Repair mocap actions across all tasks, old_data and validation; retain V8 force

ACTION_REPAIR_20260918.md

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+# Mocap action repair and review — 2026-09-18
+
+Scope: all 218 published parquet segments/episodes: main motherboard25,
+pushT68, rope70, toy10; old motherboard32 and pushT4; validation9.
+Raw sensor poses, timestamps, source frame IDs and videos are preserved.
+
+## Review first
+
+Download `action_sample25.html` and open it in a browser. It contains25
+stratified examples, original camera videos and raw/repaired pose traces.
+Video playback requires an internet connection. `action_sample25.json`
+records the selected examples and their evidence. `action_review.html`
+covers every unresolved event occurrence in published segments.
+
+**A tracking jump and genuine rapid out-and-back motion can have identical
+pose sequences. Smooth reconstruction does not prove tracking loss.**
+Synthetic reconstruction benchmarks measure interpolation accuracy conditional
+on an error being present; they do not establish real-world detection precision.
+The negative-control report documents this limitation.
+
+The strict review policy admits automatic HIGH repairs only where replaced
+observations have independent loss evidence: declared tracking gaps or invalid
+raw poses. Other proposed repairs remain MEDIUM pending human review; LOW
+events are not reconstructed. Persistent branch proposals invalidate the whole
+identified interval, including its smooth interior, not just boundary jumps.
+`action_lost_track_*` means **suspected/review-required tracking corruption**,
+not a confirmed ground-truth diagnosis. Genuine fast movement may remain
+unchanged but masked under the existing physical-action limits.
+
+## Columns and action sidecars
+
+- `sensor_{left,right}_pose`: unchanged original pose.
+- `sensor_{left,right}_pose_repaired`: proposed reconstructed pose; consult masks.
+- `pose_{left,right}_valid`, `_repaired`, `_repair_confidence`,
+  `_repair_event_id`, `_repair_method`: pose provenance.
+- `action`:14 floats, next-frame absolute left XYZ+quaternion(xyzw), then
+  right XYZ+quaternion(xyzw), in the same world coordinates as the parquet.
+  The final row repeats the final pose and is invalid, with no next transition.
+- `action_valid_{left,right}`: the authoritative per-hand training mask.
+- `action_repaired_*`, `action_repair_confidence_*`, `action_lost_track_*`,
+  `action_long_lost_track_*`: transition provenance. Long means over60 native
+  frames, retaining the original event duration across segment boundaries.
+
+Confidence codes:0 unchanged/unclassified,1 LOW,2 MEDIUM,3 HIGH.
+Confidence alone is not a validity mask.
+
+Each parquet has left/right `actions_native/...npz` and
+`actions_fps15/...npz`. Each sidecar stores9D world-frame translation delta
+plus relative-rotation6D (first two matrix columns, column by column).
+Native actions connect k→k+1.15fps actions connect k→k+2 at local rows0,2,4…;
+their validity is the AND of both native transitions. No action crosses a
+published segment boundary.
+
+Training consumers must enforce both hand masks: `action_full` rejects any
+window containing an invalid transition; `action_causal` excludes invalid
+transitions from its action loss. Parquet masks do not enforce this by themselves.
+These added columns are not a conversion of the entire repository to a new
+LeRobot storage version.
+
+## Force and reproducibility
+
+The old-data force update is separate:36 episodes rebuilt using V8 from
+released lossy tactile MP4 because the raw May H5 files are gone. See the force
+recovery documentation and each `.force.json` sidecar. MP4 recovery is not
+bit-equivalent to raw-H5 processing, and absolute force accuracy on React is
+not established. Action publication retains those newly rebuilt force fields.
+
+`action_publication_manifest.json` records the frozen source mapping, source
+hashes, output hashes, coordinate conversions and preceding force revision.
+`repair_audit/` contains reconstruction benchmarks, negative controls,
+build configurations and event counts. Counts of source events, published
+event occurrences and action transitions are different units.
+
+The source implementation is included under `action_repair_tools/`. It uses
+NumPy, SciPy, PyArrow and huggingface_hub. Candidate builds remain separate
+from raw release inputs. Review decisions must identify their source episode
+as well as the event; same local frame IDs can occur in different episodes.

action_repair_tools/README.md

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+# Action repair tools
+
+Run from this directory after `python -m pip install -r requirements.txt`.
+The package uses minimal namespace initializers to avoid pulling in unrelated
+recording/camera dependencies. It contains no model weights or credentials.
+
+The September18 build used the frozen **uncut source parquet tree**, its
+`pose_gaps.json` files and the input manifests in `../repair_audit/`.
+The public segmented parquets alone do not reproduce context that was cut
+away; do not treat a segment boundary as a new tracker dropout.
+
+Example with your original source tree and a NEW candidate output directory:
+
+```bash
+python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates audit --source /path/to/source
+python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates benchmark --max-intervals 700 --seed 17
+python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates build --branch-review --require-loss-evidence
+python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates verify
+python -m twm.scripts.propagate_mocap_repairs --help
+```
+
+For the exact September18 benchmark length set `(1,2,3,4,5,20,60)`, call
+`run_benchmarks(candidate_root, anomaly_lengths=(1,2,3,4,5,20,60), max_intervals=700, seed=17)`.
+The CLI default length set is smaller. Build the separate validation source
+with `--task validation` during audit. Use the propagation inventory to map
+every published segment by source frame ID, not inferred filename offsets.
+
+`propagate_mocap_repairs export` regenerates native/15fps sidecars and the
+lightweight HTML review, including25 examples. Pass a pinned video-file
+inventory to `--remote-videos`. The older full camera-rendering `review`
+subcommand requires the full MultimodalData repository and is not bundled.
+
+Review decisions use `task/date/source_episode::local_event_id`, mapped from
+the published segment through the inventory. `keep_raw`, `accept_repair`,
+`invalidate`, and `unsure` are supported; accepting one event never implicitly
+approves another event with the same frame indices in a different episode.
+Re-export actions after applying decisions. The publisher requires a bound
+force-commit receipt and an unchanged HF parent commit; it never uploads unless
+explicitly invoked with `--publish`.

action_repair_tools/requirements.txt

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+numpy
+scipy
+pyarrow
+huggingface_hub

action_repair_tools/twm/__init__.py

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+"""Minimal standalone namespace for the published action-repair tools."""

action_repair_tools/twm/react_preprocess/__init__.py

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+"""Repair-only package; full recording/video preprocessing is not bundled."""

action_repair_tools/twm/react_preprocess/mocap_branch.py

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+"""Conservative body-frame branch proposals, separate from training admission.
+
+A pose is a body-to-world transform T. Marker symmetry changes it to T @ B,
+so correction must be right multiplication by inverse(B), including the body
+translation lever arm. Subtracting a world offset destroys genuine motion.
+
+Paired boundaries supply two independent estimates of B using only immediate
+outer context. This never interpolates the interior of a persistent interval.
+Even matching return pairs are hypotheses: real abrupt out-and-back motion can
+be observationally indistinguishable. All results therefore remain MEDIUM;
+method/task-specific validation and any human decision belong to the caller.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+from typing import Iterable
+
+import numpy as np
+from scipy.spatial.transform import Rotation
+
+
+@dataclass(frozen=True)
+class BranchConfig:
+    rotation_seed_deg: float = 30.0
+    coupled_rotation_deg: float = 12.0
+    coupled_translation_mm: float = 20.0
+    context_frames: int = 5
+    max_interval_frames: int = 900
+    transform_rotation_tolerance_deg: float = 3.0
+    transform_translation_tolerance_mm: float = 5.0
+    boundary_rotation_tolerance_deg: float = 3.0
+    boundary_translation_tolerance_mm: float = 5.0
+    max_step_rotation_deg: float = 30.0
+    max_step_translation_mm: float = 50.0
+
+
+@dataclass(frozen=True)
+class BranchCandidate:
+    start: int
+    end: int
+    pose: np.ndarray
+    branch_rotation_xyzw: np.ndarray
+    branch_translation_m: np.ndarray
+    evidence: dict[str, float | int | bool | str]
+    confidence: str = 'MEDIUM'
+
+
+def _predict_next(context: np.ndarray) -> tuple[np.ndarray, Rotation]:
+    """One-step prediction; reversed context yields a backward prediction."""
+    rotations = Rotation.from_quat(context[:, 3:])
+    angular = np.median((rotations[1:]*rotations[:-1].inv()).as_rotvec(), axis=0)
+    velocity = np.median(np.diff(context[:, :3], axis=0), axis=0)
+    return context[-1, :3] + velocity, Rotation.from_rotvec(angular)*rotations[-1]
+
+
+def _transform(expected: tuple[np.ndarray, Rotation], observed: np.ndarray):
+    xyz, rotation = expected
+    return rotation.inv().apply(observed[:3]-xyz), rotation.inv()*Rotation.from_quat(observed[3:])
+
+
+def find_branch_candidates(
+    pose: np.ndarray,
+    known_gaps: Iterable[tuple[int, int]] = (),
+    *,
+    trusted: np.ndarray | None = None,
+    config: BranchConfig | None = None,
+) -> list[BranchCandidate]:
+    """Propose complete paired-return intervals without changing input poses.
+
+    ``trusted`` optionally identifies independently trusted outer anchors.
+    Without it, anchors are only screened for finite, contiguous, quiet local
+    observations and evidence records that limitation. Declared gaps and any
+    nonfinite/zero-quaternion rows exclude both anchors and branch interiors.
+    Only adjacent boundaries are paired: nested/chattering ambiguous branches
+    are deliberately left for a richer decoder or human review.
+    """
+    raw = np.asarray(pose, dtype=float)
+    if raw.ndim != 2 or raw.shape[1] != 7:
+        raise ValueError('pose must have shape (T, 7)')
+    cfg = config or BranchConfig()
+    if cfg.context_frames < 2 or cfg.max_interval_frames < 1:
+        raise ValueError('context_frames must be >=2 and max_interval_frames >=1')
+    finite = np.isfinite(raw).all(axis=1) & (np.linalg.norm(raw[:, 3:], axis=1) > 1e-12)
+    available = finite.copy()
+    for start, count in known_gaps:
+        if count > 0:
+            available[max(0, int(start)):max(0, min(len(raw), int(start)+int(count)))] = False
+    anchor_ok = available.copy()
+    if trusted is not None:
+        trusted = np.asarray(trusted, dtype=bool)
+        if trusted.shape != (len(raw),):
+            raise ValueError('trusted must have shape (T,)')
+        anchor_ok &= trusted
+    if len(raw) < 2*cfg.context_frames + 1:
+        return []
+    step_rot = np.full(len(raw)-1, np.nan)
+    pairs = np.flatnonzero(finite[:-1] & finite[1:])
+    if len(pairs):
+        step_rot[pairs] = np.degrees((Rotation.from_quat(raw[pairs+1, 3:])*
+                                      Rotation.from_quat(raw[pairs, 3:]).inv()).magnitude())
+    step_trans = np.linalg.norm(np.diff(raw[:, :3], axis=0), axis=1)*1000
+    edges = np.flatnonzero((step_rot > cfg.rotation_seed_deg) |
+                          ((step_rot > cfg.coupled_rotation_deg) &
+                           (step_trans > cfg.coupled_translation_mm)))
+    candidates: list[BranchCandidate] = []
+    used_until = -1
+    for entry, exit_edge in zip(edges[:-1], edges[1:]):
+        start, end = int(entry+1), int(exit_edge)
+        left, right = start-cfg.context_frames, end+1+cfg.context_frames
+        # A return boundary already used to close a proposal cannot also open
+        # its inverse over the clean interval leading to the next occurrence.
+        if start <= used_until+1 or left < 0 or right > len(raw):
+            continue
+        if end-start+1 > cfg.max_interval_frames or not available[start:end+1].all():
+            continue
+        if not anchor_ok[left:start].all() or not anchor_ok[end+1:right].all():
+            continue
+        # Contexts that cross another jump cannot establish either branch.
+        context_edges = np.r_[np.arange(left, start-1), np.arange(end+1, right-1)]
+        if (np.any(step_rot[context_edges] > cfg.coupled_rotation_deg) or
+                np.any(step_trans[context_edges] > cfg.max_step_translation_mm)):
+            continue
+        expected_entry = _predict_next(raw[left:start])
+        expected_exit = _predict_next(raw[end+1:right][::-1])
+        entry_t, entry_r = _transform(expected_entry, raw[start])
+        exit_t, exit_r = _transform(expected_exit, raw[end])
+        transform_rot = float(np.degrees((entry_r.inv()*exit_r).magnitude()))
+        transform_trans = float(np.linalg.norm(entry_t-exit_t)*1000)
+        if (transform_rot > cfg.transform_rotation_tolerance_deg or
+                transform_trans > cfg.transform_translation_tolerance_mm):
+            continue
+        branch_r = Rotation.from_quat(np.stack([entry_r.as_quat(), exit_r.as_quat()])).mean()
+        branch_t = (entry_t+exit_t)/2
+        corrected_r = Rotation.from_quat(raw[start:end+1, 3:])*branch_r.inv()
+        corrected_t = raw[start:end+1, :3]-corrected_r.apply(branch_t)
+        corrected = np.column_stack((corrected_t, corrected_r.as_quat()))
+        boundary_trans = max(float(np.linalg.norm(corrected_t[i]-pred[0])*1000)
+                             for i, pred in ((0, expected_entry), (-1, expected_exit)))
+        boundary_rot = max(float(np.degrees((pred[1].inv()*corrected_r[i]).magnitude()))
+                           for i, pred in ((0, expected_entry), (-1, expected_exit)))
+        joined = np.vstack((raw[start-1], corrected, raw[end+1]))
+        joined_r = Rotation.from_quat(joined[:, 3:])
+        max_rot = float(np.max(np.degrees((joined_r[1:]*joined_r[:-1].inv()).magnitude())))
+        max_trans = float(np.max(np.linalg.norm(np.diff(joined[:, :3], axis=0), axis=1)*1000))
+        if (boundary_trans > cfg.boundary_translation_tolerance_mm or
+                boundary_rot > cfg.boundary_rotation_tolerance_deg or
+                max_rot > cfg.max_step_rotation_deg or
+                max_trans > cfg.max_step_translation_mm):
+            continue
+        candidates.append(BranchCandidate(start, end, corrected, branch_r.as_quat(), branch_t, {
+            'method': 'paired_body_branch',
+            'duration_frames': end-start+1,
+            'bidirectional_transform_agrees': True,
+            'transform_rotation_disagreement_deg': transform_rot,
+            'transform_translation_disagreement_mm': transform_trans,
+            'boundary_prediction_rotation_max_deg': boundary_rot,
+            'boundary_prediction_translation_max_mm': boundary_trans,
+            'reconstructed_max_step_rotation_deg': max_rot,
+            'reconstructed_max_step_translation_mm': max_trans,
+            'left_anchor': start-1,
+            'right_anchor': end+1,
+            'context_frames': cfg.context_frames,
+            'independent_anchor_mask_supplied': trusted is not None,
+            'physical': True,
+            'benchmark_validated': False,
+        }))
+        used_until = end
+    # Support is restricted to this supplied recording/body stream; unrelated
+    # epochs or rigid bodies must never be pooled implicitly.
+    for index, candidate in enumerate(candidates):
+        r = Rotation.from_quat(candidate.branch_rotation_xyzw)
+        support = 0
+        for other in candidates:
+            rotation_error = np.degrees((r.inv()*Rotation.from_quat(other.branch_rotation_xyzw)).magnitude())
+            translation_error = np.linalg.norm(candidate.branch_translation_m-other.branch_translation_m)*1000
+            if (rotation_error <= cfg.transform_rotation_tolerance_deg and
+                    translation_error <= cfg.transform_translation_tolerance_mm):
+                support += 1
+        candidates[index] = replace(candidate, evidence={**candidate.evidence,
+                                                        'matching_return_pairs': support})
+    return candidates

