Add expanded 16-scene confirmatory4 offline evaluation

Frozen three-seed 1,024-decision-per-seed evaluation, no-point exact-baseline control, preregistration, audits, analyzer, exact code snapshot, and Chinese report.

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/README.md

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+# `global_semantic_v2_b64_noappend_confirmatory4` expanded offline evaluation
+
+This package freezes the three previously released adapters and evaluates them
+on 16 scene-disjoint HM3D scenes (1,024 paired decisions per seed), plus a
+1,024-decision `no_point_signal` exact-baseline audit.
+
+## Main result
+
+- Ground Top-1: `+0.5208 pp` (3/3 seeds positive), scene-cluster 95% CI
+  `[-0.7161, +1.6927] pp`.
+- Joint: `+0.4557 pp`, scene-cluster 95% CI
+  `[-0.4557, +1.3346] pp`.
+- Ground Top-5: `+1.6927 pp` (3/3 seeds positive), scene-cluster 95% CI
+  `[+0.7161, +3.0599] pp`.
+- `no_point_signal`: every discrete Ground/Joint/Decision prediction exactly
+  matches the frozen MTU3D baseline.
+
+The original five-scene confirmatory result remains the largest observed
+Top-1 effect (`Ground +4.5833 pp`); this expanded evaluation is the stronger
+generalization audit and reports the smaller effect without selecting scenes
+or seeds.
+
+## Files
+
+- `formal_r2/seed{42,123,777}/result.json`: complete per-decision results.
+- `formal_r2/no_point_seed42/result.json`: complete no-point control.
+- `formal_r2/analysis.json`: paired aggregation and scene-cluster bootstrap.
+- `preregistration.json` and `preregistration_amendment_r1.json`: locked
+  protocol and the pre-metric infrastructure amendment.
+- `evaluation_manifest.json`: scene and weight identities.
+- `payload_sha_audit.json`, `transfer_audit.json`: cross-machine provenance.
+- `analyze.py`, `run_expanded16*.sh`, `exact_code.tar.gz`: analysis and exact
+  evaluation code snapshot.
+- `global_semantic_v2_b64_noappend_confirmatory4扩大离线复测报告_20260823.md`:
+  complete Chinese report.
+
+The adapter weights are not duplicated in this folder. They are the exact
+SHA256-identified weights already published under the original
+`global_semantic_v2_b64_noappend_confirmatory4` artifact in this repository.
+Licensed HM3D assets, MTU3D, and Sonata/PTv3 base checkpoints are intentionally
+not redistributed.

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/SHA256SUMS

@@ -0,0 +1,16 @@
+f8124342beacf1a299506e3ea15cea58e2de9e73d5468fc8b018ac2b49a43bc6  ./README.md
+c4b0cafadb234cd544cfbc73845af1d6306d8c3f6963eb32d165a5c609e06f9d  ./analyze.py
+0f8c3981d0da33baccbb6da887476ee300e78716f1df0ab90163b8c46c82f729  ./evaluation_manifest.json
+1aa63f3f566ac35f2d37452dc6296cf231eb8226baa5d6639a4b40bd1ca966b6  ./exact_code.tar.gz
+a3060c3006824dcb0f9031bd6c6ec2e15ec227f8b92136870bc087da55f3bd7b  ./formal_r2/analysis.json
+f65e58efd8ab8b9591462bf90bb3ae4badb69cd3329094113b209a2f59ef8b51  ./formal_r2/no_point_seed42/result.json
+6eb2a1483de767241460e2b98cda8f1709dbe9708a277e5c86c1d654c42e42ed  ./formal_r2/seed123/result.json
+23cab2c6ee597048b0f7b66af2627a59c33270d34823d25ddad30b3918de8603  ./formal_r2/seed42/result.json
+dfd8d50fbfc36e99d5eb06fcd28df112f85eb196ffaf27e0ce1d5db9f7f79230  ./formal_r2/seed777/result.json
+b556e7c68aec54890eaed1b6d0ae25fbe8047cdff263677f25ad422139aa02b7  ./global_semantic_v2_b64_noappend_confirmatory4扩大离线复测报告_20260823.md
+22b223d7677cb3515c7b22a4b27c052d0e631ca54d9d04eddf0c3851d8f5fdc6  ./payload_sha_audit.json
+dd004361124030b3f1f14bec857163eb3dc05ee32a3390c1d72c986af6f2ebf4  ./preregistration.json
+f471c9c9f8dbe0b4494e06b7000998dd6bedb86c8a7db36e3d4851c2c08eee87  ./preregistration_amendment_r1.json
+90bd615f81dc8c234d69aafd3e063a9356f31186710657728c1c065a44b40588  ./run_expanded16.sh
+c616b94b0e1d9e47af7200b50dc984dbee26a64ec6b7a3804e1662083ddfe8dc  ./run_expanded16_r2.sh
+4df018cae8d67695d2ac2a329436b689900b8e8645d64b3a81fa574920c3951d  ./transfer_audit.json

