video_backbone剖析

scripts/evaluation/analyze_backbone_blocks.py

@@ -0,0 +1,148 @@
+import argparse
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+MID_STEP_MIN = 15
+MID_STEP_MAX = 35
+TAIL_STEP_MIN = 40
+TAIL_STEP_MAX = 49
+
+
+def describe(series: pd.Series) -> dict[str, float]:
+    numeric = pd.to_numeric(series, errors='coerce').dropna()
+    if numeric.empty:
+        return {'count': 0.0, 'mean': np.nan, 'median': np.nan, 'p90': np.nan}
+    return {
+        'count': float(numeric.count()),
+        'mean': float(numeric.mean()),
+        'median': float(numeric.median()),
+        'p90': float(numeric.quantile(0.90)),
+    }
+
+
+def make_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Aggregate backbone block profiling results.")
+    parser.add_argument("--input_csv",
+                        type=str,
+                        required=True,
+                        help="Path to backbone_block_log.csv")
+    parser.add_argument("--output_dir",
+                        type=str,
+                        default=None,
+                        help="Directory to store summaries; defaults to input parent.")
+    return parser
+
+
+def main() -> None:
+    args = make_parser().parse_args()
+    input_path = Path(args.input_csv)
+    if not input_path.exists():
+        raise FileNotFoundError(f"Missing file: {input_path}")
+
+    output_dir = Path(args.output_dir) if args.output_dir else input_path.parent / "analysis"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    df = pd.read_csv(input_path)
+    numeric_cols = [
+        'forward_time_ms',
+        'l2_delta_vs_prev',
+        'rel_l2_delta_vs_prev',
+        'cosine_vs_prev',
+        'l2_delta_vs_full50',
+        'cosine_vs_full50',
+    ]
+    for col in numeric_cols:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+
+    step_aggregate = df.groupby(
+        ['block_stage', 'block_name', 'block_index', 'step'])[numeric_cols].mean().reset_index()
+    step_aggregate.to_csv(output_dir / 'block_step_aggregate.csv', index=False)
+
+    block_summary = df.groupby(['block_stage', 'block_name', 'block_index']).agg(
+        mean_forward_time_ms=('forward_time_ms', 'mean'),
+        mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+        mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+        mean_l2_delta_vs_full50=('l2_delta_vs_full50', 'mean'),
+        mean_cosine_vs_full50=('cosine_vs_full50', 'mean'),
+    ).reset_index()
+
+    mid_df = df[(df['step'] >= MID_STEP_MIN) & (df['step'] <= MID_STEP_MAX)]
+    tail_df = df[(df['step'] >= TAIL_STEP_MIN) & (df['step'] <= TAIL_STEP_MAX)]
+    block_summary = block_summary.merge(
+        mid_df.groupby(['block_stage', 'block_name', 'block_index']).agg(
+            mid_mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+            mid_mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+            mid_mean_forward_time_ms=('forward_time_ms', 'mean'),
+        ).reset_index(),
+        on=['block_stage', 'block_name', 'block_index'],
+        how='left',
+    )
+    block_summary = block_summary.merge(
+        tail_df.groupby(['block_stage', 'block_name', 'block_index']).agg(
+            tail_mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+            tail_mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+            tail_mean_forward_time_ms=('forward_time_ms', 'mean'),
+        ).reset_index(),
+        on=['block_stage', 'block_name', 'block_index'],
+        how='left',
+    )
+    block_summary = block_summary.sort_values(
+        ['tail_mean_rel_l2_delta_vs_prev', 'mean_forward_time_ms'],
+        ascending=[True, False])
+    block_summary.to_csv(output_dir / 'block_summary.csv', index=False)
+
+    stage_summary = []
+    for stage_name, stage_df in [('all', df), ('mid', mid_df), ('tail', tail_df)]:
+        grouped = stage_df.groupby('block_stage').agg(
+            mean_forward_time_ms=('forward_time_ms', 'mean'),
+            mean_rel_l2_delta_vs_prev=('rel_l2_delta_vs_prev', 'mean'),
+            mean_cosine_vs_prev=('cosine_vs_prev', 'mean'),
+            mean_l2_delta_vs_full50=('l2_delta_vs_full50', 'mean'),
+            mean_cosine_vs_full50=('cosine_vs_full50', 'mean'),
+        ).reset_index()
+        grouped.insert(0, 'window', stage_name)
+        stage_summary.append(grouped)
+    stage_summary_df = pd.concat(stage_summary, ignore_index=True)
+    stage_summary_df.to_csv(output_dir / 'stage_summary.csv', index=False)
+
+    best_cache_candidates = block_summary.sort_values(
+        ['tail_mean_rel_l2_delta_vs_prev', 'mean_forward_time_ms'],
+        ascending=[True, False]).head(10)
+
+    lines = [
+        "# Backbone Block Profiling Report",
+        "",
+        "## Dataset Overview",
+        "",
+        f"- Rows: {len(df)}",
+        f"- Blocks: {df['block_name'].nunique()}",
+        f"- Steps: {int(df['step'].min())}-{int(df['step'].max())}",
+        f"- Pass types: {', '.join(sorted(df['pass_type'].dropna().unique()))}",
+        "",
+        "## Block Timing",
+        "",
+        f"- `forward_time_ms`: mean={describe(df['forward_time_ms'])['mean']:.4f}, median={describe(df['forward_time_ms'])['median']:.4f}, p90={describe(df['forward_time_ms'])['p90']:.4f}",
+        "",
+        "## Stability",
+        "",
+        f"- `rel_l2_delta_vs_prev`: mean={describe(df['rel_l2_delta_vs_prev'])['mean']:.6f}, median={describe(df['rel_l2_delta_vs_prev'])['median']:.6f}, p90={describe(df['rel_l2_delta_vs_prev'])['p90']:.6f}",
+        f"- `cosine_vs_prev`: mean={describe(df['cosine_vs_prev'])['mean']:.6f}, median={describe(df['cosine_vs_prev'])['median']:.6f}, p90={describe(df['cosine_vs_prev'])['p90']:.6f}",
+        "",
+        "## Top Cache Candidates",
+        "",
+    ]
+    for _, row in best_cache_candidates.iterrows():
+        lines.append(
+            f"- `{row['block_name']}` ({row['block_stage']}): tail_rel_l2={row['tail_mean_rel_l2_delta_vs_prev']:.6f}, mean_forward_time_ms={row['mean_forward_time_ms']:.4f}"
+        )
+
+    (output_dir / 'backbone_profile_report.md').write_text("\n".join(lines),
+                                                           encoding='utf-8')
+
+
+if __name__ == "__main__":
+    main()

scripts/evaluation/simulate_sparse_head_execution.py

@@ -0,0 +1,380 @@
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+DEFAULT_SCHEMES = {
+    "dense": list(range(50)),
+    "sparse_10": list(range(0, 50, 5)),
+    "sparse_5": list(range(0, 50, 10)),
+    "tail_heavy_10": [0, 5, 10, 20, 30, 38, 43, 46, 48, 49],
+    "tail_only_6": [40, 43, 46, 47, 48, 49],
+    "sparse_8": [0, 7, 14, 21, 28, 35, 42, 49],
+    "sparse_4": [0, 16, 32, 49],
+    "tail_only_4": [40, 43, 46, 49],
+    "tail_heavy_6": [0, 32, 40, 44, 47, 49],
+    "tail_heavy_8": [0, 10, 20, 30, 38, 43, 46, 49],
+}
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Offline sparse head execution simulation from existing DDIM logs."
+    )
+    parser.add_argument(
+        "--root_dir",
+        type=str,
+        default=".",
+        help="Repository root or directory under which case outputs are stored.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default=None,
+        help="Directory to write sparse-head simulation outputs. Defaults to <root_dir>/sparse_head_simulation.",
+    )
+    parser.add_argument(
+        "--target_threshold",
+        type=float,
+        default=0.95,
+        help="Target cosine threshold used by experiment 1.2.",
+    )
+    return parser.parse_args()
+
+
+def discover_stepwise_logs(root_dir: Path) -> list[Path]:
+    return sorted(root_dir.glob("unitree_*/case*/output/inference/stepwise_log.csv"))
+
+
+def load_stepwise_table(stepwise_paths: list[Path]) -> pd.DataFrame:
+    frames = []
+    for path in stepwise_paths:
+        frame = pd.read_csv(path)
+        frame["dataset"] = path.parts[-5]
+        frame["case"] = path.parts[-4]
+        frames.append(frame)
+    if not frames:
+        raise FileNotFoundError("No stepwise_log.csv files found.")
+    stepwise_df = pd.concat(frames, ignore_index=True)
+    for column in [
+            "step",
+            "step_time_s",
+            "latent_delta",
+            "action_delta",
+            "state_delta",
+            "action_cosine_vs_full50",
+            "state_cosine_vs_full50",
+            "latent_l2_vs_full50",
+    ]:
+        stepwise_df[column] = pd.to_numeric(stepwise_df[column], errors="coerce")
+    return stepwise_df
+
+
+def simulate_schemes(stepwise_df: pd.DataFrame,
+                     schemes: dict[str, list[int]]) -> pd.DataFrame:
+    rows = []
+    group_columns = [
+        "dataset",
+        "case",
+        "sample_id",
+        "scene",
+        "pass_type",
+        "round_id",
+    ]
+    grouped = stepwise_df.groupby(group_columns)
+    for keys, group in grouped:
+        group = group.sort_values("step").reset_index(drop=True)
+        action_curve = dict(zip(group["step"] - 1, group["action_cosine_vs_full50"]))
+        state_curve = dict(zip(group["step"] - 1, group["state_cosine_vs_full50"]))
+
+        for scheme_name, checkpoints in schemes.items():
+            normalized_checkpoints = sorted(
+                checkpoint for checkpoint in checkpoints if 0 <= checkpoint <= 49)
+            if not normalized_checkpoints:
+                continue
+            last_checkpoint = normalized_checkpoints[-1]
+            rows.append({
+                "dataset": keys[0],
+                "case": keys[1],
+                "sample_id": keys[2],
+                "scene": keys[3],
+                "pass_type": keys[4],
+                "round_id": keys[5],
+                "scheme": scheme_name,
+                "head_exec_steps_zero_based": json.dumps(normalized_checkpoints),
+                "head_exec_count": len(normalized_checkpoints),
+                "head_compute_saving_ratio": 1.0 - len(normalized_checkpoints) / 50.0,
+                "final_checkpoint_zero_based": last_checkpoint,
+                "final_checkpoint_one_based": last_checkpoint + 1,
+                "final_action_cosine_vs_dense": action_curve[last_checkpoint],
+                "final_state_cosine_vs_dense": state_curve[last_checkpoint],
+            })
+    return pd.DataFrame(rows)
+
+
+def summarize_scheme_results(simulation_df: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+    metric_columns = [
+        "final_action_cosine_vs_dense",
+        "final_state_cosine_vs_dense",
+        "head_exec_count",
+        "head_compute_saving_ratio",
+    ]
+    overall = simulation_df.groupby(["scheme", "pass_type"])[metric_columns].agg(
+        ["mean", "median", "std"]).reset_index()
+    per_case = simulation_df.groupby(
+        ["dataset", "case", "scheme", "pass_type"])[metric_columns].mean().reset_index()
+    return overall, per_case
+
+
+def compute_min_steps_needed(stepwise_df: pd.DataFrame,
+                             threshold: float) -> pd.DataFrame:
+    rows = []
+    group_columns = [
+        "dataset",
+        "case",
+        "sample_id",
+        "scene",
+        "pass_type",
+        "round_id",
+    ]
+    grouped = stepwise_df.groupby(group_columns)
+    for keys, group in grouped:
+        group = group.sort_values("step").reset_index(drop=True)
+        action_hits = group[group["action_cosine_vs_full50"] >= threshold]
+        state_hits = group[group["state_cosine_vs_full50"] >= threshold]
+
+        action_step = np.nan if action_hits.empty else float(action_hits.iloc[0]["step"] - 1)
+        state_step = np.nan if state_hits.empty else float(state_hits.iloc[0]["step"] - 1)
+        rows.append({
+            "dataset": keys[0],
+            "case": keys[1],
+            "sample_id": keys[2],
+            "scene": keys[3],
+            "pass_type": keys[4],
+            "round_id": keys[5],
+            "target_threshold": threshold,
+            "action_min_head_steps_needed": 1.0 if pd.notna(action_step) else np.nan,
+            "state_min_head_steps_needed": 1.0 if pd.notna(state_step) else np.nan,
+            "action_earliest_checkpoint_zero_based": action_step,
+            "state_earliest_checkpoint_zero_based": state_step,
+            "action_max_head_compute_saving_ratio": 0.98 if pd.notna(action_step) else np.nan,
+            "state_max_head_compute_saving_ratio": 0.98 if pd.notna(state_step) else np.nan,
+        })
+    return pd.DataFrame(rows)
+
+
+def compare_tail_vs_uniform(simulation_df: pd.DataFrame) -> pd.DataFrame:
+    comparison_specs = [
+        ("sparse_8", "tail_heavy_8", "budget_8_uniform_vs_tail"),
+        ("sparse_4", "tail_only_4", "budget_4_uniform_vs_tail"),
+        ("sparse_10", "tail_heavy_10", "budget_10_uniform_vs_tail"),
+    ]
+    base_columns = [
+        "dataset",
+        "case",
+        "sample_id",
+        "scene",
+        "pass_type",
+        "round_id",
+    ]
+    rows = []
+    for uniform_scheme, tail_scheme, comparison_name in comparison_specs:
+        uniform_df = simulation_df[simulation_df["scheme"] == uniform_scheme].copy()
+        tail_df = simulation_df[simulation_df["scheme"] == tail_scheme].copy()
+        merged = uniform_df.merge(
+            tail_df,
+            on=base_columns,
+            suffixes=("_uniform", "_tail"),
+            how="inner",
+        )
+        for _, row in merged.iterrows():
+            rows.append({
+                "comparison": comparison_name,
+                "dataset": row["dataset"],
+                "case": row["case"],
+                "sample_id": row["sample_id"],
+                "scene": row["scene"],
+                "pass_type": row["pass_type"],
+                "round_id": row["round_id"],
+                "uniform_scheme": uniform_scheme,
+                "tail_scheme": tail_scheme,
+                "uniform_head_exec_count": row["head_exec_count_uniform"],
+                "tail_head_exec_count": row["head_exec_count_tail"],
+                "uniform_action_cosine_vs_dense": row["final_action_cosine_vs_dense_uniform"],
+                "tail_action_cosine_vs_dense": row["final_action_cosine_vs_dense_tail"],
+                "uniform_state_cosine_vs_dense": row["final_state_cosine_vs_dense_uniform"],
+                "tail_state_cosine_vs_dense": row["final_state_cosine_vs_dense_tail"],
+                "tail_minus_uniform_action_cosine":
+                row["final_action_cosine_vs_dense_tail"] -
+                row["final_action_cosine_vs_dense_uniform"],
+                "tail_minus_uniform_state_cosine":
+                row["final_state_cosine_vs_dense_tail"] -
+                row["final_state_cosine_vs_dense_uniform"],
+                "tail_better_action": row["final_action_cosine_vs_dense_tail"] >
+                row["final_action_cosine_vs_dense_uniform"],
+                "tail_better_state": row["final_state_cosine_vs_dense_tail"] >
+                row["final_state_cosine_vs_dense_uniform"],
+            })
+    return pd.DataFrame(rows)
+
+
+def build_summary_payload(simulation_df: pd.DataFrame,
+                          min_steps_df: pd.DataFrame,
+                          tail_compare_df: pd.DataFrame,
+                          target_threshold: float) -> dict:
+    payload: dict[str, dict] = {
+        "config": {
+            "target_threshold": target_threshold,
+            "schemes": DEFAULT_SCHEMES,
+        },
+        "experiment_1_1": {},
+        "experiment_1_2": {},
+        "experiment_1_3": {},
+    }
+
+    for scheme, group in simulation_df.groupby("scheme"):
+        payload["experiment_1_1"][scheme] = {
+            "num_rows": int(len(group)),
+            "head_exec_count": float(group["head_exec_count"].iloc[0]),
+            "head_compute_saving_ratio": float(
+                group["head_compute_saving_ratio"].iloc[0]),
+            "final_action_cosine_vs_dense_mean": float(
+                group["final_action_cosine_vs_dense"].mean()),
+            "final_action_cosine_vs_dense_median": float(
+                group["final_action_cosine_vs_dense"].median()),
+            "final_state_cosine_vs_dense_mean": float(
+                group["final_state_cosine_vs_dense"].mean()),
+            "final_state_cosine_vs_dense_median": float(
+                group["final_state_cosine_vs_dense"].median()),
+        }
+
+    payload["experiment_1_2"] = {
+        "action_earliest_checkpoint_zero_based": {
+            "mean": float(
+                pd.to_numeric(
+                    min_steps_df["action_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().mean()),
+            "median": float(
+                pd.to_numeric(
+                    min_steps_df["action_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().median()),
+        },
+        "state_earliest_checkpoint_zero_based": {
+            "mean": float(
+                pd.to_numeric(
+                    min_steps_df["state_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().mean()),
+            "median": float(
+                pd.to_numeric(
+                    min_steps_df["state_earliest_checkpoint_zero_based"],
+                    errors="coerce").dropna().median()),
+        },
+        "min_head_steps_needed_action_unique": sorted(
+            pd.to_numeric(min_steps_df["action_min_head_steps_needed"],
+                          errors="coerce").dropna().unique().tolist()),
+        "min_head_steps_needed_state_unique": sorted(
+            pd.to_numeric(min_steps_df["state_min_head_steps_needed"],
+                          errors="coerce").dropna().unique().tolist()),
+    }
+
+    for comparison, group in tail_compare_df.groupby("comparison"):
+        payload["experiment_1_3"][comparison] = {
+            "num_rows": int(len(group)),
+            "tail_better_action_share": float(group["tail_better_action"].mean()),
+            "tail_better_state_share": float(group["tail_better_state"].mean()),
+            "tail_minus_uniform_action_cosine_mean": float(
+                group["tail_minus_uniform_action_cosine"].mean()),
+            "tail_minus_uniform_state_cosine_mean": float(
+                group["tail_minus_uniform_state_cosine"].mean()),
+        }
+    return payload
+
+
+def write_markdown_report(path: Path, payload: dict) -> None:
+    lines = [
+        "# Sparse Head Execution Simulation",
+        "",
+        "This report uses zero-order hold over logged stepwise action/state outputs.",
+        "For a sparse scheme, the final output at step 49 is approximated by the most recent checkpoint output.",
+        "",
+        "## Experiment 1.1",
+        "",
+    ]
+
+    for scheme, stats in payload["experiment_1_1"].items():
+        lines.extend([
+            f"### {scheme}",
+            "",
+            f"- Head exec count: {stats['head_exec_count']:.0f}",
+            f"- Head compute saving ratio: {stats['head_compute_saving_ratio']:.4f}",
+            f"- Final action cosine vs dense: mean={stats['final_action_cosine_vs_dense_mean']:.4f}, median={stats['final_action_cosine_vs_dense_median']:.4f}",
+            f"- Final state cosine vs dense: mean={stats['final_state_cosine_vs_dense_mean']:.4f}, median={stats['final_state_cosine_vs_dense_median']:.4f}",
+            "",
+        ])
+
+    lines.extend([
+        "## Experiment 1.2",
+        "",
+        f"- Target threshold: {payload['config']['target_threshold']}",
+        f"- Action earliest checkpoint mean: {payload['experiment_1_2']['action_earliest_checkpoint_zero_based']['mean']:.4f}",
+        f"- State earliest checkpoint mean: {payload['experiment_1_2']['state_earliest_checkpoint_zero_based']['mean']:.4f}",
+        f"- Unique min head steps needed for action: {payload['experiment_1_2']['min_head_steps_needed_action_unique']}",
+        f"- Unique min head steps needed for state: {payload['experiment_1_2']['min_head_steps_needed_state_unique']}",
+        "",
+        "## Experiment 1.3",
+        "",
+    ])
+
+    for comparison, stats in payload["experiment_1_3"].items():
+        lines.extend([
+            f"### {comparison}",
+            "",
+            f"- Tail better action share: {stats['tail_better_action_share']:.4f}",
+            f"- Tail better state share: {stats['tail_better_state_share']:.4f}",
+            f"- Mean tail-minus-uniform action cosine: {stats['tail_minus_uniform_action_cosine_mean']:.4f}",
+            f"- Mean tail-minus-uniform state cosine: {stats['tail_minus_uniform_state_cosine_mean']:.4f}",
+            "",
+        ])
+
+    with open(path, "w", encoding="utf-8") as file:
+        file.write("\n".join(lines) + "\n")
+
+
+def main() -> None:
+    args = parse_args()
+    root_dir = Path(args.root_dir).resolve()
+    output_dir = Path(args.output_dir).resolve(
+    ) if args.output_dir else root_dir / "sparse_head_simulation"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    stepwise_paths = discover_stepwise_logs(root_dir)
+    stepwise_df = load_stepwise_table(stepwise_paths)
+
+    simulation_df = simulate_schemes(stepwise_df, DEFAULT_SCHEMES)
+    scheme_overall_df, scheme_per_case_df = summarize_scheme_results(simulation_df)
+    min_steps_df = compute_min_steps_needed(stepwise_df, args.target_threshold)
+    tail_compare_df = compare_tail_vs_uniform(simulation_df)
+    summary_payload = build_summary_payload(simulation_df, min_steps_df,
+                                            tail_compare_df,
+                                            args.target_threshold)
+
+    simulation_df.to_csv(output_dir / "scheme_simulation_per_round.csv",
+                         index=False)
+    scheme_overall_df.to_csv(output_dir / "scheme_simulation_overall.csv",
+                             index=False)
+    scheme_per_case_df.to_csv(output_dir / "scheme_simulation_per_case.csv",
+                              index=False)
+    min_steps_df.to_csv(output_dir / "min_head_steps_needed.csv", index=False)
+    tail_compare_df.to_csv(output_dir / "tail_vs_uniform.csv", index=False)
+    with open(output_dir / "summary.json", "w", encoding="utf-8") as file:
+        json.dump(summary_payload, file, indent=2, ensure_ascii=False)
+    write_markdown_report(output_dir / "report.md", summary_payload)
+
+    print(f"Sparse head simulation written to: {output_dir}")
+
+
+if __name__ == "__main__":
+    main()