action_repair_tools/twm/react_preprocess/mocap_branch_review.py

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+"""Extend review invalidity across plausible persistent alternate branches."""
+from __future__ import annotations
+
+from dataclasses import asdict, replace
+import json
+
+import numpy as np
+
+from .mocap_branch import find_branch_candidates
+from .mocap_repair import AnomalyBout, Confidence, PoseRepairResult, RepairEvent
+
+
+def apply_loss_evidence_gate(result: PoseRepairResult, raw: np.ndarray,
+                             known_gaps=()) -> PoseRepairResult:
+    """Require declared loss or invalid raw observations for automatic HIGH."""
+    raw = np.asarray(raw, dtype=float)
+    loss = ~np.isfinite(raw).all(axis=1) | (np.linalg.norm(raw[:, 3:], axis=1) <= 1e-12)
+    for start, count in known_gaps:
+        if int(count) > 0:
+            loss[max(0, int(start)):max(0, min(len(raw), int(start)+int(count)))] = True
+    confidence, valid = result.confidence.copy(), result.valid.copy()
+    events = []
+    for event in result.events:
+        if event.confidence == Confidence.HIGH:
+            rows = np.asarray(event.replaced_frames, dtype=int)
+            supported = bool(len(rows) and loss[rows].all())
+            evidence = {**event.evidence, 'independent_loss_evidence': supported,
+                        'review_required': not supported}
+            if not supported:
+                confidence[event.bout.start:event.bout.end+1] = Confidence.MEDIUM
+                valid[event.bout.start:event.bout.end+1] = False
+                event = replace(event, confidence=Confidence.MEDIUM, evidence=evidence)
+            else:
+                event = replace(event, evidence=evidence)
+        events.append(event)
+    return replace(result, confidence=confidence, valid=valid, events=tuple(events))
+
+
+def apply_review_branches(result: PoseRepairResult, raw: np.ndarray, side: str,
+                          known_gaps=()) -> PoseRepairResult:
+    """Merge non-HIGH boundary events and invalidate the complete proposal.
+
+    Existing HIGH events take precedence. Proposals supply candidate values,
+    never training permission. Any larger overlapping original event remains
+    invalid in full; its original evidence is retained in the merged event.
+    """
+    pose, repaired = result.pose.copy(), result.repaired.copy()
+    confidence, valid = result.confidence.copy(), result.valid.copy()
+    event_ids, events = result.event_id.copy(), list(result.events)
+    for proposal in find_branch_candidates(raw, known_gaps):
+        overlapping = [e for e in events if e.bout.side == side and
+                       e.bout.start <= proposal.end+1 and e.bout.end >= proposal.start-1]
+        if not overlapping or any(e.confidence == Confidence.HIGH for e in overlapping):
+            continue
+        lo = min(proposal.start, *(e.bout.start for e in overlapping))
+        hi = max(proposal.end, *(e.bout.end for e in overlapping))
+        # A merged interval cannot overlap an event that retains a different
+        # row owner. Expand to a fixed point before checking HIGH protection.
+        while True:
+            merged = [e for e in events if e.bout.side == side and e.bout.start <= hi and e.bout.end >= lo]
+            new_lo = min(lo, *(e.bout.start for e in merged))
+            new_hi = max(hi, *(e.bout.end for e in merged))
+            if (new_lo, new_hi) == (lo, hi):
+                break
+            lo, hi = new_lo, new_hi
+        if any(e.confidence == Confidence.HIGH for e in merged):
+            continue
+        eid = f'{side}:{lo}-{hi}:branch_review'
+        rows = np.arange(proposal.start, proposal.end+1)
+        pose[rows] = proposal.pose
+        repaired[rows] = True
+        confidence[lo:hi+1] = Confidence.MEDIUM
+        valid[lo:hi+1] = False
+        event_ids[lo:hi+1] = eid
+        seeds = tuple(sorted({row for e in merged for row in e.bout.seed_frames}))
+        bout = AnomalyBout(side, lo, hi, 'branch_review', seeds,
+                           lo-1 if lo else None, hi+1 if hi+1 < len(raw) else None)
+        evidence = {**proposal.evidence,
+                    'duration_frames': hi-lo+1,
+                    'proposal_start': proposal.start, 'proposal_end': proposal.end,
+                    'requires_independent_branch_identity_evidence': True,
+                    'training_valid': False,
+                    'superseded_events_json': json.dumps([asdict(e) for e in merged],
+                                                         default=lambda value: value.item())}
+        replacement = RepairEvent(eid, bout, Confidence.MEDIUM, 'paired_body_branch_review',
+                                  tuple(np.flatnonzero(repaired[lo:hi+1])+lo), evidence)
+        removed_ids = {e.event_id for e in merged}
+        events = [e for e in events if e.event_id not in removed_ids] + [replacement]
+    return PoseRepairResult(pose, repaired, confidence, valid, event_ids,
+                            tuple(sorted(events, key=lambda e: e.bout.start)))