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/analyze.py

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+#!/usr/bin/env python3
+"""Aggregate the preregistered expanded confirmatory4 offline evaluation."""
+
+from __future__ import annotations
+
+import argparse
+from collections import defaultdict
+import hashlib
+import json
+from pathlib import Path
+import random
+from statistics import mean
+
+
+SEEDS = (42, 123, 777)
+EXPECTED_SOURCE_SHA = {
+    "training_source_sha256": "7d7df8aaf84c8f8599bea01911fd759745906fb8d0c7f594af782cff1b91b9ff",
+    "bridge_source_sha256": "85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9",
+    "token_encoder_source_sha256": "a06f756d697dc7c73d5069357556a2d0995de6b387d2103e900a87d825fc340e",
+    "mtu3d_query3d_source_sha256": "d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99",
+}
+EXPECTED_ADAPTER_SHA = {
+    42: "5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b",
+    123: "1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e",
+    777: "5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3",
+}
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def identity(record: dict) -> tuple:
+    return (
+        record["scene_id"],
+        record["episode_id"],
+        record["sub_episode_id"],
+        record["time_step"],
+    )
+
+
+def clustered_ci(records: list[dict], field: str, *, seed: int, draws: int) -> dict:
+    by_scene: dict[str, list[float]] = defaultdict(list)
+    for record in records:
+        by_scene[record["scene_id"]].append(
+            float(record["fused"][field]) - float(record["baseline"][field])
+        )
+    scenes = sorted(by_scene)
+    observed = mean(value for values in by_scene.values() for value in values)
+    generator = random.Random(seed)
+    samples = []
+    for _ in range(draws):
+        selected = [generator.choice(scenes) for _ in scenes]
+        values = [value for scene in selected for value in by_scene[scene]]
+        samples.append(mean(values))
+    samples.sort()
+    lo = samples[int(0.025 * draws)]
+    hi = samples[min(draws - 1, int(0.975 * draws))]
+    return {
+        "estimate": observed,
+        "ci95_low": lo,
+        "ci95_high": hi,
+        "scene_count": len(scenes),
+        "resamples": draws,
+    }
+
+
+def clustered_ci_across_seeds(
+    records_by_seed: dict[int, list[dict]],
+    field: str,
+    *,
+    seed: int,
+    draws: int,
+) -> dict:
+    """Bootstrap scenes while treating the three frozen seeds as replicas."""
+
+    by_scene: dict[str, list[float]] = defaultdict(list)
+    for records in records_by_seed.values():
+        for record in records:
+            by_scene[record["scene_id"]].append(
+                float(record["fused"][field])
+                - float(record["baseline"][field])
+            )
+    scenes = sorted(by_scene)
+    observed = mean(value for values in by_scene.values() for value in values)
+    generator = random.Random(seed)
+    samples = []
+    for _ in range(draws):
+        selected = [generator.choice(scenes) for _ in scenes]
+        values = [value for scene in selected for value in by_scene[scene]]
+        samples.append(mean(values))
+    samples.sort()
+    return {
+        "estimate": observed,
+        "ci95_low": samples[int(0.025 * draws)],
+        "ci95_high": samples[min(draws - 1, int(0.975 * draws))],
+        "scene_count": len(scenes),
+        "frozen_seed_count": len(records_by_seed),
+        "resamples": draws,
+        "bootstrap_unit": "scene_with_fixed_seed_replicates",
+    }
+
+
+def rescue_harm(records: list[dict], field: str) -> dict:
+    rescue = sum(
+        (not bool(record["baseline"][field])) and bool(record["fused"][field])
+        for record in records
+    )
+    harm = sum(
+        bool(record["baseline"][field]) and (not bool(record["fused"][field]))
+        for record in records
+    )
+    return {
+        "rescue": rescue,
+        "harm": harm,
+        "net": rescue - harm,
+        "unchanged": len(records) - rescue - harm,
+    }
+
+
+def load_result(root: Path, name: str) -> dict:
+    path = root / name / "result.json"
+    if not path.is_file():
+        raise FileNotFoundError(path)
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--root", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--bootstrap-resamples", type=int, default=10000)
+    args = parser.parse_args()
+
+    prereg_path = args.root / "preregistration.json"
+    prereg = json.loads(prereg_path.read_text(encoding="utf-8"))
+    expected_count = (
+        prereg["data"]["scene_count"] * prereg["data"]["decisions_per_scene"]
+    )
+    results = {seed: load_result(args.root, f"seed{seed}") for seed in SEEDS}
+    no_point = load_result(args.root, "no_point_seed42")
+
+    integrity = {
+        "preregistration_sha256": sha256(prereg_path),
+        "sample_count_exact": {},
+        "scene_set_exact": {},
+        "source_sha_exact": {},
+        "adapter_sha_exact": {},
+        "navigation_frozen": {},
+        "ptv3_frozen": {},
+    }
+    metrics = {}
+    paired = {}
+    records_by_seed: dict[int, list[dict]] = {}
+    expected_scenes = set(prereg["data"]["scene_ids"])
+    for seed, result in results.items():
+        validation = result["best_validation"]
+        baseline = validation["aggregate"]["baseline"]
+        fused = validation["aggregate"]["fused"]
+        records = validation["records"]
+        records_by_seed[seed] = records
+        if len({identity(record) for record in records}) != len(records):
+            raise RuntimeError(f"duplicate paired identities for seed {seed}")
+        integrity["sample_count_exact"][str(seed)] = len(records) == expected_count
+        integrity["scene_set_exact"][str(seed)] = {
+            record["scene_id"] for record in records
+        } == expected_scenes
+        integrity["source_sha_exact"][str(seed)] = all(
+            result["implementation"].get(key) == value
+            for key, value in EXPECTED_SOURCE_SHA.items()
+        )
+        integrity["adapter_sha_exact"][str(seed)] = (
+            result["loaded_adapter"]["sha256"] == EXPECTED_ADAPTER_SHA[seed]
+        )
+        integrity["navigation_frozen"][str(seed)] = bool(
+            result["navigation_parameters_bitwise_unchanged"]
+        )
+        integrity["ptv3_frozen"][str(seed)] = bool(
+            result["sonata_backbone_parameters_bitwise_unchanged"]
+        )
+        metrics[str(seed)] = {
+            "baseline": baseline,
+            "fused": fused,
+            "ground_gain": fused["ground_accuracy"] - baseline["ground_accuracy"],
+            "joint_gain": fused["joint_accuracy"] - baseline["joint_accuracy"],
+            "ground_top5_gain": (
+                fused["ground_top5_accuracy"] - baseline["ground_top5_accuracy"]
+            ),
+            "loss_reduction": baseline["mean_loss"] - fused["mean_loss"],
+        }
+        paired[str(seed)] = {
+            "ground": clustered_ci(
+                records,
+                "ground_correct",
+                seed=20260823 + seed,
+                draws=args.bootstrap_resamples,
+            ),
+            "joint": clustered_ci(
+                records,
+                "joint_correct",
+                seed=30260823 + seed,
+                draws=args.bootstrap_resamples,
+            ),
+            "ground_top5": clustered_ci(
+                records,
+                "ground_top5_correct",
+                seed=40260823 + seed,
+                draws=args.bootstrap_resamples,
+            ),
+            "ground_rescue_harm": rescue_harm(records, "ground_correct"),
+            "joint_rescue_harm": rescue_harm(records, "joint_correct"),
+        }
+
+    no_point_validation = no_point["best_validation"]
+    no_point_baseline = no_point_validation["aggregate"]["baseline"]
+    no_point_fused = no_point_validation["aggregate"]["fused"]
+    no_point_exact = {
+        key: no_point_fused[key] == no_point_baseline[key]
+        for key in (
+            "ground_accuracy",
+            "joint_accuracy",
+            "ground_top5_accuracy",
+            "decision_accuracy",
+        )
+    }
+    no_point_exact["record_predictions"] = all(
+        record["fused"][prediction] == record["baseline"][prediction]
+        for record in no_point_validation["records"]
+        for prediction in ("ground_prediction", "decision_prediction")
+    )
+    integrity["no_point_source_sha_exact"] = all(
+        no_point["implementation"].get(key) == value
+        for key, value in EXPECTED_SOURCE_SHA.items()
+    )
+    integrity["no_point_adapter_sha_exact"] = (
+        no_point["loaded_adapter"]["sha256"] == EXPECTED_ADAPTER_SHA[42]
+    )
+    integrity["no_point_sample_count_exact"] = (
+        len(no_point_validation["records"]) == expected_count
+    )
+    integrity_passed = all(
+        all(value.values()) if isinstance(value, dict) else bool(value)
+        for value in integrity.values()
+        if not isinstance(value, str)
+    ) and all(no_point_exact.values())
+
+    ground_gains = [metrics[str(seed)]["ground_gain"] for seed in SEEDS]
+    joint_gains = [metrics[str(seed)]["joint_gain"] for seed in SEEDS]
+    combined_bootstrap = {
+        "ground": clustered_ci_across_seeds(
+            records_by_seed,
+            "ground_correct",
+            seed=50260823,
+            draws=args.bootstrap_resamples,
+        ),
+        "joint": clustered_ci_across_seeds(
+            records_by_seed,
+            "joint_correct",
+            seed=60260823,
+            draws=args.bootstrap_resamples,
+        ),
+        "ground_top5": clustered_ci_across_seeds(
+            records_by_seed,
+            "ground_top5_correct",
+            seed=70260823,
+            draws=args.bootstrap_resamples,
+        ),
+    }
+    per_scene_three_seed_mean = {}
+    for scene in sorted(expected_scenes):
+        scene_records = [
+            record
+            for records in records_by_seed.values()
+            for record in records
+            if record["scene_id"] == scene
+        ]
+        per_scene_three_seed_mean[scene] = {
+            "replicate_decision_count": len(scene_records),
+            "ground_gain": mean(
+                float(record["fused"]["ground_correct"])
+                - float(record["baseline"]["ground_correct"])
+                for record in scene_records
+            ),
+            "joint_gain": mean(
+                float(record["fused"]["joint_correct"])
+                - float(record["baseline"]["joint_correct"])
+                for record in scene_records
+            ),
+            "ground_top5_gain": mean(
+                float(record["fused"]["ground_top5_correct"])
+                - float(record["baseline"]["ground_top5_correct"])
+                for record in scene_records
+            ),
+            "ground_rescue_harm": rescue_harm(scene_records, "ground_correct"),
+            "joint_rescue_harm": rescue_harm(scene_records, "joint_correct"),
+        }
+    acceptance = {
+        "integrity_required": integrity_passed,
+        "mean_ground_gain_strictly_positive": mean(ground_gains) > 0,
+        "positive_ground_gain_seeds_at_least_2": sum(v > 0 for v in ground_gains) >= 2,
+        "mean_joint_gain_strictly_positive": mean(joint_gains) > 0,
+        "no_point_ground_and_joint_exactly_equal_baseline": (
+            no_point_exact["ground_accuracy"]
+            and no_point_exact["joint_accuracy"]
+            and no_point_exact["record_predictions"]
+        ),
+    }
+    output = {
+        "schema": "nav3r-offline-confirmatory4-expanded16-analysis/v1",
+        "status": "passed" if all(acceptance.values()) else "failed",
+        "claim_scope": "offline_heldout_ovon_grounding_and_decision_only",
+        "preregistration": str(prereg_path),
+        "expected_decisions_per_seed": expected_count,
+        "integrity": integrity,
+        "integrity_passed": integrity_passed,
+        "per_seed": metrics,
+        "paired_scene_cluster_bootstrap": paired,
+        "three_seed_scene_cluster_bootstrap": combined_bootstrap,
+        "per_scene_three_seed_mean": per_scene_three_seed_mean,
+        "three_seed_replicate_rescue_harm": {
+            "ground": rescue_harm(
+                [record for records in records_by_seed.values() for record in records],
+                "ground_correct",
+            ),
+            "joint": rescue_harm(
+                [record for records in records_by_seed.values() for record in records],
+                "joint_correct",
+            ),
+        },
+        "three_seed_mean": {
+            "baseline_ground_accuracy": mean(
+                metrics[str(seed)]["baseline"]["ground_accuracy"] for seed in SEEDS
+            ),
+            "fused_ground_accuracy": mean(
+                metrics[str(seed)]["fused"]["ground_accuracy"] for seed in SEEDS
+            ),
+            "ground_gain": mean(ground_gains),
+            "baseline_joint_accuracy": mean(
+                metrics[str(seed)]["baseline"]["joint_accuracy"] for seed in SEEDS
+            ),
+            "fused_joint_accuracy": mean(
+                metrics[str(seed)]["fused"]["joint_accuracy"] for seed in SEEDS
+            ),
+            "joint_gain": mean(joint_gains),
+            "baseline_ground_top5_accuracy": mean(
+                metrics[str(seed)]["baseline"]["ground_top5_accuracy"] for seed in SEEDS
+            ),
+            "fused_ground_top5_accuracy": mean(
+                metrics[str(seed)]["fused"]["ground_top5_accuracy"] for seed in SEEDS
+            ),
+            "ground_top5_gain": mean(
+                metrics[str(seed)]["ground_top5_gain"] for seed in SEEDS
+            ),
+            "loss_reduction": mean(
+                metrics[str(seed)]["loss_reduction"] for seed in SEEDS
+            ),
+        },
+        "no_point_seed42": {
+            "baseline": no_point_baseline,
+            "fused": no_point_fused,
+            "exact": no_point_exact,
+            "loss_decomposition": {
+                "baseline_to_no_point_reduction_from_loaded_calibration_or_non_point_terms": (
+                    no_point_baseline["mean_loss"] - no_point_fused["mean_loss"]
+                ),
+                "no_point_to_full_seed42_incremental_point_reduction": (
+                    no_point_fused["mean_loss"]
+                    - metrics["42"]["fused"]["mean_loss"]
+                ),
+                "total_baseline_to_full_seed42_reduction": (
+                    no_point_baseline["mean_loss"]
+                    - metrics["42"]["fused"]["mean_loss"]
+                ),
+                "interpretation": (
+                    "Only no_point_to_full is point-signal-specific; total loss "
+                    "reduction must not be wholly attributed to point clouds."
+                ),
+            },
+        },
+        "acceptance": acceptance,
+        "confidence_interval_policy": "reported_descriptively_not_a_hard_gate",
+    }
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(
+        json.dumps(output, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
+    )
+    print(json.dumps({
+        "status": output["status"],
+        "integrity_passed": integrity_passed,
+        "three_seed_mean": output["three_seed_mean"],
+        "acceptance": acceptance,
+    }, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/evaluation_manifest.json

