Publish Ropedia Xperience-10M task baseline cards

README.md

@@ -18,7 +18,7 @@ metrics:
   - mean-reciprocal-rank
   - mean-squared-error
 model-index:
-  - name: Xperience-10M Minimal and Neural Task Baselines
+  - name: Ropedia Xperience-10M Task Baselines
     results:
       - task:
           type: robotics
@@ -55,7 +55,7 @@ model-index:
             name: neural MLP F1
 ---
 
-# Xperience-10M Minimal and Neural Task Baselines
+# Ropedia Xperience-10M Task Baselines
 
 This repo stores the minimal baseline weights, neural MLP task-head checkpoints, and metrics for the 12-task Xperience-10M episode suite. It is meant to be read like a model audit, not advertised as a robot foundation model.
 
@@ -113,19 +113,19 @@ transfers them to H20 for manifest building, training, and evaluation.
 
 The companion artifact dataset repo stores CSV/JSON predictions and dashboard assets:
 
-https://huggingface.co/datasets/cy0307/ropedia-episode-task-suite-artifacts
+https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts
 
 The public visual dashboard is here:
 
-https://huggingface.co/spaces/cy0307/ropedia-episode-task-suite
+https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite
 
 Direct static app:
 
-https://cy0307-ropedia-episode-task-suite.static.hf.space/
+https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/
 
 The full Hugging Face collection is here:
 
-https://huggingface.co/collections/cy0307/ropedia-episode-task-suite
+https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite
 
 ## Minimal and Neural Architecture
 
@@ -174,8 +174,8 @@ This repo does not redistribute raw Xperience-10M videos or raw `annotation.hdf5
 
 GitHub:
 
-https://github.com/ChaoYue0307/ropedia-episode-task-suite
+https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite
 
 GitHub Pages:
 
-https://chaoyue0307.github.io/ropedia-episode-task-suite/
+https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/

notes/reproducibility_audit.md

@@ -2,7 +2,7 @@
 
 Audit date: 2026-05-30 Asia/Singapore.
 
-Purpose: verify that the committed Xperience-10M Episode Task Suite artifacts are
+Purpose: verify that the committed Ropedia Xperience-10M Task Suite artifacts are
 real outputs from the scripts, not placeholder or fabricated metrics.
 
 ## Raw Inputs Checked

scripts/generate_visualizations.py

@@ -24,6 +24,22 @@ DOCS = ROOT / "docs"
 ASSETS = DOCS / "assets"
 CHARTS = ASSETS / "charts"
 
+OMNI_RELAY = {
+    "status": "pending_huggingface_gated_access",
+    "dataset": "ropedia-ai/xperience-10m",
+    "relay_server": "ANGEL-A100-80Gx4",
+    "training_server": "ANGEL-H20-96GX8",
+    "selection_strategy": "stratified_round_robin_by_top_level_session",
+    "target_episodes": 32,
+    "selected_sessions": 32,
+    "candidate_scan_top_level_sessions": 64,
+    "valid_candidates": 680,
+    "estimated_bytes": 72031620552,
+    "exclude": ["visualization.rrd"],
+    "blocker": "Hugging Face returns 403 pending review for the full Xperience-10M gated dataset.",
+    "claim_boundary": "No real 32-episode fine-tune is claimed until the watcher downloads data, transfers it to H20, and the held-out evaluation runs.",
+}
+
 