scripts/evaluation/world_model_interaction.py

@@ -53,6 +53,12 @@ def build_sample_id(dataset: str, sample: pd.Series, frame_stride: int) -> str:
     return f"{dataset}-vid{sample['videoid']}-fs{frame_stride}"
 
 
+def get_case_id(prompt_dir: str) -> str:
+    """Resolve case id from a prompt directory like */case1/world_model_interaction_prompts."""
+    normalized = os.path.normpath(prompt_dir)
+    return os.path.basename(os.path.dirname(normalized))
+
+
 def flatten_batch_tensor(tensor: torch.Tensor) -> torch.Tensor:
     """Flatten all non-batch dimensions for batched metric computation."""
     return tensor.detach().float().reshape(tensor.shape[0], -1)
@@ -124,6 +130,37 @@ def safe_mean(values: list[float]) -> float:
     return float(np.mean(valid_values))
 
 
+def flatten_tensor(tensor: torch.Tensor) -> torch.Tensor:
+    """Flatten an arbitrary tensor into one 1D float vector."""
+    return tensor.detach().float().reshape(-1)
+
+
+def tensor_l2_distance(current: torch.Tensor, reference: torch.Tensor) -> float:
+    """Compute ||current-reference|| for arbitrary tensors."""
+    current_flat = flatten_tensor(current)
+    reference_flat = flatten_tensor(reference)
+    return float(torch.linalg.vector_norm(current_flat - reference_flat).item())
+
+
+def tensor_relative_l2(current: torch.Tensor, previous: torch.Tensor) -> float:
+    """Compute ||current-previous|| / (||previous|| + eps) for arbitrary tensors."""
+    current_flat = flatten_tensor(current)
+    previous_flat = flatten_tensor(previous)
+    numerator = torch.linalg.vector_norm(current_flat - previous_flat)
+    denominator = torch.linalg.vector_norm(previous_flat).clamp_min(1e-8)
+    return float((numerator / denominator).item())
+
+
+def tensor_cosine_similarity(current: torch.Tensor,
+                             reference: torch.Tensor) -> float:
+    """Compute cosine similarity between arbitrary tensors."""
+    current_flat = flatten_tensor(current)
+    reference_flat = flatten_tensor(reference)
+    return float(
+        F.cosine_similarity(current_flat, reference_flat, dim=0,
+                            eps=1e-8).item())
+
+
 def make_sampling_noise_bundle(model: nn.Module,
                                noise_shape: list[int]) -> dict[str, torch.Tensor]:
     """Create aligned initial noise for latent, action, and state diffusion streams."""
@@ -139,6 +176,15 @@ def make_sampling_noise_bundle(model: nn.Module,
     }
 
 
+def reset_sampling_seed(seed: int) -> None:
+    """Reset RNGs so repeated dense passes follow the same stochastic DDIM path."""
+    random.seed(seed)
+    np.random.seed(seed % (2**32))
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
 def load_psnr_lookup(psnr_path: str | None) -> dict[str, float]:
     """Load optional PSNR values keyed by sample_id or videoid."""
     if not psnr_path:
@@ -447,6 +493,121 @@ class InteractionAnalysisLogger:
         summary_df.to_csv(summary_path, index=False)
 
 
+class BackboneBlockProfiler:
+    """Collect dense backbone block features and timings with a low-memory two-pass flow."""
+
+    COLUMNS = [
+        'sample_id',
+        'case_id',
+        'scene',
+        'pass_type',
+        'round_id',
+        'step',
+        'block_name',
+        'block_stage',
+        'block_index',
+        'shape',
+        'forward_time_ms',
+        'l2_delta_vs_prev',
+        'rel_l2_delta_vs_prev',
+        'cosine_vs_prev',
+        'l2_delta_vs_full50',
+        'cosine_vs_full50',
+    ]
+
+    def __init__(self, output_dir: str):
+        self.output_dir = output_dir
+        self.rows: list[dict] = []
+        self.reference_features: dict[tuple[str, str, int], dict[str,
+                                                                  torch.Tensor]] = {}
+        self.mode: str | None = None
+        self.pass_key: tuple[str, str, int] | None = None
+        self.pass_meta: dict[str, str | int] = {}
+        self.current_reference: dict[str, torch.Tensor] = {}
+        self.previous_features: dict[str, torch.Tensor] = {}
+
+    def _set_pass(self, mode: str, sample_id: str, case_id: str, scene: str,
+                  pass_type: str, round_id: int) -> None:
+        self.mode = mode
+        self.pass_key = (sample_id, pass_type, int(round_id))
+        self.pass_meta = {
+            'sample_id': sample_id,
+            'case_id': case_id,
+            'scene': scene,
+            'pass_type': pass_type,
+            'round_id': int(round_id),
+        }
+        self.current_reference = {}
+        self.previous_features = {}
+
+    def start_reference_pass(self, sample_id: str, case_id: str, scene: str,
+                             pass_type: str, round_id: int) -> None:
+        self._set_pass('reference', sample_id, case_id, scene, pass_type,
+                       round_id)
+
+    def start_target_pass(self, sample_id: str, case_id: str, scene: str,
+                          pass_type: str, round_id: int) -> None:
+        self._set_pass('target', sample_id, case_id, scene, pass_type, round_id)
+
+    def finish_pass(self) -> None:
+        if self.mode == 'reference' and self.pass_key is not None:
+            self.reference_features[self.pass_key] = self.current_reference
+        elif self.mode == 'target' and self.pass_key is not None:
+            self.reference_features.pop(self.pass_key, None)
+        self.mode = None
+        self.pass_key = None
+        self.pass_meta = {}
+        self.current_reference = {}
+        self.previous_features = {}
+
+    def record_block(self, step: int, block_name: str, block_stage: str,
+                     block_index: int | None, output: torch.Tensor,
+                     forward_time_ms: float) -> None:
+        if self.mode is None or self.pass_key is None:
+            return
+        block_output = output.detach().float().cpu()
+        if self.mode == 'reference':
+            self.current_reference[block_name] = block_output
+            return
+
+        previous = self.previous_features.get(block_name)
+        reference = self.reference_features.get(self.pass_key, {}).get(block_name)
+        row = {
+            **self.pass_meta,
+            'step': int(step),
+            'block_name': block_name,
+            'block_stage': block_stage,
+            'block_index': -1 if block_index is None else int(block_index),
+            'shape': str(tuple(block_output.shape)),
+            'forward_time_ms': float(forward_time_ms),
+            'l2_delta_vs_prev': np.nan,
+            'rel_l2_delta_vs_prev': np.nan,
+            'cosine_vs_prev': np.nan,
+            'l2_delta_vs_full50': np.nan,
+            'cosine_vs_full50': np.nan,
+        }
+        if previous is not None:
+            row['l2_delta_vs_prev'] = tensor_l2_distance(block_output, previous)
+            row['rel_l2_delta_vs_prev'] = tensor_relative_l2(
+                block_output, previous)
+            row['cosine_vs_prev'] = tensor_cosine_similarity(
+                block_output, previous)
+        if reference is not None:
+            row['l2_delta_vs_full50'] = tensor_l2_distance(
+                block_output, reference)
+            row['cosine_vs_full50'] = tensor_cosine_similarity(
+                block_output, reference)
+
+        self.previous_features[block_name] = block_output
+        self.rows.append(row)
+
+    def flush(self) -> None:
+        os.makedirs(self.output_dir, exist_ok=True)
+        path = os.path.join(self.output_dir, 'backbone_block_log.csv')
+        df = pd.DataFrame(self.rows, columns=self.COLUMNS)
+        df.to_csv(path, index=False)
+
+
 def write_video(video_path: str, stacked_frames: list, fps: int) -> None:
     """Save a list of frames to a video file.
 
@@ -765,7 +926,9 @@ def image_guided_synthesis_sim_mode(
         init_noise_bundle (dict[str, torch.Tensor] | None): Optional aligned noise inputs for latent/action/state.
         decode_video (bool): Whether to decode the final latent into pixel space.
         return_debug_info (bool): Whether to return per-step traces for analysis logging.
-        **kwargs: Additional arguments passed to the DDIM sampler.
+        **kwargs: Additional arguments passed to the DDIM sampler, including
+            sparse head controls such as `head_schedule`, `head_log_steps`,
+            and `head_skip_mode`.
 
     Returns:
         batch_variants (torch.Tensor | None): Predicted pixel-space video frames [B, C, T, H, W].
@@ -852,10 +1015,11 @@ def image_guided_synthesis_sim_mode(
         if decode_video:
             batch_variants = model.decode_first_stage(samples)
 
-        if return_debug_info:
+        if return_debug_info or intermedia.get('head_sparse_logs'):
             debug_info = {
                 'analysis_init': intermedia.get('analysis_init'),
                 'step_records': intermedia.get('step_records', []),
+                'head_sparse_logs': intermedia.get('head_sparse_logs', {}),
                 'final_latent': samples.detach().cpu(),
                 'final_action': actions.detach().cpu(),
                 'final_state': states.detach().cpu(),
@@ -883,11 +1047,22 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
     os.makedirs(log_dir, exist_ok=True)
     writer = SummaryWriter(log_dir=log_dir)
     analysis_logger = None
+    backbone_profiler = None
+    head_schedule = args.head_schedule_steps if args.head_schedule_steps else None
+    head_log_steps = args.head_log_steps if args.head_log_steps else None
+    head_skip_mode = args.head_skip_mode
     if args.analysis_log_metrics:
         analysis_logger = InteractionAnalysisLogger(
             output_dir=inference_dir,
             psnr_lookup=load_psnr_lookup(args.analysis_psnr_path),
         )
+    if args.analysis_profile_backbone_blocks:
+        if head_schedule is not None:
+            raise ValueError(
+                "Backbone block profiling expects dense DDIM runs. "
+                "Do not pass --head_schedule_steps.")
+        backbone_profiler = BackboneBlockProfiler(output_dir=inference_dir)
+    case_id = get_case_id(args.prompt_dir)
 