@@ -0,0 +1,169 @@
+{
+  "schema": "nav3r-alignment-manifest-offline-expanded16/v1",
+  "training_policy": "processed_hm3d AND official_vle_train MINUS held-out val/test benchmark scenes",
+  "counts": {
+    "processed_train": 17,
+    "processed_test": 0,
+    "official_vle_train": 145,
+    "reserved_heldout_validation_or_test": 36,
+    "eligible_train": 17,
+    "selected_train": 17
+  },
+  "selected_train_scene_ids": [],
+  "validation_scene_ids": [
+    "7MXmsvcQjpJ",
+    "BAbdmeyTvMZ",
+    "Dd4bFSTQ8gi",
+    "GLAQ4DNUx5U",
+    "LT9Jq6dN3Ea",
+    "Nfvxx8J5NCo",
+    "VBzV5z6i1WS",
+    "XB4GS9ShBRE",
+    "a8BtkwhxdRV",
+    "bxsVRursffK",
+    "mv2HUxq3B53",
+    "q3zU7Yy5E5s",
+    "svBbv1Pavdk",
+    "y9hTuugGdiq",
+    "ziup5kvtCCR",
+    "zt1RVoi7PcG"
+  ],
+  "reserved_scene_ids": [
+    "4ok3usBNeis",
+    "5cdEh9F2hJL",
+    "6s7QHgap2fW",
+    "7MXmsvcQjpJ",
+    "BAbdmeyTvMZ",
+    "CrMo8WxCyVb",
+    "DYehNKdT76V",
+    "Dd4bFSTQ8gi",
+    "GLAQ4DNUx5U",
+    "HY1NcmCgn3n",
+    "LT9Jq6dN3Ea",
+    "MHPLjHsuG27",
+    "Nfvxx8J5NCo",
+    "QaLdnwvtxbs",
+    "TEEsavR23oF",
+    "VBzV5z6i1WS",
+    "XB4GS9ShBRE",
+    "a8BtkwhxdRV",
+    "bCPU9suPUw9",
+    "bxsVRursffK",
+    "cvZr5TUy5C5",
+    "eF36g7L6Z9M",
+    "h1zeeAwLh9Z",
+    "k1cupFYWXJ6",
+    "mL8ThkuaVTM",
+    "mv2HUxq3B53",
+    "p53SfW6mjZe",
+    "q3zU7Yy5E5s",
+    "q5QZSEeHe5g",
+    "qyAac8rV8Zk",
+    "svBbv1Pavdk",
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+    "y9hTuugGdiq",
+    "yr17PDCnDDW",
+    "ziup5kvtCCR",
+    "zt1RVoi7PcG"
+  ],
+  "leakage_check_passed": true,
+  "notes": [
+    "Only these selected scenes may be used to fit the new PTv3 adapter.",
+    "The wcojb4TFT35 smoke scene is benchmark-reserved and is never eligible.",
+    "Validation selection used geometry gates and available decision count only; no navigation metric was inspected.",
+    "This panel supports offline OVON metrics only and never authorizes SR/SPL claims."
+  ],
+  "validation_selection": {
+    "policy": "require_prespecified_anchor_then_rank_quality-passed_val_seen_scenes_by_valid_decision_count_desc_then_scene_id",
+    "uses_navigation_accuracy_or_loss": false,
+    "required_scene_ids": [
+      "wcojb4TFT35"
+    ],
+    "candidate_manifest": "runtime/ptv3/alignment_manifest_17train_validation_candidates.json",
+    "candidate_manifest_sha256": "d798421d90f6a31f5450ca3f0c89b901932c34e869d13fe79baf514270de51b0",
+    "screen_result": "runtime/ptv3/status/validation_scene_screen_geometry_path.json",
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+        "error": null,
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+        "trusted_voxels": 640,
+        "grid_size_m": 0.02,
+        "official_scene_id": "00853-5cdEh9F2hJL",
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+      },
+      {
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+        "valid_ovon_decisions": 791
+      }
+    ]
+  },
+  "evaluation_only": true,
+  "source_adapter_family": "global_semantic_v2_b64_noappend_confirmatory4",
+  "original_confirmatory_scenes_excluded": [
+    "5cdEh9F2hJL",
+    "6s7QHgap2fW",
+    "cvZr5TUy5C5",
+    "p53SfW6mjZe",
+    "wcojb4TFT35"
+  ],
+  "expanded_scene_count": 16,
+  "source_adapter_train_scene_ids": [
+    "1xGrZPxG1Hz",
+    "3CBBjsNkhqW",
+    "6HRFAUDqpTb",
+    "6imZUJGRUq4",
+    "DoSbsoo4EAg",
+    "NGyoyh91xXJ",
+    "R9fYpvCUkV7",
+    "TYDavTf8oyy",
+    "U3oQjwTuMX8",
+    "WhNyDTnd9g5",
+    "X6Pct1msZv5",
+    "Z2DQddYp1fn",
+    "gQgtJ9Stk5s",
+    "ggNAcMh8JPT",
+    "kA2nG18hCAr",
+    "qgZhhx1MpTi",
+    "w8GiikYuFRk"
+  ],
+  "evaluation_only_skip_unused_train_cache": true
+}

