早停特征验证，早停不通

.gitignore

@@ -139,3 +139,4 @@ onnx__Mul*
 onnx__MatMul*
 onnx__Add_*
 Constant_*_attr__value
+*.0

configs/inference/world_model_interaction.yaml

@@ -222,7 +222,7 @@ data:
     test:
       target: unifolm_wma.data.wma_data.WMAData
       params:
-        data_dir: '/mnt/ASC1637/unifolm-world-model-action/examples/world_model_interaction_prompts'
+        data_dir: 'examples/world_model_interaction_prompts'
         video_length: ${model.params.wma_config.params.temporal_length}
         frame_stride: 2
         load_raw_resolution: True

scripts/evaluation/analyze_metrics.py

@@ -0,0 +1,611 @@
+import argparse
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+try:
+    import matplotlib.pyplot as plt
+except ModuleNotFoundError:
+    plt = None
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Analyze DDIM convergence logs for the five research directions."
+    )
+    parser.add_argument(
+        "--input_dir",
+        type=str,
+        required=True,
+        help="Directory containing stepwise_log.csv, sample_summary.csv, and round_summary.csv.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default=None,
+        help="Directory to write analysis outputs. Defaults to <input_dir>/analysis.",
+    )
+    parser.add_argument(
+        "--mid_step_start",
+        type=int,
+        default=15,
+        help="Start of the step window used for cross-step latent-delta analysis.",
+    )
+    parser.add_argument(
+        "--mid_step_end",
+        type=int,
+        default=35,
+        help="End of the step window used for cross-step latent-delta analysis.",
+    )
+    parser.add_argument(
+        "--full50_step_cap",
+        type=int,
+        default=50,
+        help="Upper bound used when summarizing step-based metrics.",
+    )
+    parser.add_argument(
+        "--stability_threshold",
+        type=float,
+        default=None,
+        help="Optional threshold for offline first_stable_step analysis. If unset, stable-step metrics are not computed.",
+    )
+    parser.add_argument(
+        "--stability_window",
+        type=int,
+        default=3,
+        help="Consecutive-step window for offline first_stable_step analysis.",
+    )
+    return parser.parse_args()
+
+
+def safe_float(value) -> float:
+    if pd.isna(value):
+        return float("nan")
+    return float(value)
+
+
+def describe_series(series: pd.Series) -> dict[str, float]:
+    series = pd.to_numeric(series, errors="coerce").dropna()
+    if series.empty:
+        return {
+            "count": 0.0,
+            "mean": float("nan"),
+            "std": float("nan"),
+            "min": float("nan"),
+            "p25": float("nan"),
+            "median": float("nan"),
+            "p75": float("nan"),
+            "p90": float("nan"),
+            "p95": float("nan"),
+            "max": float("nan"),
+        }
+    return {
+        "count": float(series.count()),
+        "mean": float(series.mean()),
+        "std": float(series.std(ddof=0)),
+        "min": float(series.min()),
+        "p25": float(series.quantile(0.25)),
+        "median": float(series.median()),
+        "p75": float(series.quantile(0.75)),
+        "p90": float(series.quantile(0.90)),
+        "p95": float(series.quantile(0.95)),
+        "max": float(series.max()),
+    }
+
+
+def ensure_columns(frame: pd.DataFrame, required: list[str], frame_name: str) -> None:
+    missing = [column for column in required if column not in frame.columns]
+    if missing:
+        raise ValueError(f"{frame_name} is missing required columns: {missing}")
+
+
+def first_consecutive_below(values: pd.Series, threshold: float,
+                            window: int) -> float:
+    cleaned = pd.to_numeric(values, errors="coerce").tolist()
+    if window <= 0 or len(cleaned) < window:
+        return float("nan")
+    for start in range(len(cleaned) - window + 1):
+        chunk = cleaned[start:start + window]
+        if all(pd.notna(value) and value < threshold for value in chunk):
+            return float(start + 1)
+    return float("nan")
+
+
+def load_tables(input_dir: Path) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    stepwise_path = input_dir / "stepwise_log.csv"
+    sample_summary_path = input_dir / "sample_summary.csv"
+    round_summary_path = input_dir / "round_summary.csv"
+
+    if not stepwise_path.exists():
+        raise FileNotFoundError(f"Missing file: {stepwise_path}")
+    if not sample_summary_path.exists():
+        raise FileNotFoundError(f"Missing file: {sample_summary_path}")
+    if not round_summary_path.exists():
+        raise FileNotFoundError(f"Missing file: {round_summary_path}")
+
+    stepwise_df = pd.read_csv(stepwise_path)
+    sample_summary_df = pd.read_csv(sample_summary_path)
+    round_summary_df = pd.read_csv(round_summary_path)
+
+    ensure_columns(
+        stepwise_df,
+        [
+            "sample_id",
+            "scene",
+            "pass_type",
+            "round_id",
+            "step",
+            "latent_delta",
+            "action_delta",
+            "state_delta",
+            "action_cosine_vs_full50",
+            "state_cosine_vs_full50",
+            "latent_l2_vs_full50",
+        ],
+        "stepwise_log.csv",
+    )
+    ensure_columns(
+        sample_summary_df,
+        [
+            "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",
+        ],
+        "sample_summary.csv",
+    )
+    ensure_columns(
+        round_summary_df,
+        [
+            "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",
+        ],
+        "round_summary.csv",
+    )
+    return stepwise_df, sample_summary_df, round_summary_df
+
+
+def aggregate_stepwise(stepwise_df: pd.DataFrame) -> pd.DataFrame:
+    metric_columns = [
+        "step_time_s",
+        "latent_delta",
+        "action_delta",
+        "state_delta",
+        "action_cosine_vs_full50",
+        "state_cosine_vs_full50",
+        "latent_l2_vs_full50",
+    ]
+    stepwise_df = stepwise_df.copy()
+    for column in metric_columns:
+        stepwise_df[column] = pd.to_numeric(stepwise_df[column], errors="coerce")
+    grouped = stepwise_df.groupby(["pass_type", "step"])[metric_columns]
+    return grouped.agg(["mean", "median", "std"]).reset_index()
+
+
+def aggregate_scene_summary(sample_summary_df: pd.DataFrame) -> pd.DataFrame:
+    metrics = [
+        "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",
+        "round_total_time_s",
+        "policy_pass_total_time_s",
+        "world_model_pass_total_time_s",
+        "latent_init_dist_to_prev_round",
+        "action_drift_vs_prev_round",
+    ]
+    sample_summary_df = sample_summary_df.copy()
+    for column in metrics:
+        sample_summary_df[column] = pd.to_numeric(sample_summary_df[column],
+                                                  errors="coerce")
+    grouped = sample_summary_df.groupby(["scene", "pass_type"])[metrics]
+    return grouped.agg(["mean", "median", "std"]).reset_index()
+
+
+def compute_stability_summary(stepwise_df: pd.DataFrame, threshold: float,
+                              window: int) -> pd.DataFrame:
+    rows = []
+    grouped = stepwise_df.sort_values("step").groupby(
+        ["sample_id", "scene", "pass_type"])
+    for (sample_id, scene, pass_type), group in grouped:
+        rows.append({
+            "sample_id": sample_id,
+            "scene": scene,
+            "pass_type": pass_type,
+            "action_first_stable_step": first_consecutive_below(
+                group["action_delta"], threshold, window),
+            "state_first_stable_step": first_consecutive_below(
+                group["state_delta"], threshold, window),
+            "latent_first_stable_step": first_consecutive_below(
+                group["latent_delta"], threshold, window),
+        })
+    return pd.DataFrame(rows)
+
+
+def compute_direction_summary(stepwise_df: pd.DataFrame,
+                              sample_summary_df: pd.DataFrame,
+                              round_summary_df: pd.DataFrame,
+                              mid_step_start: int,
+                              mid_step_end: int,
+                              stability_threshold: float | None,
+                              stability_window: int) -> dict:
+    policy_summary = sample_summary_df[
+        sample_summary_df["pass_type"] == "policy"].copy()
+    world_summary = sample_summary_df[
+        sample_summary_df["pass_type"] == "world_model"].copy()
+
+    latent_mid = stepwise_df[
+        (stepwise_df["step"] >= mid_step_start)
+        & (stepwise_df["step"] <= mid_step_end)].copy()
+
+    action_before_latent = policy_summary[[
+        "action_first_stable_step",
+        "latent_first_stable_step",
+        "action_vs_full50_95pct_step",
+    ]].dropna()
+    if action_before_latent.empty:
+        action_first_beats_latent = float("nan")
+        action_95_beats_latent = float("nan")
+    else:
+        action_first_beats_latent = float(
+            (action_before_latent["action_first_stable_step"] <
+             action_before_latent["latent_first_stable_step"]).mean())
+        action_95_beats_latent = float(
+            (action_before_latent["action_vs_full50_95pct_step"] <
+             action_before_latent["latent_first_stable_step"]).mean())
+
+    direction_summary = {
+        "analysis_config": {
+            "stability_threshold": stability_threshold,
+            "stability_window": int(stability_window),
+            "mid_step_window": [int(mid_step_start), int(mid_step_end)],
+        },
+        "dataset_overview": {
+            "num_step_rows": int(len(stepwise_df)),
+            "num_sample_rows": int(len(sample_summary_df)),
+            "num_round_rows": int(len(round_summary_df)),
+            "num_unique_samples": int(sample_summary_df["sample_id"].nunique()),
+            "scenes": sorted(sample_summary_df["scene"].dropna().unique().tolist()),
+            "pass_types": sorted(sample_summary_df["pass_type"].dropna().unique().tolist()),
+        },
+        "direction_original_early_stop": {
+            "latent_first_stable_step_policy":
+            describe_series(policy_summary["latent_first_stable_step"]),
+            "latent_first_stable_step_world_model":
+            describe_series(world_summary["latent_first_stable_step"]),
+            "latent_l2_vs_full50":
+            describe_series(stepwise_df["latent_l2_vs_full50"]),
+        },
+        "direction_c_action_converges_first": {
+            "action_first_stable_step":
+            describe_series(policy_summary["action_first_stable_step"]),
+            "latent_first_stable_step":
+            describe_series(policy_summary["latent_first_stable_step"]),
+            "action_vs_full50_95pct_step":
+            describe_series(policy_summary["action_vs_full50_95pct_step"]),
+            "share_action_first_stable_before_latent":
+            action_first_beats_latent,
+            "share_action_95pct_before_latent":
+            action_95_beats_latent,
+        },
+        "direction_d_cross_step_similarity": {
+            "latent_delta_mid_steps":
+            describe_series(latent_mid["latent_delta"]),
+            "action_delta_mid_steps":
+            describe_series(latent_mid["action_delta"]),
+            "state_delta_mid_steps":
+            describe_series(latent_mid["state_delta"]),
+        },
+        "direction_a_budget_heterogeneity": {
+            "oracle_budget_action":
+            describe_series(policy_summary["oracle_budget_action"]),
+            "oracle_budget_state":
+            describe_series(world_summary["oracle_budget_state"]),
+            "oracle_budget_action_by_scene": {
+                scene: describe_series(group["oracle_budget_action"])
+                for scene, group in policy_summary.groupby("scene")