action_repair_tools/twm/react_preprocess/mocap_candidate.py

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+"""Immutable-input writer for mocap repair candidate trees.
+
+Production release trees are read-only inputs.  Every build is pinned to a
+manifest of exact parquet digests and writes augmented metadata plus action
+sidecars beneath a separate review root.
+"""
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from datetime import datetime, timezone
+import hashlib
+import json
+import os
+from pathlib import Path
+import tempfile
+from typing import Any, Iterable
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+from .mocap_repair import Confidence, TaskGate, repair_pose_stream
+from .repaired_actions import ActionSeries, actions_fps15, native_actions
+
+
+SIDES = ("left", "right")
+MANIFEST_SCHEMA_VERSION = 1
+
+
+class SourceChangedError(RuntimeError):
+    """A manifest-pinned input no longer has its recorded content."""
+
+
+class CandidateVerificationError(RuntimeError):
+    """Candidate provenance or derived actions disagree with their contract."""
+
+
+@dataclass(frozen=True)
+class ManifestFile:
+    path: str
+    bytes: int
+    rows: int
+    mtime_ns: int
+    sha256: str
+
+
+@dataclass(frozen=True)
+class InputManifest:
+    source_root: str
+    tasks: tuple[str, ...]
+    files: tuple[ManifestFile, ...]
+    created_utc: str
+    digest: str
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "schema_version": MANIFEST_SCHEMA_VERSION,
+            "source_root": self.source_root,
+            "tasks": list(self.tasks),
+            "created_utc": self.created_utc,
+            "digest": self.digest,
+            "files": [asdict(item) for item in self.files],
+        }
+
+    @classmethod
+    def from_dict(cls, value: dict[str, Any]) -> "InputManifest":
+        if int(value.get("schema_version", -1)) != MANIFEST_SCHEMA_VERSION:
+            raise ValueError("unsupported input manifest schema")
+        return cls(
+            source_root=str(value["source_root"]),
+            tasks=tuple(map(str, value["tasks"])),
+            files=tuple(ManifestFile(**item) for item in value["files"]),
+            created_utc=str(value["created_utc"]),
+            digest=str(value["digest"]),
+        )
+
+
+@dataclass(frozen=True)
+class CandidateEpisode:
+    task: str
+    date: str
+    episode: str
+    parquet: Path
+    events: Path
+    repaired_frames: int
+
+
+@dataclass(frozen=True)
+class CandidateBuild:
+    root: Path
+    manifest_digest: str
+    episodes: tuple[CandidateEpisode, ...]
+
+
+@dataclass(frozen=True)
+class VerificationReport:
+    episodes: int
+    errors: tuple[str, ...]
+
+
+def _sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def _canonical_json(value: Any) -> bytes:
+    return json.dumps(value, sort_keys=True, separators=(",", ":"),
+                      ensure_ascii=True).encode("utf-8")
+
+
+def _manifest_identity(source_root: Path, tasks: tuple[str, ...],
+                       files: tuple[ManifestFile, ...]) -> dict[str, Any]:
+    return {
+        "schema_version": MANIFEST_SCHEMA_VERSION,
+        "source_root": str(source_root),
+        "tasks": list(tasks),
+        "files": [{
+            "path": item.path,
+            "bytes": item.bytes,
+            "rows": item.rows,
+            "sha256": item.sha256,
+        } for item in files],
+    }
+
+
+def _validate_manifest(manifest: InputManifest) -> None:
+    identity = _manifest_identity(
+        Path(manifest.source_root).resolve(), manifest.tasks, manifest.files)
+    expected = hashlib.sha256(_canonical_json(identity)).hexdigest()
+    if manifest.digest != expected:
+        raise ValueError(
+            f"input manifest digest is invalid: {manifest.digest} != {expected}")
+
+
+def snapshot_inputs(source_root: Path,
+                    tasks: Iterable[str]) -> InputManifest:
+    """Snapshot the exact parquet set used by one candidate run."""
+    source_root = Path(source_root).resolve()
+    task_tuple = tuple(sorted(dict.fromkeys(str(task) for task in tasks)))
+    files: list[ManifestFile] = []
+    for task in task_tuple:
+        for path in sorted((source_root / task / "meta").glob("*/*.parquet")):
+            stat = path.stat()
+            files.append(ManifestFile(
+                path=path.relative_to(source_root).as_posix(),
+                bytes=stat.st_size,
+                rows=pq.ParquetFile(path).metadata.num_rows,
+                mtime_ns=stat.st_mtime_ns,
+                sha256=_sha256(path),
+            ))
+    files_tuple = tuple(sorted(files, key=lambda item: item.path))
+    identity = _manifest_identity(source_root, task_tuple, files_tuple)
+    digest = hashlib.sha256(_canonical_json(identity)).hexdigest()
+    return InputManifest(
+        source_root=str(source_root),
+        tasks=task_tuple,
+        files=files_tuple,
+        created_utc=datetime.now(timezone.utc).isoformat(),
+        digest=digest,
+    )
+
+
+def save_manifest(manifest: InputManifest, path: Path) -> Path:
+    """Persist a manifest, refusing to replace a different snapshot."""
+    _validate_manifest(manifest)
+    path = Path(path)
+    if path.is_file():
+        prior = InputManifest.from_dict(json.loads(path.read_text()))
+        _validate_manifest(prior)
+        if prior.digest != manifest.digest:
+            raise ValueError(
+                f"manifest path already contains a different snapshot: {path}")
+    _atomic_json(path, manifest.to_dict())
+    return path
+
+
+def load_manifest(path: Path) -> InputManifest:
+    manifest = InputManifest.from_dict(json.loads(Path(path).read_text()))
+    _validate_manifest(manifest)
+    return manifest
+
+
+def _atomic_json(path: Path, value: Any) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with tempfile.NamedTemporaryFile(
+            mode="w", encoding="utf-8", dir=path.parent,
+            prefix=f".{path.name}.", suffix=".tmp", delete=False) as stream:
+        temp = Path(stream.name)
+        json.dump(value, stream, indent=2, sort_keys=True)
+        stream.write("\n")
+    os.replace(temp, path)
+
+
+def _is_within(path: Path, root: Path) -> bool:
+    return path == root or root in path.parents
+
+
+def _pose_list_array(value: np.ndarray) -> pa.Array:
+    return pa.array(np.asarray(value, dtype=np.float64).tolist(),
+                    type=pa.list_(pa.float64()))
+
+
+def _set_column(table: pa.Table, name: str, value: pa.Array) -> pa.Table:
+    if name in table.column_names:
+        return table.set_column(table.schema.get_field_index(name), name, value)
+    return table.append_column(name, value)
+
+
+def _jsonable(value: Any) -> Any:
+    if isinstance(value, np.generic):
+        return value.item()
+    if isinstance(value, dict):
+        return {str(key): _jsonable(item) for key, item in value.items()}
+    if isinstance(value, (list, tuple)):
+        return [_jsonable(item) for item in value]
+    return value
+
+
+def _event_dict(event: Any) -> dict[str, Any]:
+    return _jsonable({
+        "event_id": event.event_id,
+        "side": event.bout.side,
+        "start": event.bout.start,
+        "end": event.bout.end,
+        "kind": event.bout.kind,
+        "seed_frames": list(event.bout.seed_frames),
+        "left_context_end": event.bout.left_context_end,
+        "right_context_start": event.bout.right_context_start,
+        "confidence": Confidence(event.confidence).name,
+        "method": event.method,
+        "replaced_frames": list(event.replaced_frames),
+        "evidence": event.evidence,
+    })
+
+
+def _write_actions(path: Path, actions: ActionSeries) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    event_text = np.asarray(["|".join(ids) for ids in actions.event_ids],
+                            dtype=np.str_)
+    with tempfile.NamedTemporaryFile(
+            mode="wb", dir=path.parent, prefix=f".{path.name}.",
+            suffix=".tmp", delete=False) as stream:
+        temp = Path(stream.name)
+        np.savez_compressed(
+            stream,
+            values=np.asarray(actions.values, np.float32),
+            valid=np.asarray(actions.valid, bool),
+            repaired=np.asarray(actions.repaired, bool),
+            event_ids=event_text,
+            start_rows=np.asarray(actions.start_rows, np.int32),
+            end_rows=np.asarray(actions.end_rows, np.int32),
+            rotation_deg=np.asarray(actions.rotation_deg, np.float32),
+            translation_mm=np.asarray(actions.translation_mm, np.float32),
+        )
+    os.replace(temp, path)
+
+
+class CandidateWriter:
+    """Build candidate metadata pinned to one immutable input manifest."""
+
+    def __init__(self, source_root: Path, output_root: Path,
+                 manifest: InputManifest, *,
+                 task_gates: dict[str, TaskGate] | None = None,
+                 production_roots: Iterable[Path] = (),
+                 branch_review: bool = False,
+                 require_loss_evidence: bool = False):
+        self.source_root = Path(source_root).resolve()
+        self.output_root = Path(output_root).resolve()
+        self.manifest = manifest
+        _validate_manifest(manifest)
+        self.task_gates = dict(task_gates or {})
+        self.branch_review = bool(branch_review)
+        self.require_loss_evidence = bool(require_loss_evidence)
+        inferred = {
+            self.source_root,
+            self.source_root.parent / "release_zup",
+            self.source_root.parent / "release_cut",
+        }
+        inferred.update(Path(path).resolve() for path in production_roots)
+        for root in inferred:
+            if (_is_within(self.output_root, root)
+                    or _is_within(root, self.output_root)):
+                raise ValueError(
+                    f"candidate output overlaps production root: "
+                    f"{self.output_root} vs {root}")
+        if Path(manifest.source_root).resolve() != self.source_root:
+            raise ValueError("manifest source_root does not match writer source")
+
+    def _verify_source(self, item: ManifestFile) -> Path:
+        path = self.source_root / item.path
+        if not path.is_file():
+            raise SourceChangedError(f"source missing after manifest: {item.path}")
+        stat = path.stat()
+        actual = _sha256(path)
+        if actual != item.sha256:
+            raise SourceChangedError(
+                f"source digest changed after manifest: {item.path} "
+                f"(bytes {item.bytes} -> {stat.st_size})")
+        rows = pq.ParquetFile(path).metadata.num_rows
+        if rows != item.rows:
+            raise SourceChangedError(
+                f"source row count changed after manifest: {item.path}")
+        return path
+
+    def _known_gaps(self, task: str, date: str,
+                    episode: str) -> dict[str, list[tuple[int, int]]]:
+        path = self.source_root / task / "pose_gaps.json"
+        if not path.is_file():
+            return {}
+        payload = json.loads(path.read_text())
+        row = payload.get(f"{date}/{episode}", {})
+        return {side: [tuple(map(int, gap)) for gap in row.get(side, [])]
+                for side in SIDES}
+
+    def _write_parquet_atomic(self, table: pa.Table, output: Path) -> None:
+        output.parent.mkdir(parents=True, exist_ok=True)
+        with tempfile.NamedTemporaryFile(
+                dir=output.parent, prefix=f".{output.name}.",
+                suffix=".tmp", delete=False) as stream:
+            temp = Path(stream.name)
+        try:
+            pq.write_table(table, temp, compression="zstd")
+            os.replace(temp, output)
+        finally:
+            if temp.exists():
+                temp.unlink()
+
+    def _write_episode(self, item: ManifestFile) -> CandidateEpisode:
+        source = self._verify_source(item)
+        rel = Path(item.path)
+        task, marker, date, filename = rel.parts
+        if marker != "meta":
+            raise ValueError(f"manifest path is not task/meta/date: {item.path}")
+        episode = Path(filename).stem
+        table = pq.read_table(source)
+        output_table = table
+        gaps = self._known_gaps(task, date, episode)
+        events: list[dict[str, Any]] = []
+        repaired_total = 0
+        gate = self.task_gates.get(task, TaskGate())
+        for side in SIDES:
+            pose_name = f"sensor_{side}_pose"
+            if pose_name not in table.column_names:
+                continue
+            raw_pose = np.asarray(table[pose_name].to_pylist(), dtype=float)
+            result = repair_pose_stream(
+                raw_pose, side, gaps.get(side, ()), task_gate=gate)
+            if self.require_loss_evidence:
+                from .mocap_branch_review import apply_loss_evidence_gate
+                result = apply_loss_evidence_gate(result, raw_pose, gaps.get(side, ()))
+            if self.branch_review:
+                from .mocap_branch_review import apply_review_branches
+                result = apply_review_branches(result, raw_pose, side, gaps.get(side, ()))
+            output_table = _set_column(
+                output_table, pose_name, _pose_list_array(result.pose))
+            output_table = _set_column(
+                output_table, f"pose_{side}_repaired",
+                pa.array(result.repaired, type=pa.bool_()))
+            output_table = _set_column(
+                output_table, f"pose_{side}_repair_confidence",
+                pa.array(result.confidence, type=pa.uint8()))
+            output_table = _set_column(
+                output_table, f"pose_{side}_valid",
+                pa.array(result.valid, type=pa.bool_()))
+            output_table = _set_column(
+                output_table, f"pose_{side}_repair_event_id",
+                pa.array(result.event_id.astype(str), type=pa.string()))
+            repaired_total += int(result.repaired.sum())
+            events.extend(_event_dict(event) for event in result.events)
+            native = native_actions(
+                result.pose, result.valid, result.repaired, result.event_id)
+            half = actions_fps15(result.pose, native)
+            _write_actions(
+                self.output_root / task / "actions_native" / date
+                / f"{episode}_{side}.npz", native)
+            _write_actions(
+                self.output_root / task / "actions_fps15" / date
+                / f"{episode}_{side}.npz", half)
+
+        output_table = output_table.replace_schema_metadata(
+            dict(table.schema.metadata or {}))
+        output = self.output_root / item.path
+        self._write_parquet_atomic(output_table, output)
+        event_path = (self.output_root / task / "repair_events" / date
+                      / f"{episode}.json")
+        _atomic_json(event_path, {
+            "schema_version": 1,
+            "manifest_digest": self.manifest.digest,
+            "source_path": item.path,
+            "source_sha256": item.sha256,
+            "task": task,
+            "date": date,
+            "episode": episode,
+            "events": sorted(events, key=lambda event: event["event_id"]),
+        })
+        return CandidateEpisode(
+            task, date, episode, output, event_path, repaired_total)
+
+    def write_all(self) -> CandidateBuild:
+        """Verify the complete snapshot, then build only its declared files."""
+        for item in self.manifest.files:
+            self._verify_source(item)
+        manifest_path = self.output_root / "input_manifest.json"
+        if manifest_path.exists():
+            prior = json.loads(manifest_path.read_text())
+            if prior.get("digest") != self.manifest.digest:
+                raise ValueError(
+                    "candidate root contains a different input manifest digest")
+        _atomic_json(manifest_path, self.manifest.to_dict())
+        _atomic_json(self.output_root / "build_config.json", {
+            "schema_version": 1,
+            "manifest_digest": self.manifest.digest,