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+      },
+      "joint_rescue_harm": {
+        "rescue": 6,
+        "harm": 2,
+        "net": 4,
+        "unchanged": 184
+      }
+    },
+    "zt1RVoi7PcG": {
+      "replicate_decision_count": 192,
+      "ground_gain": 0.026041666666666668,
+      "joint_gain": 0.03125,
+      "ground_top5_gain": 0.0,
+      "ground_rescue_harm": {
+        "rescue": 6,
+        "harm": 1,
+        "net": 5,
+        "unchanged": 185
+      },
+      "joint_rescue_harm": {
+        "rescue": 6,
+        "harm": 0,
+        "net": 6,
+        "unchanged": 186
+      }
+    }
+  },
+  "three_seed_replicate_rescue_harm": {
+    "ground": {
+      "rescue": 81,
+      "harm": 65,
+      "net": 16,
+      "unchanged": 2926
+    },
+    "joint": {
+      "rescue": 59,
+      "harm": 45,
+      "net": 14,
+      "unchanged": 2968
+    }
+  },
+  "three_seed_mean": {
+    "baseline_ground_accuracy": 0.4521484375,
+    "fused_ground_accuracy": 0.4573567708333333,
+    "ground_gain": 0.005208333333333333,
+    "baseline_joint_accuracy": 0.404296875,
+    "fused_joint_accuracy": 0.4088541666666667,
+    "joint_gain": 0.004557291666666667,
+    "baseline_ground_top5_accuracy": 0.822265625,
+    "fused_ground_top5_accuracy": 0.8391927083333334,
+    "ground_top5_gain": 0.016927083333333332,
+    "loss_reduction": 0.052372142662875376
+  },
+  "no_point_seed42": {
+    "baseline": {
+      "mean_loss": 0.6842419945877509,
+      "ground_accuracy": 0.4521484375,
+      "ground_top5_accuracy": 0.822265625,
+      "decision_accuracy": 0.73046875,
+      "decision_balanced_accuracy": 0.73046875,
+      "decision_precision": 0.7209737827715356,
+      "decision_recall": 0.751953125,
+      "decision_f1": 0.7361376673040153,
+      "joint_accuracy": 0.404296875,
+      "sample_count": 1024
+    },
+    "fused": {
+      "mean_loss": 0.6336838341915154,
+      "ground_accuracy": 0.4521484375,
+      "ground_top5_accuracy": 0.822265625,
+      "decision_accuracy": 0.73046875,
+      "decision_balanced_accuracy": 0.73046875,
+      "decision_precision": 0.7209737827715356,
+      "decision_recall": 0.751953125,
+      "decision_f1": 0.7361376673040153,
+      "joint_accuracy": 0.404296875,
+      "sample_count": 1024
+    },
+    "exact": {
+      "ground_accuracy": true,
+      "joint_accuracy": true,
+      "ground_top5_accuracy": true,
+      "decision_accuracy": true,
+      "record_predictions": true
+    },
+    "loss_decomposition": {
+      "baseline_to_no_point_reduction_from_loaded_calibration_or_non_point_terms": 0.05055816039623551,
+      "no_point_to_full_seed42_incremental_point_reduction": 0.001694146557156273,
+      "total_baseline_to_full_seed42_reduction": 0.05225230695339178,
+      "interpretation": "Only no_point_to_full is point-signal-specific; total loss reduction must not be wholly attributed to point clouds."
+    }
+  },
+  "acceptance": {
+    "integrity_required": true,
+    "mean_ground_gain_strictly_positive": true,
+    "positive_ground_gain_seeds_at_least_2": true,
+    "mean_joint_gain_strictly_positive": true,
+    "no_point_ground_and_joint_exactly_equal_baseline": true
+  },
+  "confidence_interval_policy": "reported_descriptively_not_a_hard_gate"
+}

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/global_semantic_v2_b64_noappend_confirmatory4扩大离线复测报告_20260823.md