+            },
+        },
+        "direction_b_round_reuse": {
+            "latent_init_dist_to_prev_round":
+            describe_series(round_summary_df["latent_init_dist_to_prev_round"]),
+            "action_drift_vs_prev_round":
+            describe_series(round_summary_df["action_drift_vs_prev_round"]),
+            "round_total_time_s":
+            describe_series(round_summary_df["round_total_time_s"]),
+            "policy_pass_total_time_s":
+            describe_series(round_summary_df["policy_pass_total_time_s"]),
+            "world_model_pass_total_time_s":
+            describe_series(round_summary_df["world_model_pass_total_time_s"]),
+        },
+    }
+    return direction_summary
+
+
+def save_json(path: Path, payload: dict) -> None:
+    with open(path, "w", encoding="utf-8") as file:
+        json.dump(payload, file, indent=2, ensure_ascii=False)
+
+
+def write_markdown_report(path: Path, direction_summary: dict) -> None:
+    lines = [
+        "# DDIM Analysis Report",
+        "",
+        "## Analysis Config",
+        "",
+    ]
+    config = direction_summary["analysis_config"]
+    lines.extend([
+        f"- Stability threshold: {config['stability_threshold']}",
+        f"- Stability window: {config['stability_window']}",
+        f"- Mid-step window: {config['mid_step_window'][0]}-{config['mid_step_window'][1]}",
+        "",
+        "## Dataset Overview",
+        "",
+    ])
+    overview = direction_summary["dataset_overview"]
+    lines.extend([
+        f"- Unique samples: {overview['num_unique_samples']}",
+        f"- Step rows: {overview['num_step_rows']}",
+        f"- Sample rows: {overview['num_sample_rows']}",
+        f"- Round rows: {overview['num_round_rows']}",
+        f"- Scenes: {', '.join(overview['scenes'])}",
+        f"- Pass types: {', '.join(overview['pass_types'])}",
+        "",
+        "## Five Directions",
+        "",
+    ])
+
+    def append_stats(title: str, payload: dict) -> None:
+        lines.append(f"### {title}")
+        lines.append("")
+        for key, value in payload.items():
+            if isinstance(value, dict):
+                if {"median", "mean", "p90"} <= set(value.keys()):
+                    lines.append(
+                        f"- `{key}`: mean={value['mean']:.4f}, median={value['median']:.4f}, p90={value['p90']:.4f}, p95={value['p95']:.4f}"
+                    )
+                else:
+                    lines.append(f"- `{key}`:")
+                    for sub_key, sub_value in value.items():
+                        if isinstance(sub_value, dict) and {"median", "mean"} <= set(
+                                sub_value.keys()):
+                            lines.append(
+                                f"  - `{sub_key}`: mean={sub_value['mean']:.4f}, median={sub_value['median']:.4f}, std={sub_value['std']:.4f}"
+                            )
+                        else:
+                            lines.append(f"  - `{sub_key}`: {sub_value}")
+            else:
+                if isinstance(value, float):
+                    lines.append(f"- `{key}`: {value:.4f}")
+                else:
+                    lines.append(f"- `{key}`: {value}")
+        lines.append("")
+
+    append_stats("Original Early Stop",
+                 direction_summary["direction_original_early_stop"])
+    append_stats("Direction C: Action Converges First",
+                 direction_summary["direction_c_action_converges_first"])
+    append_stats("Direction D: Cross-Step Similarity",
+                 direction_summary["direction_d_cross_step_similarity"])
+    append_stats("Direction A: Budget Heterogeneity",
+                 direction_summary["direction_a_budget_heterogeneity"])
+    append_stats("Direction B: Round Reuse",
+                 direction_summary["direction_b_round_reuse"])
+
+    with open(path, "w", encoding="utf-8") as file:
+        file.write("\n".join(lines) + "\n")
+
+
+def save_convergence_plot(stepwise_df: pd.DataFrame, output_path: Path) -> None:
+    if plt is None:
+        return
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+    pass_types = sorted(stepwise_df["pass_type"].dropna().unique().tolist())
+    for pass_type in pass_types:
+        subset = stepwise_df[stepwise_df["pass_type"] == pass_type]
+        grouped = subset.groupby("step").mean(numeric_only=True).reset_index()
+        axes[0, 0].plot(grouped["step"],
+                        grouped["latent_delta"],
+                        label=pass_type)
+        axes[0, 1].plot(grouped["step"],
+                        grouped["action_cosine_vs_full50"],
+                        label=pass_type)
+        axes[1, 0].plot(grouped["step"],
+                        grouped["state_cosine_vs_full50"],
+                        label=pass_type)
+        axes[1, 1].plot(grouped["step"],
+                        grouped["latent_l2_vs_full50"],
+                        label=pass_type)
+
+    axes[0, 0].set_title("Mean Latent Delta")
+    axes[0, 1].set_title("Mean Action Cosine vs Full50")
+    axes[1, 0].set_title("Mean State Cosine vs Full50")
+    axes[1, 1].set_title("Mean Latent L2 vs Full50")
+    for axis in axes.flatten():
+        axis.set_xlabel("Step")
+        axis.grid(alpha=0.3)
+        axis.legend()
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def save_budget_distribution_plot(sample_summary_df: pd.DataFrame,
+                                  output_path: Path) -> None:
+    if plt is None:
+        return
+    policy_summary = sample_summary_df[
+        sample_summary_df["pass_type"] == "policy"].copy()
+    scenes = sorted(policy_summary["scene"].dropna().unique().tolist())
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+
+    for scene in scenes:
+        subset = policy_summary[policy_summary["scene"] == scene]
+        axes[0].hist(subset["oracle_budget_action"].dropna(),
+                     bins=15,
+                     alpha=0.5,
+                     label=scene)
+    axes[0].set_title("Oracle Budget Action Distribution")
+    axes[0].set_xlabel("Step")
+    axes[0].set_ylabel("Count")
+    axes[0].legend()
+    axes[0].grid(alpha=0.3)
+
+    boxplot_data = [
+        policy_summary[policy_summary["scene"] == scene]["oracle_budget_action"].dropna()
+        for scene in scenes
+    ]
+    if scenes and any(len(values) > 0 for values in boxplot_data):
+        axes[1].boxplot(boxplot_data, labels=scenes, showfliers=False)
+    axes[1].set_title("Oracle Budget Action by Scene")
+    axes[1].set_ylabel("Step")
+    axes[1].tick_params(axis="x", rotation=20)
+    axes[1].grid(alpha=0.3)
+
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def save_midstep_delta_plot(stepwise_df: pd.DataFrame, output_path: Path,
+                            mid_step_start: int, mid_step_end: int) -> None:
+    if plt is None:
+        return
+    subset = stepwise_df[(stepwise_df["step"] >= mid_step_start)
+                         & (stepwise_df["step"] <= mid_step_end)].copy()
+    steps = sorted(subset["step"].dropna().unique().tolist())
+    data = [subset[subset["step"] == step]["latent_delta"].dropna() for step in steps]
+
+    fig, ax = plt.subplots(figsize=(14, 5))
+    if steps and any(len(values) > 0 for values in data):
+        ax.boxplot(data, labels=steps, showfliers=False)
+    ax.set_title(
+        f"Latent Delta Distribution for Steps {mid_step_start}-{mid_step_end}")
+    ax.set_xlabel("Step")
+    ax.set_ylabel("latent_delta")
+    ax.grid(alpha=0.3)
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def save_round_reuse_plot(round_summary_df: pd.DataFrame, output_path: Path) -> None:
+    if plt is None:
+        return
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+
+    subset = round_summary_df.dropna(
+        subset=["latent_init_dist_to_prev_round", "action_drift_vs_prev_round"])
+    if not subset.empty:
+        for scene, group in subset.groupby("scene"):
+            axes[0].scatter(group["latent_init_dist_to_prev_round"],
+                            group["action_drift_vs_prev_round"],
+                            alpha=0.6,
+                            label=scene)
+        axes[0].legend()
+    axes[0].set_title("Round Reuse: Latent Init Dist vs Action Drift")
+    axes[0].set_xlabel("latent_init_dist_to_prev_round")
+    axes[0].set_ylabel("action_drift_vs_prev_round")
+    axes[0].grid(alpha=0.3)
+
+    scene_groups = []
+    scene_labels = []
+    for scene, group in round_summary_df.groupby("scene"):
+        values = group["round_total_time_s"].dropna()
+        if len(values) > 0:
+            scene_groups.append(values)
+            scene_labels.append(scene)
+    if scene_groups:
+        axes[1].boxplot(scene_groups, labels=scene_labels, showfliers=False)
+    axes[1].set_title("Round Total Time by Scene")
+    axes[1].set_ylabel("Seconds")
+    axes[1].tick_params(axis="x", rotation=20)
+    axes[1].grid(alpha=0.3)
+
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200, bbox_inches="tight")
+    plt.close(fig)
+
+
+def main() -> None:
+    args = parse_args()
+    input_dir = Path(args.input_dir)
+    output_dir = Path(args.output_dir) if args.output_dir else input_dir / "analysis"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    stepwise_df, sample_summary_df, round_summary_df = load_tables(input_dir)
+
+    stability_summary_df = None
+    sample_summary_for_analysis = sample_summary_df.copy()
+    if args.stability_threshold is not None:
+        stability_summary_df = compute_stability_summary(stepwise_df,
+                                                         args.stability_threshold,
+                                                         args.stability_window)
+        sample_summary_for_analysis = sample_summary_for_analysis.drop(
+            columns=[
+                "action_first_stable_step",
+                "state_first_stable_step",
+                "latent_first_stable_step",
+            ],
+            errors="ignore",
+        ).merge(stability_summary_df,
+                on=["sample_id", "scene", "pass_type"],
+                how="left")
+
+    step_aggregate_df = aggregate_stepwise(stepwise_df)
+    scene_summary_df = aggregate_scene_summary(sample_summary_for_analysis)
+    direction_summary = compute_direction_summary(
+        stepwise_df=stepwise_df,
+        sample_summary_df=sample_summary_for_analysis,
+        round_summary_df=round_summary_df,
+        mid_step_start=args.mid_step_start,
+        mid_step_end=args.mid_step_end,
+        stability_threshold=args.stability_threshold,
+        stability_window=args.stability_window,
+    )
+
+    step_aggregate_df.to_csv(output_dir / "step_aggregate.csv", index=False)
+    scene_summary_df.to_csv(output_dir / "scene_summary.csv", index=False)
+    if stability_summary_df is not None:
+        stability_summary_df.to_csv(output_dir / "stability_summary.csv",
+                                    index=False)
+    save_json(output_dir / "direction_summary.json", direction_summary)
+    write_markdown_report(output_dir / "analysis_report.md", direction_summary)
+
+    if plt is not None:
+        save_convergence_plot(stepwise_df, output_dir / "convergence_curves.png")
+        save_budget_distribution_plot(sample_summary_df,
+                                      output_dir / "budget_distribution.png")
+        save_midstep_delta_plot(stepwise_df,
+                                output_dir / "midstep_latent_delta.png",
+                                args.mid_step_start, args.mid_step_end)
+        save_round_reuse_plot(round_summary_df, output_dir / "round_reuse.png")
+    else:
+        print("matplotlib not installed; skipped PNG plots.")
+
+    print(f"Analysis written to: {output_dir}")
+
+
+if __name__ == "__main__":
+    main()