 def read_json(path: Path) -> dict:
     return json.loads(path.read_text(encoding="utf-8"))
@@ -100,7 +116,7 @@ def svg_pipeline_diagram(path: Path, summary: dict) -> None:
             "numpy softmax classifier",
             "metrics and predictions",
         ], "#1f63e9"),
-        (520, 380, 360, 168, "6. Episode task suite", [
+        (520, 380, 360, 168, "6. Ropedia Xperience-10M suite", [
             f"{task_count} supervised/self-supervised tasks",
             "chronological split",
             "retrieval, forecast, alignment",
@@ -117,7 +133,7 @@ def svg_pipeline_diagram(path: Path, summary: dict) -> None:
         f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
         '<rect width="100%" height="100%" fill="#ffffff"/>',
         '<rect x="0" y="0" width="1400" height="760" fill="#ffffff"/>',
-        '<text x="60" y="58" font-family="Arial, sans-serif" font-size="32" font-weight="700" fill="#10141f">Verified Xperience-10M Episode Pipeline</text>',
+        '<text x="60" y="58" font-family="Arial, sans-serif" font-size="32" font-weight="700" fill="#10141f">Verified Ropedia Xperience-10M Pipeline</text>',
         '<text x="60" y="88" font-family="Arial, sans-serif" font-size="16" fill="#5b6475">Generated from committed scripts and metrics; no conceptual placeholder stages.</text>',
     ]
     arrows = [
@@ -333,7 +349,7 @@ def svg_task_architectures(path: Path, summary: dict) -> None:
         f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
         '<defs><marker id="arrow2" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path d="M 0 0 L 10 5 L 0 10 z" fill="#cbd5e1"/></marker></defs>',
         '<rect width="100%" height="100%" fill="#ffffff"/>',
-        '<text x="60" y="56" font-family="Arial, sans-serif" font-size="34" font-weight="700" fill="#10141f">Minimal Architectures for the 12 Xperience-10M Episode Tasks</text>',
+        '<text x="60" y="56" font-family="Arial, sans-serif" font-size="34" font-weight="700" fill="#10141f">Minimal Architectures for 12 Ropedia Xperience-10M Tasks</text>',
         '<text x="60" y="88" font-family="Arial, sans-serif" font-size="16" fill="#5b6475">Generated from scripts/episode_task_suite.py semantics and committed summary metrics. These are minimal baselines, not deep foundation models.</text>',
     ]
 
@@ -419,6 +435,7 @@ def collect_summary() -> dict:
     suite = read_json(RESULTS / "episode_task_suite/summary_report.json")
     manifest = read_json(RESULTS / "episode_task_suite/feature_manifest.json")
     return {
+        "omni_relay": OMNI_RELAY,
         "models": {
             "motion_action": min_action,
             "motion_subtask": min_subtask,
@@ -453,13 +470,13 @@ def generate_charts(summary: dict) -> None:
     task_rows = []
     for task_name, metrics in suite.items():
         task_rows.append((task_name, task_score(metrics)))
-    svg_bar_chart(CHARTS / "episode_task_scores.svg", "Episode Task Suite: Main Scores", task_rows, max_value=1.0)
+    svg_bar_chart(CHARTS / "episode_task_scores.svg", "Ropedia Xperience-10M Suite: Main Scores", task_rows, max_value=1.0)
 
     neural = summary["suite"].get("neural_tasks", {})
     if neural:
         neural_rows = [(task_name, task_score(metrics)) for task_name, metrics in neural.items() if "error" not in metrics]
         if neural_rows:
-            svg_bar_chart(CHARTS / "episode_task_scores_neural_mlp.svg", "Episode Task Suite: Neural MLP Main Scores", neural_rows, max_value=1.0)
+            svg_bar_chart(CHARTS / "episode_task_scores_neural_mlp.svg", "Ropedia Xperience-10M Suite: Neural MLP Main Scores", neural_rows, max_value=1.0)
 
         comparison_rows = []
         for task_name, metrics in suite.items():
@@ -484,7 +501,7 @@ def generate_charts(summary: dict) -> None:
 def write_summary_data(summary: dict) -> None:
     DOCS.mkdir(parents=True, exist_ok=True)
     (DOCS / "data").mkdir(parents=True, exist_ok=True)
-    (DOCS / "data/summary_metrics.json").write_text(json.dumps(summary, indent=2), encoding="utf-8")
+    (DOCS / "data/summary_metrics.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
 