@@ -0,0 +1,192 @@
+# Nav3R `global_semantic_v2_b64_noappend_confirmatory4` 扩大离线复测报告
+
+> 日期：2026-08-23  
+> 当前状态：**正式 r2 已完成并通过预注册门槛**；formal r1 为无指标的基础设施失败，单独保留。
+
+## 1. 实验目的
+
+本实验不是重新训练或筛选一个更好的模型，而是冻结旧离线最佳版本
+`global_semantic_v2_b64_noappend_confirmatory4` 的三个既有 adapter，在更大且场景独立的
+HM3D 离线理解面板上复测其增益是否稳定。
+
+该版本在原第四组确认集上的三种子均值为：
+
+| 指标 | 冻结 MTU3D | 点云融合版 | 绝对变化 |
+|---|---:|---:|---:|
+| Ground accuracy | 35.3125% | 39.8958% | **+4.5833pp** |
+| Joint accuracy | 33.1250% | 34.2708% | **+1.1458pp** |
+| Mean loss | 0.836673 | 0.761728 | **-0.074945** |
+
+原结果只有 5 个场景、每种子 320 条决策，因此本轮重点增加场景数和配对决策数，而不改变模型、权重或门槛。
+
+## 2. 版本与融合结构
+
+### 2.1 模型身份
+
+本轮直接复测旧 H800 `confirmatory4` 权重本身，不使用 v179 中结构相同但针对
+`official_world` 坐标修正后重新训练的 `official_ovon_world_v10` 权重。
+
+| 组件 | 种子/版本 | SHA256 |
+|---|---|---|
+| adapter | seed42 | `5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b` |
+| adapter | seed123 | `1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e` |
+| adapter | seed777 | `5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3` |
+| MTU3D Stage-2 | frozen | `0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791` |
+| Sonata/PTv3 | frozen | `c5ced5acdae30d1c469713398073a866e25e6e414e23feed5dc025373657ac50` |
+
+### 2.2 点云如何接入 MTU3D
+
+1. ZPlusM/Stream3R 重建包经过固定参考相机与 Sim(3) 对齐；
+2. 冻结 Sonata/PTv3 编码点云，确定性保留 256 个点云 token；
+3. 使用宽度为 64 的显式全局场景语义 adapter，以 MTU3D 的指令条件化查询对点云语义做交叉注意力；
+4. adapter 只产生 grounding residual；不把点 token 追加进 MTU3D memory，且不修改 decision logits；
+5. 冻结 MTU3D、冻结 PTv3，只加载训练完成的 adapter 和训练集独立校准参数；本轮 `steps=0`，不发生任何训练。
+
+这个结构能证明“点云语义信号影响离线目标理解/候选 grounding”，但不能仅凭该实验声称闭环导航 SR/SPL 改善。
+
+## 3. 扩大数据面板
+
+### 3.1 规模
+
+- 16 个独立 HM3D 场景；
+- 每场景预注册 64 条平衡 object/frontier 决策；
+- 预计每种子 1024 条决策，三种子共 3072 条 full-point 评估；
+- 另用 seed42 做 1024 条 `no_point_signal` 精确基线回退审计。
+
+相较原确认集：场景数从 5 增至 16（3.2 倍），每种子决策从 320 增至 1024（3.2 倍）。
+
+### 3.2 场景独立性
+
+16 个扩大场景与原 adapter 的 17 个训练场景无交集，也与原 5 个 confirmatory4 场景无交集：
+
+`7MXmsvcQjpJ, a8BtkwhxdRV, BAbdmeyTvMZ, bxsVRursffK, Dd4bFSTQ8gi, GLAQ4DNUx5U, LT9Jq6dN3Ea, mv2HUxq3B53, Nfvxx8J5NCo, q3zU7Yy5E5s, svBbv1Pavdk, VBzV5z6i1WS, XB4GS9ShBRE, y9hTuugGdiq, ziup5kvtCCR, zt1RVoi7PcG`。
+
+需要特别说明：扩大集使用另一批通过 `fixed_reference` 与 alignment 审计的 geometry-path 重建包。因此它同时检验新场景泛化和一定程度的重建包域泛化；若增益衰减，不能直接归因为模型无效，必须拆分场景差异、重建域差异和接口实现。
+
+## 4. 预注册协议
+
+- 完整协议文件：`preregistration.json`；
+- 在读取扩大面板正式指标前锁定场景、样本数、三份权重 SHA、源码 SHA、主指标和门槛；
+- 三个 full-point 条件分别使用 seed42/123/777 原权重；
+- no-point 条件使用 seed42 原权重，但在 PTv3 cache 后施加 `no_point_signal`；
+- 主指标：Ground、Joint、Ground Top-5、Mean loss；
+- 同一 `scene/episode/sub_episode/time_step` 做逐样本配对；
+- 以场景为 cluster 做 10,000 次 bootstrap 95% CI；CI 如实报告，不设为硬门槛；
+- 接受条件：完整性全部通过、三种子平均 Ground 增益大于 0、至少 2/3 种子 Ground 为正、平均 Joint 增益大于 0、no-point Ground/Joint 和逐样本预测严格回到基线。
+
+## 5. 完整性与链路审计
+
+正式启动前已确认：
+
+- 四个关键源码 SHA 与旧确认实验一致；
+- 三份 adapter SHA 与已发布权重一致；
+- MTU3D 与 Sonata/PTv3 checkpoint SHA 一致；
+- 所有 payload 由旧机直接经内存中继到 H100，不落本机；
+- 评估在独立 staging 中运行，不覆盖当前主代码；
+- GPU4/5/6 分别运行三个 full-point 种子，GPU7 运行 no-point；
+- 导航主干与 PTv3 必须在结果文件中报告位级不变。
+
+此外，正式运行使用 `steps=0` 并从冻结 adapter 读取既有校准参数。旧 17 个训练场景在这一模式下不会参与任何计算或统计；为避免四个进程重复构建完全无用的训练场景 PTv3 cache，评估专用 manifest 将 `selected_train_scene_ids` 置空，同时在 `source_adapter_train_scene_ids` 中原样保留 17 个训练场景身份。这个纯调度优化不修改源码、权重、校准、验证样本或 forward，已先在 2-decision 烟测中验证。
+
+## 6. 扩大复测结果
+
+### 6.1 三种子主结果
+
+冻结 MTU3D 基线在三个种子中逐位相同。所有精度均基于每种子 1024 条、16 个场景的同一配对面板。
+
+| seed | Ground 基线 | Ground 融合 | ΔGround | Joint 基线 | Joint 融合 | ΔJoint | Top-5 基线 | Top-5 融合 | ΔTop-5 | Mean loss 变化 |
+|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| 42 | 45.2148% | 45.4102% | **+0.1953pp** | 40.4297% | 40.9180% | **+0.4883pp** | 82.2266% | 83.3984% | **+1.1719pp** | -0.052252 |
+| 123 | 45.2148% | 45.6055% | **+0.3906pp** | 40.4297% | 40.3320% | -0.0977pp | 82.2266% | 84.6680% | **+2.4414pp** | -0.052594 |
+| 777 | 45.2148% | 46.1914% | **+0.9766pp** | 40.4297% | 41.4062% | **+0.9766pp** | 82.2266% | 83.6914% | **+1.4648pp** | -0.052270 |
+| **三种子均值** | **45.2148%** | **45.7357%** | **+0.5208pp** | **40.4297%** | **40.8854%** | **+0.4557pp** | **82.2266%** | **83.9193%** | **+1.6927pp** | **-0.052372** |
+
+直接结论：
+
+- Ground 为 **3/3 种子正增益**；
+- Joint 为 2/3 种子正增益，三种子均值仍为正；
+- Ground Top-5 为 **3/3 种子正增益**，而且增益比 Top-1 更稳定；
+- 3072 个种子×决策配对中，Ground rescue/harm 为 `81/65`，净救回 16；Joint 为 `59/45`，净救回 14。
+
+### 6.2 场景聚类 bootstrap
+
+以场景为统计 cluster、固定三个冻结种子为模型副本，10,000 次 bootstrap：
+
+| 指标 | 三种子均值增益 | 场景 cluster 95% CI |
+|---|---:|---:|
+| Ground Top-1 | +0.5208pp | [-0.7161pp, +1.6927pp] |
+| Joint | +0.4557pp | [-0.4557pp, +1.3346pp] |
+| Ground Top-5 | **+1.6927pp** | **[+0.7161pp, +3.0599pp]** |
+
+因此应区分两种强度的证据：
+
+1. Top-1 Ground/Joint 的点估计和种子方向支持“小幅改善”，但 16 场景 cluster CI 仍跨 0，不能写成统计上已经排除零效应；
+2. Top-5 的三种子方向一致，且合并场景 CI 下界大于 0，是本轮扩大面板中更稳健的证据，说明点云主要改善了候选排序/召回，但把正确候选推到 Top-1 的能力仍有限。
+
+### 6.3 逐场景异质性
+
+| 场景 | ΔGround | ΔJoint | ΔTop-5 |
+|---|---:|---:|---:|
+| y9hTuugGdiq | +5.21pp | +3.12pp | +10.42pp |
+| 7MXmsvcQjpJ | +4.17pp | +1.56pp | +2.60pp |
+| zt1RVoi7PcG | +2.60pp | +3.12pp | 0.00pp |
+| q3zU7Yy5E5s | +2.08pp | +1.04pp | +1.04pp |
+| ziup5kvtCCR | +2.08pp | +2.08pp | +0.52pp |
+| BAbdmeyTvMZ | +1.56pp | 0.00pp | +1.56pp |
+| LT9Jq6dN3Ea | +1.56pp | +1.04pp | 0.00pp |
+| XB4GS9ShBRE | +0.52pp | +2.08pp | +2.08pp |
+| a8BtkwhxdRV | +0.52pp | +1.56pp | +1.56pp |
+| svBbv1Pavdk | +0.52pp | 0.00pp | +1.56pp |
+| bxsVRursffK | 0.00pp | -1.04pp | +2.08pp |
+| Dd4bFSTQ8gi | -1.56pp | 0.00pp | +2.08pp |
+| VBzV5z6i1WS | -1.56pp | 0.00pp | +2.60pp |
+| GLAQ4DNUx5U | -2.08pp | -3.12pp | -0.52pp |
+| Nfvxx8J5NCo | -3.65pp | -1.56pp | +0.52pp |
+| mv2HUxq3B53 | -3.65pp | -2.60pp | -1.04pp |
+
+10/16 场景的 Ground 点估计非负，6/16 为负。效应明显依赖场景，这解释了扩大后 Top-1 从原来的 +4.58pp 缩小到 +0.52pp，也说明后续不能继续只靠全局语义单残差：需要候选局部几何、重建质量和保守置信门控来避免在不适合的场景中产生 harm。
+
+### 6.4 no-point 与 loss 归因
+
+seed42 的 `no_point_signal` 在 1024 条决策上：
+
+- Ground、Joint、Top-5、Decision 均与冻结 MTU3D 基线完全相同；
+- 每条样本的 ground/decision argmax 预测也完全相同；
+- 证明 full-point 条件中的离散预测变化确实来自点云支路，而不是重新训练 MTU3D。
+
+但 no-point 的 mean loss 从 0.684242 降到 0.633684，原因是加载 adapter 时也加载了训练集独立拟合、保持 argmax 的温度/偏置校准。loss 必须拆分：
+
+| 分量 | loss reduction |
+|---|---:|
+| 基线 → no-point（校准/非点云项） | 0.050558 |
+| no-point → full-point seed42（点云特异增量） | **0.001694** |
+| 基线 → full-point seed42（总变化） | 0.052252 |
+
+所以不能把总 `-0.05237` 全部写成点云贡献；点云特异的连续 loss 证据以 seed42 的 `0.001694` 为准。离散 Ground/Top-5 改变不受这一 argmax-preserving 校准混淆。
+
+### 6.5 无指标的 formal r1 基础设施失败
+
+formal r1 在生成任何性能指标前统一退出。原因是评估专用 manifest 已正确省略训练场景，但启动参数错误保留了训练期 `selection_decisions_per_scene=32`，使旧脚本尝试对空的训练 selection panel 聚合，报 `Cannot finalize empty validation metrics`。该错误发生在验证 forward 和结果落盘前，不能解释为模型或数据表现。
+
+r2 只把 `steps=0` 时不应启用的 selection/calibration 计数改为 0；模型、三份权重、16 个场景、每场景 64 条决策、源码、forward、指标和门槛全部不变。修订已在正式 r2 前写入 `preregistration_amendment_r1.json`，r1 日志保留且不纳入性能统计。
+
+## 7. 结论边界
+
+本轮扩大复测为旧 `global_semantic_v2_b64_noappend_confirmatory4` 增加了比原确认集大 3.2 倍、场景完全独立的离线证据。结果支持以下结论：
+
+1. 冻结 ZPlusM 点云经冻结 PTv3 接入冻结 MTU3D 后，在新场景上的 Ground Top-1 和 Joint 有小幅正点估计；Ground 为 3/3 种子正增益；
+2. 更稳健的证据是 Ground Top-5：三种子均正、合并场景 cluster CI 下界大于 0，说明点云能够改善理解阶段的候选召回与排序；
+3. Top-1 增益存在明显场景异质性，合并 CI 仍跨 0；旧全局语义 adapter 能证明信号有效，但不是最终在线融合方案；
+4. no-point 的逐预测精确回退排除了“只是又训练了 MTU3D 参数”的解释；MTU3D 和 PTv3 在四组结果中均位级不变；
+5. 本实验只支持离线理解结论，不能把 Ground/Joint 写成 ObjectNav 的 SR/SPL，也不能把 v179 在线权重误写成旧 confirmatory4 权重。
+
+对下一版 v182 的直接启示是：保留已证明有效的 PTv3 语义来源，但应把候选局部 token 更早接入 MTU3D layer3→layer4，并用独立重建质量/候选置信门抑制 6 个负增益场景，而不是继续放大全局 residual 或只调最终分数。
+
+## 8. 结果文件索引
+
+- `formal_r2/analysis.json`：预注册聚合、三种子/场景 bootstrap、rescue-harm、no-point loss 拆分；
+- `formal_r2/seed{42,123,777}/result.json`：三种子完整 1024 条逐样本结果；
+- `formal_r2/no_point_seed42/result.json`：no-point 逐样本对照；
+- `payload_sha_audit.json`：旧机/H100 146 个 payload 文件 SHA256 逐项一致；
+- `transfer_audit.json`：不落本机的跨机传输记录；
+- `preregistration.json` 与 `preregistration_amendment_r1.json`：原预注册及 r1 无指标基础设施修订。