scripts/evaluation/world_model_interaction.py

@@ -1,4 +1,5 @@
 import argparse, os, glob
+import json
 import pandas as pd
 import random
 import torch
@@ -9,6 +10,7 @@ import logging
 import einops
 import warnings
 import imageio
+import time
 
 from pytorch_lightning import seed_everything
 from omegaconf import OmegaConf
@@ -16,6 +18,7 @@ from tqdm import tqdm
 from einops import rearrange, repeat
 from collections import OrderedDict
 from torch import nn
+import torch.nn.functional as F
 from eval_utils import populate_queues, log_to_tensorboard
 from collections import deque
 from torch import Tensor
@@ -38,6 +41,412 @@ def get_device_from_parameters(module: nn.Module) -> torch.device:
     return next(iter(module.parameters())).device
 
 
+def get_scene_name(sample: pd.Series, fallback: str) -> str:
+    """Resolve the scene label used in analysis logs."""
+    if 'data_dir' in sample and pd.notna(sample['data_dir']):
+        return str(sample['data_dir'])
+    return fallback
+
+
+def build_sample_id(dataset: str, sample: pd.Series, frame_stride: int) -> str:
+    """Build a stable sample id while keeping the required CSV schema flat."""
+    return f"{dataset}-vid{sample['videoid']}-fs{frame_stride}"
+
+
+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)
+
+
+def batch_relative_l2(current: torch.Tensor,
+                      previous: torch.Tensor) -> list[float]:
+    """Compute ||current-previous|| / ||previous|| for each item in the batch."""
+    current_flat = flatten_batch_tensor(current)
+    previous_flat = flatten_batch_tensor(previous)
+    numerator = torch.linalg.vector_norm(current_flat - previous_flat, dim=1)
+    denominator = torch.linalg.vector_norm(previous_flat, dim=1).clamp_min(1e-8)
+    return (numerator / denominator).cpu().tolist()
+
+
+def batch_l2_distance(current: torch.Tensor,
+                      reference: torch.Tensor) -> list[float]:
+    """Compute L2 distance against a reference tensor for each batch item."""
+    current_flat = flatten_batch_tensor(current)
+    reference_flat = flatten_batch_tensor(reference)
+    return torch.linalg.vector_norm(current_flat - reference_flat,
+                                    dim=1).cpu().tolist()
+
+
+def batch_cosine_similarity(current: torch.Tensor,
+                            reference: torch.Tensor) -> list[float]:
+    """Compute cosine similarity against a reference tensor for each batch item."""
+    current_flat = flatten_batch_tensor(current)
+    reference_flat = flatten_batch_tensor(reference)
+    return F.cosine_similarity(current_flat,
+                               reference_flat,
+                               dim=1,
+                               eps=1e-8).cpu().tolist()
+
+
+def first_consecutive_below(values: list[float], threshold: float,
+                            window: int) -> float:
+    """Return the first 1-based index where `window` consecutive values are below threshold."""
+    if window <= 0 or len(values) < window:
+        return np.nan
+    for start in range(len(values) - window + 1):
+        window_values = values[start:start + window]
+        if all(pd.notna(value) and value < threshold for value in window_values):
+            return float(start + 1)
+    return np.nan
+
+
+def first_at_least(values: list[float], threshold: float) -> float:
+    """Return the first 1-based index where the series reaches the threshold."""
+    for index, value in enumerate(values, start=1):
+        if pd.notna(value) and value >= threshold:
+            return float(index)
+    return np.nan
+
+
+def first_at_most(values: list[float], threshold: float) -> float:
+    """Return the first 1-based index where the series drops below the threshold."""
+    for index, value in enumerate(values, start=1):
+        if pd.notna(value) and value <= threshold:
+            return float(index)
+    return np.nan
+
+
+def safe_mean(values: list[float]) -> float:
+    """Average numeric values while ignoring NaNs."""
+    valid_values = [value for value in values if pd.notna(value)]
+    if not valid_values:
+        return np.nan
+    return float(np.mean(valid_values))
+
+
+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."""
+    batch_size = noise_shape[0]
+    horizon = noise_shape[2]
+    device = model.device
+    return {
+        'img': torch.randn(noise_shape, device=device),
+        'action': torch.randn((batch_size, horizon, model.agent_action_dim),
+                              device=device),
+        'state': torch.randn((batch_size, horizon, model.agent_state_dim),
+                             device=device),
+    }
+
+
+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:
+        return {}
+    if not os.path.exists(psnr_path):
+        logging.warning("PSNR file not found: %s", psnr_path)
+        return {}
+
+    suffix = os.path.splitext(psnr_path)[1].lower()
+    lookup: dict[str, float] = {}
+    if suffix == '.csv':
+        df = pd.read_csv(psnr_path)
+        key_column = 'sample_id' if 'sample_id' in df.columns else 'videoid'
+        value_column = 'psnr_full50' if 'psnr_full50' in df.columns else 'psnr'
+        for _, row in df.iterrows():
+            if pd.notna(row[key_column]) and pd.notna(row[value_column]):
+                lookup[str(row[key_column])] = float(row[value_column])
+        return lookup
+
+    if suffix == '.json':
+        with open(psnr_path, 'r', encoding='utf-8') as file:
+            data = json.load(file)
+        if isinstance(data, dict):
+            if 'sample_id' in data and ('psnr_full50' in data or 'psnr' in data):
+                lookup[str(data['sample_id'])] = float(
+                    data.get('psnr_full50', data['psnr']))
+            else:
+                for key, value in data.items():
+                    if isinstance(value, (int, float)):
+                        lookup[str(key)] = float(value)
+        elif isinstance(data, list):
+            for item in data:
+                if not isinstance(item, dict):
+                    continue
+                if 'sample_id' in item and ('psnr_full50' in item or 'psnr' in
+                                            item):
+                    lookup[str(item['sample_id'])] = float(
+                        item.get('psnr_full50', item['psnr']))
+        return lookup
+
+    logging.warning("Unsupported PSNR file format: %s", psnr_path)
+    return {}
+
+
+class InteractionAnalysisLogger:
+    """Collect stepwise metrics and aggregated per-sample summaries."""
+
+    STEP_COLUMNS = [
+        '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',
+    ]
+    SUMMARY_COLUMNS = [
+        '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',
+    ]
+    ROUND_COLUMNS = [
+        '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',
+    ]
+
+    def __init__(self, output_dir: str, psnr_lookup: dict[str, float]):
+        self.output_dir = output_dir
+        self.psnr_lookup = psnr_lookup
+        self.step_rows: list[dict] = []
+        self.summary_buckets: dict[tuple[str, str, str], dict] = {}
+        self.round_rows: list[dict] = []
+        self.round_buckets: dict[tuple[str, str], dict] = {}
+        self.prev_policy_action: dict[str, torch.Tensor] = {}
+        self.prev_world_latent: dict[str, torch.Tensor] = {}
+
+    def resolve_psnr(self, sample_id: str, videoid: int) -> float:
+        """Resolve a PSNR value by full sample id first, then by raw video id."""
+        candidates = [sample_id, sample_id.rsplit('-fs', 1)[0], str(videoid)]
+        for candidate in candidates:
+            if candidate in self.psnr_lookup:
+                return float(self.psnr_lookup[candidate])
+        return np.nan
+
+    def append_summary_row(self, row: dict) -> None:
+        """Store per-round summaries and aggregate them later by sample and pass type."""
+        key = (row['sample_id'], row['scene'], row['pass_type'])
+        metric_columns = self.SUMMARY_COLUMNS[3:]
+        if key not in self.summary_buckets:
+            self.summary_buckets[key] = {
+                'sample_id': row['sample_id'],
+                'scene': row['scene'],
+                'pass_type': row['pass_type'],
+                **{column: [] for column in metric_columns},
+            }
+        for column in metric_columns:
+            self.summary_buckets[key][column].append(row.get(column, np.nan))
+
+    def append_round_row(self, row: dict) -> None:
+        """Store per-round metrics and aggregate them later by sample."""
+        self.round_rows.append(row)
+        key = (row['sample_id'], row['scene'])
+        metric_columns = self.ROUND_COLUMNS[3:]
+        if key not in self.round_buckets:
+            self.round_buckets[key] = {
+                'sample_id': row['sample_id'],
+                'scene': row['scene'],
+                **{column: [] for column in metric_columns},
+            }
+        for column in metric_columns:
+            self.round_buckets[key][column].append(row.get(column, np.nan))
+
+    def collect_trace_series(self, debug_info: dict, reference_action: torch.Tensor,
+                             reference_state: torch.Tensor,
+                             reference_latent: torch.Tensor) -> tuple[list[float], list[float], list[float]]:
+        """Extract cosine/L2 curves for either the target pass or the full-50 reference pass."""
+        action_cosines = []
+        state_cosines = []
+        latent_l2s = []
+        for record in debug_info['step_records']:
+            action_cosines.append(
+                batch_cosine_similarity(record['action'], reference_action)[0])
+            state_cosines.append(
+                batch_cosine_similarity(record['state'], reference_state)[0])
+            latent_l2s.append(
+                batch_l2_distance(record['pred_x0'], reference_latent)[0])
+        return action_cosines, state_cosines, latent_l2s
+
+    def log_pass(self, sample_id: str, videoid: int, scene: str, pass_type: str,
+                 round_id: int, pass_total_time_s: float, target_debug: dict,
+                 reference_debug: dict) -> dict | None:
+        """Log one pass worth of stepwise and aggregated metrics."""
+        if not target_debug or not target_debug.get('step_records'):
+            return None
+        if not reference_debug or not reference_debug.get('step_records'):
+            reference_debug = target_debug
+
+        reference_final_action = reference_debug['step_records'][-1]['action']
+        reference_final_state = reference_debug['step_records'][-1]['state']
+        reference_final_latent = reference_debug['step_records'][-1]['pred_x0']
+
+        prev_img = target_debug['analysis_init']['img']
+        prev_action = target_debug['analysis_init']['action']
+        prev_state = target_debug['analysis_init']['state']
+        action_deltas: list[float] = []
+        state_deltas: list[float] = []
+        latent_deltas: list[float] = []
+        action_cosines: list[float] = []
+        state_cosines: list[float] = []
+        latent_l2s: list[float] = []
+
+        for record in target_debug['step_records']:
+            latent_delta = batch_relative_l2(record['img'], prev_img)[0]
+            action_delta = batch_relative_l2(record['action'], prev_action)[0]
+            state_delta = batch_relative_l2(record['state'], prev_state)[0]
+            action_cosine = batch_cosine_similarity(record['action'],
+                                                    reference_final_action)[0]
+            state_cosine = batch_cosine_similarity(record['state'],
+                                                   reference_final_state)[0]
+            latent_l2 = batch_l2_distance(record['pred_x0'],
+                                          reference_final_latent)[0]
+
+            action_deltas.append(action_delta)
+            state_deltas.append(state_delta)
+            latent_deltas.append(latent_delta)
+            action_cosines.append(action_cosine)
+            state_cosines.append(state_cosine)
+            latent_l2s.append(latent_l2)
+
+            self.step_rows.append({
+                'sample_id': sample_id,
+                'scene': scene,
+                'pass_type': pass_type,
+                'round_id': round_id,
+                'step': record['step_index'],
+                'step_time_s': float(record['step_time_s']),
+                'latent_delta': latent_delta,
+                'action_delta': action_delta,
+                'state_delta': state_delta,
+                'action_cosine_vs_full50': action_cosine,
+                'state_cosine_vs_full50': state_cosine,
+                'latent_l2_vs_full50': latent_l2,
+            })
+
+            prev_img = record['img']
+            prev_action = record['action']
+            prev_state = record['state']
+
+        oracle_action_cosines, oracle_state_cosines, oracle_latent_l2s = self.collect_trace_series(
+            reference_debug, reference_final_action, reference_final_state,
+            reference_final_latent)
+
+        latent_init_dist_to_prev_round = np.nan
+        action_drift_vs_prev_round = np.nan
+        if pass_type == 'policy':
+            previous_action = self.prev_policy_action.get(sample_id)
+            if previous_action is not None:
+                action_drift_vs_prev_round = 1.0 - batch_cosine_similarity(
+                    reference_final_action, previous_action)[0]
+            self.prev_policy_action[sample_id] = reference_final_action.clone()
+        elif pass_type == 'world_model':
+            previous_latent = self.prev_world_latent.get(sample_id)
+            if previous_latent is not None:
+                latent_init_dist_to_prev_round = batch_l2_distance(
+                    reference_final_latent, previous_latent)[0]
+            self.prev_world_latent[sample_id] = reference_final_latent.clone()
+
+        summary_row = {
+            'sample_id': sample_id,
+            'scene': scene,
+            'pass_type': pass_type,
+            'pass_total_time_s': float(pass_total_time_s),
+            'action_first_stable_step': np.nan,
+            'state_first_stable_step': np.nan,
+            'latent_first_stable_step': np.nan,
+            'action_vs_full50_90pct_step': first_at_least(action_cosines, 0.90),
+            'action_vs_full50_95pct_step': first_at_least(action_cosines, 0.95),
+            'oracle_budget_action': first_at_least(oracle_action_cosines, 0.95),
+            'oracle_budget_state': first_at_least(oracle_state_cosines, 0.95),
+            'oracle_budget_latent': np.nan,
+            'latent_init_dist_to_prev_round': latent_init_dist_to_prev_round,
+            'action_drift_vs_prev_round': action_drift_vs_prev_round,
+            'round_total_time_s': np.nan,
+            'policy_pass_total_time_s': np.nan,
+            'world_model_pass_total_time_s': np.nan,
+            'psnr_full50': self.resolve_psnr(sample_id, videoid),
+        }
+        self.append_summary_row(summary_row)
+        return summary_row
+
+    def log_round(self, sample_id: str, videoid: int, scene: str, round_id: int,
+                  policy_pass_total_time_s: float,
+                  world_model_pass_total_time_s: float, round_total_time_s: float,
+                  latent_init_dist_to_prev_round: float,
+                  action_drift_vs_prev_round: float) -> None:
+        """Log one interaction round consisting of one policy pass and one world-model pass."""
+        self.append_round_row({
+            'sample_id': sample_id,
+            'scene': scene,
+            'round_id': round_id,
+            'policy_pass_total_time_s': float(policy_pass_total_time_s),
+            'world_model_pass_total_time_s': float(world_model_pass_total_time_s),
+            'round_total_time_s': float(round_total_time_s),
+            'latent_init_dist_to_prev_round': latent_init_dist_to_prev_round,
+            'action_drift_vs_prev_round': action_drift_vs_prev_round,
+            'psnr_full50': self.resolve_psnr(sample_id, videoid),
+        })
+
+    def flush(self) -> None:
+        """Write analysis CSVs to disk."""
+        os.makedirs(self.output_dir, exist_ok=True)
+        stepwise_path = os.path.join(self.output_dir, 'stepwise_log.csv')
+        summary_path = os.path.join(self.output_dir, 'sample_summary.csv')
+        round_path = os.path.join(self.output_dir, 'round_summary.csv')
+
+        stepwise_df = pd.DataFrame(self.step_rows, columns=self.STEP_COLUMNS)
+        stepwise_df.to_csv(stepwise_path, index=False)
+
+        round_df = pd.DataFrame(self.round_rows, columns=self.ROUND_COLUMNS)
+        round_df.to_csv(round_path, index=False)
+
+        summary_rows = []
+        metric_columns = self.SUMMARY_COLUMNS[3:]
+        for bucket in self.summary_buckets.values():
+            round_bucket = self.round_buckets.get((bucket['sample_id'],
+                                                   bucket['scene']))
+            row = {
+                'sample_id': bucket['sample_id'],
+                'scene': bucket['scene'],
+                'pass_type': bucket['pass_type'],
+            }
+            for column in metric_columns:
+                if round_bucket is not None and column in round_bucket:
+                    row[column] = safe_mean(round_bucket[column])
+                else:
+                    row[column] = safe_mean(bucket[column])
+            summary_rows.append(row)
+        summary_df = pd.DataFrame(summary_rows, columns=self.SUMMARY_COLUMNS)
+        summary_df.to_csv(summary_path, index=False)
+
+
 def write_video(video_path: str, stacked_frames: list, fps: int) -> None:
     """Save a list of frames to a video file.
 