     # Load prompt
     csv_path = os.path.join(args.prompt_dir, f"{args.dataset}.csv")
@@ -1021,10 +1196,45 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
                 # Use world-model in policy to generate action
                 print(f'>>> Step {itr}: generating actions ...')
-                policy_noise_bundle = make_sampling_noise_bundle(
-                    model, noise_shape) if args.analysis_log_metrics else None
+                policy_noise_bundle = (
+                    make_sampling_noise_bundle(model, noise_shape)
+                    if (args.analysis_log_metrics
+                        or backbone_profiler is not None) else None)
                 policy_reference_debug = None
-                if args.analysis_log_metrics and args.analysis_reference_steps != args.ddim_steps:
+                policy_sampling_seed = int(args.seed + itr * 1000 + 11)
+                if backbone_profiler is not None:
+                    reset_sampling_seed(policy_sampling_seed)
+                    backbone_profiler.start_reference_pass(
+                        sample_id=sample_id,
+                        case_id=case_id,
+                        scene=scene,
+                        pass_type='policy',
+                        round_id=itr,
+                    )
+                    image_guided_synthesis_sim_mode(
+                        model,
+                        sample['instruction'],
+                        observation,
+                        noise_shape,
+                        action_cond_step=args.exe_steps,
+                        ddim_steps=args.ddim_steps,
+                        ddim_eta=args.ddim_eta,
+                        unconditional_guidance_scale=args.
+                        unconditional_guidance_scale,
+                        fs=model_input_fs,
+                        timestep_spacing=args.timestep_spacing,
+                        guidance_rescale=args.guidance_rescale,
+                        sim_mode=False,
+                        init_noise_bundle=policy_noise_bundle,
+                        decode_video=False,
+                        return_debug_info=False,
+                        backbone_block_profiler=backbone_profiler,
+                    )
+                    backbone_profiler.finish_pass()
+                need_policy_reference = args.analysis_log_metrics and (
+                    args.analysis_reference_steps != args.ddim_steps
+                    or head_schedule is not None)
+                if need_policy_reference:
                     _, _, _, policy_reference_debug = image_guided_synthesis_sim_mode(
                         model,
                         sample['instruction'],
@@ -1041,8 +1251,18 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                         sim_mode=False,
                         init_noise_bundle=policy_noise_bundle,
                         decode_video=False,
-                        return_debug_info=True)
+                        return_debug_info=True,
+                        head_log_steps=head_log_steps)
                 policy_pass_start = time.time()
+                if backbone_profiler is not None:
+                    reset_sampling_seed(policy_sampling_seed)
+                    backbone_profiler.start_target_pass(
+                        sample_id=sample_id,
+                        case_id=case_id,
+                        scene=scene,
+                        pass_type='policy',
+                        round_id=itr,
+                    )
                 pred_videos_0, pred_actions, _, policy_debug = image_guided_synthesis_sim_mode(
                     model,
                     sample['instruction'],
@@ -1058,7 +1278,13 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     guidance_rescale=args.guidance_rescale,
                     sim_mode=False,
                     init_noise_bundle=policy_noise_bundle,
-                    return_debug_info=args.analysis_log_metrics)
+                    return_debug_info=args.analysis_log_metrics,
+                    head_schedule=head_schedule,
+                    head_log_steps=head_log_steps,
+                    head_skip_mode=head_skip_mode,
+                    backbone_block_profiler=backbone_profiler)
+                if backbone_profiler is not None:
+                    backbone_profiler.finish_pass()
                 policy_pass_total_time_s = time.time() - policy_pass_start
                 policy_summary_row = None
                 if analysis_logger is not None:
@@ -1101,10 +1327,45 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
                 # Interaction with the world-model
                 print(f'>>> Step {itr}: interacting with world model ...')
-                world_noise_bundle = make_sampling_noise_bundle(
-                    model, noise_shape) if args.analysis_log_metrics else None
+                world_noise_bundle = (
+                    make_sampling_noise_bundle(model, noise_shape)
+                    if (args.analysis_log_metrics
+                        or backbone_profiler is not None) else None)
                 world_reference_debug = None
-                if args.analysis_log_metrics and args.analysis_reference_steps != args.ddim_steps:
+                world_sampling_seed = int(args.seed + itr * 1000 + 29)
+                if backbone_profiler is not None:
+                    reset_sampling_seed(world_sampling_seed)
+                    backbone_profiler.start_reference_pass(
+                        sample_id=sample_id,
+                        case_id=case_id,
+                        scene=scene,
+                        pass_type='world_model',
+                        round_id=itr,
+                    )
+                    image_guided_synthesis_sim_mode(
+                        model,
+                        "",
+                        observation,
+                        noise_shape,
+                        action_cond_step=args.exe_steps,
+                        ddim_steps=args.ddim_steps,
+                        ddim_eta=args.ddim_eta,
+                        unconditional_guidance_scale=args.
+                        unconditional_guidance_scale,
+                        fs=model_input_fs,
+                        text_input=False,
+                        timestep_spacing=args.timestep_spacing,
+                        guidance_rescale=args.guidance_rescale,
+                        init_noise_bundle=world_noise_bundle,
+                        decode_video=False,
+                        return_debug_info=False,
+                        backbone_block_profiler=backbone_profiler,
+                    )
+                    backbone_profiler.finish_pass()
+                need_world_reference = args.analysis_log_metrics and (
+                    args.analysis_reference_steps != args.ddim_steps
+                    or head_schedule is not None)
+                if need_world_reference:
                     _, _, _, world_reference_debug = image_guided_synthesis_sim_mode(
                         model,
                         "",
@@ -1121,8 +1382,18 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                         guidance_rescale=args.guidance_rescale,
                         init_noise_bundle=world_noise_bundle,
                         decode_video=False,
-                        return_debug_info=True)
+                        return_debug_info=True,
+                        head_log_steps=head_log_steps)
                 world_pass_start = time.time()
+                if backbone_profiler is not None:
+                    reset_sampling_seed(world_sampling_seed)
+                    backbone_profiler.start_target_pass(
+                        sample_id=sample_id,
+                        case_id=case_id,
+                        scene=scene,
+                        pass_type='world_model',
+                        round_id=itr,
+                    )
                 pred_videos_1, _, pred_states, world_debug = image_guided_synthesis_sim_mode(
                     model,
                     "",
@@ -1138,7 +1409,13 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     timestep_spacing=args.timestep_spacing,
                     guidance_rescale=args.guidance_rescale,
                     init_noise_bundle=world_noise_bundle,
-                    return_debug_info=args.analysis_log_metrics)
+                    return_debug_info=args.analysis_log_metrics,
+                    head_schedule=head_schedule,
+                    head_log_steps=head_log_steps,
+                    head_skip_mode=head_skip_mode,
+                    backbone_block_profiler=backbone_profiler)
+                if backbone_profiler is not None:
+                    backbone_profiler.finish_pass()
                 world_pass_total_time_s = time.time() - world_pass_start
                 world_summary_row = None
                 if analysis_logger is not None:
@@ -1224,6 +1501,8 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
     if analysis_logger is not None:
         analysis_logger.flush()
+    if backbone_profiler is not None:
+        backbone_profiler.flush()
     writer.close()
 
 
@@ -1357,6 +1636,29 @@ def get_parser():
                         type=str,
                         default=None,
                         help="Optional CSV/JSON file with psnr_full50 values keyed by sample_id or videoid.")
+    parser.add_argument(
+        "--analysis_profile_backbone_blocks",
+        action='store_true',
+        default=False,
+        help="Run dense two-pass backbone block profiling and export backbone_block_log.csv.")
+    parser.add_argument(
+        "--head_schedule_steps",
+        type=int,
+        nargs='*',
+        default=None,
+        help="Zero-based DDIM loop indices where action/state heads execute. Omit for dense execution.")
+    parser.add_argument(
+        "--head_log_steps",
+        type=int,
+        nargs='*',
+        default=None,
+        help="Zero-based DDIM loop indices to snapshot sparse action/state/latent outputs for dense-vs-sparse comparison.")
+    parser.add_argument(
+        "--head_skip_mode",
+        type=str,
+        default="reuse_prediction",
+        choices=["reuse_prediction", "freeze_state"],
+        help="Behavior on non-checkpoint steps: reuse cached head predictions while still running scheduler.step, or freeze action/state entirely.")
     return parser
 
 

scripts/run_all_world_model_cases.sh

@@ -7,6 +7,22 @@ cd "${repo_root}"
 
 run_analysis="${RUN_ANALYSIS:-1}"
 case_filter="${CASE_FILTER:-}"
+python_bin="${PYTHON_BIN:-}"
+
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
 
 case_scripts=(
     "unitree_z1_stackbox/case1/run_world_model_interaction.sh"
@@ -36,23 +52,9 @@ for case_script in "${case_scripts[@]}"; do
         continue
     fi
 
-    case_dir="$(dirname "${case_script}")"
-    inference_dir="${repo_root}/${case_dir}/output/inference"
-
     echo "============================================================"
     echo "Running ${case_script}"
     echo "============================================================"
-    bash "${repo_root}/${case_script}"
-
-    if [[ "${run_analysis}" == "1" ]]; then
-        if [[ -f "${inference_dir}/stepwise_log.csv" ]] && \
-           [[ -f "${inference_dir}/sample_summary.csv" ]] && \
-           [[ -f "${inference_dir}/round_summary.csv" ]]; then
-            echo "Analyzing ${case_dir}"
-            python3 "${repo_root}/scripts/evaluation/analyze_metrics.py" \
-                --input_dir "${inference_dir}"
-        else
-            echo "Skipping analysis for ${case_dir}: missing analysis CSVs."
-        fi
-    fi
+    echo "Using Python: ${python_bin}"
+    RUN_ANALYSIS="${run_analysis}" PYTHON_BIN="${python_bin}" bash "${repo_root}/${case_script}"
 done

scripts/run_case3_backbone_profiles.sh

@@ -0,0 +1,53 @@
+#!/usr/bin/env bash
+
+set -uo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+gpu_devices="${CUDA_VISIBLE_DEVICES:-1}"
+python_bin="${PYTHON_BIN:-}"
+
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
+
+case_scripts=(
+    "unitree_z1_dual_arm_stackbox/case3/run_world_model_backbone_profile.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case3/run_world_model_backbone_profile.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case3/run_world_model_backbone_profile.sh"
+    "unitree_g1_pack_camera/case3/run_world_model_backbone_profile.sh"
+)
+
+failed_cases=()
+
+for case_script in "${case_scripts[@]}"; do
+    echo "============================================================"
+    echo "Running ${case_script} on GPU ${gpu_devices}"
+    echo "============================================================"
+    echo "Using Python: ${python_bin}"
+    if ! CUDA_VISIBLE_DEVICES="${gpu_devices}" PYTHON_BIN="${python_bin}" bash "${repo_root}/${case_script}"; then
+        echo "FAILED: ${case_script}" >&2
+        failed_cases+=("${case_script}")
+    fi
+done
+
+if [[ ${#failed_cases[@]} -gt 0 ]]; then
+    echo "============================================================" >&2
+    echo "Backbone profiling finished with failures:" >&2
+    for case_script in "${failed_cases[@]}"; do
+        echo "  - ${case_script}" >&2
+    done
+    exit 1
+fi

scripts/run_world_model_backbone_profile_case.sh

@@ -0,0 +1,86 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+if [[ $# -ne 4 ]]; then
+    echo "Usage: $0 <res_dir> <dataset> <frame_stride> <n_iter>" >&2
+    exit 1
+fi
+
+res_dir="$1"
+dataset="$2"
+frame_stride="$3"
+n_iter="$4"
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+gpu_devices="${CUDA_VISIBLE_DEVICES:-1}"
+python_bin="${PYTHON_BIN:-}"
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
+save_dir="${res_dir}/backbone_profile_dense"
+log_path="${save_dir}/run.log"
+
+mkdir -p "${save_dir}"
+
+interaction_cmd=(
+    "${python_bin}" scripts/evaluation/world_model_interaction.py
+    --seed 123
+    --ckpt_path ckpts/unifolm_wma_dual.ckpt
+    --config configs/inference/world_model_interaction.yaml
+    --savedir "${save_dir}"
+    --bs 1
+    --height 320
+    --width 512
+    --unconditional_guidance_scale 1.0
+    --ddim_steps 50
+    --ddim_eta 1.0
+    --prompt_dir "${res_dir}/world_model_interaction_prompts"
+    --dataset "${dataset}"
+    --video_length 16
+    --frame_stride "${frame_stride}"
+    --n_action_steps 16
+    --exe_steps 16
+    --n_iter "${n_iter}"
+    --timestep_spacing uniform_trailing
+    --guidance_rescale 0.7
+    --perframe_ae
+    --analysis_log_metrics
+    --analysis_reference_steps 50
+    --analysis_profile_backbone_blocks
+)
+
+metrics_analysis_cmd=(
+    "${python_bin}" scripts/evaluation/analyze_metrics.py
+    --input_dir "${save_dir}/inference"
+    --output_dir "${save_dir}/inference/analysis"
+)
+
+backbone_analysis_cmd=(
+    "${python_bin}" scripts/evaluation/analyze_backbone_blocks.py
+    --input_csv "${save_dir}/inference/backbone_block_log.csv"
+    --output_dir "${save_dir}/inference/backbone_analysis"
+)
+
+{
+    echo "============================================================"
+    echo "Running dense backbone profiling for ${res_dir} on GPU ${gpu_devices}"
+    echo "============================================================"
+    echo "Using Python: ${python_bin}"
+    time env CUDA_VISIBLE_DEVICES="${gpu_devices}" "${interaction_cmd[@]}"
+
+    "${metrics_analysis_cmd[@]}"
+    "${backbone_analysis_cmd[@]}"
+} 2>&1 | tee "${log_path}"

scripts/run_world_model_case_schemes.sh

@@ -0,0 +1,106 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+if [[ $# -ne 4 ]]; then
+    echo "Usage: $0 <res_dir> <dataset> <frame_stride> <n_iter>" >&2
+    exit 1
+fi
+
+res_dir="$1"
+dataset="$2"
+frame_stride="$3"
+n_iter="$4"
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+run_analysis="${RUN_ANALYSIS:-1}"
+scheme_filter="${SCHEME_FILTER:-}"
+gpu_devices="${CUDA_VISIBLE_DEVICES:-0}"
+python_bin="${PYTHON_BIN:-}"
+if [[ -z "${python_bin}" ]]; then
+    if [[ -n "${CONDA_PREFIX:-}" ]] && [[ -x "${CONDA_PREFIX}/bin/python" ]]; then
+        python_bin="${CONDA_PREFIX}/bin/python"
+    elif [[ -n "${VIRTUAL_ENV:-}" ]] && [[ -x "${VIRTUAL_ENV}/bin/python" ]]; then
+        python_bin="${VIRTUAL_ENV}/bin/python"
+    else
+        python_bin="$(command -v python || true)"
+    fi
+fi
+if [[ -z "${python_bin}" ]] || [[ ! -x "${python_bin}" ]]; then
+    echo "Unable to resolve a usable Python interpreter. Set PYTHON_BIN explicitly." >&2
+    exit 1
+fi
+head_log_steps=(40 43 46 47 48 49)
+scheme_names=(sparse_8)
+
+get_head_schedule() {
+    case "$1" in
+        sparse_8)
+            echo "0 7 14 21 28 35 42 49"
+            ;;
+        sparse_4)
+            echo "0 16 32 49"
+            ;;
+        tail_only)
+            echo "40 43 46 49"
+            ;;
+        tail_heavy)
+            echo "0 32 40 44 47 49"
+            ;;
+        *)
+            echo "Unknown scheme: $1" >&2
+            exit 1
+            ;;
+    esac
+}
+
+mkdir -p "${res_dir}/output_logs"
+
+for scheme_name in "${scheme_names[@]}"; do
+    if [[ -n "${scheme_filter}" ]] && [[ "${scheme_name}" != *"${scheme_filter}"* ]]; then
+        continue
+    fi
+
+    read -r -a head_schedule <<< "$(get_head_schedule "${scheme_name}")"
+    scheme_savedir="${res_dir}/output/${scheme_name}"
+    scheme_log_path="${res_dir}/output_logs/${scheme_name}.log"
+
+    {
+        echo "============================================================"
+        echo "Running ${res_dir} with ${scheme_name}"
+        echo "============================================================"
+        echo "Using Python: ${python_bin}"
+        time CUDA_VISIBLE_DEVICES="${gpu_devices}" "${python_bin}" scripts/evaluation/world_model_interaction.py \
+            --seed 123 \
+            --ckpt_path ckpts/unifolm_wma_dual.ckpt \
+            --config configs/inference/world_model_interaction.yaml \
+            --savedir "${scheme_savedir}" \
+            --bs 1 --height 320 --width 512 \
+            --unconditional_guidance_scale 1.0 \
+            --ddim_steps 50 \
+            --ddim_eta 1.0 \
+            --prompt_dir "${res_dir}/world_model_interaction_prompts" \
+            --dataset "${dataset}" \
+            --video_length 16 \
+            --frame_stride "${frame_stride}" \
+            --n_action_steps 16 \
+            --exe_steps 16 \
+            --n_iter "${n_iter}" \
+            --timestep_spacing uniform_trailing \
+            --guidance_rescale 0.7 \
+            --perframe_ae \
+            --analysis_log_metrics \
+            --analysis_reference_steps 50 \
+            --head_schedule_steps "${head_schedule[@]}" \
+            --head_skip_mode reuse_prediction \
+            --head_log_steps "${head_log_steps[@]}"
+
+        if [[ "${run_analysis}" == "1" ]]; then
+            "${python_bin}" scripts/evaluation/analyze_metrics.py \
+                --input_dir "${scheme_savedir}/inference" \
+                --output_dir "${scheme_savedir}/inference/analysis"
+        fi
+    } 2>&1 | tee "${scheme_log_path}"
+done

sparse_head_simulation/min_head_steps_needed.csv

@@ -0,0 +1,393 @@
+dataset,case,sample_id,scene,pass_type,round_id,target_threshold,action_min_head_steps_needed,state_min_head_steps_needed,action_earliest_checkpoint_zero_based,state_earliest_checkpoint_zero_based,action_max_head_compute_saving_ratio,state_max_head_compute_saving_ratio
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,6,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,9,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,10,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,1,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,4,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,5,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,6,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,9,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case1,unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,10,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,7,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,8,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,9,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,10,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,0,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,3,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,4,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,5,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,7,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,9,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case2,unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,10,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,1,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,7,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,8,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,9,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,10,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,0,0.95,1.0,1.0,45.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,1,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,3,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,5,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,6,0.95,1.0,1.0,46.0,46.0,0.98,0.98
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+unitree_g1_pack_camera,case3,unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,9,0.95,1.0,1.0,45.0,46.0,0.98,0.98
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+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,0,0.95,1.0,1.0,45.0,46.0,0.98,0.98
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+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,2,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,3,0.95,1.0,1.0,46.0,45.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,4,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_g1_pack_camera,case4,unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,5,0.95,1.0,1.0,46.0,47.0,0.98,0.98
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+unitree_z1_stackbox,case3,unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,world_model,7,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_stackbox,case3,unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,world_model,8,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_stackbox,case3,unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,world_model,9,0.95,1.0,1.0,45.0,47.0,0.98,0.98
+unitree_z1_stackbox,case3,unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,world_model,10,0.95,1.0,1.0,45.0,46.0,0.98,0.98
+unitree_z1_stackbox,case3,unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,world_model,11,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,0,0.95,1.0,1.0,46.0,46.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,1,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,2,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,3,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,4,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,5,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,6,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,7,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,8,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,9,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,10,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,policy,11,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,0,0.95,1.0,1.0,47.0,46.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,1,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,2,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,3,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,4,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,5,0.95,1.0,1.0,47.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,6,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,7,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,8,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,9,0.95,1.0,1.0,46.0,47.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,10,0.95,1.0,1.0,47.0,48.0,0.98,0.98
+unitree_z1_stackbox,case4,unitree_z1_stackbox-vid35-fs4,unitree_z1_stackbox,world_model,11,0.95,1.0,1.0,47.0,48.0,0.98,0.98