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/payload_sha_audit.json

@@ -0,0 +1,1032 @@
+{
+  "schema": "nav3r-offline-expanded16-cross-host-sha-audit/v1",
+  "status": "passed",
+  "local_payload_persisted": false,
+  "scene_count": 16,
+  "file_count": 146,
+  "all_sha256_match": true,
+  "records": [
+    {
+      "source": "/opt/data/gaomingju/Nav3R0805/runtime/ptv3/reconstructed_hm3d_geometry_path/scans_train/7MXmsvcQjpJ/fixed_reference.json",
+      "target": "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814/runtime/ptv3/reconstructed_hm3d_geometry_path_16unseen/scans_train/7MXmsvcQjpJ/fixed_reference.json",
+      "source_sha256": "abe4043663b4254483cfe6482258b74fd8beb320f8aa6ec1001e2c7bc0f24025",
+      "target_sha256": "abe4043663b4254483cfe6482258b74fd8beb320f8aa6ec1001e2c7bc0f24025",
+      "match": true
+    },
+    {
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experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/preregistration.json

@@ -0,0 +1,94 @@
+{
+  "schema": "nav3r-offline-confirmatory4-expanded16-preregistration/v1",
+  "created_at": "2026-08-23T00:00:00+08:00",
+  "status": "preregistered_before_evaluation",
+  "objective": "冻结旧离线最佳 global_semantic_v2_b64_noappend_confirmatory4，在更大的独立未见 HM3D 场景集上复测点云对离线理解指标的增益",
+  "claim_scope": "offline_heldout_ovon_grounding_and_decision_only",
+  "closed_loop_navigation_claim_allowed": false,
+  "sr_spl_claim_allowed": false,
+  "data": {
+    "source_split": "official HM3D OVON val_seen features/annotations plus fixed-reference reconstructed geometry packages",
+    "scene_count": 16,
+    "decisions_per_scene": 64,
+    "decision_offset": 0,
+    "expected_decisions_per_seed": 1024,
+    "scene_ids": [
+      "7MXmsvcQjpJ",
+      "a8BtkwhxdRV",
+      "BAbdmeyTvMZ",
+      "bxsVRursffK",
+      "Dd4bFSTQ8gi",
+      "GLAQ4DNUx5U",
+      "LT9Jq6dN3Ea",
+      "mv2HUxq3B53",
+      "Nfvxx8J5NCo",
+      "q3zU7Yy5E5s",
+      "svBbv1Pavdk",
+      "VBzV5z6i1WS",
+      "XB4GS9ShBRE",
+      "y9hTuugGdiq",
+      "ziup5kvtCCR",
+      "zt1RVoi7PcG"
+    ],
+    "excluded_original_confirmatory_scenes": [
+      "5cdEh9F2hJL",
+      "6s7QHgap2fW",
+      "cvZr5TUy5C5",
+      "p53SfW6mjZe",
+      "wcojb4TFT35"
+    ],
+    "required_train_scene_overlap": 0,
+    "required_original_confirmatory_scene_overlap": 0,
+    "local_payload_persistence_allowed": false
+  },
+  "frozen_adapters": {
+    "42": "5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b",
+    "123": "1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e",
+    "777": "5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3"
+  },
+  "frozen_backbones": {
+    "mtu3d_sha256": "0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791",
+    "sonata_ptv3_sha256": "c5ced5acdae30d1c469713398073a866e25e6e414e23feed5dc025373657ac50"
+  },
+  "required_source_sha256": {
+    "training_source_sha256": "7d7df8aaf84c8f8599bea01911fd759745906fb8d0c7f594af782cff1b91b9ff",
+    "bridge_source_sha256": "85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9",
+    "token_encoder_source_sha256": "a06f756d697dc7c73d5069357556a2d0995de6b387d2103e900a87d825fc340e",
+    "mtu3d_query3d_source_sha256": "d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99"
+  },
+  "architecture": {
+    "point_encoder": "frozen Sonata/PTv3",
+    "adapter": "bottleneck64 explicit global scene semantic grounding-only residual",
+    "point_memory_append": false,
+    "decision_logits": "frozen MTU3D, unchanged by point adapter",
+    "evaluation_steps": 0,
+    "adapter_training_allowed": false
+  },
+  "conditions": {
+    "seed42": "full point cloud",
+    "seed123": "full point cloud",
+    "seed777": "full point cloud",
+    "no_point_seed42": "no_point_signal exact-baseline control"
+  },
+  "primary_metrics": [
+    "ground_accuracy",
+    "joint_accuracy",
+    "ground_top5_accuracy",
+    "mean_loss"
+  ],
+  "analysis": {
+    "paired_unit": "same scene/episode/sub_episode/time_step",
+    "uncertainty": "scene-cluster bootstrap 95% CI",
+    "bootstrap_seed": 20260823,
+    "bootstrap_resamples": 10000
+  },
+  "acceptance": {
+    "integrity_required": true,
+    "mean_ground_gain_strictly_positive": true,
+    "positive_ground_gain_seeds_at_least": 2,
+    "mean_joint_gain_strictly_positive": true,
+    "no_point_ground_and_joint_exactly_equal_baseline": true,
+    "confidence_intervals_are_reported_not_used_as_a_hard_gate": true
+  },
+  "interpretation_note": "该扩大集与原确认集在场景上独立，并采用另一批经 fixed-reference/alignment 审计的重建包；若结果不一致，必须先区分场景泛化、重建域差异和实现链路，不允许按结果修改门槛。"
+}

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/preregistration_amendment_r1.json

@@ -0,0 +1,34 @@
+{
+  "schema": "nav3r-offline-confirmatory4-expanded16-preregistration-amendment/v1",
+  "date": "2026-08-23",
+  "reason": "formal_r1 implementation-control failure before any formal metric was produced",
+  "formal_r1_status": "invalid_no_metrics",
+  "observed_exception": "RuntimeError: Cannot finalize empty validation metrics",
+  "root_cause": {
+    "evaluation_manifest_selected_train_scene_ids": 0,
+    "steps": 0,
+    "selection_decisions_per_scene": 32,
+    "calibration_decisions_per_scene": 32,
+    "control_flow": "the nonzero selection count selected an empty training-scene selection panel instead of the 16-scene validation panel"
+  },
+  "amendment": {
+    "selection_decisions_per_scene": 0,
+    "calibration_decisions_per_scene": 0,
+    "train_decisions_per_scene": 1,
+    "explanation": "steps=0 loads the adapter's already-fitted calibration and performs no training or checkpoint selection; these parameters are therefore inactive evaluation infrastructure and must not select a nonexistent training panel"
+  },
+  "unchanged": [
+    "16 validation scenes",
+    "64 decisions per validation scene",
+    "decision offset 0",
+    "all three adapter files and SHA256 values",
+    "frozen MTU3D checkpoint",
+    "frozen Sonata/PTv3 checkpoint",
+    "four authoritative source files and SHA256 values",
+    "point cloud preprocessing and model forward",
+    "metrics, bootstrap policy and acceptance rules",
+    "GPU assignment"
+  ],
+  "evidence_policy": "formal_r1 is retained as an implementation failure and excluded from all performance summaries; formal_r2 is the first valid formal evaluation",
+  "not_result_contingent": true
+}