@@ -327,7 +736,10 @@ def image_guided_synthesis_sim_mode(
         timestep_spacing: str = 'uniform',
         guidance_rescale: float = 0.0,
         sim_mode: bool = True,
-        **kwargs) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        init_noise_bundle: dict[str, torch.Tensor] | None = None,
+        decode_video: bool = True,
+        return_debug_info: bool = False,
+        **kwargs) -> tuple[torch.Tensor | None, torch.Tensor, torch.Tensor, dict | None]:
     """
     Performs image-guided video generation in a simulation-style mode with optional multimodal guidance (image, state, action, text).
 
@@ -350,16 +762,22 @@ def image_guided_synthesis_sim_mode(
         timestep_spacing (str): Timestep sampling method in DDIM sampler. Typically "uniform" or "linspace".
         guidance_rescale (float): Guidance rescaling factor to mitigate overexposure from classifier-free guidance.
         sim_mode (bool): Whether to perform world-model interaction or decision-making using the world-model.
+        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.
 
     Returns:
-        batch_variants (torch.Tensor): Predicted pixel-space video frames [B, C, T, H, W].
+        batch_variants (torch.Tensor | None): Predicted pixel-space video frames [B, C, T, H, W].
         actions (torch.Tensor): Predicted action sequences [B, T, D] from diffusion decoding.
         states (torch.Tensor): Predicted state sequences [B, T, D] from diffusion decoding.
+        debug_info (dict | None): Optional per-step trace used for convergence analysis.
     """
     b, _, t, _, _ = noise_shape
     ddim_sampler = DDIMSampler(model)
     batch_size = noise_shape[0]
+    batch_variants = None
+    debug_info = None
 
     fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device)
 
@@ -422,13 +840,28 @@ def image_guided_synthesis_sim_mode(
             fs=fs,
             timestep_spacing=timestep_spacing,
             guidance_rescale=guidance_rescale,
+            x_T=None if init_noise_bundle is None else init_noise_bundle['img'],
+            action_T=None if init_noise_bundle is None else
+            init_noise_bundle['action'],
+            state_T=None if init_noise_bundle is None else
+            init_noise_bundle['state'],
+            record_step_outputs=return_debug_info,
             **kwargs)
 
-        # Reconstruct from latent to pixel space
-        batch_images = model.decode_first_stage(samples)
-        batch_variants = batch_images
+        batch_variants = None
+        if decode_video:
+            batch_variants = model.decode_first_stage(samples)
 
-    return batch_variants, actions, states
+        if return_debug_info:
+            debug_info = {
+                'analysis_init': intermedia.get('analysis_init'),
+                'step_records': intermedia.get('step_records', []),
+                'final_latent': samples.detach().cpu(),
+                'final_action': actions.detach().cpu(),
+                'final_state': states.detach().cpu(),
+            }
+
+    return batch_variants, actions, states, debug_info
 
 
 def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
@@ -444,10 +877,17 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
         None
     """
     # Create inference and tensorboard dirs
-    os.makedirs(args.savedir + '/inference', exist_ok=True)
+    inference_dir = args.savedir + '/inference'
+    os.makedirs(inference_dir, exist_ok=True)
     log_dir = args.savedir + f"/tensorboard"
     os.makedirs(log_dir, exist_ok=True)
     writer = SummaryWriter(log_dir=log_dir)
+    analysis_logger = None
+    if args.analysis_log_metrics:
+        analysis_logger = InteractionAnalysisLogger(
+            output_dir=inference_dir,
+            psnr_lookup=load_psnr_lookup(args.analysis_psnr_path),
+        )
 
     # Load prompt
     csv_path = os.path.join(args.prompt_dir, f"{args.dataset}.csv")
@@ -478,6 +918,10 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
         args.width % 16
         == 0), "Error: image size [h,w] should be multiples of 16!"
     assert args.bs == 1, "Current implementation only support [batch size = 1]!"
+    if args.analysis_log_metrics:
+        assert args.ddim_steps > 0, "analysis_log_metrics requires positive --ddim_steps."
+        assert args.analysis_reference_steps > 0, (
+            "analysis_log_metrics requires positive --analysis_reference_steps.")
 