+            "branch_review": self.branch_review,
+            "require_loss_evidence": self.require_loss_evidence,
+            "task_gates": {
+                task: asdict(self.task_gates.get(task, TaskGate()))
+                for task in self.manifest.tasks
+            },
+        })
+        episodes = tuple(self._write_episode(item) for item in self.manifest.files)
+        _atomic_json(self.output_root / "build_summary.json", {
+            "schema_version": 1,
+            "manifest_digest": self.manifest.digest,
+            "episodes": [{
+                "task": episode.task,
+                "date": episode.date,
+                "episode": episode.episode,
+                "parquet": episode.parquet.relative_to(self.output_root).as_posix(),
+                "events": episode.events.relative_to(self.output_root).as_posix(),
+                "repaired_frames": episode.repaired_frames,
+            } for episode in episodes],
+        })
+        return CandidateBuild(self.output_root, self.manifest.digest, episodes)
+
+
+def _load_candidate(candidate_root: Path) -> tuple[InputManifest, dict[str, TaskGate]]:
+    candidate_root = Path(candidate_root).resolve()
+    manifest_path = candidate_root / "input_manifest.json"
+    config_path = candidate_root / "build_config.json"
+    if not manifest_path.is_file() or not config_path.is_file():
+        raise ValueError("candidate root is missing manifest or build config")
+    manifest = InputManifest.from_dict(json.loads(manifest_path.read_text()))
+    config = json.loads(config_path.read_text())
+    if config.get("manifest_digest") != manifest.digest:
+        raise ValueError("build config manifest digest mismatch")
+    gates = {task: TaskGate(**value)
+             for task, value in config.get("task_gates", {}).items()}
+    return manifest, gates
+
+
+def _scoped_event_id(path: Path, event_id: str) -> str:
+    return f"{path.parent.parent.parent.name}/{path.parent.name}/{path.stem}::{event_id}"
+
+
+def _event_index(candidate_root: Path) -> dict[str, tuple[Path, dict[str, Any]]]:
+    """Index review IDs as task/date/source_episode::local_event_id.
+
+    Legacy local IDs are accepted only when globally unique. Exported samples
+    keyed remote_path::local_event_id must first be mapped through publication
+    inventory to the unsegmented source episode; segmented filenames are not
+    sufficient to infer this mapping.
+    """
+    index: dict[str, tuple[Path, dict[str, Any]]] = {}
+    aliases: dict[str, list[tuple[Path, dict[str, Any]]]] = {}
+    for path in sorted(Path(candidate_root).glob("*/repair_events/*/*.json")):
+        payload = json.loads(path.read_text())
+        for event in payload.get("events", []):
+            event_id = str(event["event_id"])
+            scoped = _scoped_event_id(path, event_id)
+            if scoped in index:
+                raise ValueError(f"duplicate event within episode: {scoped}")
+            index[scoped] = (path, event)
+            aliases.setdefault(event_id, []).append((path, event))
+    index.update({alias: values[0] for alias, values in aliases.items() if len(values) == 1})
+    return index
+
+
+def _canonical_decisions(payload: dict[str, Any],
+                         manifest: InputManifest,
+                         events: dict[str, tuple[Path, dict[str, Any]]]) -> list[dict[str, str]]:
+    if int(payload.get("schema_version", -1)) != 1:
+        raise ValueError("unsupported decisions schema")
+    if payload.get("manifest_digest") != manifest.digest:
+        raise ValueError("decisions manifest digest mismatch")
+    allowed = {"keep_raw", "accept_repair", "invalidate", "unsure"}
+    seen: set[str] = set()
+    out = []
+    for row in payload.get("decisions", []):
+        event_id = str(row.get("event_id", ""))
+        decision = str(row.get("decision", ""))
+        if event_id not in events:
+            raise ValueError(f"unknown event in decisions: {event_id}")
+        path, event = events[event_id]
+        event_id = _scoped_event_id(path, str(event["event_id"]))
+        if event_id in seen:
+            raise ValueError(f"duplicate decision for event: {event_id}")
+        if decision not in allowed:
+            raise ValueError(f"invalid decision {decision!r} for {event_id}")
+        seen.add(event_id)
+        out.append({"event_id": event_id, "decision": decision})
+    return sorted(out, key=lambda row: row["event_id"])
+
+
+def _finite_unit_pose(pose: np.ndarray) -> np.ndarray:
+    norm = np.linalg.norm(pose[:, 3:], axis=1)
+    return np.isfinite(pose).all(axis=1) & (norm > 1e-12)
+
+
+def apply_decisions(candidate_root: Path,
+                    payload: dict[str, Any]) -> CandidateBuild:
+    """Replay canonical review decisions from the immutable source snapshot."""
+    candidate_root = Path(candidate_root).resolve()
+    manifest, gates = _load_candidate(candidate_root)
+    events_before = _event_index(candidate_root)
+    decisions = _canonical_decisions(payload, manifest, events_before)
+
+    # Rebuild the undecided baseline first.  This makes decision application
+    # independent of whatever decision was applied on the previous run.
+    writer = CandidateWriter(
+        Path(manifest.source_root), candidate_root, manifest,
+        task_gates=gates,
+        branch_review=bool(json.loads((candidate_root / "build_config.json").read_text()).get("branch_review", False)),
+        require_loss_evidence=bool(json.loads((candidate_root / "build_config.json").read_text()).get("require_loss_evidence", False)))
+    build = writer.write_all()
+    events = _event_index(candidate_root)
+    grouped: dict[Path, list[dict[str, str]]] = {}
+    for row in decisions:
+        path, event = events[row["event_id"]]
+        grouped.setdefault(path, []).append({"event_id": str(event["event_id"]),
+                                            "decision": row["decision"]})
+
+    for event_path, rows in grouped.items():
+        event_payload = json.loads(event_path.read_text())
+        by_id = {event["event_id"]: event
+                 for event in event_payload.get("events", [])}
+        task = str(event_payload["task"])
+        date = str(event_payload["date"])
+        episode = str(event_payload["episode"])
+        candidate_path = candidate_root / task / "meta" / date / f"{episode}.parquet"
+        source_path = Path(manifest.source_root) / event_payload["source_path"]
+        table = pq.read_table(candidate_path)
+        source = pq.read_table(source_path)
+        side_state: dict[str, dict[str, np.ndarray]] = {}
+        for side in SIDES:
+            pose_name = f"sensor_{side}_pose"
+            if pose_name not in table.column_names:
+                continue
+            side_state[side] = {
+                "pose": np.asarray(table[pose_name].to_pylist(), float),
+                "raw": np.asarray(source[pose_name].to_pylist(), float),
+                "repaired": np.asarray(
+                    table[f"pose_{side}_repaired"], bool).copy(),
+                "confidence": np.asarray(
+                    table[f"pose_{side}_repair_confidence"], np.uint8).copy(),
+                "valid": np.asarray(
+                    table[f"pose_{side}_valid"], bool).copy(),
+                "event_id": np.asarray(
+                    table[f"pose_{side}_repair_event_id"].to_pylist(), object),
+            }
+        for row in rows:
+            event = by_id[row["event_id"]]
+            side = str(event["side"])
+            state = side_state[side]
+            selected = np.arange(int(event["start"]), int(event["end"]) + 1)
+            decision = row["decision"]
+            if decision == "accept_repair":
+                state["valid"][selected] = _finite_unit_pose(state["pose"][selected])
+            else:
+                state["pose"][selected] = state["raw"][selected]
+                state["repaired"][selected] = False
+                if decision == "keep_raw":
+                    state["confidence"][selected] = Confidence.NONE
+                    state["valid"][selected] = _finite_unit_pose(state["raw"][selected])
+                elif decision == "invalidate":
+                    state["confidence"][selected] = Confidence.LOW
+                    state["valid"][selected] = False
+                else:  # unsure
+                    state["valid"][selected] = False
+            event["decision"] = decision
+
+        output_table = table
+        for side, state in side_state.items():
+            output_table = _set_column(
+                output_table, f"sensor_{side}_pose", _pose_list_array(state["pose"]))
+            output_table = _set_column(
+                output_table, f"pose_{side}_repaired",
+                pa.array(state["repaired"], type=pa.bool_()))
+            output_table = _set_column(
+                output_table, f"pose_{side}_repair_confidence",
+                pa.array(state["confidence"], type=pa.uint8()))
+            output_table = _set_column(
+                output_table, f"pose_{side}_valid",
+                pa.array(state["valid"], type=pa.bool_()))
+            native = native_actions(
+                state["pose"], state["valid"], state["repaired"],
+                state["event_id"])
+            half = actions_fps15(state["pose"], native)
+            _write_actions(
+                candidate_root / task / "actions_native" / date
+                / f"{episode}_{side}.npz", native)
+            _write_actions(
+                candidate_root / task / "actions_fps15" / date
+                / f"{episode}_{side}.npz", half)
+        writer._write_parquet_atomic(output_table, candidate_path)
+        _atomic_json(event_path, event_payload)
+
+    _atomic_json(candidate_root / "decisions.json", {
+        "schema_version": 1,
+        "manifest_digest": manifest.digest,
+        "decisions": decisions,
+    })
+    return build
+
+
+def _rows_equal(left: np.ndarray, right: np.ndarray) -> np.ndarray:
+    same = left == right
+    if np.issubdtype(left.dtype, np.floating):
+        same |= np.isnan(left) & np.isnan(right)
+    return np.all(same, axis=1)
+
+
+def _columns_equal(left: pa.ChunkedArray,
+                   right: pa.ChunkedArray) -> bool:
+    """Arrow equality with IEEE NaNs treated as equal at identical positions."""
+    if left.type != right.type or len(left) != len(right):
+        return False
+    if left.equals(right):
+        return True
+    try:
+        if (pa.types.is_list(left.type)
+                or pa.types.is_large_list(left.type)
+                or pa.types.is_fixed_size_list(left.type)):
+            a = np.asarray(left.to_pylist(), dtype=float)
+            b = np.asarray(right.to_pylist(), dtype=float)
+        elif pa.types.is_floating(left.type):
+            a = np.asarray(left.to_numpy(zero_copy_only=False), dtype=float)
+            b = np.asarray(right.to_numpy(zero_copy_only=False), dtype=float)
+        else:
+            return False
+    except (TypeError, ValueError):
+        return False
+    return bool(np.array_equal(a, b, equal_nan=True))
+
+
+def _verify_action_file(path: Path, expected: ActionSeries,
+                        errors: list[str]) -> None:
+    if not path.is_file():
+        errors.append(f"missing action sidecar: {path}")
+        return
+    try:
+        with np.load(path, allow_pickle=False) as data:
+            for name, value in (
+                    ("values", expected.values), ("valid", expected.valid),
+                    ("repaired", expected.repaired),
+                    ("start_rows", expected.start_rows),
+                    ("end_rows", expected.end_rows)):
+                if name not in data.files or not np.array_equal(data[name], value):
+                    errors.append(f"action mismatch {path}: {name}")
+            event_text = np.asarray(["|".join(ids) for ids in expected.event_ids],
+                                    dtype=np.str_)
+            if "event_ids" not in data.files or not np.array_equal(
+                    data["event_ids"], event_text):
+                errors.append(f"action mismatch {path}: event_ids")
+    except Exception as exc:
+        errors.append(f"unreadable action sidecar {path}: {exc}")
+
+
+def verify_candidate(candidate_root: Path) -> VerificationReport:
+    """Verify source immutability, declared changes, and action derivations."""
+    candidate_root = Path(candidate_root).resolve()
+    manifest, gates = _load_candidate(candidate_root)
+    writer = CandidateWriter(
+        Path(manifest.source_root), candidate_root, manifest,
+        task_gates=gates)
+    errors: list[str] = []
+    for item in manifest.files:
+        try:
+            source_path = writer._verify_source(item)
+        except SourceChangedError as exc:
+            errors.append(str(exc)); continue
+        candidate_path = candidate_root / item.path
+        if not candidate_path.is_file():
+            errors.append(f"missing candidate parquet: {item.path}"); continue
+        try:
+            source = pq.read_table(source_path)
+            candidate = pq.read_table(candidate_path)
+        except Exception as exc:
+            errors.append(f"unreadable candidate {item.path}: {exc}"); continue
+        if source.num_rows != candidate.num_rows:
+            errors.append(f"row count mismatch: {item.path}"); continue
+        for name in source.column_names:
+            if name.startswith("sensor_") and name.endswith("_pose"):
+                continue
+            if (name not in candidate.column_names
+                    or not _columns_equal(source[name], candidate[name])):
+                errors.append(f"source column changed: {item.path}:{name}")
+        rel = Path(item.path)
+        task, _, date, filename = rel.parts
+        episode = Path(filename).stem
+        for side in SIDES:
+            pose_name = f"sensor_{side}_pose"
+            required = [pose_name, f"pose_{side}_repaired",
+                        f"pose_{side}_repair_confidence", f"pose_{side}_valid",
+                        f"pose_{side}_repair_event_id"]
+            if pose_name not in source.column_names:
+                continue
+            if any(name not in candidate.column_names for name in required):
+                errors.append(f"missing repair columns: {item.path}:{side}"); continue
+            raw = np.asarray(source[pose_name].to_pylist(), float)
+            pose = np.asarray(candidate[pose_name].to_pylist(), float)
+            repaired = np.asarray(candidate[f"pose_{side}_repaired"], bool)
+            valid = np.asarray(candidate[f"pose_{side}_valid"], bool)
+            event_id = np.asarray(
+                candidate[f"pose_{side}_repair_event_id"].to_pylist(), object)
+            changed = ~_rows_equal(raw, pose)
+            if np.any(changed & ~repaired):
+                errors.append(f"undeclared pose change: {item.path}:{side}")
+            if np.any(repaired & (event_id == "")):
+                errors.append(f"repaired pose missing event id: {item.path}:{side}")
+            finite_unit = _finite_unit_pose(pose)
+            if np.any(valid & ~finite_unit):
+                errors.append(f"valid pose is nonfinite/nonunit: {item.path}:{side}")
+            norm = np.linalg.norm(pose[valid, 3:], axis=1)
+            if len(norm) and not np.allclose(norm, 1.0, atol=1e-6):
+                errors.append(f"valid quaternion is not unit: {item.path}:{side}")
+            native = native_actions(pose, valid, repaired, event_id)
+            half = actions_fps15(pose, native)
+            _verify_action_file(
+                candidate_root / task / "actions_native" / date
+                / f"{episode}_{side}.npz", native, errors)
+            _verify_action_file(
+                candidate_root / task / "actions_fps15" / date
+                / f"{episode}_{side}.npz", half, errors)
+    report = VerificationReport(len(manifest.files), tuple(errors))
+    if errors:
+        raise CandidateVerificationError("; ".join(errors))
+    return report