 
 def main() -> int:

scripts/omni/run_omni_finetune_8gpu.sh

@@ -0,0 +1,138 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+WORKSPACE="${WORKSPACE:-/home/cy/Ropedia/ropedia-xperience-10m-task-suite}"
+PROJECT_ROOT="${PROJECT_ROOT:-/home/cy/Ropedia}"
+VENV_PY="${VENV_PY:-$WORKSPACE/.venv/bin/python}"
+RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_32ep}"
+DATA_ROOT="${DATA_ROOT:-$PROJECT_ROOT/modelscope_data}"
+MAX_EPISODES="${MAX_EPISODES:-32}"
+MAX_WINDOWS_PER_EPISODE="${MAX_WINDOWS_PER_EPISODE:-128}"
+MAX_VIDEO_FRAMES="${MAX_VIDEO_FRAMES:-16}"
+EPOCHS="${EPOCHS:-1}"
+TRAIN_SPLIT="${TRAIN_SPLIT:-train}"
+VAL_SPLIT="${VAL_SPLIT:-val}"
+EVAL_SPLIT="${EVAL_SPLIT:-test}"
+MODEL_ID="${MODEL_ID:-Qwen/Qwen3-Omni-30B-A3B-Instruct}"
+LOCAL_MODEL_DIR="${LOCAL_MODEL_DIR:-$PROJECT_ROOT/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct}"
+
+RESULT_DIR="$WORKSPACE/results/omni_finetune/$RUN_ID"
+DATASET_RUN_ID="${RUN_ID}_dataset"
+DATASET_DIR="$WORKSPACE/results/omni_finetune/$DATASET_RUN_ID"
+MANIFEST="$RESULT_DIR/episode_manifest.json"
+LOG_DIR="$RESULT_DIR/logs"
+mkdir -p "$LOG_DIR" "$LOCAL_MODEL_DIR"
+
+exec > >(tee -a "$LOG_DIR/pipeline.log") 2>&1
+
+cd "$WORKSPACE"
+
+phase() {
+  echo "PHASE: $1"
+  "$VENV_PY" - <<PY
+import json, time
+path = "$RESULT_DIR/pipeline_status.jsonl"
+with open(path, "a", encoding="utf-8") as fp:
+    fp.write(json.dumps({"event": "phase", "phase": "$1", "timestamp": time.time()}) + "\\n")
+PY
+}
+
+phase "preflight"
+nvidia-smi --query-gpu=index,name,memory.total,memory.used,utilization.gpu --format=csv,noheader,nounits
+"$VENV_PY" - <<'PY'
+mods = ["torch", "transformers", "accelerate", "peft", "qwen_omni_utils", "soundfile", "librosa", "imageio_ffmpeg", "modelscope"]
+for mod in mods:
+    __import__(mod)
+    print(f"{mod}: ok")
+PY
+
+phase "download_qwen3_omni_instruct"
+if ! compgen -G "$LOCAL_MODEL_DIR/*.safetensors" > /dev/null && ! compgen -G "$LOCAL_MODEL_DIR/*.bin" > /dev/null; then
+  if command -v modelscope >/dev/null 2>&1; then
+    modelscope download --model "$MODEL_ID" --local_dir "$LOCAL_MODEL_DIR"
+  else
+    "$VENV_PY" -m modelscope download --model "$MODEL_ID" --local_dir "$LOCAL_MODEL_DIR"
+  fi
+else
+  echo "Model weights already present in $LOCAL_MODEL_DIR"
+fi
+
+phase "build_manifest"