     # Get latent noise shape
     h, w = args.height // 8, args.width // 8
@@ -510,6 +954,8 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
         # If many, test various frequence control and world-model generation
         for fs in args.frame_stride:
+            sample_id = build_sample_id(args.dataset, sample, fs)
+            scene = get_scene_name(sample, args.dataset)
 
             # For saving imagens in policy
             sample_save_dir = f'{video_save_dir}/dm/{fs}'
@@ -554,6 +1000,7 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
             # Multi-round interaction with the world-model
             for itr in tqdm(range(args.n_iter)):
+                round_start_time = time.time()
 
                 # Get observation
                 observation = {
@@ -574,7 +1021,29 @@ 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 ...')
-                pred_videos_0, pred_actions, _ = image_guided_synthesis_sim_mode(
+                policy_noise_bundle = make_sampling_noise_bundle(
+                    model, noise_shape) if args.analysis_log_metrics else None
+                policy_reference_debug = None
+                if args.analysis_log_metrics and args.analysis_reference_steps != args.ddim_steps:
+                    _, _, _, policy_reference_debug = image_guided_synthesis_sim_mode(
+                        model,
+                        sample['instruction'],
+                        observation,
+                        noise_shape,
+                        action_cond_step=args.exe_steps,
+                        ddim_steps=args.analysis_reference_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=True)
+                policy_pass_start = time.time()
+                pred_videos_0, pred_actions, _, policy_debug = image_guided_synthesis_sim_mode(
                     model,
                     sample['instruction'],
                     observation,
@@ -587,7 +1056,24 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     fs=model_input_fs,
                     timestep_spacing=args.timestep_spacing,
                     guidance_rescale=args.guidance_rescale,
-                    sim_mode=False)
+                    sim_mode=False,
+                    init_noise_bundle=policy_noise_bundle,
+                    return_debug_info=args.analysis_log_metrics)
+                policy_pass_total_time_s = time.time() - policy_pass_start
+                policy_summary_row = None
+                if analysis_logger is not None:
+                    if policy_reference_debug is None:
+                        policy_reference_debug = policy_debug
+                    policy_summary_row = analysis_logger.log_pass(
+                        sample_id=sample_id,
+                        videoid=int(sample['videoid']),
+                        scene=scene,
+                        pass_type='policy',
+                        round_id=itr,
+                        pass_total_time_s=policy_pass_total_time_s,
+                        target_debug=policy_debug,
+                        reference_debug=policy_reference_debug,
+                    )
 
                 # Update future actions in the observation queues
                 for idx in range(len(pred_actions[0])):
@@ -615,7 +1101,29 @@ 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 ...')
-                pred_videos_1, _, pred_states = image_guided_synthesis_sim_mode(
+                world_noise_bundle = make_sampling_noise_bundle(
+                    model, noise_shape) if args.analysis_log_metrics else None
+                world_reference_debug = None
+                if args.analysis_log_metrics and args.analysis_reference_steps != args.ddim_steps:
+                    _, _, _, world_reference_debug = image_guided_synthesis_sim_mode(
+                        model,
+                        "",
+                        observation,
+                        noise_shape,
+                        action_cond_step=args.exe_steps,
+                        ddim_steps=args.analysis_reference_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=True)
+                world_pass_start = time.time()
+                pred_videos_1, _, pred_states, world_debug = image_guided_synthesis_sim_mode(
                     model,
                     "",
                     observation,
@@ -628,7 +1136,40 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     fs=model_input_fs,
                     text_input=False,
                     timestep_spacing=args.timestep_spacing,
-                    guidance_rescale=args.guidance_rescale)
+                    guidance_rescale=args.guidance_rescale,
+                    init_noise_bundle=world_noise_bundle,
+                    return_debug_info=args.analysis_log_metrics)
+                world_pass_total_time_s = time.time() - world_pass_start
+                world_summary_row = None
+                if analysis_logger is not None:
+                    if world_reference_debug is None:
+                        world_reference_debug = world_debug
+                    world_summary_row = analysis_logger.log_pass(
+                        sample_id=sample_id,
+                        videoid=int(sample['videoid']),
+                        scene=scene,
+                        pass_type='world_model',
+                        round_id=itr,
+                        pass_total_time_s=world_pass_total_time_s,
+                        target_debug=world_debug,
+                        reference_debug=world_reference_debug,
+                    )
+                    analysis_logger.log_round(
+                        sample_id=sample_id,
+                        videoid=int(sample['videoid']),
+                        scene=scene,
+                        round_id=itr,
+                        policy_pass_total_time_s=policy_pass_total_time_s,
+                        world_model_pass_total_time_s=
+                        world_pass_total_time_s,
+                        round_total_time_s=time.time() - round_start_time,
+                        latent_init_dist_to_prev_round=np.nan
+                        if world_summary_row is None else
+                        world_summary_row['latent_init_dist_to_prev_round'],
+                        action_drift_vs_prev_round=np.nan
+                        if policy_summary_row is None else
+                        policy_summary_row['action_drift_vs_prev_round'],
+                    )
 
                 for idx in range(args.exe_steps):
                     observation = {
@@ -681,6 +1222,10 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
             sample_full_video_file = f"{video_save_dir}/../{sample['videoid']}_full_fs{fs}.mp4"
             save_results(full_video, sample_full_video_file, fps=args.save_fps)
 
+    if analysis_logger is not None:
+        analysis_logger.flush()
+    writer.close()
+
 
 def get_parser():
     parser = argparse.ArgumentParser()
@@ -798,6 +1343,20 @@ def get_parser():
                         type=int,
                         default=8,
                         help="fps for the saving video")
+    parser.add_argument("--analysis_log_metrics",
+                        action='store_true',
+                        default=False,
+                        help="Enable DDIM convergence logging and export analysis CSVs.")
+    parser.add_argument(
+        "--analysis_reference_steps",
+        type=int,
+        default=50,
+        help="Reference DDIM steps used to build the full-step baseline for *_vs_full50 metrics."
+    )
+    parser.add_argument("--analysis_psnr_path",
+                        type=str,
+                        default=None,
+                        help="Optional CSV/JSON file with psnr_full50 values keyed by sample_id or videoid.")
     return parser
 
 

scripts/run_all_world_model_cases.sh

@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+run_analysis="${RUN_ANALYSIS:-1}"
+case_filter="${CASE_FILTER:-}"
+
+case_scripts=(
+    "unitree_z1_stackbox/case1/run_world_model_interaction.sh"
+    "unitree_z1_stackbox/case2/run_world_model_interaction.sh"
+    "unitree_z1_stackbox/case3/run_world_model_interaction.sh"
+    "unitree_z1_stackbox/case4/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case1/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case2/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case3/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox/case4/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case1/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case2/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case3/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case4/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case1/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case2/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case3/run_world_model_interaction.sh"
+    "unitree_z1_dual_arm_cleanup_pencils/case4/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case1/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case2/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case3/run_world_model_interaction.sh"
+    "unitree_g1_pack_camera/case4/run_world_model_interaction.sh"
+)
+
+for case_script in "${case_scripts[@]}"; do
+    if [[ -n "${case_filter}" ]] && [[ "${case_script}" != *"${case_filter}"* ]]; then
+        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
+done

scripts/run_world_model_interaction.sh

@@ -3,6 +3,8 @@ ckpt=/path/to/model/checkpoint
 config=configs/inference/world_model_interaction.yaml
 seed=123
 res_dir="/path/to/result/directory"
+analysis_reference_steps=50
+# analysis_psnr_path="/path/to/psnr_full50.csv"
 
 datasets=(
     "unitree_z1_stackbox"
@@ -19,6 +21,13 @@ for i in "${!datasets[@]}"; do
     dataset=${datasets[$i]}
     n_iter=${n_iters[$i]}
     fs=${fses[$i]}
+    analysis_args=(
+        --analysis_log_metrics
+        --analysis_reference_steps ${analysis_reference_steps}
+    )
+    if [ -n "${analysis_psnr_path:-}" ]; then
+        analysis_args+=(--analysis_psnr_path "${analysis_psnr_path}")
+    fi
 
     CUDA_VISIBLE_DEVICES=0 python3 scripts/evaluation/world_model_interaction.py \
     --seed ${seed} \
@@ -38,5 +47,6 @@ for i in "${!datasets[@]}"; do
     --n_iter ${n_iter} \
     --timestep_spacing 'uniform_trailing' \
     --guidance_rescale 0.7 \
-    --perframe_ae
+    --perframe_ae \
+    "${analysis_args[@]}"
 done

src/unifolm_wma/models/samplers/ddim.py

@@ -1,6 +1,7 @@
 import numpy as np
 import torch
 import copy
+import time
 
 from unifolm_wma.utils.diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg
 from unifolm_wma.utils.common import noise_like
@@ -106,6 +107,9 @@ class DDIMSampler(object):
             fs=None,
             timestep_spacing='uniform',  #uniform_trailing for starting from last timestep
             guidance_rescale=0.0,
+            action_T=None,
+            state_T=None,
+            record_step_outputs=False,
             **kwargs):
 
         # Check condition bs
@@ -161,6 +165,9 @@ class DDIMSampler(object):
             precision=precision,
             fs=fs,
             guidance_rescale=guidance_rescale,
+            action_T=action_T,
+            state_T=state_T,
+            record_step_outputs=record_step_outputs,
             **kwargs)
         return samples, actions, states, intermediates
 