action_repair_tools/twm/react_preprocess/mocap_repair.py

@@ -0,0 +1,624 @@
+"""Conservative, provenance-preserving OptiTrack trajectory repair.
+
+This module starts by grouping discontinuity seeds into complete tracking-loss
+bouts.  Detection is deliberately separate from reconstruction: a surprising
+edge is evidence to inspect, never by itself permission to replace a pose.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import IntEnum
+from typing import Iterable
+
+import numpy as np
+from scipy.spatial.transform import Rotation, Slerp
+
+
+@dataclass(frozen=True)
+class RepairConfig:
+    """Physical and temporal gates expressed at the native 30 fps rate."""
+
+    rotation_seed_deg: float = 30.0
+    coupled_rotation_deg: float = 12.0
+    coupled_translation_mm: float = 20.0
+    branch_agreement_deg: float = 12.0
+    branch_context_frames: int = 15
+    stable_frames: int = 10
+    context_frames: int = 15
+    translation_inlier_mm: float = 20.0
+    rotation_inlier_deg: float = 12.0
+    max_high_frames: int = 60
+    max_step_translation_mm: float = 50.0
+    max_step_rotation_deg: float = 30.0
+    max_prediction_translation_mm: float = 10.0
+    max_prediction_rotation_deg: float = 5.0
+
+
+@dataclass(frozen=True)
+class AnomalyBout:
+    """One complete interval requiring reconstruction or review."""
+
+    side: str
+    start: int
+    end: int
+    kind: str
+    seed_frames: tuple[int, ...]
+    left_context_end: int | None
+    right_context_start: int | None
+
+
+class Confidence(IntEnum):
+    NONE = 0
+    LOW = 1
+    MEDIUM = 2
+    HIGH = 3
+
+
+@dataclass(frozen=True)
+class TaskGate:
+    """Held-out clean-data evidence allowed to enable automatic training."""
+
+    high_confidence_enabled: bool = False
+    validated_max_frames: int = 0
+    endpoint_max_frames: int = 0
+
+
+@dataclass(frozen=True)
+class RepairEvent:
+    event_id: str
+    bout: AnomalyBout
+    confidence: Confidence
+    method: str
+    replaced_frames: tuple[int, ...]
+    evidence: dict[str, float | int | bool | str]
+
+
+@dataclass(frozen=True)
+class PoseRepairResult:
+    pose: np.ndarray
+    repaired: np.ndarray
+    confidence: np.ndarray
+    valid: np.ndarray
+    event_id: np.ndarray
+    events: tuple[RepairEvent, ...]
+
+
+def _unit_quaternion(q: np.ndarray) -> np.ndarray:
+    norm = np.linalg.norm(q, axis=-1, keepdims=True)
+    return q / np.where(norm > 1e-12, norm, 1.0)
+
+
+def _quaternion_angle_deg(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+    a, b = _unit_quaternion(a), _unit_quaternion(b)
+    dot = np.abs(np.sum(a * b, axis=-1))
+    return np.degrees(2.0 * np.arccos(np.clip(dot, -1.0, 1.0)))
+
+
+def transition_metrics(pose: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """Return sign-insensitive rotation degrees and translation millimetres."""
+    pose = np.asarray(pose, dtype=float)
+    if pose.ndim != 2 or pose.shape[1] != 7:
+        raise ValueError(f"pose must have shape (T, 7), got {pose.shape}")
+    if len(pose) < 2:
+        return np.empty(0, dtype=float), np.empty(0, dtype=float)
+    finite = np.isfinite(pose).all(axis=1)
+    rot = _quaternion_angle_deg(pose[:-1, 3:], pose[1:, 3:])
+    trans = np.linalg.norm(np.diff(pose[:, :3], axis=0), axis=1) * 1000.0
+    invalid = ~(finite[:-1] & finite[1:])
+    rot[invalid] = np.nan
+    trans[invalid] = np.nan
+    return rot, trans
+
+
+def _branch_support(pose: np.ndarray, rows: set[int], config: RepairConfig) -> dict[int, float]:
+    """Fraction of nearby finite observations agreeing with each candidate row."""
+    q = np.asarray(pose, dtype=float)[:, 3:]
+    finite = np.isfinite(q).all(axis=1) & (np.linalg.norm(q, axis=1) > 1e-12)
+    out: dict[int, float] = {}
+    for row in sorted(rows):
+        if not finite[row]:
+            out[row] = -1.0
+            continue
+        lo = max(0, row - config.branch_context_frames)
+        hi = min(len(q), row + config.branch_context_frames + 1)
+        neighbours = np.arange(lo, hi)
+        neighbours = neighbours[(neighbours != row) & finite[lo:hi]]
+        if not len(neighbours):
+            out[row] = 1.0
+            continue
+        angle = _quaternion_angle_deg(
+            np.repeat(q[row][None], len(neighbours), axis=0), q[neighbours])
+        out[row] = float(np.mean(angle <= config.branch_agreement_deg))
+    return out
+
+
+def _discontinuity_seed_frames(pose: np.ndarray, config: RepairConfig) -> set[int]:
+    rot, trans = transition_metrics(pose)
+    edges = np.where(
+        (rot > config.rotation_seed_deg)
+        | ((rot > config.coupled_rotation_deg)
+           & (trans > config.coupled_translation_mm))
+    )[0]
+    if not len(edges):
+        return set()
+    endpoints = {int(i) for edge in edges for i in (edge, edge + 1)}
+    support = _branch_support(pose, endpoints, config)
+    seeds: set[int] = set()
+    for edge in edges:
+        left, right = int(edge), int(edge + 1)
+        ls, rs = support[left], support[right]
+        # The alternate OptiTrack branch has less local temporal support.  A
+        # tie remains a review seed on the post-transition side; reconstruction
+        # later requires two trustworthy anchors before changing anything.
+        seeds.add(left if ls < rs else right)
+    return seeds
+
+
+def detect_bouts(
+    pose: np.ndarray,
+    side: str,
+    known_gaps: Iterable[tuple[int, int]] = (),
+    *,
+    config: RepairConfig | None = None,
+) -> list[AnomalyBout]:
+    """Group discontinuities and declared gaps into complete anomaly bouts.
+
+    Seeds separated by fewer than ``stable_frames`` clean native frames are
+    one bout.  Consequently a returning A/B flicker is represented once rather
+    than as a list of unrelated >30 degree transitions.
+    """
+    pose = np.asarray(pose, dtype=float)
+    if pose.ndim != 2 or pose.shape[1] != 7:
+        raise ValueError(f"pose must have shape (T, 7), got {pose.shape}")
+    if side not in {"left", "right"}:
+        raise ValueError(f"side must be left or right, got {side!r}")
+    cfg = config or RepairConfig()
+    n_rows = len(pose)
+    known: set[int] = set()
+    for start, count in known_gaps:
+        start, count = int(start), int(count)
+        if count > 0:
+            known.update(range(max(0, start), min(n_rows, start + count)))
+    nonfinite = set(np.where(~np.isfinite(pose).all(axis=1))[0].astype(int))
+    discontinuity = _discontinuity_seed_frames(pose, cfg)
+    # A declared gap already owns its entry and return boundaries.  Counting
+    # the first clean row after it as another seed makes an N-row gap appear
+    # to be N+1 rows and can incorrectly cross a confidence-duration gate.
+    gap_boundaries: set[int] = set()
+    for start, count in known_gaps:
+        gap_boundaries.update(range(max(0, int(start) - 1),
+                                    min(n_rows, int(start) + int(count) + 1)))
+    discontinuity -= gap_boundaries
+    seeds = sorted(known | nonfinite | discontinuity)
+    if not seeds:
+        return []
+
+    groups: list[list[int]] = [[seeds[0]]]
+    for row in seeds[1:]:
+        if row - groups[-1][-1] <= cfg.stable_frames:
+            groups[-1].append(row)
+        else:
+            groups.append([row])
+
+    bouts: list[AnomalyBout] = []
+    for group in groups:
+        start, end = group[0], group[-1]
+        group_set = set(group)
+        if group_set <= known and group_set:
+            kind = "known_gap"
+        elif group_set & known:
+            kind = "gap_with_discontinuity"
+        elif group_set & nonfinite:
+            kind = "nonfinite"
+        else:
+            kind = "branch_discontinuity"
+        left = start - 1 if start > 0 else None
+        right = end + 1 if end + 1 < n_rows else None
+        bouts.append(AnomalyBout(
+            side=side,
+            start=start,
+            end=end,
+            kind=kind,
+            seed_frames=tuple(group),
+            left_context_end=left,
+            right_context_start=right,
+        ))
+    return bouts
+
+
+def _finite_pose_rows(pose: np.ndarray) -> np.ndarray:
+    q_norm = np.linalg.norm(pose[:, 3:], axis=1)
+    return np.isfinite(pose).all(axis=1) & (q_norm > 1e-12)
+
+
+def _context_rows(finite: np.ndarray, start: int, end: int,
+                  count: int) -> tuple[np.ndarray, np.ndarray]:
+    left = np.flatnonzero(finite[:start])[-count:]
+    right = np.flatnonzero(finite[end + 1:])[:count] + end + 1
+    return left, right
+
+
+def _median_linear_velocity(xyz: np.ndarray, rows: np.ndarray) -> np.ndarray:
+    if len(rows) < 2:
+        return np.zeros(3, dtype=float)
+    consecutive = np.diff(rows) == 1
+    delta = np.diff(xyz[rows], axis=0)[consecutive]
+    return np.median(delta, axis=0) if len(delta) else np.zeros(3, dtype=float)
+
+
+def _median_angular_velocity(quat: np.ndarray, rows: np.ndarray) -> np.ndarray:
+    if len(rows) < 2:
+        return np.zeros(3, dtype=float)
+    pairs = np.flatnonzero(np.diff(rows) == 1)
+    if not len(pairs):
+        return np.zeros(3, dtype=float)
+    first = Rotation.from_quat(quat[rows[pairs]])
+    second = Rotation.from_quat(quat[rows[pairs + 1]])
+    return np.median((second * first.inv()).as_rotvec(), axis=0)
+
+
+def _hermite(p0: np.ndarray, p1: np.ndarray, v0: np.ndarray,
+             v1: np.ndarray, duration: int, offsets: np.ndarray) -> np.ndarray:
+    s = (offsets / float(duration))[:, None]
+    h00 = 2 * s**3 - 3 * s**2 + 1
+    h10 = s**3 - 2 * s**2 + s
+    h01 = -2 * s**3 + 3 * s**2
+    h11 = s**3 - s**2
+    return h00 * p0 + h10 * duration * v0 + h01 * p1 + h11 * duration * v1
+
+
+def _rotation_error_deg(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+    return np.degrees(
+        (Rotation.from_quat(a) * Rotation.from_quat(b).inv()).magnitude())
+
+
+def _predict_bout(pose: np.ndarray, bout: AnomalyBout, left: np.ndarray,
+                  right: np.ndarray) -> tuple[np.ndarray, dict[str, float]]:
+    """Boundary-constrained SE(3) prediction and independent check evidence."""
+    lo, hi = int(left[-1]), int(right[0])
+    rows = np.arange(bout.start, bout.end + 1)
+    duration = hi - lo
+    offsets = rows - lo
+    xyz = pose[:, :3]
+    quat = _unit_quaternion(pose[:, 3:])
+    v0 = _median_linear_velocity(xyz, left)
+    v1 = _median_linear_velocity(xyz, right)
+    # Both effectively stationary: the minimum-assumption path is linear.
+    if max(np.linalg.norm(v0), np.linalg.norm(v1)) < 0.0005:
+        v0 = v1 = (xyz[hi] - xyz[lo]) / duration
+    pred_xyz = _hermite(xyz[lo], xyz[hi], v0, v1, duration, offsets)
+
+    r0, r1 = Rotation.from_quat(quat[lo]), Rotation.from_quat(quat[hi])
+    end_rotvec = (r1 * r0.inv()).as_rotvec()
+    w0 = _median_angular_velocity(quat, left)
+    w1 = _median_angular_velocity(quat, right)
+    pred_rotvec = _hermite(
+        np.zeros(3), end_rotvec, w0, w1, duration, offsets)
+    pred_quat = (Rotation.from_rotvec(pred_rotvec) * r0).as_quat()
+
+    forward_xyz = xyz[lo] + offsets[:, None] * v0
+    backward_xyz = xyz[hi] - (hi - rows)[:, None] * v1
+    pred_trans_disagreement = float(np.max(
+        np.linalg.norm(forward_xyz - backward_xyz, axis=1) * 1000.0))
+    forward_q = (Rotation.from_rotvec(offsets[:, None] * w0) * r0).as_quat()
+    backward_q = (
+        Rotation.from_rotvec(-(hi - rows)[:, None] * w1) * r1).as_quat()
+    pred_rot_disagreement = float(np.max(
+        _rotation_error_deg(forward_q, backward_q)))
+    pred = np.concatenate([pred_xyz, pred_quat], axis=1)
+    evidence = {
+        "left_anchor": lo,
+        "right_anchor": hi,
+        "left_context_frames": int(len(left)),
+        "right_context_frames": int(len(right)),
+        "prediction_translation_max_mm": pred_trans_disagreement,
+        "prediction_rotation_max_deg": pred_rot_disagreement,
+    }
+    return pred, evidence
+
+
+def _endpoint_se3(pose: np.ndarray, bout: AnomalyBout) -> tuple[
+        np.ndarray, dict[str, float]]:
+    """Interpolate a short bout from its immediate finite SE(3) anchors."""
+    lo, hi = bout.start - 1, bout.end + 1
+    rows = np.arange(bout.start, bout.end + 1)
+    duration = hi - lo
+    fraction = (rows - lo) / float(duration)
+    xyz = ((1.0 - fraction[:, None]) * pose[lo, :3]
+           + fraction[:, None] * pose[hi, :3])
+    rotations = Slerp(
+        [0.0, 1.0], Rotation.from_quat(_unit_quaternion(pose[[lo, hi], 3:])))
+    quat = rotations(fraction).as_quat()
+    anchor_translation_mm = float(
+        np.linalg.norm(pose[hi, :3] - pose[lo, :3]) * 1000.0)
+    anchor_rotation_deg = float(_quaternion_angle_deg(
+        pose[lo:lo + 1, 3:], pose[hi:hi + 1, 3:])[0])
+    return np.concatenate([xyz, quat], axis=1), {
+        "left_anchor": int(lo),
+        "right_anchor": int(hi),
+        "anchor_translation_mm": anchor_translation_mm,
+        "anchor_rotation_deg": anchor_rotation_deg,
+        "prediction_translation_max_mm": anchor_translation_mm / duration,
+        "prediction_rotation_max_deg": anchor_rotation_deg / duration,
+    }
+
+
+def _trajectory_is_physical(pose: np.ndarray, config: RepairConfig) -> tuple[bool, float, float]:
+    rot, trans = transition_metrics(pose)
+    finite = np.isfinite(rot) & np.isfinite(trans)
+    if not finite.all():
+        return False, float("inf"), float("inf")
+    max_rot = float(np.max(rot, initial=0.0))
+    max_trans = float(np.max(trans, initial=0.0))
+    return (max_rot <= config.max_step_rotation_deg
+            and max_trans <= config.max_step_translation_mm), max_rot, max_trans
+
+
+def _residuals(observed: np.ndarray,
+               predicted: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    finite = _finite_pose_rows(observed)
+    trans = np.full(len(observed), np.inf)
+    rot = np.full(len(observed), np.inf)
+    trans[finite] = np.linalg.norm(
+        observed[finite, :3] - predicted[finite, :3], axis=1) * 1000.0
+    if finite.any():
+        rot[finite] = _rotation_error_deg(
+            observed[finite, 3:], predicted[finite, 3:])
+    return trans, rot, finite
+
+
+def _refit_from_inliers(predicted: np.ndarray, observed: np.ndarray,
+                        inlier: np.ndarray, offsets: np.ndarray,
+                        duration: int) -> np.ndarray:
+    """Fit a boundary-zero cubic correction without moving either anchor."""
+    if int(inlier.sum()) < 2:
+        return predicted
+    s = offsets / float(duration)
+    basis = np.stack([s * (1.0 - s),
+                      s * (1.0 - s) * (2.0 * s - 1.0)], axis=1)
+    design = basis[inlier]
+    xyz_residual = observed[inlier, :3] - predicted[inlier, :3]
+    xyz_coef, *_ = np.linalg.lstsq(design, xyz_residual, rcond=None)
+    corrected_xyz = predicted[:, :3] + basis @ xyz_coef
+
+    measured_r = Rotation.from_quat(observed[inlier, 3:])
+    predicted_r = Rotation.from_quat(predicted[inlier, 3:])
+    rot_residual = (measured_r * predicted_r.inv()).as_rotvec()
+    rot_coef, *_ = np.linalg.lstsq(design, rot_residual, rcond=None)
+    correction = Rotation.from_rotvec(basis @ rot_coef)
+    corrected_q = (correction * Rotation.from_quat(predicted[:, 3:])).as_quat()
+    return np.concatenate([corrected_xyz, corrected_q], axis=1)
+
+
+def _robust_upper(values: np.ndarray, floor: float, cap: float) -> float:
+    values = np.asarray(values, dtype=float)
+    values = values[np.isfinite(values)]
+    if not len(values):
+        return float(floor)
+    centre = float(np.median(values))
+    mad = float(np.median(np.abs(values - centre)))
+    return float(min(cap, max(floor, centre + 6.0 * 1.4826 * mad)))
+
+
+def _local_inlier_thresholds(pose: np.ndarray, left: np.ndarray,
+                             right: np.ndarray,
+                             config: RepairConfig) -> tuple[float, float]:
+    """Estimate clean local jitter, bounded by global physical residual caps."""
+    trans_accel: list[np.ndarray] = []
+    rot_accel: list[np.ndarray] = []
+    for rows in (left, right):
+        runs = np.split(rows, np.where(np.diff(rows) != 1)[0] + 1)
+        for run in runs:
+            if len(run) < 3:
+                continue
+            xyz = pose[run, :3]
+            trans_accel.append(
+                np.linalg.norm(np.diff(xyz, n=2, axis=0), axis=1) * 1000.0)
+            rotations = Rotation.from_quat(_unit_quaternion(pose[run, 3:]))
+            velocity = (rotations[1:] * rotations[:-1].inv()).as_rotvec()
+            rot_accel.append(np.degrees(
+                np.linalg.norm(np.diff(velocity, axis=0), axis=1)))
+    trans_values = np.concatenate(trans_accel) if trans_accel else np.empty(0)
+    rot_values = np.concatenate(rot_accel) if rot_accel else np.empty(0)
+    return (
+        _robust_upper(trans_values, 3.0, config.translation_inlier_mm),
+        _robust_upper(rot_values, 2.0, config.rotation_inlier_deg),
+    )
+
+
+def _robust_branch_fit(observed: np.ndarray, initial: np.ndarray,
+                       forced: np.ndarray, offsets: np.ndarray, duration: int,
+                       translation_threshold_mm: float,
+                       rotation_threshold_deg: float) -> tuple[
+                           np.ndarray, np.ndarray, np.ndarray, np.ndarray,
+                           int, bool]:
+    """Alternate branch classification and trajectory correction up to 5 times."""
+    predicted = initial.copy()
+    previous: np.ndarray | None = None
+    converged = False
+    iterations = 0
+    for iterations in range(1, 6):
+        trans, rot, finite = _residuals(observed, predicted)
+        inlier = (finite
+                  & (trans <= translation_threshold_mm)
+                  & (rot <= rotation_threshold_deg)
+                  & ~forced)
+        if previous is not None and np.array_equal(inlier, previous):
+            converged = True
+            break
+        previous = inlier.copy()
+        predicted = _refit_from_inliers(
+            predicted, observed, inlier, offsets, duration)
+    trans, rot, finite = _residuals(observed, predicted)
+    inlier = (finite
+              & (trans <= translation_threshold_mm)
+              & (rot <= rotation_threshold_deg)
+              & ~forced)
+    if previous is not None and np.array_equal(inlier, previous):
+        converged = True
+    return predicted, inlier, trans, rot, iterations, converged
+
+
+def repair_pose_stream(
+    pose: np.ndarray,
+    side: str,
+    known_gaps: Iterable[tuple[int, int]] = (),
+    *,
+    task_gate: TaskGate | None = None,
+    config: RepairConfig | None = None,
+) -> PoseRepairResult:
+    """Return candidate poses and explicit side-specific repair provenance.
+
+    HIGH candidates are valid for training.  MEDIUM candidates contain the
+    proposed reconstruction but remain invalid pending a review decision.
+    LOW events preserve the measured poses and remain invalid.
+    """
+    raw = np.asarray(pose, dtype=float)
+    if raw.ndim != 2 or raw.shape[1] != 7:
+        raise ValueError(f"pose must have shape (T, 7), got {raw.shape}")
+    cfg = config or RepairConfig()
+    gate = task_gate or TaskGate()
+    gap_list = [(int(start), int(count)) for start, count in known_gaps]
+    bouts = detect_bouts(raw, side, gap_list, config=cfg)
+    finite = _finite_pose_rows(raw)
+    candidate = raw.copy()
+    repaired = np.zeros(len(raw), dtype=bool)
+    confidence = np.full(len(raw), Confidence.NONE, dtype=np.uint8)
+    valid = finite.copy()
+    event_id = np.full(len(raw), "", dtype=object)
+    events: list[RepairEvent] = []
+    known_rows = {
+        row for start, count in gap_list
+        for row in range(max(0, start), min(len(raw), start + count))
+    }
+
+    for bout in bouts:
+        rows = np.arange(bout.start, bout.end + 1)
+        left, right = _context_rows(finite, bout.start, bout.end,
+                                    cfg.context_frames)
+        eid = f"{side}:{bout.start}-{bout.end}:{bout.kind}"
+        event_id[rows] = eid
+        if not len(left) or not len(right):
+            confidence[rows] = Confidence.LOW
+            valid[rows] = False
+            events.append(RepairEvent(
+                eid, bout, Confidence.LOW, "unrepaired_missing_anchor", (), {
+                    "duration_frames": int(len(rows)),
+                    "left_context_frames": int(len(left)),
+                    "right_context_frames": int(len(right)),
+                }))
+            continue
+
+        endpoint_method = (
+            1 <= len(rows) <= 5
+            and int(left[-1]) == bout.start - 1
+            and int(right[0]) == bout.end + 1
+        )
+        if endpoint_method:
+            predicted, evidence = _endpoint_se3(raw, bout)
+            evidence.update({
+                "left_context_frames": int(len(left)),
+                "right_context_frames": int(len(right)),
+            })
+        else:
+            predicted, evidence = _predict_bout(raw, bout, left, right)
+        observed = raw[rows]
+        forced = np.array([int(row) in known_rows for row in rows])
+        lo = int(left[-1]); hi = int(right[0])
+        trans_threshold, rot_threshold = _local_inlier_thresholds(
+            raw, left, right, cfg)
+        if endpoint_method:
+            # Keep this identical to the benchmarked linear/SLERP model;
+            # fitting a polynomial to possible wrong-branch observations
+            # would quietly turn it into a different estimator.
+            trans_residual, rot_residual, observed_finite = _residuals(observed, predicted)
+            inlier = (observed_finite & ~forced
+                      & (trans_residual <= trans_threshold)
+                      & (rot_residual <= rot_threshold))
+            iterations, converged = 0, True
+        else:
+            predicted, inlier, trans_residual, rot_residual, iterations, converged = (
+                _robust_branch_fit(observed, predicted, forced, rows - lo,
+                                   hi - lo, trans_threshold, rot_threshold))
+        replace = ~inlier | forced
+        classified_outlier = replace & ~forced
+        if classified_outlier.any():
+            outlier_separation = np.maximum(
+                trans_residual[classified_outlier] / trans_threshold,
+                rot_residual[classified_outlier] / rot_threshold)
+            branch_unambiguous = bool(np.min(outlier_separation) >= 1.5)
+        else:
+            branch_unambiguous = True
+        proposed = raw.copy()
+        proposed[rows[replace]] = predicted[replace]
+        physical, max_rot, max_trans = _trajectory_is_physical(
+            proposed[max(0, bout.start - 1):min(len(raw), bout.end + 2)], cfg)
+        evidence.update({
+            "duration_frames": int(len(rows)),
+            "observed_inlier_frames": int(inlier.sum()),
+            "replaced_frames": int(replace.sum()),
+            "translation_residual_p95_mm": float(np.percentile(trans_residual, 95)),
+            "rotation_residual_p95_deg": float(np.percentile(rot_residual, 95)),
+            "reconstructed_max_step_translation_mm": max_trans,
+            "reconstructed_max_step_rotation_deg": max_rot,
+            "physical": bool(physical),
+            "fit_iterations": int(iterations),
+            "fit_converged": bool(converged),
+            "translation_inlier_threshold_mm": trans_threshold,
+            "rotation_inlier_threshold_deg": rot_threshold,
+            "branch_unambiguous": branch_unambiguous,
+        })
+        extreme_prediction_disagreement = (not endpoint_method and (
+            evidence["prediction_translation_max_mm"]
+            > 3.0 * cfg.max_prediction_translation_mm
+            or evidence["prediction_rotation_max_deg"]
+            > 3.0 * cfg.max_prediction_rotation_deg))
+        if not physical or extreme_prediction_disagreement:
+            confidence[rows] = Confidence.LOW
+            valid[rows] = False
+            evidence["rejection"] = (
+                "nonphysical_reconstruction" if not physical
+                else "incompatible_two_sided_prediction")
+            events.append(RepairEvent(
+                eid, bout, Confidence.LOW, "unrepaired_ambiguous_branch", (),
+                evidence))
+            continue
+        two_full_contexts = (len(left) >= cfg.context_frames
+                             and len(right) >= cfg.context_frames)
+        # A short boundary seed may actually be one end of a persistent
+        # alternate branch. Physical step limits alone cannot establish
+        # that the two anchors belong to the same trajectory branch.
+        endpoint_return = (not endpoint_method or
+                           evidence["anchor_rotation_deg"] <= 20.0)
+        if endpoint_method:
+            evidence["endpoint_return_consistent"] = endpoint_return
+        prediction_agrees = endpoint_method or (
+            evidence["prediction_translation_max_mm"]
+            <= cfg.max_prediction_translation_mm
+            and evidence["prediction_rotation_max_deg"]
+            <= cfg.max_prediction_rotation_deg)
+        high = (
+            gate.high_confidence_enabled
+            and len(rows) <= min(cfg.max_high_frames, gate.validated_max_frames)
+            and (not endpoint_method or len(rows) <= gate.endpoint_max_frames)
+            and endpoint_return
+            and two_full_contexts and prediction_agrees and physical
+            and branch_unambiguous and bool(replace.any()))
+        tier = Confidence.HIGH if high else Confidence.MEDIUM
+        candidate[rows[replace]] = predicted[replace]
+        repaired[rows[replace]] = True
+        confidence[rows] = tier
+        valid[rows] = high
+        events.append(RepairEvent(
+            eid, bout, tier, ("endpoint_se3" if endpoint_method
+                              else "robust_se3_hermite"),
+            tuple(rows[replace].astype(int)), evidence))
+
+    return PoseRepairResult(
+        pose=candidate,
+        repaired=repaired,
+        confidence=confidence,
+        valid=valid,
+        event_id=event_id,
+        events=tuple(events),
+    )