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16.sh

@@ -0,0 +1,137 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT=/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814
+STAGE="$ROOT/runtime/staging/offline_confirmatory4_expand_20260823"
+OUT="$ROOT/runtime/ptv3/offline_confirmatory4_expanded16_20260823"
+PY="$ROOT/runtime/physbench_envs/envs/physbench-objectnav-mtu3d/bin/python"
+TRAIN="$STAGE/scripts/train_ptv3_mtu3d_real_ovon_l40s.py"
+MTU3D="$STAGE/third_party/MTU3D"
+CHECKPOINT="$ROOT/runtime/mtu3d/checkpoints/stage2-pretrain-all/pytorch_model.bin"
+SONATA="$ROOT/runtime/ptv3/checkpoints/sonata.pth"
+AUDIT_MANIFEST="$ROOT/runtime/ptv3/alignment_manifest_17train_16unseen_offline_expand_20260823.json"
+MANIFEST="$OUT/evaluation_manifest.json"
+FEATURE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_stage2_feat/HM3D"
+VLE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_vle_data/ovon"
+PROCESSED="$ROOT/runtime/ptv3/data/processed_hm3d_official_train"
+TRAIN_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_multiwindow_131072_canonicalref_17train/scans_train"
+VAL_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_geometry_path_16unseen/scans_train"
+TOKENIZER="$ROOT/runtime/ptv3/tokenizers/clip-vit-large-patch14"
+ADAPTER_ROOT="$ROOT/runtime/publication_staging/20260822/global_semantic_v2_b64_noappend_confirmatory4"
+
+mkdir -p "$OUT"
+
+check_sha() {
+  local expected="$1"
+  local path="$2"
+  local actual
+  actual=$(sha256sum "$path" | awk '{print $1}')
+  if [[ "$actual" != "$expected" ]]; then
+    echo "SHA mismatch: $path expected=$expected actual=$actual" >&2
+    exit 2
+  fi
+}
+
+check_sha 7d7df8aaf84c8f8599bea01911fd759745906fb8d0c7f594af782cff1b91b9ff "$TRAIN"
+check_sha 85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9 "$STAGE/nav3r/integration/mtu3d_ptv3_bridge.py"
+check_sha a06f756d697dc7c73d5069357556a2d0995de6b387d2103e900a87d825fc340e "$STAGE/nav3r/integration/ptv3_token_encoder.py"
+check_sha d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99 "$MTU3D/model/query3d_vle.py"
+check_sha 0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791 "$CHECKPOINT"
+check_sha c5ced5acdae30d1c469713398073a866e25e6e414e23feed5dc025373657ac50 "$SONATA"
+check_sha 5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b "$ADAPTER_ROOT/seed42/best_adapter.pt"
+check_sha 1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e "$ADAPTER_ROOT/seed123/best_adapter.pt"
+check_sha 5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3 "$ADAPTER_ROOT/seed777/best_adapter.pt"
+
+"$PY" - "$AUDIT_MANIFEST" "$MANIFEST" "$VAL_RECON" <<'PY'
+import json
+from pathlib import Path
+import sys
+audit_manifest = json.loads(Path(sys.argv[1]).read_text())
+validation = audit_manifest["validation_scene_ids"]
+training = audit_manifest["selected_train_scene_ids"]
+if len(validation) != 16 or set(validation) & set(training):
+    raise SystemExit("invalid expanded validation manifest")
+root = Path(sys.argv[3])
+missing = [scene for scene in validation if not (root / scene / "zplusm_scene_input.npz").is_file()]
+if missing:
+    raise SystemExit(f"missing reconstruction packages: {missing}")
+# steps=0 loads the frozen adapter's already-fitted calibration.  Training
+# scenes cannot influence evaluation, so omit their costly feature/PTv3 cache
+# construction while retaining their identities as immutable provenance.
+evaluation_manifest = dict(audit_manifest)
+evaluation_manifest["source_adapter_train_scene_ids"] = list(training)
+evaluation_manifest["selected_train_scene_ids"] = []
+evaluation_manifest["evaluation_only_skip_unused_train_cache"] = True
+Path(sys.argv[2]).write_text(json.dumps(evaluation_manifest, indent=2) + "\n")
+print(json.dumps({
+    "preflight": "passed",
+    "validation_scene_count": len(validation),
+    "source_adapter_train_scene_count": len(training),
+    "evaluation_train_scene_count": 0,
+}))
+PY
+
+common=(
+  --project-root "$STAGE"
+  --mtu3d-root "$MTU3D"
+  --checkpoint "$CHECKPOINT"
+  --sonata-checkpoint "$SONATA"
+  --alignment-manifest "$MANIFEST"
+  --feature-root "$FEATURE"
+  --vle-root "$VLE"
+  --processed-root "$PROCESSED"
+  --reconstruction-root "$TRAIN_RECON"
+  --validation-reconstruction-root "$VAL_RECON"
+  --tokenizer "$TOKENIZER"
+  --train-decisions-per-scene 128
+  --selection-decisions-per-scene 32
+  --calibration-decisions-per-scene 32
+  --checkpoint-selection-objective loss_then_accuracy
+  --validation-decisions 64
+  --validation-decision-offset 0
+  --steps 0
+  --eval-every 1
+  --adapter-lr 0.0001
+  --gate-lr 0.001
+  --initial-fusion-scale 0.35
+  --point-token-count 256
+  --enable-global-semantic-residual
+  --global-semantic-ground-only
+  --global-semantic-bottleneck-size 64
+  --disable-point-memory-append
+  --raw-fused-aux-loss-weight 1.0
+  --ground-listwise-loss-weight 0.25
+  --ground-bce-loss-weight 1.0
+  --decision-loss-weight 1.0
+)
+
+launch() {
+  local gpu="$1"
+  local name="$2"
+  local seed="$3"
+  local adapter="$4"
+  local ablation="$5"
+  local run_out="$OUT/$name"
+  if [[ -e "$run_out/result.json" || -e "$OUT/$name.pid" ]]; then
+    echo "Refusing to overwrite existing run: $name" >&2
+    exit 3
+  fi
+  mkdir -p "$run_out"
+  nohup env CUDA_VISIBLE_DEVICES="$gpu" PYTHONPATH="$STAGE:$ROOT/runtime/ptv3/sonata" \
+    "$PY" "$TRAIN" "${common[@]}" \
+    --seed "$seed" \
+    --load-adapter "$adapter" \
+    --point-ablation "$ablation" \
+    --output-dir "$run_out" \
+    >"$OUT/$name.log" 2>&1 &
+  local pid=$!
+  echo "$pid" >"$OUT/$name.pid"
+  echo "$name gpu=$gpu pid=$pid"
+}
+
+launch 4 seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" full
+launch 5 seed123 123 "$ADAPTER_ROOT/seed123/best_adapter.pt" full
+launch 6 seed777 777 "$ADAPTER_ROOT/seed777/best_adapter.pt" full
+launch 7 no_point_seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" no_point_signal
+
+date --iso-8601=seconds >"$OUT/launched_at.txt"

