact_root, viewer_dir)
     (viewer_dir / "dataset_viewer_summary.jsonl").unlink(missing_ok=True)
 
     if not readme_path.exists():

scripts/sync_hf_publish_mirrors.py

@@ -90,7 +90,13 @@ XPERIENCE128_ARTIFACT_ROW = (
     "`XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md`, "
     "`docs/data/xperience10m_128_episode_feature_index.json` |"
 )
-XPERIENCE128_CARD_BLOCK = """
+XPERIENCE128_VIEWER_NOTE = """The Hugging Face artifact dataset exposes the 34,269 selected-128 exported
+windows as a separate viewer config, `selected_128_windows`, with split
+`selected_128` at `viewer/selected128_windows.parquet`. The one-sample episode
+viewer remains separate as `episode_sample/public_sample`; do not concatenate
+the two evidence lines when reading scores or dataset rows.
+"""
+XPERIENCE128_CARD_BLOCK = f"""
 ## 128-Episode Source and Feature Index
 
 The selected 128-episode split is linked back to the official gated
@@ -100,6 +106,8 @@ The selected 128-episode split is linked back to the official gated
 carry only public-safe processed artifacts: selection files, inspected
 manifests, dense multiscale window rows, metadata feature matrices, and result
 summaries.
+
+{XPERIENCE128_VIEWER_NOTE.rstrip()}
 """
 LANGUAGE_VERSIONS_MARKER = "docs/data/language_versions.json"
 LANGUAGE_VERSIONS_ROW = (
@@ -399,18 +407,26 @@ def ensure_xperience128_card_links(hf_root: Path, *, dry_run: bool) -> list[str]
         return updated
     text = artifacts_readme.read_text(encoding="utf-8")
     original = text
+    while text.count(XPERIENCE128_VIEWER_NOTE) > 1:
+        text = text.replace("\n" + XPERIENCE128_VIEWER_NOTE, "", 1)
     if XPERIENCE128_ARTIFACT_ROW not in text:
         table_anchor = "| Reader goal | Artifact |\n| --- | --- |\n"
         if table_anchor in text:
             text = text.replace(table_anchor, table_anchor + XPERIENCE128_ARTIFACT_ROW + "\n", 1)
         else:
             text = text.rstrip() + "\n\n" + XPERIENCE128_ARTIFACT_ROW + "\n"
-    if XPERIENCE128_MARKER not in text:
+    if "## 128-Episode Source and Feature Index" not in text:
         insert_before = "\n## Dataset Boundary"
         if insert_before in text:
             text = text.replace(insert_before, XPERIENCE128_CARD_BLOCK + insert_before, 1)
         else:
             text = text.rstrip() + "\n" + XPERIENCE128_CARD_BLOCK
+    elif "do not concatenate" not in text:
+        insert_before = "\n## Dataset Boundary"
+        if insert_before in text:
+            text = text.replace(insert_before, "\n" + XPERIENCE128_VIEWER_NOTE + insert_before, 1)
+        else:
+            text = text.rstrip() + "\n" + XPERIENCE128_VIEWER_NOTE
     if text != original:
         updated.append("artifacts/README.md")
         if not dry_run:

scripts/validate_publication_package.py

@@ -109,6 +109,8 @@ CARD_FRESHNESS_EXPECTATIONS = [
             "xperience10m-logo-social-card.png",
             "derived artifacts",
             "Raw Xperience-10M videos",
+            "selected_128_windows",
+            "viewer/selected128_windows.parquet",
             "Qwen3-Omni",
         ],
     },

viewer/selected128_windows.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a76b266e5ffc3de2cb0824762e4a6b84e398c59445b6c430387d85066c04e63
+size 31784559

viewer/selected128_windows.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60efd53743a10376f7583056b7c433424fb5074db8d41473197a85ee24dea2ae
+size 361234