action_repair_tools/twm/react_preprocess/repaired_actions.py

@@ -0,0 +1,125 @@
+"""Repair-aware action construction from candidate OptiTrack poses.
+
+Actions are 9-D ``[dx, dy, dz, relative_rotation_6d]``.  Translation is in
+metres.  Rotation uses the first two columns of the world-frame relative
+rotation matrix, concatenated column by column.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+from scipy.spatial.transform import Rotation
+
+
+MAX_ROTATION_DEG = 30.0
+MAX_TRANSLATION_MM = 50.0
+
+
+@dataclass(frozen=True)
+class ActionSeries:
+    values: np.ndarray
+    valid: np.ndarray
+    repaired: np.ndarray
+    event_ids: tuple[tuple[str, ...], ...]
+    start_rows: np.ndarray
+    end_rows: np.ndarray
+    rotation_deg: np.ndarray
+    translation_mm: np.ndarray
+
+
+def _check_pose_arrays(pose: np.ndarray, pose_valid: np.ndarray,
+                       pose_repaired: np.ndarray,
+                       pose_event_id: np.ndarray) -> tuple[
+                           np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    pose = np.asarray(pose, dtype=float)
+    if pose.ndim != 2 or pose.shape[1] != 7:
+        raise ValueError(f"pose must have shape (T, 7), got {pose.shape}")
+    valid = np.asarray(pose_valid, dtype=bool)
+    repaired = np.asarray(pose_repaired, dtype=bool)
+    event_id = np.asarray(pose_event_id, dtype=object)
+    for name, value in (("pose_valid", valid), ("pose_repaired", repaired),
+                        ("pose_event_id", event_id)):
+        if value.shape != (len(pose),):
+            raise ValueError(f"{name} must have shape ({len(pose)},), got {value.shape}")
+    return pose, valid, repaired, event_id
+
+
+def _event_union(*values: object) -> tuple[str, ...]:
+    out: set[str] = set()
+    for value in values:
+        if isinstance(value, str):
+            if value:
+                out.add(value)
+        elif value is not None:
+            out.update(str(item) for item in value if str(item))
+    return tuple(sorted(out))
+
+
+def _relative_values(pose: np.ndarray, start: np.ndarray,
+                     end: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    n = len(start)
+    values = np.zeros((n, 9), dtype=np.float32)
+    values[:, 3:] = np.array([1, 0, 0, 0, 1, 0], dtype=np.float32)
+    rotation_deg = np.full(n, np.inf, dtype=float)
+    translation_mm = np.full(n, np.inf, dtype=float)
+    endpoints_finite = np.isfinite(pose[start]).all(axis=1) & np.isfinite(pose[end]).all(axis=1)
+    q0_norm = np.linalg.norm(pose[start, 3:], axis=1)
+    q1_norm = np.linalg.norm(pose[end, 3:], axis=1)
+    endpoints_finite &= (q0_norm > 1e-12) & (q1_norm > 1e-12)
+    usable = np.flatnonzero(endpoints_finite)
+    if len(usable):
+        s, e = start[usable], end[usable]
+        delta = pose[e, :3] - pose[s, :3]
+        q0 = pose[s, 3:] / np.linalg.norm(pose[s, 3:], axis=1, keepdims=True)
+        q1 = pose[e, 3:] / np.linalg.norm(pose[e, 3:], axis=1, keepdims=True)
+        relative = Rotation.from_quat(q1) * Rotation.from_quat(q0).inv()
+        matrix = relative.as_matrix()
+        rot6d = np.concatenate([matrix[:, :, 0], matrix[:, :, 1]], axis=1)
+        values[usable, :3] = delta.astype(np.float32)
+        values[usable, 3:] = rot6d.astype(np.float32)
+        rotation_deg[usable] = np.degrees(relative.magnitude())
+        translation_mm[usable] = np.linalg.norm(delta, axis=1) * 1000.0
+    return values, rotation_deg, translation_mm, endpoints_finite
+
+
+def native_actions(pose: np.ndarray, pose_valid: np.ndarray,
+                   pose_repaired: np.ndarray,
+                   pose_event_id: np.ndarray) -> ActionSeries:
+    """Build each native transition ``k -> k+1`` with explicit validity."""
+    pose, pose_valid, pose_repaired, pose_event_id = _check_pose_arrays(
+        pose, pose_valid, pose_repaired, pose_event_id)
+    start = np.arange(max(0, len(pose) - 1), dtype=np.int32)
+    end = start + 1
+    values, rotation_deg, translation_mm, finite = _relative_values(
+        pose, start, end)
+    valid = (pose_valid[start] & pose_valid[end] & finite
+             & (rotation_deg <= MAX_ROTATION_DEG)
+             & (translation_mm <= MAX_TRANSLATION_MM))
+    repaired = pose_repaired[start] | pose_repaired[end]
+    event_ids = tuple(_event_union(pose_event_id[a], pose_event_id[b])
+                      for a, b in zip(start, end))
+    return ActionSeries(values, valid, repaired, event_ids, start, end,
+                        rotation_deg, translation_mm)
+
+
+def actions_fps15(pose: np.ndarray, native: ActionSeries) -> ActionSeries:
+    """Build ``k -> k+2`` actions; validity is AND and repair is OR."""
+    pose = np.asarray(pose, dtype=float)
+    expected_native = max(0, len(pose) - 1)
+    if len(native.valid) != expected_native:
+        raise ValueError(
+            f"native has {len(native.valid)} rows for {len(pose)} poses; "
+            f"expected {expected_native}")
+    start = np.arange(0, max(0, len(pose) - 2), 2, dtype=np.int32)
+    end = start + 2
+    values, rotation_deg, translation_mm, _ = _relative_values(pose, start, end)
+    first = start
+    second = start + 1
+    valid = native.valid[first] & native.valid[second]
+    repaired = native.repaired[first] | native.repaired[second]
+    event_ids = tuple(_event_union(native.event_ids[a], native.event_ids[b])
+                      for a, b in zip(first, second))
+    return ActionSeries(values, valid, repaired, event_ids, start, end,
+                        rotation_deg, translation_mm)
+

action_repair_tools/twm/react_toolbox/__init__.py

@@ -0,0 +1 @@
+"""Coordinate conversion helpers used by action propagation."""

action_repair_tools/twm/react_toolbox/frames.py

@@ -0,0 +1,150 @@
+"""Which way is up — declared, and converted as one piece or not at all.
+
+WHAT THE DATA IS, AND WHAT IT WAS
+
+The release is now **Z-up, right-handed**: the table normal in world
+coordinates is (0.053, -0.056, 0.997), 4.4 degrees off +z, and both the pose
+rotations and `T_mocap_to_cam` have determinant +1.
+
+OptiTrack RECORDS Y-up. The release used to ship that unchanged and said so
+nowhere, which is the worse half of the problem: robotics code overwhelmingly
+assumes Z-up, and a reader taking `pose[2]` as height got a horizontal
+coordinate with nothing to complain — plausible numbers, fine-looking plots,
+and an error that surfaces only as a model that never learns which way gravity
+points.
+
+Poses and extrinsics were converted together, so every projection is
+unchanged and every rendered preview, overlay and clip stayed valid. Only the
+numbers moved. `ZUP_TO_YUP` converts back for anything that still wants the
+raw OptiTrack convention.
+
+THE CONVERSION, AND THE TRAP IN IT
+
+Y-up to Z-up preserving handedness is a rotation of -90 degrees about x:
+
+    (x, y, z)  ->  (x, -z, y)
+
+There are two right-handed candidates; this is the one that sends UP to **+z**
+rather than -z. The other, R_x(+90), is equally right-handed and puts the table
+normal at (0.053, 0.056, -0.997) — upside down, det still +1, and no assertion
+about handedness would catch it.
+
+THE TRAP: a world-frame rotation must be applied to the POSES **and** to
+`T_mocap_to_cam` together. Applied to one only, every projection moves and
+nothing raises. So this module converts a BUNDLE — poses and cameras — and
+`scripts/test_frames.py` asserts that projections are bit-identical afterwards.
+Invariance is the test: if a pixel moves, the conversion was half-applied.
+
+The gel centre is in the sensor's own RIGID frame and is therefore untouched.
+"""
+from __future__ import annotations
+
+import numpy as np
+
+UP_AXIS_RECORDED = "z"
+UP_AXIS_ROBOTICS = "z"
+
+# (x, y, z)_yup -> (x, -z, y)_zup.  det = +1, and it sends +y to +z.
+YUP_TO_ZUP = np.array([[1.0, 0.0, 0.0],
+                       [0.0, 0.0, -1.0],
+                       [0.0, 1.0, 0.0]])
+ZUP_TO_YUP = YUP_TO_ZUP.T
+
+
+def _R(to_zup: bool) -> np.ndarray:
+    return YUP_TO_ZUP if to_zup else ZUP_TO_YUP
+
+
+def convert_poses(poses7, to_zup: bool = True) -> np.ndarray:
+    """Rotate a pose array into the other up-convention. (N, 7) or (7,).
+
+    Positions rotate; orientations PRE-multiply, because this is a change of
+    the world frame, not a motion of the body.
+    """
+    from scipy.spatial.transform import Rotation
+
+    p = np.atleast_2d(np.asarray(poses7, float)).copy()
+    M = _R(to_zup)
+    ok = np.isfinite(p).all(1) & (np.linalg.norm(p[:, 3:7], axis=1) > 0.5)
+    # A column can be entirely untracked -- pushT never tracked an object
+    # body, so its object_pose is NaN from end to end. scipy raises "Found
+    # zero norm quaternions" on the resulting EMPTY array, and that exception
+    # is why the pushT half of the release stayed unconverted while the
+    # motherboard half went Z-up. Nothing to rotate is not an error.
+    if ok.any():
+        p[ok, :3] = p[ok, :3] @ M.T
+        p[ok, 3:7] = (Rotation.from_matrix(M)
+                      * Rotation.from_quat(p[ok, 3:7])).as_quat()
+    return p[0] if np.ndim(poses7) == 1 else p
+
+
+def convert_calibration(cal: dict, to_zup: bool = True) -> dict:
+    """The same rotation applied to every camera extrinsic. Returns a copy.
+
+    `T_mocap_to_cam` maps mocap -> camera. Rotating the mocap frame by M means
+    the new matrix is `T @ M^-1`, so that `T_new @ (M @ x) == T @ x` and every
+    projection is unchanged. Getting this inverse backwards is the other way to
+    break it silently; the invariance test catches that too.
+    """
+    M = _R(to_zup)
+    out = {k: v for k, v in cal.items()}
+    out["up_axis"] = "z" if to_zup else "y"
+    out["cams"] = {}
+    for name, c in cal["cams"].items():
+        T = np.asarray(c["T_mocap_to_cam"], float).copy()
+        T[:3, :3] = T[:3, :3] @ M.T           # M^-1 == M.T for a rotation
+        out["cams"][name] = {**c, "T_mocap_to_cam": T}
+    return out
+
+
+def to_zup(poses7, cal: dict):
+    """Convert a pose array and its calibration together. Returns (poses, cal).
+
+    Use this rather than the two halves: the whole point is that they move as
+    one piece.
+    """
+    return convert_poses(poses7, True), convert_calibration(cal, True)
+
+
+def require_up_axis(cal: dict, expected: str = UP_AXIS_RECORDED, where: str = ""):
+    """Raise unless `cal` declares the up-axis convention `expected`.
+
+    WHY THIS EXISTS. Poses and calibration are two halves of one convention,
+    and they are read from two paths. Rotating only one half leaves every
+    self-consistency check green -- projections still recompute exactly from
+    the same wrong matrix -- while the pictures are wrong. That happened: the
+    probe test set drew its poses from the Z-up release and its calibration
+    from a Y-up tree, and every overlay was a median 153 px off.
+
+    So the halves must be paired loudly, not by convention. A file with no
+    declaration is treated as the pre-conversion Y-up it was.
+    """
+    got = cal.get("up_axis")
+    if got != expected:
+        raise ValueError(
+            f"calibration up-axis mismatch{' in ' + where if where else ''}: "
+            f"declared {got!r}, need {expected!r}. Poses and calibration must "
+            f"come from the same release. A missing declaration means a "
+            f"pre-conversion Y-up file -- convert it with "
+            f"scripts/convert_release_zup.py, or take the calibration from "
+            f"the release the poses came from.")
+    return cal
+
+
+def as_up_axis(cal: dict, want: str) -> dict:
+    """Return `cal` in the convention `want`, converting only if it must.
+
+    `require_up_axis` refuses a mismatch; this one repairs it. Use it when the
+    caller genuinely knows which convention its POSES are in -- a raw-HDF5
+    reader wants "y", a release reader wants "z" -- and should not have to care
+    which directory the calibration happened to come from.
+
+    An undeclared calibration is the pre-conversion Y-up it was, so this
+    converts it rather than trusting it.
+    """
+    if want not in ("y", "z"):
+        raise ValueError(f"up axis must be 'y' or 'z', got {want!r}")
+    got = cal.get("up_axis") or "y"
+    if got == want:
+        return cal
+    return convert_calibration(cal, to_zup=(want == "z"))

action_repair_tools/twm/scripts/__init__.py

@@ -0,0 +1 @@
+"""Action audit, reconstruction, publication and review commands."""