sparse_head_simulation/report.md

@@ -0,0 +1,108 @@
+# Sparse Head Execution Simulation
+
+This report uses zero-order hold over logged stepwise action/state outputs.
+For a sparse scheme, the final output at step 49 is approximated by the most recent checkpoint output.
+
+## Experiment 1.1
+
+### dense
+
+- Head exec count: 50
+- Head compute saving ratio: 0.0000
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### sparse_10
+
+- Head exec count: 10
+- Head compute saving ratio: 0.8000
+- Final action cosine vs dense: mean=0.9341, median=0.9357
+- Final state cosine vs dense: mean=0.9130, median=0.9207
+
+### sparse_4
+
+- Head exec count: 4
+- Head compute saving ratio: 0.9200
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### sparse_5
+
+- Head exec count: 5
+- Head compute saving ratio: 0.9000
+- Final action cosine vs dense: mean=0.7747, median=0.7703
+- Final state cosine vs dense: mean=0.7463, median=0.7481
+
+### sparse_8
+
+- Head exec count: 8
+- Head compute saving ratio: 0.8400
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_heavy_10
+
+- Head exec count: 10
+- Head compute saving ratio: 0.8000
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_heavy_6
+
+- Head exec count: 6
+- Head compute saving ratio: 0.8800
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_heavy_8
+
+- Head exec count: 8
+- Head compute saving ratio: 0.8400
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_only_4
+
+- Head exec count: 4
+- Head compute saving ratio: 0.9200
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+### tail_only_6
+
+- Head exec count: 6
+- Head compute saving ratio: 0.8800
+- Final action cosine vs dense: mean=1.0000, median=1.0000
+- Final state cosine vs dense: mean=1.0000, median=1.0000
+
+## Experiment 1.2
+
+- Target threshold: 0.95
+- Action earliest checkpoint mean: 46.0179
+- State earliest checkpoint mean: 46.5357
+- Unique min head steps needed for action: [1.0]
+- Unique min head steps needed for state: [1.0]
+
+## Experiment 1.3
+
+### budget_10_uniform_vs_tail
+
+- Tail better action share: 1.0000
+- Tail better state share: 1.0000
+- Mean tail-minus-uniform action cosine: 0.0659
+- Mean tail-minus-uniform state cosine: 0.0870
+
+### budget_4_uniform_vs_tail
+
+- Tail better action share: 0.0000
+- Tail better state share: 0.0000
+- Mean tail-minus-uniform action cosine: 0.0000
+- Mean tail-minus-uniform state cosine: 0.0000
+
+### budget_8_uniform_vs_tail
+
+- Tail better action share: 0.0000
+- Tail better state share: 0.0000
+- Mean tail-minus-uniform action cosine: 0.0000
+- Mean tail-minus-uniform state cosine: 0.0000
+

sparse_head_simulation/scheme_simulation_overall.csv

@@ -0,0 +1,22 @@
+scheme,pass_type,final_action_cosine_vs_dense,final_action_cosine_vs_dense,final_action_cosine_vs_dense,final_state_cosine_vs_dense,final_state_cosine_vs_dense,final_state_cosine_vs_dense,head_exec_count,head_exec_count,head_exec_count,head_compute_saving_ratio,head_compute_saving_ratio,head_compute_saving_ratio
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+dense,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,50.0,50.0,0.0,0.0,0.0,0.0
+dense,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,50.0,50.0,0.0,0.0,0.0,0.0
+sparse_10,policy,0.9357762607384701,0.9370943009853362,0.018183880133080282,0.9124557038350981,0.9198094010353088,0.03185505383051429,10.0,10.0,0.0,0.8,0.8,0.0
+sparse_10,world_model,0.9325069240769561,0.9341191649436951,0.0185309603092533,0.9134697318077087,0.9217162132263184,0.032954335242965455,10.0,10.0,0.0,0.8,0.8,0.0
+sparse_4,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+sparse_4,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+sparse_5,policy,0.7812822941614657,0.7787671983242035,0.03869909771001223,0.7465329766273499,0.7454181015491486,0.05109581488207182,5.0,5.0,0.0,0.9,0.9,0.0
+sparse_5,world_model,0.7681533368874569,0.7654141485691071,0.03844469190746414,0.7460347663383095,0.7492875158786774,0.05257641591339498,5.0,5.0,0.0,0.9,0.9,0.0
+sparse_8,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,8.0,8.0,0.0,0.84,0.84,0.0
+sparse_8,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,8.0,8.0,0.0,0.84,0.84,0.0
+tail_heavy_10,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,10.0,10.0,0.0,0.8,0.8,0.0
+tail_heavy_10,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,10.0,10.0,0.0,0.8,0.8,0.0
+tail_heavy_6,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0
+tail_heavy_6,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0
+tail_heavy_8,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,8.0,8.0,0.0,0.84,0.84,0.0
+tail_heavy_8,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,8.0,8.0,0.0,0.84,0.84,0.0
+tail_only_4,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+tail_only_4,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,4.0,4.0,0.0,0.9200000000000002,0.92,0.0
+tail_only_6,policy,1.000000006386212,1.0,8.6717418229413e-08,0.9999999939178934,1.0,9.826164033417286e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0
+tail_only_6,world_model,1.0000000069944226,1.0,9.373933947591167e-08,0.9999999936137881,1.0,9.102258232536112e-08,6.0,6.0,0.0,0.8799999999999999,0.88,0.0

sparse_head_simulation/scheme_simulation_per_case.csv

@@ -0,0 +1,401 @@
+dataset,case,scheme,pass_type,final_action_cosine_vs_dense,final_state_cosine_vs_dense,head_exec_count,head_compute_saving_ratio
+unitree_g1_pack_camera,case1,dense,policy,1.000000005418604,1.0000000216744163,50.0,0.0
+unitree_g1_pack_camera,case1,dense,world_model,1.0000000162558123,0.9999999891627919,50.0,0.0
+unitree_g1_pack_camera,case1,sparse_10,policy,0.9458914236588911,0.9406864263794639,10.0,0.8
+unitree_g1_pack_camera,case1,sparse_10,world_model,0.943276660008864,0.94043137810447,10.0,0.8
+unitree_g1_pack_camera,case1,sparse_4,policy,1.000000005418604,1.0000000216744163,4.0,0.92
+unitree_g1_pack_camera,case1,sparse_4,world_model,1.0000000162558123,0.9999999891627919,4.0,0.92
+unitree_g1_pack_camera,case1,sparse_5,policy,0.7835968299345537,0.7882764935493469,5.0,0.9
+unitree_g1_pack_camera,case1,sparse_5,world_model,0.768740404735912,0.7820447520776228,5.0,0.9
+unitree_g1_pack_camera,case1,sparse_8,policy,1.000000005418604,1.0000000216744163,8.0,0.84
+unitree_g1_pack_camera,case1,sparse_8,world_model,1.0000000162558123,0.9999999891627919,8.0,0.84
+unitree_g1_pack_camera,case1,tail_heavy_10,policy,1.000000005418604,1.0000000216744163,10.0,0.8
+unitree_g1_pack_camera,case1,tail_heavy_10,world_model,1.0000000162558123,0.9999999891627919,10.0,0.8
+unitree_g1_pack_camera,case1,tail_heavy_6,policy,1.000000005418604,1.0000000216744163,6.0,0.88
+unitree_g1_pack_camera,case1,tail_heavy_6,world_model,1.0000000162558123,0.9999999891627919,6.0,0.88
+unitree_g1_pack_camera,case1,tail_heavy_8,policy,1.000000005418604,1.0000000216744163,8.0,0.84
+unitree_g1_pack_camera,case1,tail_heavy_8,world_model,1.0000000162558123,0.9999999891627919,8.0,0.84
+unitree_g1_pack_camera,case1,tail_only_4,policy,1.000000005418604,1.0000000216744163,4.0,0.92
+unitree_g1_pack_camera,case1,tail_only_4,world_model,1.0000000162558123,0.9999999891627919,4.0,0.92
+unitree_g1_pack_camera,case1,tail_only_6,policy,1.000000005418604,1.0000000216744163,6.0,0.88
+unitree_g1_pack_camera,case1,tail_only_6,world_model,1.0000000162558123,0.9999999891627919,6.0,0.88
+unitree_g1_pack_camera,case2,dense,policy,1.0,1.0000000108372082,50.0,0.0
+unitree_g1_pack_camera,case2,dense,world_model,1.0000000433488325,1.0,50.0,0.0
+unitree_g1_pack_camera,case2,sparse_10,policy,0.9434121738780629,0.9354661919853904,10.0,0.8
+unitree_g1_pack_camera,case2,sparse_10,world_model,0.9378488930788907,0.9352135224775835,10.0,0.8
+unitree_g1_pack_camera,case2,sparse_4,policy,1.0,1.0000000108372082,4.0,0.92
+unitree_g1_pack_camera,case2,sparse_4,world_model,1.0000000433488325,1.0,4.0,0.92
+unitree_g1_pack_camera,case2,sparse_5,policy,0.7797025496309454,0.7743967554785989,5.0,0.9
+unitree_g1_pack_camera,case2,sparse_5,world_model,0.7510665926066312,0.7674615762450478,5.0,0.9
+unitree_g1_pack_camera,case2,sparse_8,policy,1.0,1.0000000108372082,8.0,0.84
+unitree_g1_pack_camera,case2,sparse_8,world_model,1.0000000433488325,1.0,8.0,0.84
+unitree_g1_pack_camera,case2,tail_heavy_10,policy,1.0,1.0000000108372082,10.0,0.8
+unitree_g1_pack_camera,case2,tail_heavy_10,world_model,1.0000000433488325,1.0,10.0,0.8
+unitree_g1_pack_camera,case2,tail_heavy_6,policy,1.0,1.0000000108372082,6.0,0.88
+unitree_g1_pack_camera,case2,tail_heavy_6,world_model,1.0000000433488325,1.0,6.0,0.88
+unitree_g1_pack_camera,case2,tail_heavy_8,policy,1.0,1.0000000108372082,8.0,0.84
+unitree_g1_pack_camera,case2,tail_heavy_8,world_model,1.0000000433488325,1.0,8.0,0.84
+unitree_g1_pack_camera,case2,tail_only_4,policy,1.0,1.0000000108372082,4.0,0.92
+unitree_g1_pack_camera,case2,tail_only_4,world_model,1.0000000433488325,1.0,4.0,0.92
+unitree_g1_pack_camera,case2,tail_only_6,policy,1.0,1.0000000108372082,6.0,0.88
+unitree_g1_pack_camera,case2,tail_only_6,world_model,1.0000000433488325,1.0,6.0,0.88
+unitree_g1_pack_camera,case3,dense,policy,0.9999999729069796,0.9999999945813959,50.0,0.0
+unitree_g1_pack_camera,case3,dense,world_model,0.9999999891627919,0.9999999729069796,50.0,0.0
+unitree_g1_pack_camera,case3,sparse_10,policy,0.9479800787839022,0.9408404772931879,10.0,0.8
+unitree_g1_pack_camera,case3,sparse_10,world_model,0.9443203156644647,0.9426048723134127,10.0,0.8
+unitree_g1_pack_camera,case3,sparse_4,policy,0.9999999729069796,0.9999999945813959,4.0,0.92
+unitree_g1_pack_camera,case3,sparse_4,world_model,0.9999999891627919,0.9999999729069796,4.0,0.92
+unitree_g1_pack_camera,case3,sparse_5,policy,0.7867171547629617,0.7888143712824042,5.0,0.9
+unitree_g1_pack_camera,case3,sparse_5,world_model,0.7702723687345331,0.7856205864386125,5.0,0.9
+unitree_g1_pack_camera,case3,sparse_8,policy,0.9999999729069796,0.9999999945813959,8.0,0.84
+unitree_g1_pack_camera,case3,sparse_8,world_model,0.9999999891627919,0.9999999729069796,8.0,0.84
+unitree_g1_pack_camera,case3,tail_heavy_10,policy,0.9999999729069796,0.9999999945813959,10.0,0.8
+unitree_g1_pack_camera,case3,tail_heavy_10,world_model,0.9999999891627919,0.9999999729069796,10.0,0.8
+unitree_g1_pack_camera,case3,tail_heavy_6,policy,0.9999999729069796,0.9999999945813959,6.0,0.88
+unitree_g1_pack_camera,case3,tail_heavy_6,world_model,0.9999999891627919,0.9999999729069796,6.0,0.88
+unitree_g1_pack_camera,case3,tail_heavy_8,policy,0.9999999729069796,0.9999999945813959,8.0,0.84
+unitree_g1_pack_camera,case3,tail_heavy_8,world_model,0.9999999891627919,0.9999999729069796,8.0,0.84
+unitree_g1_pack_camera,case3,tail_only_4,policy,0.9999999729069796,0.9999999945813959,4.0,0.92
+unitree_g1_pack_camera,case3,tail_only_4,world_model,0.9999999891627919,0.9999999729069796,4.0,0.92
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+unitree_z1_stackbox,case3,tail_only_4,world_model,1.0000000298023224,1.0,4.0,0.92
+unitree_z1_stackbox,case3,tail_only_6,policy,1.0000000447034836,0.9999999950329462,6.0,0.88
+unitree_z1_stackbox,case3,tail_only_6,world_model,1.0000000298023224,1.0,6.0,0.88
+unitree_z1_stackbox,case4,dense,policy,1.00000003973643,1.0000000298023224,50.0,0.0
+unitree_z1_stackbox,case4,dense,world_model,1.0000000099341075,1.0000000248352687,50.0,0.0
+unitree_z1_stackbox,case4,sparse_10,policy,0.9115401953458786,0.8695555130640665,10.0,0.8000000000000002
+unitree_z1_stackbox,case4,sparse_10,world_model,0.9073014905055364,0.8689398914575577,10.0,0.8000000000000002
+unitree_z1_stackbox,case4,sparse_4,policy,1.00000003973643,1.0000000298023224,4.0,0.92
+unitree_z1_stackbox,case4,sparse_4,world_model,1.0000000099341075,1.0000000248352687,4.0,0.92
+unitree_z1_stackbox,case4,sparse_5,policy,0.7569213857253393,0.6986446877320608,5.0,0.9
+unitree_z1_stackbox,case4,sparse_5,world_model,0.7459165553251902,0.6989829589923223,5.0,0.9
+unitree_z1_stackbox,case4,sparse_8,policy,1.00000003973643,1.0000000298023224,8.0,0.84
+unitree_z1_stackbox,case4,sparse_8,world_model,1.0000000099341075,1.0000000248352687,8.0,0.84
+unitree_z1_stackbox,case4,tail_heavy_10,policy,1.00000003973643,1.0000000298023224,10.0,0.8000000000000002
+unitree_z1_stackbox,case4,tail_heavy_10,world_model,1.0000000099341075,1.0000000248352687,10.0,0.8000000000000002
+unitree_z1_stackbox,case4,tail_heavy_6,policy,1.00000003973643,1.0000000298023224,6.0,0.88
+unitree_z1_stackbox,case4,tail_heavy_6,world_model,1.0000000099341075,1.0000000248352687,6.0,0.88
+unitree_z1_stackbox,case4,tail_heavy_8,policy,1.00000003973643,1.0000000298023224,8.0,0.84
+unitree_z1_stackbox,case4,tail_heavy_8,world_model,1.0000000099341075,1.0000000248352687,8.0,0.84
+unitree_z1_stackbox,case4,tail_only_4,policy,1.00000003973643,1.0000000298023224,4.0,0.92
+unitree_z1_stackbox,case4,tail_only_4,world_model,1.0000000099341075,1.0000000248352687,4.0,0.92
+unitree_z1_stackbox,case4,tail_only_6,policy,1.00000003973643,1.0000000298023224,6.0,0.88
+unitree_z1_stackbox,case4,tail_only_6,world_model,1.0000000099341075,1.0000000248352687,6.0,0.88

src/unifolm_wma/models/samplers/ddim.py

@@ -110,6 +110,9 @@ class DDIMSampler(object):
             action_T=None,
             state_T=None,
             record_step_outputs=False,
+            head_schedule=None,
+            head_log_steps=None,
+            head_skip_mode="reuse_prediction",
             **kwargs):
 
         # Check condition bs
@@ -168,6 +171,9 @@ class DDIMSampler(object):
             action_T=action_T,
             state_T=state_T,
             record_step_outputs=record_step_outputs,
+            head_schedule=head_schedule,
+            head_log_steps=head_log_steps,
+            head_skip_mode=head_skip_mode,
             **kwargs)
         return samples, actions, states, intermediates
 