+"$VENV_PY" scripts/omni/build_episode_manifest.py \
+  --data-root "$DATA_ROOT" \
+  --max-episodes "$MAX_EPISODES" \
+  --train-fraction 0.8 \
+  --val-fraction 0.0 \
+  --test-fraction 0.2 \
+  --output "$MANIFEST"
+
+EVAL_SPLIT="$("$VENV_PY" - <<PY
+import json
+payload = json.load(open("$MANIFEST", "r", encoding="utf-8"))
+counts = payload.get("summary", {}).get("split_counts", {})
+requested = "$EVAL_SPLIT"
+if counts.get(requested, 0):
+    print(requested)
+elif counts.get("test", 0):
+    print("test")
+elif counts.get("val", 0):
+    print("val")
+else:
+    print("train")
+PY
+)"
+echo "Using eval split: $EVAL_SPLIT"
+
+phase "export_dataset"
+"$VENV_PY" scripts/omni/export_qwen3_omni_action_dataset.py \
+  --manifest "$MANIFEST" \
+  --run-id "$DATASET_RUN_ID" \
+  --max-windows-per-episode "$MAX_WINDOWS_PER_EPISODE" \
+  --max-video-frames "$MAX_VIDEO_FRAMES"
+
+DATASET_JSONL="$DATASET_DIR/dataset.jsonl"
+
+phase "qwen_zero_shot_smoke"
+"$VENV_PY" scripts/omni/qwen3_omni_inference_smoke.py \
+  --dataset-jsonl "$DATASET_JSONL" \
+  --model-id "$LOCAL_MODEL_DIR" \
+  --split "$EVAL_SPLIT" \
+  --sample-limit 3 \
+  --run-id "${RUN_ID}_zero_shot" \
+  --local-files-only || true
+
+phase "train_8gpu_lora"
+CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}" \
+"$VENV_PY" -m accelerate.commands.launch \
+  --num_processes 8 \
+  --mixed_precision bf16 \
+  scripts/omni/train_qwen3_omni_lora.py \
+  --dataset-jsonl "$DATASET_JSONL" \
+  --model-id "$LOCAL_MODEL_DIR" \
+  --run-id "${RUN_ID}_lora" \
+  --train-split "$TRAIN_SPLIT" \
+  --val-split "$VAL_SPLIT" \
+  --epochs "$EPOCHS" \
+  --batch-size 1 \
+  --gradient-accumulation-steps 8 \
+  --max-train-samples 0 \
+  --max-val-samples 64 \
+  --local-files-only
+
+phase "eval_lora"
+"$VENV_PY" scripts/omni/eval_qwen3_omni_lora.py \
+  --dataset-jsonl "$DATASET_JSONL" \
+  --model-id "$LOCAL_MODEL_DIR" \
+  --adapter-dir "$WORKSPACE/checkpoints/${RUN_ID}_lora/adapter_lora" \
+  --run-id "${RUN_ID}_eval" \
+  --eval-split "$EVAL_SPLIT" \
+  --local-files-only
+
+phase "runbook"
+"$VENV_PY" scripts/omni/omni_finetune_runbook.py \
+  --run-id "$RUN_ID" \
+  --manifest "$MANIFEST" \
+  --metric-file "$WORKSPACE/results/omni_finetune/${RUN_ID}_eval/metrics.json" || true
+
+phase "complete"
+echo "DONE: $RUN_ID"