action_repair_tools/twm/scripts/benchmark_mocap_branch.py

@@ -0,0 +1,119 @@
+"""Benchmark body-frame branch correction on frozen clean pose fragments."""
+from __future__ import annotations
+
+import argparse
+from dataclasses import asdict
+from pathlib import Path
+
+import numpy as np
+from scipy.spatial.transform import Rotation
+
+from twm.react_preprocess.mocap_branch import find_branch_candidates
+from twm.react_preprocess.mocap_candidate import load_manifest
+from twm.scripts.benchmark_mocap_repair import (
+    _candidate_order, _native_errors, _rotation_angle_deg, _select_disjoint, _summary,
+)
+from twm.scripts.build_mocap_repair_candidates import _atomic_json, _clean_fragments
+
+
+def _inject(pose, start, length):
+    observed = pose.copy()
+    rotations = Rotation.from_quat(pose[start:start+length, 3:])
+    observed[start:start+length, :3] += rotations.apply([0.029, 0, 0])
+    observed[start:start+length, 3:] = (rotations*Rotation.from_euler('yx', [100, 8], degrees=True)).as_quat()
+    return observed
+
+
+def _negative_controls():
+    pose = np.zeros((180, 7)); pose[:, 6] = 1
+    fast = pose.copy()
+    fast[:, 3:] = Rotation.from_euler('z', 35*np.arange(len(fast)), degrees=True).as_quat()
+    out_back = pose.copy()
+    angle = np.r_[np.zeros(30), np.linspace(0, 100, 60), np.linspace(100, 0, 60), np.zeros(30)]
+    out_back[:, 3:] = Rotation.from_euler('z', angle, degrees=True).as_quat()
+    abrupt = find_branch_candidates(_inject(pose, 40, 60))
+    repeated = find_branch_candidates(_inject(_inject(pose, 20, 30), 100, 30))
+    return {
+        'continuous_fast_turn_candidates': len(find_branch_candidates(fast)),
+        'continuous_out_back_candidates': len(find_branch_candidates(out_back)),
+        'abrupt_out_back_candidates': len(abrupt),
+        'abrupt_out_back_automatic_admissions': sum(c.confidence == 'HIGH' for c in abrupt),
+        'repeated_abrupt_out_back_candidates': len(repeated),
+        'repeated_abrupt_out_back_repeat_supported_candidates': sum(c.evidence['matching_return_pairs'] >= 2 for c in repeated),
+        'caveat': 'Repeated abrupt genuine motion is observationally indistinguishable from a tracker branch; repetition alone is not independent ground truth.',
+    }
+
+
+def benchmark_branch_task(clean_episodes, *, task, max_intervals=200, seed=17):
+    episodes = [np.asarray(p, dtype=float) for p in clean_episodes]
+    lengths = (6, 20, 60, 120)
+    selected = _select_disjoint(_candidate_order(
+        episodes, lengths, np.random.default_rng(seed), pool_size=max(1000, max_intervals*50)), max_intervals)
+    names = ('translation_mm', 'orientation_deg', 'native_translation_mm', 'native_rotation_deg')
+    values = {name: [] for name in names}
+    all_values = {name: [] for name in names}
+    by_length = {length: {name: [] for name in names} for length in lengths}
+    counts = {length: 0 for length in lengths}
+    for interval in selected:
+        truth = episodes[interval.episode][interval.start-15:interval.start+interval.length+15]
+        observed = _inject(truth, 15, interval.length)
+        candidates = find_branch_candidates(observed)
+        matches = [c for c in candidates if c.start == 15 and c.end == 15+interval.length-1]
+        result = observed.copy()
+        if matches:
+            result[15:15+interval.length] = matches[0].pose
+            counts[interval.length] += 1
+        rows = slice(15, 15+interval.length)
+        trans = np.linalg.norm(result[rows, :3]-truth[rows, :3], axis=1)*1000
+        rot = _rotation_angle_deg(result[rows, 3:], truth[rows, 3:])
+        action_trans, action_rot = _native_errors(result, truth, np.arange(14, 15+interval.length))
+        for name, error in zip(names, (trans, rot, action_trans, action_rot)):
+            all_values[name].extend(error.tolist())
+            if matches:
+                values[name].extend(error.tolist())
+                by_length[interval.length][name].extend(error.tolist())
+    metrics = {name: asdict(_summary(v)) for name, v in values.items()}
+    per_length = {str(length): {'qualifying_intervals': counts[length],
+                              'metrics': {name: asdict(_summary(v)) for name, v in by_length[length].items()}}
+                  for length in lengths}
+    def passes(measured):
+        caps = {'translation_mm': (2, 10), 'orientation_deg': (1, 5),
+                'native_translation_mm': (2, 5), 'native_rotation_deg': (1, 3)}
+        return all(measured[name]['count'] > 0 and measured[name]['median'] <= median and
+                   measured[name]['p95'] <= p95 for name, (median, p95) in caps.items())
+    negative = _negative_controls()
+    passed = (sum(counts.values()) >= 100 and passes(metrics) and
+              all(counts[length] >= 10 and passes(per_length[str(length)]['metrics']) for length in lengths) and
+              negative['continuous_fast_turn_candidates'] == 0 and
+              negative['continuous_out_back_candidates'] == 0)
+    return {
+        'schema_version': 1, 'task': task, 'seed': seed, 'method': 'paired_body_branch',
+        'intervals': len(selected), 'qualifying_intervals': sum(counts.values()),
+        'rejected_intervals': len(selected)-sum(counts.values()), 'anomaly_lengths': list(lengths),
+        'metrics': metrics, 'metrics_all': {name: asdict(_summary(v)) for name, v in all_values.items()},
+        'by_length': per_length, 'negative_controls': negative,
+        'gate': {'high_confidence_enabled': False, 'reconstruction_accuracy_passed': bool(passed),
+                 'validated_max_frames': max(lengths) if passed else 0,
+                 'requires_independent_branch_identity_evidence': True},
+        'scope': 'Reconstruction accuracy conditional on exact paired-boundary identification; does not validate anomaly classification or branch identity.',
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--candidate-root', type=Path, required=True)
+    parser.add_argument('--output-root', type=Path, required=True)
+    parser.add_argument('--max-intervals', type=int, default=200)
+    parser.add_argument('--seed', type=int, default=17)
+    args = parser.parse_args()
+    manifest = load_manifest(args.candidate_root/'input_manifest.json')
+    for task in manifest.tasks:
+        fragments = _clean_fragments(manifest, task)
+        report = benchmark_branch_task(fragments, task=task, max_intervals=args.max_intervals, seed=args.seed)
+        report['manifest_digest'] = manifest.digest
+        path = _atomic_json(args.output_root/f'{task}.json', report)
+        print(task, report['qualifying_intervals'], report['gate'], path, flush=True)
+
+
+if __name__ == '__main__':
+    main()

action_repair_tools/twm/scripts/benchmark_mocap_repair.py

@@ -0,0 +1,315 @@
+#!/usr/bin/env python3
+"""Calibrate mocap repair confidence with deterministic clean-data masking."""
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+from typing import Iterable
+
+import numpy as np
+from scipy.spatial.transform import Rotation
+
+from twm.react_preprocess.mocap_repair import TaskGate, repair_pose_stream
+
+
+@dataclass(frozen=True)
+class MetricSummary:
+    median: float
+    p95: float
+    count: int
+
+
+@dataclass(frozen=True)
+class BenchmarkReport:
+    task: str
+    seed: int
+    intervals: int
+    high_intervals: int
+    anomaly_lengths: tuple[int, ...]
+    confidence_counts: dict[str, int]
+    metrics: dict[str, MetricSummary]
+    metrics_all: dict[str, MetricSummary]
+    gate: TaskGate
+    methods: dict = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        return {
+            "schema_version": 1,
+            "task": self.task,
+            "seed": self.seed,
+            "intervals": self.intervals,
+            "high_intervals": self.high_intervals,
+            "anomaly_lengths": list(self.anomaly_lengths),
+            "confidence_counts": dict(sorted(self.confidence_counts.items())),
+            "metrics": {name: asdict(self.metrics[name])
+                        for name in sorted(self.metrics)},
+            "metrics_all": {name: asdict(self.metrics_all[name])
+                            for name in sorted(self.metrics_all)},
+            "gate": asdict(self.gate),
+            "methods": self.methods,
+        }
+
+
+@dataclass(frozen=True)
+class _Candidate:
+    episode: int
+    start: int
+    length: int
+    motion: float
+
+
+def _validate_episode(pose: np.ndarray) -> np.ndarray:
+    pose = np.asarray(pose, dtype=float)
+    if pose.ndim != 2 or pose.shape[1] != 7:
+        raise ValueError(f"clean episode must have shape (T, 7), got {pose.shape}")
+    return pose
+
+
+def _rotation_angle_deg(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+    return np.degrees(
+        (Rotation.from_quat(a) * Rotation.from_quat(b).inv()).magnitude())
+
+
+def _motion_score(pose: np.ndarray, start: int, length: int) -> float:
+    lo, hi = start - 1, start + length
+    trans = np.linalg.norm(pose[hi, :3] - pose[lo, :3]) * 1000.0
+    rot = float(_rotation_angle_deg(
+        pose[hi:hi + 1, 3:], pose[lo:lo + 1, 3:])[0])
+    return float(trans + rot)
+
+
+def _candidate_order(episodes: list[np.ndarray], lengths: tuple[int, ...],
+                     rng: np.random.Generator, *,
+                     pool_size: int) -> list[_Candidate]:
+    """Sample a bounded candidate pool before motion-stratified sorting."""
+    context = 15
+    spans: list[tuple[int, int, int]] = []
+    for episode_index, pose in enumerate(episodes):
+        for length in lengths:
+            count = max(0, len(pose) - 2 * context - length)
+            if count:
+                spans.append((episode_index, length, count))
+    total = sum(count for _, _, count in spans)
+    if not total:
+        return []
+    take = min(int(pool_size), total)
+    flat_rows = (np.arange(total, dtype=np.int64) if take == total
+                 else np.sort(rng.choice(total, size=take, replace=False)))
+    cumulative = np.cumsum([count for _, _, count in spans])
+    candidates = []
+    for flat in flat_rows:
+        span_index = int(np.searchsorted(cumulative, flat, side="right"))
+        prior = int(cumulative[span_index - 1]) if span_index else 0
+        episode_index, length, _ = spans[span_index]
+        start = context + int(flat) - prior
+        pose = episodes[episode_index]
+        rows = pose[start - context:start + length + context]
+        norm = np.linalg.norm(rows[:, 3:], axis=1)
+        if not np.isfinite(rows).all() or np.any(norm <= 1e-12):
+            continue
+        candidates.append(_Candidate(
+            episode_index, start, length,
+            _motion_score(pose, start, length)))
+    candidates.sort(key=lambda row: (row.motion, row.episode, row.start, row.length))
+    bins = [list(chunk) for chunk in np.array_split(
+        np.asarray(candidates, dtype=object), 3) if len(chunk)]
+    for rows in bins:
+        rng.shuffle(rows)
+    ordered: list[_Candidate] = []
+    while any(bins):
+        for rows in bins:
+            if rows:
+                ordered.append(rows.pop())
+    return ordered
+
+
+def _select_disjoint(candidates: list[_Candidate], count: int) -> list[_Candidate]:
+    used: dict[int, list[tuple[int, int]]] = {}
+    out = []
+    for candidate in candidates:
+        end = candidate.start + candidate.length
+        overlaps = any(candidate.start < prior_end and prior_start < end
+                       for prior_start, prior_end in used.get(candidate.episode, []))
+        if overlaps:
+            continue
+        used.setdefault(candidate.episode, []).append((candidate.start, end))
+        out.append(candidate)
+        if len(out) >= count:
+            break
+    return out
+
+
+def _inject_wrong_branch(pose: np.ndarray, rows: np.ndarray) -> np.ndarray:
+    observed = pose.copy()
+    observed[rows, :3] += np.array([0.029, -0.003, 0.0])
+    wrong = Rotation.from_euler("yx", [100.0, 8.0], degrees=True)
+    observed[rows, 3:] = (
+        wrong * Rotation.from_quat(pose[rows, 3:])).as_quat()
+    return observed
+
+
+def _native_errors(candidate: np.ndarray, truth: np.ndarray,
+                   transitions: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    truth_delta = truth[transitions + 1, :3] - truth[transitions, :3]
+    candidate_delta = candidate[transitions + 1, :3] - candidate[transitions, :3]
+    trans = np.linalg.norm(candidate_delta - truth_delta, axis=1) * 1000.0
+    truth_relative = (Rotation.from_quat(truth[transitions + 1, 3:])
+                      * Rotation.from_quat(truth[transitions, 3:]).inv())
+    candidate_relative = (Rotation.from_quat(candidate[transitions + 1, 3:])
+                          * Rotation.from_quat(candidate[transitions, 3:]).inv())
+    rot = np.degrees((candidate_relative * truth_relative.inv()).magnitude())
+    return trans, rot
+
+
+def _summary(values: list[float]) -> MetricSummary:
+    data = np.asarray(values, dtype=float)
+    if not len(data):
+        return MetricSummary(float("inf"), float("inf"), 0)
+    return MetricSummary(
+        median=float(np.median(data)),
+        p95=float(np.percentile(data, 95)),
+        count=int(len(data)),
+    )
+
+
+def benchmark_task(
+    clean_episodes: Iterable[np.ndarray],
+    *,
+    task: str,
+    anomaly_lengths: Iterable[int] = (1, 5, 20, 60),
+    max_intervals: int = 300,
+    seed: int = 0,
+) -> BenchmarkReport:
+    """Mask held-out clean intervals and score reconstruction/action errors."""
+    episodes = [_validate_episode(pose) for pose in clean_episodes]
+    lengths = tuple(sorted({int(length) for length in anomaly_lengths
+                            if int(length) > 0}))
+    if not episodes:
+        raise ValueError("at least one clean episode is required")
+    if not lengths:
+        raise ValueError("at least one positive anomaly length is required")
+    if max_intervals <= 0:
+        raise ValueError("max_intervals must be positive")
+    rng = np.random.default_rng(int(seed))
+    selected = _select_disjoint(
+        _candidate_order(
+            episodes, lengths, rng,
+            pool_size=max(1000, int(max_intervals) * 50)),
+        int(max_intervals))
+
+    names = ("translation_mm", "orientation_deg",
+             "native_translation_mm", "native_rotation_deg")
+    all_values: dict[str, list[float]] = {name: [] for name in names}
+    high_values: dict[str, list[float]] = {name: [] for name in names}
+    high_by_length: dict[int, dict[str, list[float]]] = {
+        length: {name: [] for name in names} for length in lengths}
+    high_interval_count = {length: 0 for length in lengths}
+    method_values = {}
+    method_counts = {}
+    confidence_counts = {"HIGH": 0, "MEDIUM": 0, "LOW": 0}
+    endpoint_lengths = [length for length in lengths if length <= 5]
+    endpoint_trial_max = max(endpoint_lengths, default=0)
+    gate_for_reconstruction = TaskGate(
+        True, max(lengths), endpoint_max_frames=endpoint_trial_max)
+    for index, interval in enumerate(selected):
+        # Only the 15-frame contexts influence reconstruction. Evaluating
+        # an entire many-minute clean episode for every masked window adds
+        # quadratic detection work without supplying any extra evidence.
+        context_start = interval.start - 15
+        context_end = interval.start + interval.length + 15
+        truth = episodes[interval.episode][context_start:context_end]
+        interval = _Candidate(interval.episode, 15, interval.length, interval.motion)
+        all_rows = np.arange(interval.start, interval.start + interval.length)
+        if index % 2:
+            corrupt_rows = all_rows[::2]
+            known_gaps: list[tuple[int, int]] = []
+        else:
+            corrupt_rows = all_rows
+            known_gaps = [(interval.start, interval.length)]
+        observed = _inject_wrong_branch(truth, corrupt_rows)
+        result = repair_pose_stream(
+            observed, "left", known_gaps,
+            task_gate=gate_for_reconstruction)
+        pose_error = np.linalg.norm(
+            result.pose[all_rows, :3] - truth[all_rows, :3], axis=1) * 1000.0
+        rot_error = _rotation_angle_deg(
+            result.pose[all_rows, 3:], truth[all_rows, 3:])
+        lo = max(0, interval.start - 1)
+        hi = min(len(truth) - 1, interval.start + interval.length)
+        transitions = np.arange(lo, hi, dtype=int)
+        trans_action, rot_action = _native_errors(
+            result.pose, truth, transitions)
+        interval_values = {
+            "translation_mm": list(map(float, pose_error)),
+            "orientation_deg": list(map(float, rot_error)),
+            "native_translation_mm": list(map(float, trans_action)),
+            "native_rotation_deg": list(map(float, rot_action)),
+        }
+        for name in names:
+            all_values[name].extend(interval_values[name])
+        corrupt_confidence = result.confidence[corrupt_rows]
+        if (len(corrupt_rows)
+                and result.repaired[corrupt_rows].all()
+                and np.all(corrupt_confidence == 3)):
+            tier = "HIGH"
+        elif (result.repaired[corrupt_rows].any()
+              or np.any(corrupt_confidence == 2)):
+            tier = "MEDIUM"
+        else:
+            tier = "LOW"
+        confidence_counts[tier] += 1
+        if tier == "HIGH":
+            methods = {event.method for event in result.events
+                       if event.bout.start <= interval.start + interval.length - 1
+                       and event.bout.end >= interval.start}
+            method = next(iter(methods)) if len(methods) == 1 else "mixed"
+            method_counts[method] = method_counts.get(method, 0) + 1
+            values = method_values.setdefault(method, {name: [] for name in names})
+            for name in names:
+                values[name].extend(interval_values[name])
+            high_interval_count[interval.length] += 1
+            for name in names:
+                high_values[name].extend(interval_values[name])
+                high_by_length[interval.length][name].extend(
+                    interval_values[name])
+
+    metrics = {name: _summary(high_values[name]) for name in names}
+    metrics_all = {name: _summary(all_values[name]) for name in names}
+
+    def passes(values: dict[str, list[float]], interval_count: int) -> bool:
+        summary = {name: _summary(values[name]) for name in names}
+        return (
+            interval_count >= 100
+            and summary["translation_mm"].median <= 2.0
+            and summary["translation_mm"].p95 <= 10.0
+            and summary["orientation_deg"].median <= 1.0
+            and summary["orientation_deg"].p95 <= 5.0
+            and summary["native_translation_mm"].p95 <= 5.0
+            and summary["native_rotation_deg"].p95 <= 3.0)
+
+    validated_max = 0
+    for max_length in lengths:
+        prefix = {name: [] for name in names}
+        prefix_intervals = 0
+        for length in lengths:
+            if length > max_length:
+                continue
+            prefix_intervals += high_interval_count[length]
+            for name in names:
+                prefix[name].extend(high_by_length[length][name])
+        if passes(prefix, prefix_intervals):
+            validated_max = max_length
+    gate = TaskGate(
+        high_confidence_enabled=validated_max > 0,
+        validated_max_frames=validated_max,
+        endpoint_max_frames=(endpoint_trial_max if passes(
+            method_values.get("endpoint_se3", {name: [] for name in names}),
+            method_counts.get("endpoint_se3", 0)) else 0))
+    return BenchmarkReport(
+        task=str(task), seed=int(seed), intervals=len(selected),
+        high_intervals=confidence_counts["HIGH"],
+        anomaly_lengths=lengths, confidence_counts=confidence_counts,
+        metrics=metrics, metrics_all=metrics_all, gate=gate,
+        methods={method: {"intervals": method_counts[method],
+                          "metrics": {name: asdict(_summary(values[name]))
+                                      for name in names}}
+                 for method, values in method_values.items()})