@@ -197,6 +203,9 @@ class DDIMSampler(object):
                       action_T=None,
                       state_T=None,
                       record_step_outputs=False,
+                      head_schedule=None,
+                      head_log_steps=None,
+                      head_skip_mode="reuse_prediction",
                       **kwargs):
         device = self.model.betas.device
         dp_ddim_scheduler_action = self.model.dp_noise_scheduler_action
@@ -248,6 +257,14 @@ class DDIMSampler(object):
                 'state': state.detach().cpu(),
             }
             intermediates['step_records'] = []
+        head_schedule_set = None if head_schedule is None else {
+            int(step_index) for step_index in head_schedule
+        }
+        head_log_steps_set = None if head_log_steps is None else {
+            int(step_index) for step_index in head_log_steps
+        }
+        if head_log_steps_set is not None:
+            intermediates['head_sparse_logs'] = {}
         time_range = reversed(range(
             0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)
         total_steps = timesteps if ddim_use_original_steps else timesteps.shape[
@@ -261,6 +278,14 @@ class DDIMSampler(object):
         sync_device = device if isinstance(device, torch.device) else torch.device(
             device)
         should_sync = record_step_outputs and sync_device.type == "cuda"
+        if head_skip_mode not in {"reuse_prediction", "freeze_state"}:
+            raise ValueError(
+                f"Unsupported head_skip_mode={head_skip_mode!r}. "
+                "Expected 'reuse_prediction' or 'freeze_state'.")
+        x_action_frozen = action.detach().clone()
+        x_state_frozen = state.detach().clone()
+        action_pred_cache = None
+        state_pred_cache = None
 
         dp_ddim_scheduler_action.set_timesteps(len(timesteps))
         dp_ddim_scheduler_state.set_timesteps(len(timesteps))
@@ -280,6 +305,11 @@ class DDIMSampler(object):
             if should_sync:
                 torch.cuda.synchronize(sync_device)
             step_start_time = time.time()
+            scheduled_head = head_schedule_set is None or i in head_schedule_set
+            if head_skip_mode == "reuse_prediction":
+                run_head = scheduled_head or action_pred_cache is None or state_pred_cache is None
+            else:
+                run_head = scheduled_head
 
             outs = self.p_sample_ddim(
                 img,
@@ -300,22 +330,48 @@ class DDIMSampler(object):
                 x0=x0,
                 fs=fs,
                 guidance_rescale=guidance_rescale,
+                backbone_step_index=i + 1,
+                run_head=run_head,
                 **kwargs)
 
             img, pred_x0, model_output_action, model_output_state = outs
 
-            action = dp_ddim_scheduler_action.step(
-                model_output_action,
-                step,
-                action,
-                generator=None,
-            ).prev_sample
-            state = dp_ddim_scheduler_state.step(
-                model_output_state,
-                step,
-                state,
-                generator=None,
-            ).prev_sample
+            if run_head:
+                action_pred_cache = model_output_action.detach().clone()
+                state_pred_cache = model_output_state.detach().clone()
+                action = dp_ddim_scheduler_action.step(
+                    model_output_action,
+                    step,
+                    action,
+                    generator=None,
+                ).prev_sample
+                state = dp_ddim_scheduler_state.step(
+                    model_output_state,
+                    step,
+                    state,
+                    generator=None,
+                ).prev_sample
+                x_action_frozen = action.detach().clone()
+                x_state_frozen = state.detach().clone()
+            else:
+                if head_skip_mode == "reuse_prediction":
+                    action = dp_ddim_scheduler_action.step(
+                        action_pred_cache,
+                        step,
+                        action,
+                        generator=None,
+                    ).prev_sample
+                    state = dp_ddim_scheduler_state.step(
+                        state_pred_cache,
+                        step,
+                        state,
+                        generator=None,
+                    ).prev_sample
+                    x_action_frozen = action.detach().clone()
+                    x_state_frozen = state.detach().clone()
+                else:
+                    action = x_action_frozen
+                    state = x_state_frozen
 
             if should_sync:
                 torch.cuda.synchronize(sync_device)
@@ -329,10 +385,21 @@ class DDIMSampler(object):
                 intermediates['pred_x0'].append(pred_x0)
                 intermediates['x_inter_action'].append(action)
                 intermediates['x_inter_state'].append(state)
+            if head_log_steps_set is not None and i in head_log_steps_set:
+                intermediates['head_sparse_logs'][i] = {
+                    'step_index': i,
+                    'ddim_timestep': int(step),
+                    'head_executed': run_head,
+                    'img': img.detach().cpu(),
+                    'pred_x0': pred_x0.detach().cpu(),
+                    'action': action.detach().cpu(),
+                    'state': state.detach().cpu(),
+                }
             if record_step_outputs:
                 intermediates['step_records'].append({
                     'step_index': i + 1,
                     'ddim_timestep': int(step),
+                    'head_executed': run_head,
                     'img': img.detach().cpu(),
                     'pred_x0': pred_x0.detach().cpu(),
                     'action': action.detach().cpu(),
@@ -364,6 +431,7 @@ class DDIMSampler(object):
                       mask=None,
                       x0=None,
                       guidance_rescale=0.0,
+                      run_head=True,
                       **kwargs):
         b, *_, device = *x.shape, x.device
         if x.dim() == 5:
@@ -373,35 +441,42 @@ class DDIMSampler(object):
 
         if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
             model_output, model_output_action, model_output_state = self.model.apply_model(
-                x, x_action, x_state, t, c, **kwargs)  # unet denoiser
+                x, x_action, x_state, t, c, run_head=run_head,
+                **kwargs)  # unet denoiser
         else:
             # do_classifier_free_guidance
             if isinstance(c, torch.Tensor) or isinstance(c, dict):
                 e_t_cond, e_t_cond_action, e_t_cond_state = self.model.apply_model(
-                    x, x_action, x_state, t, c, **kwargs)
+                    x, x_action, x_state, t, c, run_head=run_head, **kwargs)
                 e_t_uncond, e_t_uncond_action, e_t_uncond_state = self.model.apply_model(
                     x, x_action, x_state, t, unconditional_conditioning,
+                    run_head=run_head,
                     **kwargs)
             else:
                 raise NotImplementedError
             model_output = e_t_uncond + unconditional_guidance_scale * (
                 e_t_cond - e_t_uncond)
-            model_output_action = e_t_uncond_action + unconditional_guidance_scale * (
-                e_t_cond_action - e_t_uncond_action)
-            model_output_state = e_t_uncond_state + unconditional_guidance_scale * (
-                e_t_cond_state - e_t_uncond_state)
+            if run_head:
+                model_output_action = e_t_uncond_action + unconditional_guidance_scale * (
+                    e_t_cond_action - e_t_uncond_action)
+                model_output_state = e_t_uncond_state + unconditional_guidance_scale * (
+                    e_t_cond_state - e_t_uncond_state)
+            else:
+                model_output_action = None
+                model_output_state = None
 
             if guidance_rescale > 0.0:
                 model_output = rescale_noise_cfg(
                     model_output, e_t_cond, guidance_rescale=guidance_rescale)
-                model_output_action = rescale_noise_cfg(
-                    model_output_action,
-                    e_t_cond_action,
-                    guidance_rescale=guidance_rescale)
-                model_output_state = rescale_noise_cfg(
-                    model_output_state,
-                    e_t_cond_state,
-                    guidance_rescale=guidance_rescale)
+                if run_head:
+                    model_output_action = rescale_noise_cfg(
+                        model_output_action,
+                        e_t_cond_action,
+                        guidance_rescale=guidance_rescale)
+                    model_output_state = rescale_noise_cfg(
+                        model_output_state,
+                        e_t_cond_state,
+                        guidance_rescale=guidance_rescale)
 
         if self.model.parameterization == "v":
             e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)

src/unifolm_wma/modules/networks/wma_model.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import time
 
 from torch import Tensor
 from functools import partial
@@ -715,6 +716,9 @@ class WMAModel(nn.Module):
         """
 
         b, _, t, _, _ = x.shape
+        run_head = kwargs.pop("run_head", True)
+        backbone_block_profiler = kwargs.pop("backbone_block_profiler", None)
+        backbone_step_index = kwargs.pop("backbone_step_index", None)
         t_emb = timestep_embedding(timesteps,
                                    self.model_channels,
                                    repeat_only=False).type(x.dtype)
@@ -791,14 +795,47 @@ class WMAModel(nn.Module):
             fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0)
             emb = emb + fs_embed
 
+        def run_block_with_profile(block_name: str, block_stage: str,
+                                   block_index: int | None,
+                                   fn: Callable[[], Tensor]) -> Tensor:
+            if backbone_block_profiler is None or backbone_step_index is None:
+                return fn()
+            if x.device.type == "cuda":
+                torch.cuda.synchronize(x.device)
+            start_time = time.perf_counter()
+            out = fn()
+            if x.device.type == "cuda":
+                torch.cuda.synchronize(x.device)
+            backbone_block_profiler.record_block(
+                step=int(backbone_step_index),
+                block_name=block_name,
+                block_stage=block_stage,
+                block_index=block_index,
+                output=out,
+                forward_time_ms=(time.perf_counter() - start_time) * 1000.0,
+            )
+            return out
+
         h = x.type(self.dtype)
         adapter_idx = 0
         hs = []
         hs_a = []
         for id, module in enumerate(self.input_blocks):
-            h = module(h, emb, context=context, batch_size=b)
-            if id == 0 and self.addition_attention:
-                h = self.init_attn(h, emb, context=context, batch_size=b)
+            def run_input_block() -> Tensor:
+                block_out = module(h, emb, context=context, batch_size=b)
+                if id == 0 and self.addition_attention:
+                    block_out = self.init_attn(block_out,
+                                               emb,
+                                               context=context,
+                                               batch_size=b)
+                return block_out
+
+            h = run_block_with_profile(
+                block_name=f"input_{id}",
+                block_stage="input_blocks",
+                block_index=id,
+                fn=run_input_block,
+            )
             # plug-in adapter features
             if ((id + 1) % 3 == 0) and features_adapter is not None:
                 h = h + features_adapter[adapter_idx]
@@ -813,13 +850,30 @@ class WMAModel(nn.Module):
         if features_adapter is not None:
             assert len(
                 features_adapter) == adapter_idx, 'Wrong features_adapter'
-        h = self.middle_block(h, emb, context=context, batch_size=b)
+        h = run_block_with_profile(
+            block_name="middle",
+            block_stage="middle_block",
+            block_index=0,
+            fn=lambda: self.middle_block(h, emb, context=context, batch_size=b),
+        )
         hs_a.append(rearrange(h, '(b t) c h w -> b t c h w', t=t))
 
         hs_out = []
-        for module in self.output_blocks:
-            h = torch.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context=context, batch_size=b)
+        for id, module in enumerate(self.output_blocks):
+            skip_h = hs.pop()
+
+            def run_output_block() -> Tensor:
+                return module(torch.cat([h, skip_h], dim=1),
+                              emb,
+                              context=context,
+                              batch_size=b)
+
+            h = run_block_with_profile(
+                block_name=f"output_{id}",
+                block_stage="output_blocks",
+                block_index=id,
+                fn=run_output_block,
+            )
             if isinstance(module[-1], Upsample):
                 hs_a.append(
                     rearrange(hs_out[-1], '(b t) c h w -> b t c h w', t=t))
@@ -830,7 +884,7 @@ class WMAModel(nn.Module):
         y = self.out(h)
         y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
 
-        if not self.base_model_gen_only:
+        if not self.base_model_gen_only and run_head:
             ba, _, _ = x_action.shape
             a_y = self.action_unet(x_action, timesteps[:ba], hs_a,
                                    context_action[:2], **kwargs)
@@ -841,6 +895,9 @@ class WMAModel(nn.Module):
             else:
                 s_y = self.state_unet(x_state, timesteps, hs_a,
                                       context_action[:2], **kwargs)
+        elif not self.base_model_gen_only:
+            a_y = None
+            s_y = None
         else:
             a_y = torch.zeros_like(x_action)
             s_y = torch.zeros_like(x_state)

unitree_g1_pack_camera/case1/output/inference/analysis/analysis_report.md

@@ -21,34 +21,34 @@
 
 - `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
 - `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
-- `latent_l2_vs_full50`: mean=119.6001, median=138.8359, p90=169.4805, p95=174.5434
+- `latent_l2_vs_full50`: mean=172.5522, median=175.0850, p90=194.0716, p95=200.4957
 
 ### Direction C: Action Converges First
 
 - `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
 - `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
-- `action_vs_full50_95pct_step`: mean=46.6364, median=46.6364, p90=46.6364, p95=46.6364
+- `action_vs_full50_95pct_step`: mean=47.2727, median=47.2727, p90=47.2727, p95=47.2727
 - `share_action_first_stable_before_latent`: nan
 - `share_action_95pct_before_latent`: nan
 
 ### Direction D: Cross-Step Similarity
 
-- `latent_delta_mid_steps`: mean=0.3526, median=0.3488, p90=0.4169, p95=0.4275
-- `action_delta_mid_steps`: mean=0.0318, median=0.0300, p90=0.0453, p95=0.0481
-- `state_delta_mid_steps`: mean=0.0311, median=0.0302, p90=0.0437, p95=0.0460
+- `latent_delta_mid_steps`: mean=0.3526, median=0.3486, p90=0.4168, p95=0.4280
+- `action_delta_mid_steps`: mean=0.0301, median=0.0287, p90=0.0431, p95=0.0455
+- `state_delta_mid_steps`: mean=0.0312, median=0.0299, p90=0.0444, p95=0.0465
 
 ### Direction A: Budget Heterogeneity
 
-- `oracle_budget_action`: mean=46.6364, median=46.6364, p90=46.6364, p95=46.6364
-- `oracle_budget_state`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `oracle_budget_action`: mean=46.8182, median=46.8182, p90=46.8182, p95=46.8182
+- `oracle_budget_state`: mean=46.5455, median=46.5455, p90=46.5455, p95=46.5455
 - `oracle_budget_action_by_scene`:
-  - `unitree_g1_pack_camera`: mean=46.6364, median=46.6364, std=0.0000
+  - `unitree_g1_pack_camera`: mean=46.8182, median=46.8182, std=0.0000
 
 ### Direction B: Round Reuse
 
-- `latent_init_dist_to_prev_round`: mean=229.0488, median=228.5274, p90=251.0416, p95=257.7575
-- `action_drift_vs_prev_round`: mean=0.1520, median=0.1298, p90=0.2520, p95=0.3113
-- `round_total_time_s`: mean=150.0686, median=151.2592, p90=152.1900, p95=152.5157
-- `policy_pass_total_time_s`: mean=73.6966, median=74.1877, p90=74.7325, p95=74.8893
-- `world_model_pass_total_time_s`: mean=73.2965, median=74.3135, p90=74.6243, p95=74.6295
+- `latent_init_dist_to_prev_round`: mean=231.3224, median=234.0656, p90=246.2483, p95=251.1132
+- `action_drift_vs_prev_round`: mean=0.1699, median=0.1659, p90=0.2361, p95=0.3048
+- `round_total_time_s`: mean=136.4094, median=136.1244, p90=137.2721, p95=137.4062
+- `policy_pass_total_time_s`: mean=33.5494, median=33.5109, p90=33.7524, p95=33.8025
+- `world_model_pass_total_time_s`: mean=33.6186, median=33.5301, p90=33.9147, p95=34.0011
 

unitree_g1_pack_camera/case1/output/inference/analysis/scene_summary.csv

@@ -1,4 +1,4 @@
 scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
 ,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
-unitree_g1_pack_camera,policy,73.69664512981068,73.69664512981068,,,,,,,,,,,44.72727272727273,44.72727272727273,,46.63636363636363,46.63636363636363,,46.63636363636363,46.63636363636363,,46.90909090909091,46.90909090909091,,150.06858123432505,150.06858123432505,,73.69664512981068,73.69664512981068,,73.29652458971196,73.29652458971196,,229.048844909668,229.048844909668,,0.1520089924335479,0.1520089924335479,
-unitree_g1_pack_camera,world_model,73.29652458971196,73.29652458971196,,,,,,,,,,,44.81818181818182,44.81818181818182,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,150.06858123432505,150.06858123432505,,73.69664512981068,73.69664512981068,,73.29652458971196,73.29652458971196,,229.048844909668,229.048844909668,,0.1520089924335479,0.1520089924335479,
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unitree_g1_pack_camera/case1/output/inference/analysis/step_aggregate.csv

@@ -1,102 +1,102 @@
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unitree_g1_pack_camera/case1/output/inference/round_summary.csv

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unitree_g1_pack_camera/case1/output/inference/sample_summary.csv

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unitree_g1_pack_camera/case1/output/sparse_8/inference/round_summary.csv

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+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,1,35.05562353134155,34.18100881576538,143.12280178070068,214.95654296875,0.1811339259147644,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,2,34.80648851394653,34.815757036209106,143.93157863616943,240.22900390625,0.18993812799453735,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,3,35.42078423500061,34.078826665878296,143.7456750869751,206.12351989746094,0.026802361011505127,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,4,34.88056492805481,34.83683705329895,145.21864223480225,216.09486389160156,0.16485852003097534,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,5,34.707502365112305,34.06549119949341,142.62004137039185,227.89527893066406,0.3844761848449707,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,6,34.08131694793701,34.453927993774414,141.4016900062561,236.65484619140625,0.21243447065353394,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,7,35.12070393562317,35.34656286239624,145.56022691726685,246.84864807128906,0.1723371148109436,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,8,34.99006509780884,34.1045401096344,142.48975276947021,236.0821533203125,0.10035890340805054,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,9,34.99584174156189,35.086113691329956,143.1674451828003,235.0965576171875,0.1056985855102539,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,10,35.301815032958984,34.37025237083435,144.55097150802612,241.2891845703125,0.16251075267791748,

unitree_g1_pack_camera/case1/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,policy,34.943129019303754,,,,45.36363636363637,47.36363636363637,46.81818181818182,46.72727272727273,,230.12705993652344,0.17005489468574525,143.59034397385338,34.943129019303754,34.55225768956271,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,34.55225768956271,,,,45.9,48.0,46.81818181818182,46.54545454545455,,230.12705993652344,0.17005489468574525,143.59034397385338,34.943129019303754,34.55225768956271,

unitree_g1_pack_camera/case1/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case1"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_g1_pack_camera"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case2/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,0,34.508949756622314,34.254820823669434,140.46258544921875,,,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,1,34.90244817733765,34.26566195487976,141.24445700645447,216.52996826171875,0.24411267042160034,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,2,34.450573444366455,33.74520945549011,140.44232392311096,210.6576385498047,0.211828351020813,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,3,34.227173805236816,34.19744801521301,139.57855439186096,231.77476501464844,0.10076236724853516,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,4,34.42516374588013,33.859012603759766,140.9917664527893,196.30520629882812,0.24305355548858643,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,5,34.275354862213135,34.270713329315186,143.11559915542603,270.99072265625,0.9365180283784866,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,6,34.44929575920105,33.572381019592285,141.9271743297577,227.52503967285156,0.14944738149642944,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,7,34.52991771697998,34.2509183883667,141.21305179595947,256.9947509765625,0.1445719599723816,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,8,34.30908131599426,33.91361689567566,140.56905364990234,263.05572509765625,0.0568811297416687,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,9,34.12396168708801,34.13224148750305,139.65425729751587,249.03759765625,0.11548089981079102,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,10,34.32340431213379,34.3162624835968,142.47088241577148,246.99588012695312,0.5883344411849976,

unitree_g1_pack_camera/case2/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,policy,34.41139314391396,,,,45.90909090909091,47.81818181818182,47.0,47.09090909090909,,236.98672943115236,0.279099078476429,141.06088235161522,34.41139314391396,34.07075331427834,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,34.07075331427834,,,,46.27272727272727,47.77777777777778,47.0,47.09090909090909,,236.98672943115236,0.279099078476429,141.06088235161522,34.41139314391396,34.07075331427834,

unitree_g1_pack_camera/case2/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case2"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_g1_pack_camera"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 1100
+- Sample rows: 2
+- Round rows: 11
+- Scenes: unitree_g1_pack_camera
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=118.1510, median=137.4783, p90=165.7335, p95=172.5465
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3528, median=0.3487, p90=0.4164, p95=0.4276
+- `action_delta_mid_steps`: mean=0.0317, median=0.0304, p90=0.0455, p95=0.0480
+- `state_delta_mid_steps`: mean=0.0317, median=0.0308, p90=0.0444, p95=0.0465
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `oracle_budget_state`: mean=46.8182, median=46.8182, p90=46.8182, p95=46.8182
+- `oracle_budget_action_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.7273, median=46.7273, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=226.9825, median=228.4588, p90=251.2165, p95=255.6648
+- `action_drift_vs_prev_round`: mean=0.2812, median=0.1662, p90=0.6238, p95=0.6602
+- `round_total_time_s`: mean=559.6007, median=588.8439, p90=625.1090, p95=625.4309
+- `policy_pass_total_time_s`: mean=231.2165, median=252.6857, p90=269.4816, p95=270.8949
+- `world_model_pass_total_time_s`: mean=239.7890, median=256.9119, p90=265.6770, p95=268.7066
+