@@ -187,24 +194,30 @@ class DDIMSampler(object):
                       precision=None,
                       fs=None,
                       guidance_rescale=0.0,
+                      action_T=None,
+                      state_T=None,
+                      record_step_outputs=False,
                       **kwargs):
         device = self.model.betas.device
         dp_ddim_scheduler_action = self.model.dp_noise_scheduler_action
         dp_ddim_scheduler_state = self.model.dp_noise_scheduler_state
 
         b = shape[0]
+        horizon = shape[2] if len(shape) >= 3 else 16
         if x_T is None:
             img = torch.randn(shape, device=device)
-            action = torch.randn((b, 16, self.model.agent_action_dim),
-                                 device=device)
-            state = torch.randn((b, 16, self.model.agent_state_dim),
-                                device=device)
         else:
             img = x_T
-            action = torch.randn((b, 16, self.model.agent_action_dim),
+        if action_T is None:
+            action = torch.randn((b, horizon, self.model.agent_action_dim),
                                  device=device)
-            state = torch.randn((b, 16, self.model.agent_state_dim),
+        else:
+            action = action_T
+        if state_T is None:
+            state = torch.randn((b, horizon, self.model.agent_state_dim),
                                 device=device)
+        else:
+            state = state_T
 
         if precision is not None:
             if precision == 16:
@@ -228,6 +241,13 @@ class DDIMSampler(object):
             'x_inter_state': [state],
             'pred_x0_state': [state],
         }
+        if record_step_outputs:
+            intermediates['analysis_init'] = {
+                'img': img.detach().cpu(),
+                'action': action.detach().cpu(),
+                'state': state.detach().cpu(),
+            }
+            intermediates['step_records'] = []
         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[
@@ -238,6 +258,9 @@ class DDIMSampler(object):
             iterator = time_range
 
         clean_cond = kwargs.pop("clean_cond", False)
+        sync_device = device if isinstance(device, torch.device) else torch.device(
+            device)
+        should_sync = record_step_outputs and sync_device.type == "cuda"
 
         dp_ddim_scheduler_action.set_timesteps(len(timesteps))
         dp_ddim_scheduler_state.set_timesteps(len(timesteps))
@@ -254,6 +277,10 @@ class DDIMSampler(object):
                     img_orig = self.model.q_sample(x0, ts)
                 img = img_orig * mask + (1. - mask) * img
 
+            if should_sync:
+                torch.cuda.synchronize(sync_device)
+            step_start_time = time.time()
+
             outs = self.p_sample_ddim(
                 img,
                 action,
@@ -290,6 +317,10 @@ class DDIMSampler(object):
                 generator=None,
             ).prev_sample
 
+            if should_sync:
+                torch.cuda.synchronize(sync_device)
+            step_time_s = time.time() - step_start_time
+
             if callback: callback(i)
             if img_callback: img_callback(pred_x0, i)
 
@@ -298,6 +329,16 @@ class DDIMSampler(object):
                 intermediates['pred_x0'].append(pred_x0)
                 intermediates['x_inter_action'].append(action)
                 intermediates['x_inter_state'].append(state)
+            if record_step_outputs:
+                intermediates['step_records'].append({
+                    'step_index': i + 1,
+                    'ddim_timestep': int(step),
+                    'img': img.detach().cpu(),
+                    'pred_x0': pred_x0.detach().cpu(),
+                    'action': action.detach().cpu(),
+                    'state': state.detach().cpu(),
+                    'step_time_s': step_time_s,
+                })
 
         return img, action, state, intermediates
 

unitree_g1_pack_camera/case1/output/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=119.6001, median=138.8359, p90=169.4805, p95=174.5434
+
+### 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
+- `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
+
+### 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_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.6364, median=46.6364, 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
+

unitree_g1_pack_camera/case1/output/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,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,

unitree_g1_pack_camera/case1/output/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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unitree_g1_pack_camera/case1/output/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-vid0-fs6,unitree_g1_pack_camera,0,75.04600715637207,74.63465595245361,152.84141325950623,,,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,1,74.28308296203613,74.47904086112976,151.1198422908783,226.4590301513672,0.19760429859161377,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,2,74.51005530357361,74.53634142875671,152.12407612800598,239.2180633544922,0.16546988487243652,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,3,73.78234267234802,74.31759691238403,151.25920009613037,204.92880249023438,0.037446677684783936,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,4,74.18772149085999,74.25887393951416,151.73889231681824,201.3100128173828,0.10639482736587524,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,5,74.7325496673584,74.31347179412842,152.19000148773193,207.3344268798828,0.04424440860748291,
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+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,10,69.33188319206238,68.21298360824585,140.671804189682,249.54913330078125,0.11264693737030029,

unitree_g1_pack_camera/case1/output/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,73.69664512981068,,,,44.72727272727273,46.63636363636363,46.63636363636363,46.90909090909091,,229.04884490966796,0.15200899243354798,150.06858123432505,73.69664512981068,73.29652458971196,
+unitree_g1_pack_camera-vid0-fs6,unitree_g1_pack_camera,world_model,73.29652458971196,,,,44.81818181818182,46.72727272727273,46.72727272727273,46.72727272727273,,229.04884490966796,0.15200899243354798,150.06858123432505,73.69664512981068,73.29652458971196,

unitree_g1_pack_camera/case1/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case2/output/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=118.1448, median=137.2515, p90=165.2197, p95=172.7743
+
+### 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=0.3529, median=0.3489, p90=0.4169, p95=0.4276
+- `action_delta_mid_steps`: mean=0.0313, median=0.0298, p90=0.0451, p95=0.0483
+- `state_delta_mid_steps`: mean=0.0310, median=0.0300, p90=0.0436, p95=0.0460
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.7273, median=46.7273, p90=46.7273, p95=46.7273
+- `oracle_budget_state`: mean=47.0000, median=47.0000, p90=47.0000, p95=47.0000
+- `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=236.4804, median=243.4704, p90=257.5054, p95=261.9766
+- `action_drift_vs_prev_round`: mean=0.2621, median=0.2435, p90=0.4256, p95=0.5909
+- `round_total_time_s`: mean=151.4911, median=151.4212, p90=152.2260, p95=152.4231
+- `policy_pass_total_time_s`: mean=74.2250, median=74.3476, p90=75.0393, p95=75.0564
+- `world_model_pass_total_time_s`: mean=74.2693, median=74.3542, p90=74.6399, p95=74.6751
+

unitree_g1_pack_camera/case2/output/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,74.22499192844738,74.22499192844738,,,,,,,,,,,44.90909090909091,44.90909090909091,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,47.09090909090909,47.09090909090909,,151.4910772713748,151.4910772713748,,74.22499192844738,74.22499192844738,,74.26925277709961,74.26925277709961,,236.48041229248048,236.48041229248048,,0.2620766624808311,0.2620766624808311,
+unitree_g1_pack_camera,world_model,74.26925277709961,74.26925277709961,,,,,,,,,,,45.09090909090909,45.09090909090909,,46.81818181818182,46.81818181818182,,46.81818181818182,46.81818181818182,,47.0,47.0,,151.4910772713748,151.4910772713748,,74.22499192844738,74.22499192844738,,74.26925277709961,74.26925277709961,,236.48041229248048,236.48041229248048,,0.2620766624808311,0.2620766624808311,

unitree_g1_pack_camera/case2/output/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
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+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,1,74.23106241226196,73.55208587646484,150.9755437374115,214.81951904296875,0.24937200546264648,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,2,75.07349109649658,74.42506957054138,152.62019896507263,202.9042205810547,0.24527257680892944,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,3,73.82551193237305,74.7103636264801,150.684387922287,223.25729370117188,0.0750700831413269,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,4,74.71211385726929,74.36838006973267,152.22601079940796,210.7346954345703,0.241740882396698,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,5,73.80026388168335,74.63985013961792,151.58087468147278,266.4477844238281,0.7560845464468002,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,6,74.41286873817444,74.4742693901062,151.42124199867249,239.9493865966797,0.1602541208267212,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,7,74.38386011123657,74.27649450302124,151.78351974487305,247.0240936279297,0.13166594505310059,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,8,73.25273704528809,74.35415315628052,150.77343583106995,256.5118408203125,0.06467467546463013,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,9,74.34760189056396,73.83727645874023,151.31240582466125,256.1639709472656,0.30772316455841064,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,10,73.39611744880676,74.33566379547119,150.86029887199402,246.99131774902344,0.38890862464904785,

unitree_g1_pack_camera/case2/output/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,74.22499192844738,,,,44.90909090909091,46.72727272727273,46.72727272727273,47.09090909090909,,236.48041229248048,0.26207666248083117,151.4910772713748,74.22499192844738,74.26925277709961,
+unitree_g1_pack_camera-vid50-fs6,unitree_g1_pack_camera,world_model,74.26925277709961,,,,45.09090909090909,46.81818181818182,46.81818181818182,47.0,,236.48041229248048,0.26207666248083117,151.4910772713748,74.22499192844738,74.26925277709961,

unitree_g1_pack_camera/case2/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case3/output/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=116.0635, median=135.1986, p90=161.0852, p95=169.8322
+
+### 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
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3529, median=0.3489, p90=0.4169, p95=0.4276
+- `action_delta_mid_steps`: mean=0.0320, median=0.0307, p90=0.0455, p95=0.0488
+- `state_delta_mid_steps`: mean=0.0312, median=0.0301, p90=0.0439, 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_by_scene`:
+  - `unitree_g1_pack_camera`: mean=46.6364, median=46.6364, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=215.2431, median=230.0003, p90=237.2570, p95=239.0189
+- `action_drift_vs_prev_round`: mean=0.2697, median=0.2050, p90=0.4657, p95=0.6923
+- `round_total_time_s`: mean=147.1026, median=152.2600, p90=152.9385, p95=152.9569
+- `policy_pass_total_time_s`: mean=71.2595, median=74.7608, p90=74.9093, p95=75.1472
+- `world_model_pass_total_time_s`: mean=73.1823, median=74.7396, p90=74.9509, p95=74.9599
+

unitree_g1_pack_camera/case3/output/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,71.25950481674887,71.25950481674887,,,,,,,,,,,44.72727272727273,44.72727272727273,,46.63636363636363,46.63636363636363,,46.63636363636363,46.63636363636363,,46.72727272727273,46.72727272727273,,147.10261366584083,147.10261366584083,,71.25950481674887,71.25950481674887,,73.18230964920737,73.18230964920737,,215.24309921264648,215.24309921264648,,0.2696936547756195,0.2696936547756195,
+unitree_g1_pack_camera,world_model,73.18230964920737,73.18230964920737,,,,,,,,,,,45.0,45.0,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,46.72727272727273,46.72727272727273,,147.10261366584083,147.10261366584083,,71.25950481674887,71.25950481674887,,73.18230964920737,73.18230964920737,,215.24309921264648,215.24309921264648,,0.2696936547756195,0.2696936547756195,