scripts/omni/transfer_xperience10m_a100_to_h20.sh

@@ -13,4 +13,4 @@ rsync -avP --partial --append-verify \
   "${H20_HOST}:${H20_DATA_ROOT}"
 
 ssh -i "${SSH_KEY}" -o BatchMode=yes -o StrictHostKeyChecking=accept-new "${H20_HOST}" \
-  "cd /home/cy/Ropedia/ropedia-episode-task-suite && python3 scripts/omni/discover_xperience10m_sources.py --workspace /home/cy/Ropedia/ropedia-episode-task-suite --data-root /home/cy/Ropedia/modelscope_data --output results/omni_finetune/source_discovery.json --report-output results/omni_finetune/DATA_BLOCKER_REPORT.md"
+  "cd /home/cy/Ropedia/ropedia-xperience-10m-task-suite && python3 scripts/omni/discover_xperience10m_sources.py --workspace /home/cy/Ropedia/ropedia-xperience-10m-task-suite --data-root /home/cy/Ropedia/modelscope_data --output results/omni_finetune/source_discovery.json --report-output results/omni_finetune/DATA_BLOCKER_REPORT.md"

scripts/render_overview_figures.py

@@ -94,6 +94,7 @@ def arrow() -> str:
 def build_pipeline_html(summary: dict, base_path: Path) -> str:
     suite = summary["suite"]
     task_count = len(suite["tasks"])
+    neural_count = len(suite.get("neural_tasks", {}))
     stage_rows = [
         [
             stage_card(
@@ -132,21 +133,21 @@ def build_pipeline_html(summary: dict, base_path: Path) -> str:
             stage_card(
                 "05",
                 "Baseline models",
-                ["motion-only classifiers", "current all-feature classifiers", "stored weights + predictions"],
+                ["motion-only classifiers", "current all-feature classifiers", "neural MLP task heads"],
                 COLORS["blue"],
             ),
             arrow(),
             stage_card(
                 "06",
-                "Episode task suite",
-                [f"{task_count} task contracts", "forecast, retrieval, alignment", "chronological evaluation"],
+                "Ropedia Xperience-10M suite",
+                [f"{task_count} minimal + {neural_count} neural results", "forecast, retrieval, alignment", "chronological evaluation"],
                 COLORS["teal"],
             ),
             arrow(),
             stage_card(
                 "07",
                 "Published artifacts",
-                ["metrics.json / csv / npz", "GitHub Pages dashboard", "HF Space + dataset + model card"],
+                ["metrics.json / csv / npz / pt", "GitHub Pages dashboard", "NN comparison charts"],
                 COLORS["green"],
             ),
         ],
@@ -156,6 +157,7 @@ def build_pipeline_html(summary: dict, base_path: Path) -> str:
         "Audit check: rerunning scripts to /private/tmp reproduced the committed metrics exactly.",
         "Modality check: sample covers video, AAC audio, depth, pose/SLAM, mocap, IMU, and language annotation.",
         "Feature check: current baseline manifest has video/depth/pose/mocap/IMU/language blocks, but no audio feature block.",
+        "Neural check: lightweight PyTorch MLP heads are reported beside the minimal task heads under neural_mlp/.",
         "Scope check: this validates one public sample episode, not cross-episode generalization.",
     ]
     checks_html = "".join(f"<li>{esc(line)}</li>" for line in checks)
@@ -356,14 +358,14 @@ def build_pipeline_html(summary: dict, base_path: Path) -> str:
       <header>
         <div>
           <div class="kicker">verified single-episode pipeline</div>
-          <h1>From Xperience-10M episode to reproducible artifacts</h1>
-          <p class="subtitle">The figure follows the actual code path and separates the full Xperience-10M sample modalities from the current baseline feature manifest.</p>
+          <h1>From Ropedia Xperience-10M episode to reproducible artifacts</h1>
+          <p class="subtitle">The figure follows the actual code path and includes minimal heads plus neural MLP results. Next TODO: Qwen3-Omni fine-tuning and sensor-bridge evaluation on multi-episode splits.</p>
         </div>
         <div class="metrics">
           <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}</strong><span>tasks</span></div>
+          <div class="metric"><strong>{task_count}+{neural_count}</strong><span>min + NN tasks</span></div>
         </div>
       </header>
       {rows_html}
@@ -404,6 +406,7 @@ 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", {}))
     rows_by_task = {row["task"]: row for row in task_architecture_rows(summary)}
     group_html = []
     for title, color, task_names in TASK_GROUPS:
@@ -421,10 +424,10 @@ def build_architecture_html(summary: dict, base_path: Path) -> str:
         )
 