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
+scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_g1_pack_camera,policy,231.21650622107765,231.21650622107765,,,,,,,,,,,44.90909090909091,44.90909090909091,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,46.54545454545455,46.54545454545455,,559.6006835373965,559.6006835373965,,231.21650622107765,231.21650622107765,,239.78899411721665,239.78899411721665,,226.98248596191405,226.98248596191405,,0.2811613976955414,0.2811613976955414,
+unitree_g1_pack_camera,world_model,239.78899411721665,239.78899411721665,,,,,,,,,,,45.18181818181818,45.18181818181818,,46.90909090909091,46.90909090909091,,46.90909090909091,46.90909090909091,,46.81818181818182,46.81818181818182,,559.6006835373965,559.6006835373965,,231.21650622107765,231.21650622107765,,239.78899411721665,239.78899411721665,,226.98248596191405,226.98248596191405,,0.2811613976955414,0.2811613976955414,

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
+pass_type,step,step_time_s,step_time_s,step_time_s,latent_delta,latent_delta,latent_delta,action_delta,action_delta,action_delta,state_delta,state_delta,state_delta,action_cosine_vs_full50,action_cosine_vs_full50,action_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+policy,1,2.380662376230413,2.44140100479126,0.541582686726123,1.4142944487658413,1.4138203859329224,0.0021484383984422544,0.03989342092113057,0.0394162014126777,0.005337447312609944,0.03817017190158364,0.0320768542587757,0.019583009653493806,0.05996392396363342,0.052498735487461,0.060609924998752535,0.011052970520474692,0.0090840049088001,0.06042950452013075,170.6597373268821,169.39004516601562,11.4294487197575
+policy,2,4.017352884465998,4.391762018203735,1.1861475768626526,1.0213339003649624,1.0217310190200806,0.0024567828937138454,0.030183969742872463,0.0292389243841171,0.003662848656161182,0.024038863114335264,0.0234161801636219,0.0032601327954641397,0.05937811004167251,0.0544382371008396,0.060633432820552245,0.012036324648017217,0.0120233483612537,0.06094917187960682,170.7233359596946,170.29335021972656,11.105094617742372
+policy,3,4.122266856106845,4.671510457992554,1.2492590505795216,0.8495142514055426,0.8491835594177246,0.0019042913048274144,0.024150980805808797,0.0233028475195169,0.003964547134585321,0.018858069130642793,0.017807736992836,0.003598174444944763,0.062450069764798305,0.0594579800963401,0.06071553439145037,0.016193428500132116,0.0185147151350975,0.06139902092154736,169.90216341885653,168.07110595703125,11.037017541704445
+policy,4,4.412413705479015,5.128522157669067,1.3165137220462457,0.7499381845647638,0.7500395774841309,0.0020271416241545914,0.020568183030594437,0.0197155363857746,0.0039035814396075276,0.015956267629834684,0.0148289687931537,0.003215750250852455,0.06775069778615776,0.0672371089458465,0.06082244383055722,0.02261141416701403,0.0265998505055904,0.061885995271197446,169.1625615900213,167.7893829345703,10.199674945051715
+policy,5,4.495447418906472,5.103692531585693,1.3613295706748316,0.6822635748169639,0.6824440956115723,0.001260418909258719,0.016741364432329427,0.0162334702908992,0.0016007561725265203,0.014321604574268464,0.0139080937951803,0.002077126750241558,0.07432248680429021,0.0766021013259887,0.06090986389856854,0.030568414621732418,0.0359245762228965,0.06248923823081076,168.0842548717152,167.58656311035156,9.961248376480912
+policy,6,4.286584984172475,4.554376840591431,1.1852548776190672,0.6324766224080866,0.6318691968917847,0.0013462505728320158,0.0152664176103743,0.0151082212105393,0.0012230947816413758,0.0133105415681546,0.0132776191458106,0.001395284315979568,0.08171069994568822,0.0861552059650421,0.06093303013960668,0.03943565123799172,0.0461441427469253,0.0630760748117669,166.63392639160156,168.35958862304688,8.833516870372689
+policy,7,4.33748459815979,4.564946174621582,1.3863692798168286,0.5942527489228682,0.5944616198539734,0.001976783762122132,0.015312190421602893,0.014985267072916,0.001638173799170164,0.013621900823305909,0.0135518694296479,0.0014665356533676765,0.08973098343068898,0.0958151817321777,0.06098438272106316,0.04919455475597217,0.0572784952819347,0.06355077318461783,164.18314014781606,166.34677124023438,8.027096309427666
+policy,8,4.2186902436343106,4.780451536178589,1.288645832478222,0.5637131929397583,0.5641032457351685,0.0016790883821424748,0.01538657388565212,0.0147423753514885,0.0020803765192660517,0.014508365992117965,0.0140916323289275,0.0019343280256098468,0.0983921946449713,0.1056336611509323,0.06111284151095299,0.059545462824065545,0.0688173174858093,0.06396367833187915,161.15547596324575,162.53857421875,8.137841060967315
+policy,9,4.398435310883955,5.180230379104614,1.367872184846212,0.5373825810172341,0.5370771884918213,0.0015751094289736469,0.015588559464297474,0.0149363698437809,0.0019265773400665184,0.015275996263054236,0.0148359052836894,0.0021276673255401514,0.10758543488654217,0.1149793863296508,0.061177656636853754,0.07046273240650239,0.0792663246393203,0.06435803388848475,159.6385928067294,159.92527770996094,7.355049739101368
+policy,10,4.302034052935514,4.888946056365967,1.301449561747903,0.5159985748204318,0.5161250233650208,0.0012208308458228404,0.01593946022066199,0.0153064019978046,0.0016648655269842138,0.015570431439713945,0.0154294883832335,0.0018166444651003101,0.1174858405508778,0.1241016089916229,0.06124378948636174,0.08186532387679271,0.0882695466279983,0.06464688150998767,157.74214727228338,159.52822875976562,7.073231212176352
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unitree_g1_pack_camera/case3/backbone_profile_dense/inference/backbone_analysis/backbone_profile_report.md

@@ -0,0 +1,30 @@
+# Backbone Block Profiling Report
+
+## Dataset Overview
+
+- Rows: 27500
+- Blocks: 25
+- Steps: 1-50
+- Pass types: policy, world_model
+
+## Block Timing
+
+- `forward_time_ms`: mean=29.5324, median=33.0049, p90=53.1777
+
+## Stability
+
+- `rel_l2_delta_vs_prev`: mean=0.144351, median=0.122058, p90=0.238142
+- `cosine_vs_prev`: mean=0.986104, median=0.993058, p90=0.998209
+
+## Top Cache Candidates
+
+- `output_5` (output_blocks): tail_rel_l2=0.047653, mean_forward_time_ms=40.3060
+- `output_4` (output_blocks): tail_rel_l2=0.055735, mean_forward_time_ms=38.0808
+- `output_3` (output_blocks): tail_rel_l2=0.059179, mean_forward_time_ms=38.4340
+- `output_2` (output_blocks): tail_rel_l2=0.059520, mean_forward_time_ms=4.2037
+- `output_1` (output_blocks): tail_rel_l2=0.069876, mean_forward_time_ms=3.3372
+- `output_0` (output_blocks): tail_rel_l2=0.071423, mean_forward_time_ms=3.4297
+- `output_6` (output_blocks): tail_rel_l2=0.074268, mean_forward_time_ms=40.6642
+- `middle` (middle_block): tail_rel_l2=0.075425, mean_forward_time_ms=24.7022
+- `input_7` (input_blocks): tail_rel_l2=0.075788, mean_forward_time_ms=38.4152
+- `input_11` (input_blocks): tail_rel_l2=0.077308, mean_forward_time_ms=2.5828

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/backbone_analysis/block_summary.csv

@@ -0,0 +1,26 @@
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unitree_g1_pack_camera/case3/backbone_profile_dense/inference/backbone_analysis/stage_summary.csv

@@ -0,0 +1,10 @@
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+tail,input_blocks,26.541225201534953,0.12918178639939107,0.9904847125438127,880.7333542390303,0.9334695872032281
+tail,middle_block,24.817648800936613,0.07542458674447099,0.9971520776098425,731.289155925404,0.9663700651038777
+tail,output_blocks,33.29980809022813,0.10081260910991462,0.9936340190695994,3178.3527087298307,0.9287726470466816

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,0,272.30813097953796,258.77083349227905,625.7526919841766,,,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,1,186.9196274280548,263.2243719100952,540.1022210121155,221.82896423339844,0.33534055948257446,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,2,244.72048449516296,254.23945784568787,588.8439288139343,235.9840545654297,0.1256718635559082,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,3,263.5875928401947,254.7561550140381,607.7824947834015,199.4680633544922,0.18907040357589722,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,4,217.18552041053772,256.91192841529846,562.6434659957886,260.11322021484375,0.6156945526599884,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,5,252.68572807312012,261.74088191986084,602.5384485721588,190.06353759765625,0.43064773082733154,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,6,255.90784645080566,249.05019426345825,593.9222755432129,228.24305725097656,0.09470903873443604,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,7,226.4355342388153,271.73611545562744,585.1254336833954,228.67453002929688,0.14335262775421143,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,8,269.48164653778076,265.67703223228455,625.1090137958527,250.22793579101562,0.6966221630573273,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,9,259.6198160648346,210.60337662696838,558.2484686374664,248.954345703125,0.14137792587280273,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,10,94.52964091300964,90.96858811378479,265.539076089859,206.26715087890625,0.03912711143493652,

unitree_g1_pack_camera/case3/backbone_profile_dense/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,231.21650622107765,,,,44.90909090909091,46.72727272727273,46.72727272727273,46.54545454545455,,226.98248596191405,0.2811613976955414,559.6006835373965,231.21650622107765,239.78899411721662,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,239.78899411721662,,,,45.18181818181818,46.90909090909091,46.90909090909091,46.81818181818182,,226.98248596191405,0.2811613976955414,559.6006835373965,231.21650622107765,239.78899411721662,

unitree_g1_pack_camera/case3/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,0,34.73047733306885,34.12665319442749,143.00190567970276,,,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,1,34.77882504463196,35.024171590805054,142.87922620773315,238.14430236816406,0.34216785430908203,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,2,34.65710210800171,34.31918025016785,141.75025510787964,244.6288299560547,0.10849124193191528,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,3,34.64808392524719,34.763160705566406,142.0598931312561,250.288330078125,0.4585834741592407,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,4,34.43521761894226,34.34122323989868,141.00554966926575,224.81471252441406,0.6429577469825745,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,5,34.357038497924805,34.85403919219971,141.1495542526245,195.2209014892578,0.1021263599395752,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,6,35.0233428478241,34.67922353744507,144.0218186378479,226.24966430664062,0.08848029375076294,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,7,34.45149564743042,34.6253707408905,143.35860419273376,245.75660705566406,0.17197155952453613,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,8,34.7300283908844,34.399852991104126,143.3491985797882,238.90292358398438,0.6555709540843964,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,9,34.11410880088806,34.445759534835815,142.07709193229675,226.96424865722656,0.0632086992263794,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,10,34.794456481933594,34.530436992645264,141.20812225341797,223.38699340820312,0.012062489986419678,

unitree_g1_pack_camera/case3/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,policy,34.610925154252485,,,,45.54545454545455,47.45454545454545,46.72727272727273,46.72727272727273,,231.43575134277344,0.26456206738948823,142.35101996768606,34.610925154252485,34.555370179089635,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,34.555370179089635,,,,45.90909090909091,48.0,46.90909090909091,46.72727272727273,,231.43575134277344,0.26456206738948823,142.35101996768606,34.610925154252485,34.555370179089635,

unitree_g1_pack_camera/case3/run_world_model_backbone_profile.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-1}" \
+bash "${repo_root}/scripts/run_world_model_backbone_profile_case.sh" \
+    "unitree_g1_pack_camera/case3" \
+    "unitree_g1_pack_camera" \
+    "6" \
+    "11"

unitree_g1_pack_camera/case3/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case3"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_g1_pack_camera"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case4/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,12 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,0,34.357739210128784,34.586522579193115,140.96898198127747,,,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,1,34.29071259498596,35.00397062301636,141.79955863952637,229.6180419921875,0.32235634326934814,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,2,34.918580293655396,34.66921854019165,145.18225049972534,231.34951782226562,0.08397442102432251,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,3,33.87128400802612,34.726802349090576,141.85736179351807,220.95947265625,0.16776376962661743,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,4,34.74736452102661,34.61718559265137,141.8124771118164,257.54010009765625,0.5236250162124634,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,5,33.95931363105774,34.89797496795654,140.93837070465088,257.5311279296875,0.28147268295288086,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,6,34.651822328567505,34.53969979286194,141.137797832489,236.1876983642578,0.1898796558380127,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,7,34.88510847091675,34.01325011253357,139.92974042892456,259.3033752441406,0.1549433469772339,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,8,34.31177830696106,34.80933094024658,142.9210181236267,282.3804016113281,0.5841841697692871,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,9,34.6734459400177,33.96956968307495,141.88233709335327,262.050048828125,0.22107374668121338,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,10,34.9462194442749,34.84579944610596,142.91395735740662,199.34121704101562,0.014238595962524414,

unitree_g1_pack_camera/case4/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,policy,34.51030624996532,,,,45.81818181818182,47.6,46.63636363636363,46.72727272727273,,243.6261001586914,0.25435117483139036,141.94035014239225,34.51030624996532,34.607211329720236,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,34.607211329720236,,,,46.36363636363637,47.77777777777778,46.81818181818182,46.81818181818182,,243.6261001586914,0.25435117483139036,141.94035014239225,34.51030624996532,34.607211329720236,

unitree_g1_pack_camera/case4/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_g1_pack_camera/case4"
 dataset="unitree_g1_pack_camera"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_g1_pack_camera"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case1/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,9 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,0,34.73653507232666,34.70670413970947,145.22932696342468,,,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,1,34.74703526496887,34.542370319366455,141.5734007358551,231.2671661376953,0.25567227602005005,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,2,34.430612087249756,34.426183462142944,142.15385103225708,192.78646850585938,0.5568674504756927,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,3,34.53444242477417,34.48149752616882,142.79624843597412,262.8606872558594,1.0051207467913628,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,4,34.31733536720276,34.096614837646484,139.36779808998108,275.00848388671875,0.6432150900363922,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,5,34.413142919540405,34.334357500076294,141.52803897857666,260.6252746582031,0.5582498610019684,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,6,34.51011800765991,34.09811472892761,140.9669303894043,261.7527160644531,0.795854464173317,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,7,34.41221499443054,34.01149654388428,139.1915683746338,245.52186584472656,0.9279392808675766,

unitree_z1_dual_arm_cleanup_pencils/case1/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,34.512679517269135,,,,46.75,48.666666666666664,46.5,47.125,,247.11752319335938,0.6775598813380513,141.60089537501335,34.512679517269135,34.337167382240295,
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,34.337167382240295,,,,47.75,48.5,46.625,47.125,,247.11752319335938,0.6775598813380513,141.60089537501335,34.512679517269135,34.337167382240295,

unitree_z1_dual_arm_cleanup_pencils/case1/output/sparse_8/inference/stepwise_log.csv