unitree_g1_pack_camera/case3/output/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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+world_model,43,1.3999147848649458,1.469254970550537,0.21781755323622595,0.201913679187948,0.2019428163766861,0.0010410762372436981,0.0725201530890031,0.0733591392636299,0.0054560703243076375,0.06670289317315271,0.0654317140579223,0.004435684288902853,0.848534806208177,0.853638768196106,0.01919457673872723,0.8544365763664246,0.8620296716690063,0.023104866360112424,53.92282971468839,54.19173431396485,2.3018759645793496
+world_model,44,1.3944376165216619,1.4681918621063232,0.23179871564208238,0.1918082237243652,0.1917132884263992,0.0011266145758682539,0.07411486790938808,0.0744659677147865,0.0053340624463093615,0.06928501210429448,0.0674033015966415,0.005014592152386143,0.8842862573536959,0.8876720070838928,0.014797954371457913,0.8867509202523665,0.8930128812789917,0.01875954773917384,47.76955067027699,47.98651123046875,2.1773311004029763
+world_model,45,1.3962215943769976,1.4676592350006104,0.2336440161230607,0.18133696642788968,0.1811342984437942,0.0011126272096607466,0.07610171288251871,0.0750593394041061,0.0052231679024188175,0.07038058814677318,0.0686565786600112,0.0052372791660646265,0.9166603738611395,0.9177016615867616,0.010872336039513642,0.9161591854962435,0.921278953552246,0.014550500550090738,41.6348710493608,41.83378601074219,2.0061604610575925
+world_model,46,1.3959437933835117,1.4677042961120603,0.2334022765221959,0.1709649183533408,0.170794740319252,0.0009963828332423168,0.07662165910005567,0.0747245773673057,0.005026059803525407,0.0733391405506567,0.0711352825164794,0.0056793837645177404,0.9443203156644647,0.9427329897880554,0.007506113742280097,0.9426048723134127,0.946301519870758,0.010402146317958022,35.394454609264024,35.55489730834961,1.8793098762078355
+world_model,47,1.3919809081337668,1.4672982692718506,0.22987843257648233,0.16032402759248557,0.1601537615060806,0.001161449976876946,0.07885337891903786,0.0767847225069999,0.00498972098124497,0.07695143127983263,0.0743001103401184,0.006415993376916115,0.9672025279565291,0.9668025970458984,0.0046594976464106605,0.9653134400194342,0.9674853682518004,0.006501365069714958,29.084233544089578,29.25230026245117,1.7000212458647328
+world_model,48,1.3910502953962847,1.4627628326416016,0.2319554021951319,0.14953668144616208,0.1492391377687454,0.0010335816728866416,0.08014944602142676,0.0796511098742485,0.005092453726361779,0.07987065748734902,0.0776736214756965,0.0069555070170309605,0.9843516566536643,0.9842669367790222,0.002386530317992733,0.9830300320278514,0.9840595722198486,0.0032879741632763707,22.524729295210406,22.64668846130371,1.3865496963055841
+world_model,49,1.394185109571977,1.4651310443878174,0.23014187168674302,0.13735197755423456,0.1375972926616668,0.001190005738145348,0.0817304903810674,0.0838558226823806,0.005614900667317128,0.0799507004293528,0.0789465606212616,0.00683753993571891,0.9953602010553534,0.9953088164329528,0.0007703843441456976,0.99452528628436,0.9947948455810548,0.0011420244573810449,15.268142700195312,15.346698760986328,0.95061216057606
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unitree_g1_pack_camera/case3/output/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,0,75.38506054878235,74.50816583633423,151.84783244132996,,,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,1,74.49218130111694,74.62240505218506,152.25997352600098,224.97146606445312,0.2796536087989807,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,2,74.69758439064026,74.56192207336426,151.35133409500122,228.21055603027344,0.15541577339172363,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,3,74.7608425617218,74.91416335105896,152.6145670413971,236.63555908203125,0.25457870960235596,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,4,74.69965195655823,74.96893620491028,151.8847451210022,231.79010009765625,0.918834924697876,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,5,74.81953477859497,74.73957371711731,152.68081712722778,198.5364227294922,0.08309799432754517,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,6,74.90933179855347,74.88512587547302,152.9385278224945,231.7943572998047,0.11995601654052734,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,7,74.84911894798279,74.95093822479248,152.97526359558105,236.86546325683594,0.39963626861572266,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,8,74.88821697235107,67.77415251731873,145.36549997329712,240.78086853027344,0.4154035449028015,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,9,35.5922589302063,64.26668500900269,101.50574612617493,198.4535675048828,0.06449002027511597,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,10,74.76077079772949,74.81333827972412,152.70444345474243,124.39263153076172,0.005869686603546143,

unitree_g1_pack_camera/case3/output/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,71.25950481674887,,,,44.72727272727273,46.63636363636363,46.63636363636363,46.72727272727273,,215.24309921264648,0.2696936547756195,147.10261366584083,71.25950481674887,73.18230964920737,
+unitree_g1_pack_camera-vid100-fs6,unitree_g1_pack_camera,world_model,73.18230964920737,,,,45.0,46.72727272727273,46.72727272727273,46.72727272727273,,215.24309921264648,0.2696936547756195,147.10261366584083,71.25950481674887,73.18230964920737,

unitree_g1_pack_camera/case3/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_g1_pack_camera/case4/output/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=123.5010, median=143.1178, p90=173.0218, p95=183.1080
+
+### 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
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3512, median=0.3474, p90=0.4163, p95=0.4272
+- `action_delta_mid_steps`: mean=0.0318, median=0.0302, p90=0.0453, p95=0.0481
+- `state_delta_mid_steps`: mean=0.0313, median=0.0303, p90=0.0440, p95=0.0461
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.6364, median=46.6364, p90=46.6364, p95=46.6364
+- `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
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=231.5603, median=231.6096, p90=244.0158, p95=252.6133
+- `action_drift_vs_prev_round`: mean=0.2541, median=0.1514, p90=0.5899, p95=0.6868
+- `round_total_time_s`: mean=149.5150, median=149.4089, p90=150.2778, p95=150.4611
+- `policy_pass_total_time_s`: mean=73.3453, median=73.5693, p90=73.9486, p95=74.0089
+- `world_model_pass_total_time_s`: mean=73.2888, median=73.4965, p90=73.8629, p95=74.1038
+

unitree_g1_pack_camera/case4/output/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,73.34526510672136,73.34526510672136,,,,,,,,,,,44.54545454545455,44.54545454545455,,46.63636363636363,46.63636363636363,,46.63636363636363,46.63636363636363,,46.81818181818182,46.81818181818182,,149.51496249979192,149.51496249979192,,73.34526510672136,73.34526510672136,,73.28878194635564,73.28878194635564,,231.5602996826172,231.5602996826172,,0.2541187256574631,0.2541187256574631,
+unitree_g1_pack_camera,world_model,73.28878194635564,73.28878194635564,,,,,,,,,,,44.72727272727273,44.72727272727273,,46.54545454545455,46.54545454545455,,46.54545454545455,46.54545454545455,,46.54545454545455,46.54545454545455,,149.51496249979192,149.51496249979192,,73.34526510672136,73.34526510672136,,73.28878194635564,73.28878194635564,,231.5602996826172,231.5602996826172,,0.2541187256574631,0.2541187256574631,

unitree_g1_pack_camera/case4/output/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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+world_model,43,1.443560535257513,1.4650578498840332,0.045814004461066433,0.1969182735139673,0.1964625120162964,0.0013121706408338579,0.07144649259068746,0.0712295323610305,0.00656471212099753,0.06569696827368299,0.0664753913879394,0.004482763263597362,0.8524552096020092,0.8551205396652222,0.025934691946022607,0.8590117476203225,0.8559869527816772,0.02436736747279204,56.32756805419922,56.08658218383789,2.2465044133211203
+world_model,44,1.4414773204109885,1.4683103561401367,0.0731052117132173,0.18684981763362882,0.1866772472858429,0.0015595534961808917,0.07308493927121157,0.0729752704501152,0.006468718441838349,0.06808482550761913,0.0692135766148567,0.005182186436035626,0.8874038728800687,0.8898999691009521,0.020159636196815274,0.8901988105340437,0.8878442049026489,0.019920234334543954,49.828060150146484,49.56086730957031,2.0835507283147505
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unitree_g1_pack_camera/case4/output/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,0,73.94856452941895,73.82903981208801,150.27776408195496,,,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,1,73.56928658485413,72.59175229072571,149.55499029159546,239.52206420898438,0.4142507314682007,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,2,73.70886850357056,73.86287522315979,149.8262255191803,221.16651916503906,0.03018885850906372,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,3,73.6336178779602,74.3447675704956,148.92326378822327,235.0866241455078,0.33992570638656616,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,4,73.71470236778259,73.62404441833496,150.64440989494324,234.82203674316406,0.7838059663772583,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,5,72.88737607002258,73.55074310302734,149.81744408607483,228.39707946777344,0.14519017934799194,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,6,72.78406834602356,73.49648332595825,149.40886855125427,261.2109069824219,0.1576852798461914,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,7,72.29405403137207,73.04967951774597,148.47189116477966,242.10520935058594,0.5683176815509796,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,8,74.06924510002136,72.0180025100708,149.22126245498657,220.84542846679688,0.03633618354797363,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,9,73.14275288581848,72.9461841583252,149.21196603775024,221.39315795898438,0.020627498626708984,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,10,73.04537987709045,72.86302947998047,149.30650162696838,211.05397033691406,0.04485917091369629,

unitree_g1_pack_camera/case4/output/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,73.34526510672136,,,,44.54545454545455,46.63636363636363,46.63636363636363,46.81818181818182,,231.5602996826172,0.2541187256574631,149.51496249979192,73.34526510672136,73.28878194635564,
+unitree_g1_pack_camera-vid200-fs6,unitree_g1_pack_camera,world_model,73.28878194635564,,,,44.72727272727273,46.54545454545455,46.54545454545455,46.54545454545455,,231.5602996826172,0.2541187256574631,149.51496249979192,73.34526510672136,73.28878194635564,