     family_cards = [
-        ("Linear softmax", "Class-weighted CE + L2 for action, subtask, next-action, transition, contact, order, and alignment classifiers.", COLORS["blue"]),
-        ("Ridge regression", "Closed-form dual ridge for hand trajectory forecasting and modality reconstruction.", COLORS["green"]),
-        ("Ridge + cosine rank", "Project sensor features into text or visual space, then rank candidate windows by cosine similarity.", COLORS["teal"]),
-        ("Multi-label logistic", "One-vs-rest sigmoid heads over the object vocabulary with top-1 fallback.", COLORS["orange"]),
+        ("Linear softmax", "Minimal classifier for action, subtask, transition, contact, order, and alignment tasks.", COLORS["blue"]),
+        ("Ridge regression", "Minimal closed-form projection for forecasting, reconstruction, and retrieval spaces.", COLORS["green"]),
+        ("Multi-label logistic", "Minimal one-vs-rest sigmoid heads over the object vocabulary with top-1 fallback.", COLORS["orange"]),
+        ("Neural MLP", "Optional PyTorch nonlinear classifier/regressor over the same features, splits, and metrics.", COLORS["red"]),
     ]
     families = "".join(
         f"""
@@ -693,17 +696,17 @@ def build_architecture_html(summary: dict, base_path: Path) -> str:
     <div class="content">
       <header>
         <div>
-          <div class="kicker">minimal verified model architectures</div>
-          <h1>12 Xperience-10M episode tasks, four reusable heads</h1>
-          <p class="subtitle">Each task uses the same aligned episode-window contract, then swaps only the minimal output head needed for labels, forecasting, grounding, reconstruction, or temporal diagnostics.</p>
+          <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 TODO is Qwen3-Omni fine-tuning with sensor-bridge evaluation.</p>
         </div>
-        <div class="summary-pill"><strong>{len(suite['tasks'])}</strong><span>end-to-end tasks</span></div>
+        <div class="summary-pill"><strong>{len(suite['tasks'])}+{neural_count}</strong><span>min + NN tasks</span></div>
       </header>
       <section class="shared">
         <article><h2>Shared windows</h2><p>{suite['num_frames']:,} frames to {suite['num_windows']:,} windows over video, depth, pose, mocap, inertial, and language features.</p></article>
         <article><h2>Feature vector</h2><p>X_all is {suite['feature_dim']:,} dimensions with 17 named blocks; sample audio is documented but not featurized here.</p></article>
-        <article><h2>Reusable heads</h2><p>Softmax, ridge, ridge ranking, and multi-label logistic heads cover the whole suite.</p></article>
-        <article><h2>Artifacts</h2><p>Metrics, predictions, models, manifests, and the source summary report are committed.</p></article>
+        <article><h2>Reusable heads</h2><p>Minimal softmax/ridge/logistic heads plus optional PyTorch MLP heads cover the whole suite.</p></article>
+        <article><h2>Artifacts</h2><p>Metrics, predictions, model weights, neural checkpoints, manifests, and the source summary report are committed.</p></article>
       </section>
       <section class="families">{families}</section>
       <section class="task-groups">{"".join(group_html)}</section>

scripts/render_task_suite_infographic.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Render a polished 12-task Xperience-10M episode-suite infographic.
+Render a polished Ropedia Xperience-10M 12-task infographic.
 
 The task names, inputs, and metrics are read from
 results/episode_task_suite/summary_report.json. The output is a deterministic
@@ -470,8 +470,17 @@ def short_io(task_name: str, metrics: dict) -> str:
     return custom.get(task_name, metrics.get("input", ""))
 