@@ -0,0 +1,801 @@
+sample_id,scene,pass_type,round_id,step,step_time_s,latent_delta,action_delta,state_delta,action_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,1,0.7122414112091064,1.4160507917404175,0.06852739304304123,0.06137954071164131,0.18718847632408142,-0.004861224442720413,168.77923583984375
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,2,0.6561942100524902,1.0182937383651733,0.03162701055407524,0.03109455108642578,0.19554947316646576,-0.004426291212439537,168.93414306640625
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,3,0.6647841930389404,0.8496333956718445,0.031183363869786263,0.03083704225718975,0.20361268520355225,-0.003990776836872101,167.3546905517578
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,4,0.6669423580169678,0.750101625919342,0.030745884403586388,0.030582891777157784,0.2113816738128662,-0.0035551153123378754,166.9009552001953
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,5,0.6745193004608154,0.680191159248352,0.030315997079014778,0.030333001166582108,0.21886061131954193,-0.003119673579931259,167.56417846679688
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,6,0.6680634021759033,0.6306217312812805,0.029895056039094925,0.03008825145661831,0.22605419158935547,-0.002684846520423889,167.7312469482422
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,7,0.6541545391082764,0.5951859354972839,0.02948428876698017,0.029849419370293617,0.2329675257205963,-0.0022509507834911346,166.72650146484375
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,8,0.729128360748291,0.5636720061302185,0.02783939801156521,0.02415986731648445,0.2390400916337967,0.007997291162610054,165.8634490966797
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,9,0.6487884521484375,0.536273717880249,0.016048109158873558,0.01591581106185913,0.25219130516052246,0.02130279131233692,166.0988311767578
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,10,0.6702709197998047,0.5150058269500732,0.016097143292427063,0.016329871490597725,0.2652962803840637,0.03475123643875122,168.0155029296875
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,11,0.660832405090332,0.4949251711368561,0.016200361773371696,0.01679488644003868,0.2783699631690979,0.04836190491914749,167.63259887695312
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+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,12,0.656801700592041,0.48119306564331055,0.01292175892740488,0.013438896276056767,0.2061273157596588,0.05607526749372482,185.42430114746094
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,13,0.6571307182312012,0.46460065245628357,0.013298465870320797,0.01397185493260622,0.2150251269340515,0.06591391563415527,186.39157104492188
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,14,0.659062385559082,0.4508553147315979,0.01373815257102251,0.014561095274984837,0.22398293018341064,0.07588662207126617,187.8498992919922
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,15,0.7439024448394775,0.43971195816993713,0.015466650947928429,0.017673928290605545,0.23303061723709106,0.08614902198314667,188.07550048828125
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,16,0.6562745571136475,0.4276149868965149,0.015401441603899002,0.017061272636055946,0.24318790435791016,0.09736223518848419,189.05242919921875
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,17,0.6755261421203613,0.41660284996032715,0.016124995425343513,0.017931390553712845,0.2535009980201721,0.10883507132530212,189.79916381835938
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,18,0.6578946113586426,0.406071275472641,0.01689627766609192,0.01884688250720501,0.2639884948730469,0.12059372663497925,190.8991241455078
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,19,0.6590268611907959,0.3968207538127899,0.01771392859518528,0.019807808101177216,0.2746700644493103,0.13266630470752716,190.22689819335938
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,20,0.6596348285675049,0.3860952854156494,0.018577180802822113,0.020814815536141396,0.28556621074676514,0.14508292078971863,191.00311279296875
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,21,0.6633315086364746,0.3797665536403656,0.019485807046294212,0.02186909317970276,0.296698659658432,0.15787598490715027,191.46054077148438
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,22,0.7159254550933838,0.37099358439445496,0.023033050820231438,0.02443133480846882,0.30890852212905884,0.17186808586120605,192.27610778808594
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,23,0.6450660228729248,0.36427178978919983,0.023413371294736862,0.02563125267624855,0.3221328854560852,0.1864166557788849,192.43960571289062
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,24,0.6651468276977539,0.35718628764152527,0.02453743666410446,0.02692265436053276,0.3357490003108978,0.2015353888273239,192.8955841064453
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,25,0.651209831237793,0.34959977865219116,0.025713296607136726,0.028275372460484505,0.34978950023651123,0.21727591753005981,192.9987030029297
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,26,0.6553688049316406,0.34247925877571106,0.026942985132336617,0.029693031683564186,0.3642888069152832,0.2336941659450531,193.3661346435547
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,27,0.6506743431091309,0.3371198773384094,0.02822938561439514,0.03118024952709675,0.37928304076194763,0.25085073709487915,193.58798217773438
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,28,0.657891035079956,0.3304320275783539,0.029575645923614502,0.03274202719330788,0.3948102295398712,0.2688112258911133,193.5550079345703
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,29,0.6962895393371582,0.3241632878780365,0.03257455676794052,0.03535132482647896,0.4113161861896515,0.28853973746299744,193.7281951904297
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,30,0.6497697830200195,0.31803643703460693,0.03329634666442871,0.03712935745716095,0.42904773354530334,0.3092895448207855,193.95281982421875
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,31,0.6482515335083008,0.3129523694515228,0.03488079831004143,0.03900369629263878,0.4474930763244629,0.3311469554901123,191.74447631835938
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,32,0.6555080413818359,0.30641603469848633,0.036542996764183044,0.040981169790029526,0.4666968286037445,0.3542022705078125,189.9975128173828
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,33,0.6663320064544678,0.29813867807388306,0.038287077099084854,0.04306861013174057,0.48670241236686707,0.37854862213134766,188.2086639404297
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,34,0.6506719589233398,0.28971973061561584,0.040118537843227386,0.045274265110492706,0.5075504183769226,0.40428000688552856,186.79959106445312
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,35,0.6765928268432617,0.28157147765159607,0.042041558772325516,0.047604914754629135,0.5292758345603943,0.4314876198768616,185.61317443847656
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,36,0.698028564453125,0.2729538381099701,0.046847596764564514,0.05227617174386978,0.5536013841629028,0.46226316690444946,184.37457275390625
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,37,0.666555643081665,0.26331228017807007,0.0485270619392395,0.05505317822098732,0.5793052315711975,0.49487417936325073,183.31686401367188
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,38,0.653270959854126,0.25501778721809387,0.050900451838970184,0.0579850934445858,0.6060606241226196,0.5293647646903992,182.65696716308594
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,39,0.6630251407623291,0.2447291761636734,0.05338544026017189,0.06107184663414955,0.6338224411010742,0.565727174282074,182.1029815673828
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,40,0.6491975784301758,0.23495987057685852,0.055981025099754333,0.06430923938751221,0.6624996662139893,0.6038781404495239,181.8994140625
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,41,0.6493136882781982,0.22535791993141174,0.058679331094026566,0.06768161058425903,0.6919381618499756,0.6436243057250977,181.8695831298828
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,42,0.6611580848693848,0.21515575051307678,0.06147031858563423,0.07116605341434479,0.721904993057251,0.684626579284668,182.30111694335938
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,43,0.6954045295715332,0.20518597960472107,0.06752856820821762,0.07687032222747803,0.7542182207107544,0.7285979986190796,182.7427520751953
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,44,0.6617844104766846,0.19523076713085175,0.07019168883562088,0.08064644038677216,0.7865615487098694,0.7726260423660278,183.57443237304688
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,45,0.6877803802490234,0.18498127162456512,0.07339219003915787,0.08436547219753265,0.8180994987487793,0.8155986666679382,184.3668670654297
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,46,0.6545851230621338,0.17482146620750427,0.07657540589570999,0.08793147653341293,0.8479973077774048,0.85612553358078,185.53445434570312
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,47,0.6526172161102295,0.16440321505069733,0.07970612496137619,0.09125193953514099,0.8752629160881042,0.8926078677177429,186.76036071777344
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,48,0.6728317737579346,0.15358862280845642,0.08283405750989914,0.09433086216449738,0.8988037109375,0.9233900308609009,188.21078491210938
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,49,0.6672816276550293,0.14192815124988556,0.08652210235595703,0.09775611758232117,0.9175449013710022,0.9470097422599792,190.0011444091797
+unitree_z1_dual_arm_cleanup_pencils-vid0-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,50,0.6894626617431641,0.1207786500453949,0.11806239932775497,0.16291578114032745,0.9347217679023743,0.9663683176040649,190.32652282714844

unitree_z1_dual_arm_cleanup_pencils/case1/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_z1_dual_arm_cleanup_pencils/case1"
 dataset="unitree_z1_dual_arm_cleanup_pencils"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case2/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,9 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,0,33.99187660217285,34.31168270111084,139.47581481933594,,,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,1,34.43045139312744,34.62503695487976,139.876394033432,239.56658935546875,0.7500274181365967,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,2,34.425429344177246,34.365243911743164,141.03927159309387,246.70318603515625,1.0935600325465202,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,3,34.37379479408264,34.461503982543945,140.49598693847656,280.9136657714844,0.5302876830101013,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,4,34.351977586746216,34.01735472679138,140.40994095802307,252.80796813964844,0.556597113609314,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,5,34.36850380897522,34.59166765213013,140.12164878845215,250.94566345214844,0.4579753279685974,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,6,34.57366609573364,33.870989084243774,141.94088697433472,240.7681427001953,0.9360233545303345,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,7,34.280773401260376,34.23922944068909,141.10703015327454,247.4376220703125,0.21265381574630737,

unitree_z1_dual_arm_cleanup_pencils/case2/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,34.349559128284454,,,,46.875,48.5,46.625,47.0,,251.30611964634485,0.6481606779353959,140.55837178230286,34.349559128284454,34.31033855676651,
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,34.31033855676651,,,,46.857142857142854,48.6,46.5,47.0,,251.30611964634485,0.6481606779353959,140.55837178230286,34.349559128284454,34.31033855676651,

unitree_z1_dual_arm_cleanup_pencils/case2/output/sparse_8/inference/stepwise_log.csv

@@ -0,0 +1,801 @@
+sample_id,scene,pass_type,round_id,step,step_time_s,latent_delta,action_delta,state_delta,action_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,1,0.6877856254577637,1.4160624742507935,0.06979220360517502,0.06737218052148819,0.18364007771015167,0.03536028042435646,169.32699584960938
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,2,0.6450870037078857,1.018276572227478,0.03172800689935684,0.03107280842959881,0.1927938461303711,0.03825446218252182,168.96142578125
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,3,0.6473150253295898,0.8496280312538147,0.03140920400619507,0.030713876709342003,0.2016696184873581,0.04108085855841637,168.41268920898438
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,4,0.6476390361785889,0.7501278519630432,0.03109269216656685,0.03036072663962841,0.2102663516998291,0.043839097023010254,167.6234130859375
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,5,0.6474502086639404,0.6801994442939758,0.030779661610722542,0.03001450002193451,0.21858367323875427,0.04652892053127289,168.23016357421875
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,6,0.6504988670349121,0.6306536197662354,0.030471201986074448,0.02967633120715618,0.22662192583084106,0.04915037006139755,166.92282104492188
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,7,0.6502184867858887,0.5952312350273132,0.030168339610099792,0.02934723161160946,0.23438197374343872,0.051703572273254395,169.09803771972656
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,8,0.69171142578125,0.5637052059173584,0.03083827719092369,0.024765927344560623,0.23625528812408447,0.06087241694331169,169.31729125976562
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,9,0.6487724781036377,0.53630530834198,0.015721386298537254,0.015314269810914993,0.24915850162506104,0.07364700734615326,169.1455841064453
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,10,0.6517491340637207,0.5150970816612244,0.01578763872385025,0.01570291444659233,0.26202404499053955,0.08653585612773895,169.788818359375
+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,11,0.6496729850769043,0.4950089752674103,0.015909362584352493,0.01614486053586006,0.2748669385910034,0.09955724328756332,171.30694580078125
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+unitree_z1_dual_arm_cleanup_pencils-vid50-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,50,0.7025055885314941,0.12353639304637909,0.1361432671546936,0.15854808688163757,0.9812995195388794,0.9908853769302368,165.34307861328125

unitree_z1_dual_arm_cleanup_pencils/case2/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_z1_dual_arm_cleanup_pencils/case2"
 dataset="unitree_z1_dual_arm_cleanup_pencils"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case3/backbone_profile_dense/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 800
+- Sample rows: 2
+- Round rows: 8
+- Scenes: unitree_z1_dual_arm_cleanup_pencils
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=115.8025, median=136.8204, p90=163.1501, p95=165.9780
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=46.6250, median=46.6250, p90=46.6250, p95=46.6250
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3539, median=0.3497, p90=0.4171, p95=0.4282
+- `action_delta_mid_steps`: mean=0.0293, median=0.0282, p90=0.0415, p95=0.0434
+- `state_delta_mid_steps`: mean=0.0290, median=0.0278, p90=0.0412, p95=0.0430
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.6250, median=46.6250, p90=46.6250, p95=46.6250
+- `oracle_budget_state`: mean=47.0000, median=47.0000, p90=47.0000, p95=47.0000
+- `oracle_budget_action_by_scene`:
+  - `unitree_z1_dual_arm_cleanup_pencils`: mean=46.6250, median=46.6250, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=245.2738, median=241.8205, p90=256.2588, p95=264.7285
+- `action_drift_vs_prev_round`: mean=0.7145, median=0.7974, p90=0.9565, p95=1.0102
+- `round_total_time_s`: mean=572.9500, median=579.7652, p90=602.7283, p95=603.3178
+- `policy_pass_total_time_s`: mean=238.1444, median=246.9240, p90=257.0490, p95=257.6652
+- `world_model_pass_total_time_s`: mean=246.7932, median=254.5509, p90=258.5156, p95=258.7368
+

unitree_z1_dual_arm_cleanup_pencils/case3/backbone_profile_dense/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
+scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_z1_dual_arm_cleanup_pencils,policy,238.1443801224232,238.1443801224232,,,,,,,,,,,44.75,44.75,,46.625,46.625,,46.625,46.625,,47.125,47.125,,572.950019210577,572.950019210577,,238.1443801224232,238.1443801224232,,246.79315009713173,246.79315009713173,,245.27383640834265,245.27383640834265,,0.7144961793507848,0.7144961793507848,
+unitree_z1_dual_arm_cleanup_pencils,world_model,246.79315009713173,246.79315009713173,,,,,,,,,,,44.625,44.625,,46.625,46.625,,46.625,46.625,,47.0,47.0,,572.950019210577,572.950019210577,,238.1443801224232,238.1443801224232,,246.79315009713173,246.79315009713173,,245.27383640834265,245.27383640834265,,0.7144961793507848,0.7144961793507848,

unitree_z1_dual_arm_cleanup_pencils/case3/backbone_profile_dense/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
+pass_type,step,step_time_s,step_time_s,step_time_s,latent_delta,latent_delta,latent_delta,action_delta,action_delta,action_delta,state_delta,state_delta,state_delta,action_cosine_vs_full50,action_cosine_vs_full50,action_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+policy,1,2.6775122582912445,2.74997079372406,0.4581601264569805,1.4141224920749664,1.4141692519187927,0.0020055387897567477,0.08804251160472626,0.08834772184491149,0.020569038722695585,0.06856756331399079,0.06852775439620015,0.011545716129159516,0.1227689683437347,0.11538696289062494,0.060491925080694266,0.10053079714998601,0.1198897436261177,0.06459447388816172,166.89146423339844,166.0311279296875,3.4163694111657725
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unitree_z1_dual_arm_cleanup_pencils/case3/backbone_profile_dense/inference/backbone_analysis/backbone_profile_report.md

@@ -0,0 +1,30 @@
+# Backbone Block Profiling Report
+
+## Dataset Overview
+
+- Rows: 20000
+- Blocks: 25
+- Steps: 1-50
+- Pass types: policy, world_model
+
+## Block Timing
+
+- `forward_time_ms`: mean=29.6888, median=33.0428, p90=53.5421
+
+## Stability
+
+- `rel_l2_delta_vs_prev`: mean=0.148623, median=0.131480, p90=0.234975
+- `cosine_vs_prev`: mean=0.985607, median=0.991779, p90=0.998233
+
+## Top Cache Candidates
+
+- `output_5` (output_blocks): tail_rel_l2=0.040660, mean_forward_time_ms=40.9982
+- `output_4` (output_blocks): tail_rel_l2=0.053289, mean_forward_time_ms=38.4154
+- `output_6` (output_blocks): tail_rel_l2=0.056536, mean_forward_time_ms=40.9847
+- `output_2` (output_blocks): tail_rel_l2=0.057318, mean_forward_time_ms=4.3035
+- `output_3` (output_blocks): tail_rel_l2=0.060226, mean_forward_time_ms=38.9804
+- `output_8` (output_blocks): tail_rel_l2=0.062275, mean_forward_time_ms=42.2770
+- `input_5` (input_blocks): tail_rel_l2=0.072118, mean_forward_time_ms=42.6592
+- `output_7` (output_blocks): tail_rel_l2=0.073231, mean_forward_time_ms=41.0011
+- `output_1` (output_blocks): tail_rel_l2=0.075050, mean_forward_time_ms=3.4058
+- `input_7` (input_blocks): tail_rel_l2=0.076069, mean_forward_time_ms=38.5863

unitree_z1_dual_arm_cleanup_pencils/case3/backbone_profile_dense/inference/backbone_analysis/block_summary.csv

@@ -0,0 +1,26 @@
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unitree_z1_dual_arm_cleanup_pencils/case3/output/sparse_8/inference/round_summary.csv

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unitree_z1_dual_arm_cleanup_pencils/case3/output/sparse_8/inference/sample_summary.csv

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unitree_z1_dual_arm_cleanup_pencils/case3/output/sparse_8/inference/stepwise_log.csv