unitree_g1_pack_camera/case4/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_g1_pack_camera"
         --n_iter 11 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case1/output/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=116.1877, median=137.0989, p90=163.0532, p95=167.3646
+
+### 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=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3542, median=0.3501, p90=0.4173, p95=0.4280
+- `action_delta_mid_steps`: mean=0.0295, median=0.0283, p90=0.0414, p95=0.0439
+- `state_delta_mid_steps`: mean=0.0287, median=0.0276, p90=0.0404, p95=0.0427
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `oracle_budget_state`: mean=47.1250, median=47.1250, p90=47.1250, p95=47.1250
+- `oracle_budget_action_by_scene`:
+  - `unitree_z1_dual_arm_cleanup_pencils`: mean=46.5000, median=46.5000, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=242.4520, median=230.2455, p90=269.5033, p95=273.0074
+- `action_drift_vs_prev_round`: mean=0.6782, median=0.6411, p90=0.9664, p95=0.9760
+- `round_total_time_s`: mean=146.9209, median=150.5785, p90=151.7969, p95=152.0286
+- `policy_pass_total_time_s`: mean=70.4979, median=73.9005, p90=74.3969, p95=74.5318
+- `world_model_pass_total_time_s`: mean=73.7692, median=74.0447, p90=74.5227, p95=74.5574
+

unitree_z1_dual_arm_cleanup_pencils/case1/output/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,70.49793663620949,70.49793663620949,,,,,,,,,,,44.25,44.25,,46.5,46.5,,46.5,46.5,,47.125,47.125,,146.92093321681023,146.92093321681023,,70.49793663620949,70.49793663620949,,73.76916161179543,73.76916161179543,,242.45204162597656,242.45204162597656,,0.6781738067844084,0.6781738067844084,
+unitree_z1_dual_arm_cleanup_pencils,world_model,73.76916161179543,73.76916161179543,,,,,,,,,,,44.625,44.625,,46.5,46.5,,46.5,46.5,,47.125,47.125,,146.92093321681023,146.92093321681023,,70.49793663620949,70.49793663620949,,73.76916161179543,73.76916161179543,,242.45204162597656,242.45204162597656,,0.6781738067844084,0.6781738067844084,

unitree_z1_dual_arm_cleanup_pencils/case1/output/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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unitree_z1_dual_arm_cleanup_pencils/case1/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --n_iter 8 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case2/output/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=114.6183, median=135.5744, p90=160.0172, p95=166.5222
+
+### 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=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3542, median=0.3500, p90=0.4173, p95=0.4280
+- `action_delta_mid_steps`: mean=0.0291, median=0.0283, p90=0.0414, p95=0.0432
+- `state_delta_mid_steps`: mean=0.0286, median=0.0275, p90=0.0406, p95=0.0427
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `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=46.5000, median=46.5000, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=242.5424, median=236.8695, p90=270.3207, p95=274.0847
+- `action_drift_vs_prev_round`: mean=0.6918, median=0.6892, p90=0.9659, p95=0.9869
+- `round_total_time_s`: mean=151.1099, median=151.1618, p90=151.8859, p95=151.9580
+- `policy_pass_total_time_s`: mean=74.3568, median=74.5224, p90=74.8165, p95=74.8505
+- `world_model_pass_total_time_s`: mean=74.3846, median=74.3322, p90=74.7877, p95=74.7988
+

unitree_z1_dual_arm_cleanup_pencils/case2/output/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,74.3567801117897,74.3567801117897,,,,,,,,,,,44.375,44.375,,46.5,46.5,,46.5,46.5,,47.125,47.125,,151.10987955331802,151.10987955331802,,74.3567801117897,74.3567801117897,,74.38460382819176,74.38460382819176,,242.54239109584265,242.54239109584265,,0.6918000605489526,0.6918000605489526,
+unitree_z1_dual_arm_cleanup_pencils,world_model,74.38460382819176,74.38460382819176,,,,,,,,,,,44.625,44.625,,46.625,46.625,,46.625,46.625,,47.0,47.0,,151.10987955331802,151.10987955331802,,74.3567801117897,74.3567801117897,,74.38460382819176,74.38460382819176,,242.54239109584265,242.54239109584265,,0.6918000605489526,0.6918000605489526,

unitree_z1_dual_arm_cleanup_pencils/case2/output/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
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unitree_z1_dual_arm_cleanup_pencils/case2/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --n_iter 8 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case3/output/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=116.0821, median=136.9002, p90=164.2210, p95=167.5451
+
+### 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=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3540, median=0.3499, p90=0.4173, p95=0.4278
+- `action_delta_mid_steps`: mean=0.0290, median=0.0279, p90=0.0416, p95=0.0440
+- `state_delta_mid_steps`: mean=0.0287, median=0.0276, p90=0.0415, p95=0.0440
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.5000, median=46.5000, p90=46.5000, p95=46.5000
+- `oracle_budget_state`: mean=47.3750, median=47.3750, p90=47.3750, p95=47.3750
+- `oracle_budget_action_by_scene`:
+  - `unitree_z1_dual_arm_cleanup_pencils`: mean=46.5000, median=46.5000, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=252.5237, median=253.4787, p90=261.2025, p95=264.1173
+- `action_drift_vs_prev_round`: mean=0.7196, median=0.7119, p90=0.9656, p95=1.0833
+- `round_total_time_s`: mean=150.3981, median=150.4490, p90=151.9224, p95=152.0112
+- `policy_pass_total_time_s`: mean=73.8629, median=74.0025, p90=74.5856, p95=74.7925
+- `world_model_pass_total_time_s`: mean=73.6713, median=73.7090, p90=74.1184, p95=74.2374
+

unitree_z1_dual_arm_cleanup_pencils/case3/output/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,73.86289298534393,73.86289298534393,,,,,,,,,,,44.75,44.75,,46.5,46.5,,46.5,46.5,,47.5,47.5,,150.39811876416206,150.39811876416206,,73.86289298534393,73.86289298534393,,73.67131742835045,73.67131742835045,,252.52374049595423,252.52374049595423,,0.7196291791541236,0.7196291791541236,
+unitree_z1_dual_arm_cleanup_pencils,world_model,73.67131742835045,73.67131742835045,,,,,,,,,,,44.875,44.875,,46.625,46.625,,46.625,46.625,,47.375,47.375,,150.39811876416206,150.39811876416206,,73.86289298534393,73.86289298534393,,73.67131742835045,73.67131742835045,,252.52374049595423,252.52374049595423,,0.7196291791541236,0.7196291791541236,

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

@@ -0,0 +1,102 @@
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unitree_z1_dual_arm_cleanup_pencils/case3/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --n_iter 8 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_cleanup_pencils/case4/output/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=115.4132, median=136.2936, p90=162.1835, p95=165.4857
+
+### 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=0.3538, median=0.3497, p90=0.4173, p95=0.4278
+- `action_delta_mid_steps`: mean=0.0294, median=0.0283, p90=0.0416, p95=0.0439
+- `state_delta_mid_steps`: mean=0.0290, median=0.0278, p90=0.0415, p95=0.0435
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=46.6250, median=46.6250, p90=46.6250, p95=46.6250
+- `oracle_budget_state`: mean=47.1250, median=47.1250, p90=47.1250, p95=47.1250
+- `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=247.6047, median=242.3108, p90=265.5381, p95=269.2301
+- `action_drift_vs_prev_round`: mean=0.6564, median=0.6522, p90=1.0083, p95=1.0942
+- `round_total_time_s`: mean=152.5698, median=152.6913, p90=152.7720, p95=152.7943
+- `policy_pass_total_time_s`: mean=74.7410, median=74.8334, p90=74.9096, p95=74.9110
+- `world_model_pass_total_time_s`: mean=74.7841, median=74.7825, p90=74.8573, p95=74.8680
+

unitree_z1_dual_arm_cleanup_pencils/case4/output/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,74.74103125929832,74.74103125929832,,,,,,,,,,,44.5,44.5,,46.625,46.625,,46.625,46.625,,47.375,47.375,,152.56982910633087,152.56982910633087,,74.74103125929832,74.74103125929832,,74.78411230444908,74.78411230444908,,247.60467747279577,247.60467747279577,,0.6563876037086759,0.6563876037086759,
+unitree_z1_dual_arm_cleanup_pencils,world_model,74.78411230444908,74.78411230444908,,,,,,,,,,,44.625,44.625,,46.625,46.625,,46.625,46.625,,47.125,47.125,,152.56982910633087,152.56982910633087,,74.74103125929832,74.74103125929832,,74.78411230444908,74.78411230444908,,247.60467747279577,247.60467747279577,,0.6563876037086759,0.6563876037086759,

unitree_z1_dual_arm_cleanup_pencils/case4/output/inference/analysis/step_aggregate.csv

@@ -0,0 +1,102 @@
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unitree_z1_dual_arm_cleanup_pencils/case4/run_world_model_interaction.sh

@@ -20,5 +20,7 @@ dataset="unitree_z1_dual_arm_cleanup_pencils"
         --n_iter 8 \
         --timestep_spacing 'uniform_trailing' \
         --guidance_rescale 0.7 \
-        --perframe_ae
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50
 } 2>&1 | tee "${res_dir}/output.log"

unitree_z1_dual_arm_stackbox/case1/output/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=115.2143, median=137.1965, p90=159.5011, p95=163.5747
+
+### 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=47.0000, median=47.0000, p90=47.0000, p95=47.0000
+- `share_action_first_stable_before_latent`: nan
+- `share_action_95pct_before_latent`: nan
+
+### Direction D: Cross-Step Similarity
+
+- `latent_delta_mid_steps`: mean=0.3535, median=0.3495, p90=0.4168, p95=0.4276
+- `action_delta_mid_steps`: mean=0.0288, median=0.0275, p90=0.0422, p95=0.0447
+- `state_delta_mid_steps`: mean=0.0286, median=0.0274, p90=0.0428, p95=0.0451
+
+### Direction A: Budget Heterogeneity
+
+- `oracle_budget_action`: mean=47.0000, median=47.0000, p90=47.0000, p95=47.0000
+- `oracle_budget_state`: mean=47.5714, median=47.5714, p90=47.5714, p95=47.5714
+- `oracle_budget_action_by_scene`:
+  - `unitree_z1_dual_arm_stackbox`: mean=47.0000, median=47.0000, std=0.0000
+
+### Direction B: Round Reuse
+
+- `latent_init_dist_to_prev_round`: mean=200.3434, median=201.0335, p90=212.8264, p95=213.8231
+- `action_drift_vs_prev_round`: mean=0.3645, median=0.3807, p90=0.4751, p95=0.4780
+- `round_total_time_s`: mean=150.7918, median=150.8132, p90=151.4651, p95=151.4691
+- `policy_pass_total_time_s`: mean=74.0397, median=73.9827, p90=74.7022, p95=74.8505
+- `world_model_pass_total_time_s`: mean=73.8573, median=73.8913, p90=74.3682, p95=74.4975
+