 
-def task_card(task_name: str, kind: str, metrics: dict, group: dict, index: int) -> 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:
+        neural_label, neural_value = metric_for(task_name, neural_metrics)
+        neural_html = f"""
+        <div class="metric neural">
+          <span>NN {html.escape(neural_label)}</span>
+          <strong>{html.escape(neural_value)}</strong>
+        </div>
+        """
     io = short_io(task_name, metrics)
     return f"""
       <article class="task-card" style="--accent:{group['color']};--soft:{group['soft']};">
@@ -482,9 +491,10 @@ def task_card(task_name: str, kind: str, metrics: dict, group: dict, index: int)
         <h3>{html.escape(task_name)}</h3>
         <p>{html.escape(io)}</p>
         <div class="metric">
-          <span>{html.escape(label)}</span>
+          <span>min {html.escape(label)}</span>
           <strong>{html.escape(value)}</strong>
         </div>
+        {neural_html}
       </article>
     """
 
@@ -506,6 +516,7 @@ def modality_card(name: str, line_one: str, line_two: str, index: int, thumbnail
 
 def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None) -> str:
     suite = summary["tasks"]
+    neural_suite = summary.get("neural_tasks", {})
     thumbnails = load_sample_thumbnails(sample_dir)
     base_layer = ""
     if base_image is not None and base_image.exists():
@@ -514,7 +525,7 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
         (f"{summary['num_frames']:,}", "frames"),
         (f"{summary['num_windows']:,}", "windows"),
         (f"{summary['feature_dim']:,}", "features"),
-        (f"{len(suite)}", "tasks"),
+        (f"{len(suite)}+{len(neural_suite)}", "min + NN tasks"),
         ("70/30", "chronological split"),
     ]
     stats_html = "".join(
@@ -531,7 +542,7 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
     for group in GROUPS:
         cards = []
         for task_name, kind in group["tasks"]:
-            cards.append(task_card(task_name, kind, suite[task_name], group, task_index))
+            cards.append(task_card(task_name, kind, suite[task_name], group, task_index, neural_suite.get(task_name)))
             task_index += 1
         families.append(
             f"""
@@ -852,13 +863,18 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
       display: inline-flex;
       align-items: baseline;
       gap: 10px;
-      margin-top: 14px;
+      margin-top: 10px;
       min-height: 32px;
       padding: 7px 10px;
       border-radius: 8px;
       border: 1px solid color-mix(in srgb, var(--accent) 32%, #ffffff);
       background: rgba(255,255,255,0.82);
     }}
+    .metric.neural {{
+      margin-left: 8px;
+      border-color: rgba(31,36,33,0.18);
+      background: rgba(245,241,233,0.82);
+    }}
     .metric span {{
       color: #64748b;
       font-size: 13px;
@@ -897,14 +913,14 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
   </style>
 </head>
 <body>
-  <main class="canvas" aria-label="Xperience-10M 12-task episode suite infographic">
+  <main class="canvas" aria-label="Ropedia Xperience-10M 12-task suite infographic">
     {base_layer}
     <div class="content">
     <header class="header">
       <div>
         <div class="kicker">verified single-episode task suite</div>
-        <h1>Xperience-10M 12-task episode suite</h1>
-        <p class="subtitle">A clean map from synchronized multimodal windows to 12 auditable task heads, with metrics loaded from the committed summary report.</p>
+        <h1>Ropedia Xperience-10M 12-task suite</h1>
+        <p class="subtitle">A clean map from synchronized multimodal windows to 12 auditable task heads, comparing minimal heads with neural MLP results. Next TODO: Qwen3-Omni fine-tuning plus sensor-bridge evaluation.</p>
       </div>
       <div class="stats">{stats_html}</div>
     </header>
@@ -922,7 +938,7 @@ def build_html(summary: dict, base_image: Path | None, sample_dir: Path | None)
       <div class="arrow">-></div>
       <div class="step"><strong>8,378-d vector</strong><span>current manifest excludes audio features</span></div>
       <div class="arrow">-></div>
-      <div class="step"><strong>12 minimal heads</strong><span>softmax, ridge, logistic</span></div>
+      <div class="step"><strong>12 minimal + NN heads</strong><span>softmax/ridge/logistic plus PyTorch MLP</span></div>
     </section>
 
     <section class="families">{''.join(families)}</section>