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+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,39,0.6560242176055908,0.24612320959568024,0.05490987002849579,0.05908980593085289,0.6400021910667419,0.6086591482162476,181.10409545898438
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,40,0.653799295425415,0.23649759590625763,0.057822857052087784,0.06228401139378548,0.6704978942871094,0.6447045803070068,180.9855194091797
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,41,0.6604995727539062,0.2269001603126526,0.06088797748088837,0.06563553214073181,0.7022124528884888,0.6823261380195618,180.9955596923828
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,42,0.6577277183532715,0.21680843830108643,0.06410104781389236,0.06913115829229355,0.734958827495575,0.7212564945220947,181.4691162109375
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,43,0.7010416984558105,0.20679448544979095,0.07089077681303024,0.0737539604306221,0.7698583602905273,0.76194167137146,181.90528869628906
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,44,0.6533682346343994,0.1969878375530243,0.07356980443000793,0.07756061106920242,0.8058331608772278,0.8029186129570007,182.69383239746094
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,45,0.6558592319488525,0.1866701990365982,0.07738089561462402,0.081395223736763,0.8415144085884094,0.843254029750824,183.64479064941406
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,46,0.6550247669219971,0.17648696899414062,0.08124037086963654,0.08518433570861816,0.8759417533874512,0.8817307949066162,184.9155731201172
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,47,0.6546435356140137,0.16608528792858124,0.08510251343250275,0.0888548195362091,0.9078992009162903,0.9168746471405029,186.11227416992188
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,48,0.6583943367004395,0.15522052347660065,0.08900417387485504,0.09242091327905655,0.9359601140022278,0.9470533728599548,187.5897979736328
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,49,0.654116153717041,0.1435481756925583,0.09352952986955643,0.09646458923816681,0.9586333632469177,0.9706863760948181,189.4212646484375
+unitree_z1_dual_arm_cleanup_pencils-vid100-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,50,0.7028539180755615,0.12265245616436005,0.1435292661190033,0.16214682161808014,0.9825617074966431,0.9918212890625,189.74871826171875

unitree_z1_dual_arm_cleanup_pencils/case3/run_world_model_backbone_profile.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-1}" \
+bash "${repo_root}/scripts/run_world_model_backbone_profile_case.sh" \
+    "unitree_z1_dual_arm_cleanup_pencils/case3" \
+    "unitree_z1_dual_arm_cleanup_pencils" \
+    "4" \
+    "8"

unitree_z1_dual_arm_cleanup_pencils/case3/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_z1_dual_arm_cleanup_pencils/case3"
 dataset="unitree_z1_dual_arm_cleanup_pencils"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case4/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,9 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,0,34.17999196052551,33.92979121208191,139.10489463806152,,,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,1,34.89252185821533,34.62525296211243,143.00065779685974,235.68756103515625,0.6498753130435944,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,2,34.27174615859985,33.688376903533936,138.82233262062073,256.54522705078125,1.1396523118019104,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,3,34.255370140075684,34.916423082351685,142.16555285453796,256.36724853515625,0.5497420132160187,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,4,34.34192728996277,33.572452783584595,140.31931447982788,243.2056121826172,0.4506574869155884,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,5,34.29056000709534,34.0456440448761,140.76101326942444,263.9020690917969,0.6666657030582428,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,6,34.29332089424133,33.41105055809021,140.9711766242981,264.82269287109375,0.9396226517856121,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,7,34.068970680236816,34.633495569229126,142.26596093177795,214.09054565429688,0.16155409812927246,

unitree_z1_dual_arm_cleanup_pencils/case4/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,34.32430112361908,,,,46.714285714285715,48.2,46.5,47.25,,247.80299377441406,0.651109939707177,140.92636290192604,34.32430112361908,34.1028108894825,
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,34.1028108894825,,,,46.625,48.57142857142857,46.75,47.0,,247.80299377441406,0.651109939707177,140.92636290192604,34.32430112361908,34.1028108894825,

unitree_z1_dual_arm_cleanup_pencils/case4/output/sparse_8/inference/stepwise_log.csv

@@ -0,0 +1,801 @@
+sample_id,scene,pass_type,round_id,step,step_time_s,latent_delta,action_delta,state_delta,action_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,1,0.6880984306335449,1.4160549640655518,0.06707990169525146,0.06701512634754181,0.16676631569862366,-0.013001399114727974,165.47959899902344
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,2,0.6404731273651123,1.018290400505066,0.03170491009950638,0.03128667548298836,0.17448890209197998,-0.006608366966247559,165.18072509765625
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,3,0.6616852283477783,0.8496241569519043,0.03141256794333458,0.031186813488602638,0.18197126686573029,-0.00022935494780540466,164.0499267578125
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,4,0.6534245014190674,0.7501158714294434,0.031122006475925446,0.03108721226453781,0.1892121434211731,0.006129594519734383,165.2473907470703
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,5,0.661870002746582,0.6801887154579163,0.030834302306175232,0.03098822385072708,0.19621095061302185,0.012462560087442398,165.2728729248047
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,6,0.6562111377716064,0.6306071281433105,0.030550498515367508,0.03089025802910328,0.202967569231987,0.018763870000839233,166.8395233154297
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,7,0.6506836414337158,0.595181405544281,0.03027154877781868,0.030793650075793266,0.2094823718070984,0.025028038769960403,167.64724731445312
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,8,0.7021369934082031,0.5636259913444519,0.03212101757526398,0.028622178360819817,0.21139118075370789,0.0339856818318367,170.9114227294922
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,9,0.6657814979553223,0.5362586975097656,0.01700483448803425,0.01732577197253704,0.22578637301921844,0.04921547323465347,171.34056091308594
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,10,0.6452083587646484,0.5150431990623474,0.01710130088031292,0.017690259963274002,0.24015609920024872,0.06458741426467896,173.2724151611328
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,policy,0,11,0.6472418308258057,0.4949326515197754,0.017247607931494713,0.018101641908288002,0.25451523065567017,0.08012040704488754,173.38270568847656
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+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,38,0.6864757537841797,0.25685596466064453,0.0502350740134716,0.05466224625706673,0.620384693145752,0.598240077495575,174.46156311035156
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,39,0.7162935733795166,0.24657273292541504,0.0526559017598629,0.05745788663625717,0.648139238357544,0.6302155256271362,173.45912170410156
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,40,0.6682875156402588,0.23691636323928833,0.05518181622028351,0.0603843592107296,0.6767637729644775,0.663445234298706,172.6646728515625
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,41,0.6794540882110596,0.2272820919752121,0.05780508369207382,0.06343020498752594,0.7061041593551636,0.6977360844612122,172.0179901123047
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+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,43,0.6898267269134521,0.20735548436641693,0.06692087650299072,0.07105041295289993,0.7678955793380737,0.7693809866905212,171.6037139892578
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,44,0.6987981796264648,0.1974572241306305,0.06864629685878754,0.07440546154975891,0.8004235029220581,0.805675208568573,171.79794311523438
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+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,46,0.6805813312530518,0.17715197801589966,0.07460834085941315,0.08100370317697525,0.8620632886886597,0.8737053871154785,173.0034637451172
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,47,0.7169170379638672,0.16670779883861542,0.0775168165564537,0.08412803709506989,0.8894479274749756,0.903157114982605,173.8084259033203
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,48,0.6714901924133301,0.15608692169189453,0.08042218536138535,0.08715099096298218,0.9132195115089417,0.9278624653816223,174.8671875
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,49,0.6646618843078613,0.14476294815540314,0.08387859910726547,0.09064489603042603,0.9324020743370056,0.9466094374656677,176.3357391357422
+unitree_z1_dual_arm_cleanup_pencils-vid200-fs4,unitree_z1_dual_arm_cleanup_pencils,world_model,7,50,0.7178804874420166,0.12500335276126862,0.12268003821372986,0.14430758357048035,0.9528319239616394,0.9645417928695679,176.6387481689453

unitree_z1_dual_arm_cleanup_pencils/case4/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_z1_dual_arm_cleanup_pencils/case4"
 dataset="unitree_z1_dual_arm_cleanup_pencils"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_stackbox/case1/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,8 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,0,33.82403302192688,33.88991904258728,137.83710050582886,,,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,1,33.60768103599548,33.290879249572754,136.70592331886292,211.3185577392578,0.4284072518348694,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,2,33.32481932640076,33.29386496543884,135.5947766304016,212.13265991210938,0.7351764142513275,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,3,33.305495500564575,33.303760051727295,135.52519917488098,219.74609375,0.4086533188819885,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,4,33.33557486534119,33.425875186920166,135.7887668609619,176.02040100097656,0.2585596442222595,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,5,33.343204975128174,33.31395173072815,135.5879945755005,207.51541137695312,0.42041802406311035,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,6,33.2964301109314,33.34376573562622,135.58371686935425,161.896728515625,0.35986077785491943,

unitree_z1_dual_arm_stackbox/case1/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,policy,33.433891262326924,,,,47.666666666666664,49.6,47.142857142857146,47.857142857142854,,198.10497538248697,0.4351792385180791,136.08906827654158,33.433891262326924,33.40885942322867,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,world_model,33.40885942322867,,,,48.2,49.666666666666664,47.285714285714285,47.714285714285715,,198.10497538248697,0.4351792385180791,136.08906827654158,33.433891262326924,33.40885942322867,

unitree_z1_dual_arm_stackbox/case1/output/sparse_8/inference/stepwise_log.csv

@@ -0,0 +1,701 @@
+sample_id,scene,pass_type,round_id,step,step_time_s,latent_delta,action_delta,state_delta,action_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50
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+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,policy,0,2,0.6475434303283691,1.0183463096618652,0.03177296370267868,0.03130107745528221,0.1281455010175705,0.06896268576383591,167.2060546875
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+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,policy,0,4,0.6493279933929443,0.7501328587532043,0.031063811853528023,0.030766472220420837,0.14402353763580322,0.07701630890369415,164.4588623046875
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+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,world_model,6,45,0.646920919418335,0.18453563749790192,0.07931271940469742,0.08518534898757935,0.788742184638977,0.8269493579864502,114.15694427490234
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,world_model,6,46,0.6486127376556396,0.1735396832227707,0.08337537944316864,0.08937568217515945,0.8236280679702759,0.8681553602218628,114.41376495361328
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,world_model,6,47,0.6468186378479004,0.16370512545108795,0.08743951469659805,0.09343000501394272,0.8562096953392029,0.9058783650398254,114.93212127685547
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+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,world_model,6,50,0.6877295970916748,0.1225716769695282,0.13820946216583252,0.15931637585163116,0.9374991655349731,0.9875130653381348,117.61994171142578

unitree_z1_dual_arm_stackbox/case1/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_z1_dual_arm_stackbox/case1"
 dataset="unitree_z1_dual_arm_stackbox"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_z1_dual_arm_stackbox"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_stackbox/case2/output/sparse_8/inference/round_summary.csv

@@ -0,0 +1,8 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,0,33.80240821838379,33.88466477394104,137.54542684555054,,,
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,1,33.70159864425659,33.714595794677734,137.00638723373413,221.9322052001953,0.35321664810180664,
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,2,33.641886472702026,33.71323919296265,137.2907555103302,226.0243377685547,0.969904225319624,
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,3,33.302470207214355,33.31880760192871,135.6334729194641,187.4794921875,0.164162278175354,
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,4,33.28122162818909,33.32221722602844,135.5430121421814,183.0623779296875,0.2753981947898865,
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+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,6,33.817909240722656,33.4320924282074,138.2575352191925,147.5497589111328,0.11231422424316406,

unitree_z1_dual_arm_stackbox/case2/output/sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,policy,33.55556167875017,,,,47.833333333333336,49.0,47.285714285714285,48.142857142857146,,196.19026692708334,0.3762244253108899,136.73422333172388,33.55556167875017,33.532714571271626,
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,world_model,33.532714571271626,,,,47.333333333333336,49.5,47.42857142857143,47.857142857142854,,196.19026692708334,0.3762244253108899,136.73422333172388,33.55556167875017,33.532714571271626,

unitree_z1_dual_arm_stackbox/case2/output/sparse_8/inference/stepwise_log.csv

@@ -0,0 +1,701 @@
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+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,world_model,6,45,0.6512765884399414,0.18519873917102814,0.08509787917137146,0.08283906430006027,0.7265743017196655,0.7708598971366882,143.42025756835938
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,world_model,6,46,0.6476829051971436,0.17422692477703094,0.08932304382324219,0.08683417737483978,0.7510722875595093,0.8076889514923096,144.13925170898438
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,world_model,6,47,0.6520655155181885,0.16425509750843048,0.09342308342456818,0.09071682393550873,0.7717578411102295,0.8414976000785828,145.02569580078125
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,world_model,6,48,0.652416467666626,0.15385012328624725,0.09738895297050476,0.09449256956577301,0.7871288061141968,0.8706705570220947,146.04827880859375
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,world_model,6,49,0.6634759902954102,0.14302082359790802,0.1018056645989418,0.09874552488327026,0.7957057952880859,0.8936233520507812,147.49221801757812
+unitree_z1_dual_arm_stackbox-vid15-fs4,unitree_z1_dual_arm_stackbox,world_model,6,50,0.6962172985076904,0.12300201505422592,0.14138983190059662,0.14956267178058624,0.8013524413108826,0.9117910861968994,147.8792724609375

unitree_z1_dual_arm_stackbox/case2/run_world_model_interaction.sh

@@ -2,11 +2,11 @@ res_dir="unitree_z1_dual_arm_stackbox/case2"
 dataset="unitree_z1_dual_arm_stackbox"
 
 {
-    time CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
         --seed 123 \
         --ckpt_path ckpts/unifolm_wma_dual.ckpt \
         --config configs/inference/world_model_interaction.yaml \
-        --savedir "${res_dir}/output" \
+        --savedir "${res_dir}/output/sparse_8" \
         --bs 1 --height 320 --width 512 \
         --unconditional_guidance_scale 1.0 \
         --ddim_steps 50 \
@@ -22,5 +22,8 @@ dataset="unitree_z1_dual_arm_stackbox"
         --guidance_rescale 0.7 \
         --perframe_ae \
         --analysis_log_metrics \
-        --analysis_reference_steps 50
+        --analysis_reference_steps 50 \
+        --head_schedule_steps 0 7 14 21 28 35 42 49 \
+        --head_skip_mode reuse_prediction \
+        --head_log_steps 40 43 46 47 48 49
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_stackbox/case3/backbone_profile_dense/inference/analysis/analysis_report.md

@@ -0,0 +1,54 @@
+# DDIM Analysis Report
+
+## Analysis Config
+
+- Stability threshold: None
+- Stability window: 3
+- Mid-step window: 15-35
+
+## Dataset Overview
+
+- Unique samples: 1
+- Step rows: 700
+- Sample rows: 2
+- Round rows: 7
+- Scenes: unitree_z1_dual_arm_stackbox
+- Pass types: policy, world_model
+
+## Five Directions
+
+### Original Early Stop
+
+- `latent_first_stable_step_policy`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step_world_model`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_l2_vs_full50`: mean=111.5273, median=134.0007, p90=152.2725, p95=155.6022
+
+### Direction C: Action Converges First
+
+- `action_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `latent_first_stable_step`: mean=nan, median=nan, p90=nan, p95=nan
+- `action_vs_full50_95pct_step`: mean=47.1429, median=47.1429, p90=47.1429, p95=47.1429
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3536, median=0.3491, p90=0.4171, p95=0.4282
+- `action_delta_mid_steps`: mean=0.0294, median=0.0278, p90=0.0426, p95=0.0452
+- `state_delta_mid_steps`: mean=0.0295, median=0.0280, p90=0.0439, p95=0.0464
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=47.1429, median=47.1429, p90=47.1429, p95=47.1429
+- `oracle_budget_state`: mean=48.0000, median=48.0000, p90=48.0000, p95=48.0000
+- `oracle_budget_action_by_scene`:
+  - `unitree_z1_dual_arm_stackbox`: mean=47.1429, median=47.1429, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=179.2887, median=182.8145, p90=193.0819, p95=194.5104
+- `action_drift_vs_prev_round`: mean=0.3701, median=0.3073, p90=0.5790, p95=0.5933
+- `round_total_time_s`: mean=519.5323, median=520.0046, p90=565.6002, p95=568.2485
+- `policy_pass_total_time_s`: mean=215.4158, median=218.7563, p90=240.6576, p95=243.1630
+- `world_model_pass_total_time_s`: mean=215.6475, median=226.8410, p90=236.4690, p95=236.9555
+

unitree_z1_dual_arm_stackbox/case3/backbone_profile_dense/inference/analysis/scene_summary.csv

@@ -0,0 +1,4 @@
+scene,pass_type,pass_total_time_s,pass_total_time_s,pass_total_time_s,action_first_stable_step,action_first_stable_step,action_first_stable_step,state_first_stable_step,state_first_stable_step,state_first_stable_step,latent_first_stable_step,latent_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_action,oracle_budget_action,oracle_budget_state,oracle_budget_state,oracle_budget_state,round_total_time_s,round_total_time_s,round_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,world_model_pass_total_time_s,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,latent_init_dist_to_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round,action_drift_vs_prev_round
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+unitree_z1_dual_arm_stackbox,policy,215.41583268983024,215.41583268983024,,,,,,,,,,,45.42857142857143,45.42857142857143,,47.142857142857146,47.142857142857146,,47.142857142857146,47.142857142857146,,48.0,48.0,,519.5322721004486,519.5322721004486,,215.41583268983024,215.41583268983024,,215.64754690442768,215.64754690442768,,179.28866322835287,179.28866322835287,,0.3700742026170094,0.3700742026170094,
+unitree_z1_dual_arm_stackbox,world_model,215.64754690442768,215.64754690442768,,,,,,,,,,,45.285714285714285,45.285714285714285,,47.142857142857146,47.142857142857146,,47.142857142857146,47.142857142857146,,48.0,48.0,,519.5322721004486,519.5322721004486,,215.41583268983024,215.41583268983024,,215.64754690442768,215.64754690442768,,179.28866322835287,179.28866322835287,,0.3700742026170094,0.3700742026170094,

unitree_z1_dual_arm_stackbox/case3/backbone_profile_dense/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
+pass_type,step,step_time_s,step_time_s,step_time_s,latent_delta,latent_delta,latent_delta,action_delta,action_delta,action_delta,state_delta,state_delta,state_delta,action_cosine_vs_full50,action_cosine_vs_full50,action_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50,latent_l2_vs_full50
+,,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
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