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/run_expanded16_r2.sh

@@ -0,0 +1,140 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT=/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814
+STAGE="$ROOT/runtime/staging/offline_confirmatory4_expand_20260823"
+OUT="$ROOT/runtime/ptv3/offline_confirmatory4_expanded16_20260823"
+RUN_ROOT="$OUT/formal_r2"
+PY="$ROOT/runtime/physbench_envs/envs/physbench-objectnav-mtu3d/bin/python"
+TRAIN="$STAGE/scripts/train_ptv3_mtu3d_real_ovon_l40s.py"
+MTU3D="$STAGE/third_party/MTU3D"
+CHECKPOINT="$ROOT/runtime/mtu3d/checkpoints/stage2-pretrain-all/pytorch_model.bin"
+SONATA="$ROOT/runtime/ptv3/checkpoints/sonata.pth"
+AUDIT_MANIFEST="$ROOT/runtime/ptv3/alignment_manifest_17train_16unseen_offline_expand_20260823.json"
+MANIFEST="$RUN_ROOT/evaluation_manifest.json"
+FEATURE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_stage2_feat/HM3D"
+VLE="$ROOT/runtime/ptv3/data/extracted/embodied_scan_vle_data/ovon"
+PROCESSED="$ROOT/runtime/ptv3/data/processed_hm3d_official_train"
+TRAIN_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_multiwindow_131072_canonicalref_17train/scans_train"
+VAL_RECON="$ROOT/runtime/ptv3/reconstructed_hm3d_geometry_path_16unseen/scans_train"
+TOKENIZER="$ROOT/runtime/ptv3/tokenizers/clip-vit-large-patch14"
+ADAPTER_ROOT="$ROOT/runtime/publication_staging/20260822/global_semantic_v2_b64_noappend_confirmatory4"
+
+mkdir -p "$OUT"
+mkdir -p "$RUN_ROOT"
+cp "$OUT/preregistration.json" "$RUN_ROOT/preregistration.json"
+
+check_sha() {
+  local expected="$1"
+  local path="$2"
+  local actual
+  actual=$(sha256sum "$path" | awk '{print $1}')
+  if [[ "$actual" != "$expected" ]]; then
+    echo "SHA mismatch: $path expected=$expected actual=$actual" >&2
+    exit 2
+  fi
+}
+
+check_sha 7d7df8aaf84c8f8599bea01911fd759745906fb8d0c7f594af782cff1b91b9ff "$TRAIN"
+check_sha 85da4b42b10b332b2f9314d672db72480d43eb6689e50f2548552ade3583f1b9 "$STAGE/nav3r/integration/mtu3d_ptv3_bridge.py"
+check_sha a06f756d697dc7c73d5069357556a2d0995de6b387d2103e900a87d825fc340e "$STAGE/nav3r/integration/ptv3_token_encoder.py"
+check_sha d9c315a54b092da96e74279c46e331ca02c6cac6891c06c4b2f962637a49ac99 "$MTU3D/model/query3d_vle.py"
+check_sha 0bd24d081257ecba1b58df4203ecaa657763a6c2928e59fb3a0efe8aef3fc791 "$CHECKPOINT"
+check_sha c5ced5acdae30d1c469713398073a866e25e6e414e23feed5dc025373657ac50 "$SONATA"
+check_sha 5e1f360483acff9b43d4e42ab28faea5b133c4965acc42646912312e101c713b "$ADAPTER_ROOT/seed42/best_adapter.pt"
+check_sha 1cc590fc4280a81b265ddada7831a89f29a72233b849d3a03906a0189110a94e "$ADAPTER_ROOT/seed123/best_adapter.pt"
+check_sha 5c620c3e4722b148f474642a71b7c04f7e0d9de415acc436f6f1f91f8eb89de3 "$ADAPTER_ROOT/seed777/best_adapter.pt"
+
+"$PY" - "$AUDIT_MANIFEST" "$MANIFEST" "$VAL_RECON" <<'PY'
+import json
+from pathlib import Path
+import sys
+audit_manifest = json.loads(Path(sys.argv[1]).read_text())
+validation = audit_manifest["validation_scene_ids"]
+training = audit_manifest["selected_train_scene_ids"]
+if len(validation) != 16 or set(validation) & set(training):
+    raise SystemExit("invalid expanded validation manifest")
+root = Path(sys.argv[3])
+missing = [scene for scene in validation if not (root / scene / "zplusm_scene_input.npz").is_file()]
+if missing:
+    raise SystemExit(f"missing reconstruction packages: {missing}")
+# steps=0 loads the frozen adapter's already-fitted calibration.  Training
+# scenes cannot influence evaluation, so omit their costly feature/PTv3 cache
+# construction while retaining their identities as immutable provenance.
+evaluation_manifest = dict(audit_manifest)
+evaluation_manifest["source_adapter_train_scene_ids"] = list(training)
+evaluation_manifest["selected_train_scene_ids"] = []
+evaluation_manifest["evaluation_only_skip_unused_train_cache"] = True
+Path(sys.argv[2]).write_text(json.dumps(evaluation_manifest, indent=2) + "\n")
+print(json.dumps({
+    "preflight": "passed",
+    "validation_scene_count": len(validation),
+    "source_adapter_train_scene_count": len(training),
+    "evaluation_train_scene_count": 0,
+}))
+PY
+
+common=(
+  --project-root "$STAGE"
+  --mtu3d-root "$MTU3D"
+  --checkpoint "$CHECKPOINT"
+  --sonata-checkpoint "$SONATA"
+  --alignment-manifest "$MANIFEST"
+  --feature-root "$FEATURE"
+  --vle-root "$VLE"
+  --processed-root "$PROCESSED"
+  --reconstruction-root "$TRAIN_RECON"
+  --validation-reconstruction-root "$VAL_RECON"
+  --tokenizer "$TOKENIZER"
+  --train-decisions-per-scene 1
+  --selection-decisions-per-scene 0
+  --calibration-decisions-per-scene 0
+  --checkpoint-selection-objective loss_then_accuracy
+  --validation-decisions 64
+  --validation-decision-offset 0
+  --steps 0
+  --eval-every 1
+  --adapter-lr 0.0001
+  --gate-lr 0.001
+  --initial-fusion-scale 0.35
+  --point-token-count 256
+  --enable-global-semantic-residual
+  --global-semantic-ground-only
+  --global-semantic-bottleneck-size 64
+  --disable-point-memory-append
+  --raw-fused-aux-loss-weight 1.0
+  --ground-listwise-loss-weight 0.25
+  --ground-bce-loss-weight 1.0
+  --decision-loss-weight 1.0
+)
+
+launch() {
+  local gpu="$1"
+  local name="$2"
+  local seed="$3"
+  local adapter="$4"
+  local ablation="$5"
+  local run_out="$RUN_ROOT/$name"
+  if [[ -e "$run_out/result.json" || -e "$RUN_ROOT/$name.pid" ]]; then
+    echo "Refusing to overwrite existing run: $name" >&2
+    exit 3
+  fi
+  mkdir -p "$run_out"
+  nohup env CUDA_VISIBLE_DEVICES="$gpu" PYTHONPATH="$STAGE:$ROOT/runtime/ptv3/sonata" \
+    "$PY" "$TRAIN" "${common[@]}" \
+    --seed "$seed" \
+    --load-adapter "$adapter" \
+    --point-ablation "$ablation" \
+    --output-dir "$run_out" \
+    >"$RUN_ROOT/$name.log" 2>&1 &
+  local pid=$!
+  echo "$pid" >"$RUN_ROOT/$name.pid"
+  echo "$name gpu=$gpu pid=$pid"
+}
+
+launch 4 seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" full
+launch 5 seed123 123 "$ADAPTER_ROOT/seed123/best_adapter.pt" full
+launch 6 seed777 777 "$ADAPTER_ROOT/seed777/best_adapter.pt" full
+launch 7 no_point_seed42 42 "$ADAPTER_ROOT/seed42/best_adapter.pt" no_point_signal
+
+date --iso-8601=seconds >"$RUN_ROOT/launched_at.txt"

experiments/offline_global_semantic_v2_b64_noappend_confirmatory4_expanded16_20260823/transfer_audit.json

@@ -0,0 +1,913 @@
+{
+  "schema": "nav3r-old-to-h100-offline-expanded16-transfer/v1",
+  "status": "passed",
+  "local_payload_persisted": false,
+  "scene_ids": [
+    "7MXmsvcQjpJ",
+    "BAbdmeyTvMZ",
+    "Dd4bFSTQ8gi",
+    "GLAQ4DNUx5U",
+    "LT9Jq6dN3Ea",
+    "Nfvxx8J5NCo",
+    "VBzV5z6i1WS",
+    "XB4GS9ShBRE",
+    "a8BtkwhxdRV",
+    "bxsVRursffK",
+    "mv2HUxq3B53",
+    "q3zU7Yy5E5s",
+    "svBbv1Pavdk",
+    "y9hTuugGdiq",
+    "ziup5kvtCCR",
+    "zt1RVoi7PcG"
+  ],
+  "excluded_original_confirmatory_scenes": [
+    "5cdEh9F2hJL",
+    "6s7QHgap2fW",
+    "cvZr5TUy5C5",
+    "p53SfW6mjZe",
+    "wcojb4TFT35"
+  ],
+  "training_scene_overlap": [],
+  "manifest": "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/workspace/physbench_nav3r_objectnav_20260814/runtime/ptv3/alignment_manifest_17train_16unseen_offline_expand_20260823.json",
+  "record_count": 146,
+  "copied_bytes": 0,
+  "records": [
+    {
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