action_repair_tools/twm/scripts/build_mocap_repair_candidates.py

@@ -0,0 +1,350 @@
+#!/usr/bin/env python3
+"""Audit, calibrate, build, review, and verify mocap repair candidates."""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from dataclasses import asdict
+import json
+import os
+from pathlib import Path
+import tempfile
+from typing import Any, Iterable
+
+import numpy as np
+import pyarrow.parquet as pq
+
+from twm.react_preprocess.mocap_candidate import (
+    CandidateBuild,
+    CandidateWriter,
+    InputManifest,
+    VerificationReport,
+    apply_decisions,
+    load_manifest,
+    save_manifest,
+    snapshot_inputs,
+    verify_candidate,
+)
+from twm.react_preprocess.mocap_repair import (
+    TaskGate,
+    detect_bouts,
+)
+from twm.scripts.benchmark_mocap_repair import (
+    BenchmarkReport,
+    benchmark_task,
+)
+
+
+TASKS = ("motherboard", "pushT", "rope", "toy")
+DEFAULT_SOURCE = Path("/media/yxma/Disk1/twm/release")
+DEFAULT_OUTPUT = Path(
+    "/media/yxma/Disk1/twm/review/mocap_repair_2026-09-17/candidate_release")
+
+
+def _atomic_json(path: Path, value: Any) -> Path:
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with tempfile.NamedTemporaryFile(
+            mode="w", encoding="utf-8", dir=path.parent,
+            prefix=f".{path.name}.", suffix=".tmp", delete=False) as stream:
+        temp = Path(stream.name)
+        json.dump(value, stream, indent=2, sort_keys=True)
+        stream.write("\n")
+    os.replace(temp, path)
+    return path
+
+
+def _gap_map(source_root: Path, task: str) -> dict[str, Any]:
+    path = source_root / task / "pose_gaps.json"
+    return json.loads(path.read_text()) if path.is_file() else {}
+
+
+def _known_gaps(gaps: dict[str, Any], date: str, episode: str,
+                side: str) -> list[tuple[int, int]]:
+    return [tuple(map(int, row)) for row in
+            gaps.get(f"{date}/{episode}", {}).get(side, [])]
+
+
+def _load_pinned_manifest(candidate_root: Path) -> InputManifest:
+    path = Path(candidate_root) / "input_manifest.json"
+    if not path.is_file():
+        raise ValueError(f"run audit first; missing {path}")
+    return load_manifest(path)
+
+
+def run_audit(source_root: Path = DEFAULT_SOURCE,
+              candidate_root: Path = DEFAULT_OUTPUT,
+              tasks: Iterable[str] = TASKS) -> InputManifest:
+    """Freeze input membership and report metadata-only anomaly bouts."""
+    source_root = Path(source_root).resolve()
+    candidate_root = Path(candidate_root).resolve()
+    manifest = snapshot_inputs(source_root, tasks)
+    # Constructor performs the production-root overlap safety check before the
+    # audit writes even its first JSON file.
+    CandidateWriter(source_root, candidate_root, manifest)
+    save_manifest(manifest, candidate_root / "input_manifest.json")
+
+    counts: dict[str, Counter] = {task: Counter() for task in manifest.tasks}
+    frames: dict[str, int] = {task: 0 for task in manifest.tasks}
+    gap_cache = {task: _gap_map(source_root, task) for task in manifest.tasks}
+    for item in manifest.files:
+        task, _, date, filename = Path(item.path).parts
+        episode = Path(filename).stem
+        table = pq.read_table(source_root / item.path)
+        for side in ("left", "right"):
+            name = f"sensor_{side}_pose"
+            if name not in table.column_names:
+                continue
+            pose = np.asarray(table[name].to_pylist(), float)
+            bouts = detect_bouts(
+                pose, side,
+                _known_gaps(gap_cache[task], date, episode, side))
+            for bout in bouts:
+                counts[task][bout.kind] += 1
+                frames[task] += bout.end - bout.start + 1
+    _atomic_json(candidate_root / "audit_summary.json", {
+        "schema_version": 1,
+        "manifest_digest": manifest.digest,
+        "source_root": str(source_root),
+        "episode_count": len(manifest.files),
+        "tasks": {task: {
+            "episodes": sum(Path(item.path).parts[0] == task
+                            for item in manifest.files),
+            "bouts": dict(sorted(counts[task].items())),
+            "candidate_frames": frames[task],
+        } for task in manifest.tasks},
+    })
+    return manifest
+
+
+def _clean_fragments(manifest: InputManifest, task: str,
+                     context: int = 15) -> list[np.ndarray]:
+    source_root = Path(manifest.source_root)
+    gaps = _gap_map(source_root, task)
+    fragments: list[np.ndarray] = []
+    for item in manifest.files:
+        if Path(item.path).parts[0] != task:
+            continue
+        _, _, date, filename = Path(item.path).parts
+        episode = Path(filename).stem
+        table = pq.read_table(source_root / item.path)
+        for side in ("left", "right"):
+            name = f"sensor_{side}_pose"
+            if name not in table.column_names:
+                continue
+            pose = np.asarray(table[name].to_pylist(), float)
+            blocked = ~np.isfinite(pose).all(axis=1)
+            for bout in detect_bouts(
+                    pose, side, _known_gaps(gaps, date, episode, side)):
+                lo = max(0, bout.start - context)
+                hi = min(len(pose), bout.end + context + 1)
+                blocked[lo:hi] = True
+            starts = np.flatnonzero(~blocked & np.r_[True, blocked[:-1]])
+            ends = np.flatnonzero(~blocked & np.r_[blocked[1:], True]) + 1
+            for start, end in zip(starts, ends):
+                if end - start >= 40:
+                    fragments.append(pose[start:end].copy())
+    return fragments
+
+
+def run_benchmarks(candidate_root: Path = DEFAULT_OUTPUT, *,
+                   anomaly_lengths: Iterable[int] = (1, 5, 20, 60),
+                   max_intervals: int = 300,
+                   seed: int = 0) -> dict[str, BenchmarkReport]:
+    """Calibrate one immutable, explicit confidence gate per task."""
+    candidate_root = Path(candidate_root).resolve()
+    manifest = _load_pinned_manifest(candidate_root)
+    # Preflight source digests before treating any source pose as clean truth.
+    verifier = CandidateWriter(
+        Path(manifest.source_root), candidate_root, manifest)
+    for item in manifest.files:
+        verifier._verify_source(item)
+    reports: dict[str, BenchmarkReport] = {}
+    benchmark_root = candidate_root / "benchmarks"
+    for task in manifest.tasks:
+        fragments = _clean_fragments(manifest, task)
+        if not fragments:
+            raise ValueError(f"no clean pose fragments available for {task}")
+        report = benchmark_task(
+            fragments, task=task, anomaly_lengths=anomaly_lengths,
+            max_intervals=max_intervals, seed=seed)
+        reports[task] = report
+        _atomic_json(benchmark_root / f"{task}.json", report.to_dict())
+    _atomic_json(candidate_root / "task_gates.json", {
+        "schema_version": 1,
+        "manifest_digest": manifest.digest,
+        "tasks": {task: asdict(report.gate)
+                  for task, report in sorted(reports.items())},
+    })
+    return reports
+
+
+def _load_task_gates(candidate_root: Path,
+                     manifest: InputManifest) -> dict[str, TaskGate]:
+    path = Path(candidate_root) / "task_gates.json"
+    if not path.is_file():
+        raise ValueError(f"run benchmark first; missing {path}")
+    payload = json.loads(path.read_text())
+    if payload.get("manifest_digest") != manifest.digest:
+        raise ValueError("task gates manifest digest mismatch")
+    missing = set(manifest.tasks) - set(payload.get("tasks", {}))
+    if missing:
+        raise ValueError(f"task gates missing tasks: {sorted(missing)}")
+    return {task: TaskGate(**payload["tasks"][task])
+            for task in manifest.tasks}
+
+
+def run_build(candidate_root: Path = DEFAULT_OUTPUT, *,
+              task_gates: dict[str, TaskGate] | None = None,
+              branch_review: bool = False,
+              require_loss_evidence: bool = False) -> CandidateBuild:
+    candidate_root = Path(candidate_root).resolve()
+    manifest = _load_pinned_manifest(candidate_root)
+    gates = task_gates or _load_task_gates(candidate_root, manifest)
+    missing = set(manifest.tasks) - set(gates)
+    if missing:
+        raise ValueError(f"task gates missing tasks: {sorted(missing)}")
+    writer = CandidateWriter(
+        Path(manifest.source_root), candidate_root, manifest,
+        task_gates=gates, branch_review=branch_review,
+        require_loss_evidence=require_loss_evidence)
+    build = writer.write_all()
+    _atomic_json(candidate_root / "candidate_summary.json",
+                 summarize_candidate(candidate_root))
+    return build
+
+
+def run_verify(candidate_root: Path = DEFAULT_OUTPUT) -> VerificationReport:
+    report = verify_candidate(candidate_root)
+    _atomic_json(Path(candidate_root) / "verification.json", {
+        "schema_version": 1,
+        "episodes": report.episodes,
+        "errors": list(report.errors),
+    })
+    return report
+
+
+def summarize_candidate(candidate_root: Path = DEFAULT_OUTPUT) -> dict[str, Any]:
+    candidate_root = Path(candidate_root).resolve()
+    manifest = _load_pinned_manifest(candidate_root)
+    summary: dict[str, Any] = {
+        "schema_version": 1,
+        "manifest_digest": manifest.digest,
+        "tasks": {},
+    }
+    for task in manifest.tasks:
+        row = {
+            "events": {"HIGH": 0, "MEDIUM": 0, "LOW": 0},
+            "repaired_frames": 0,
+            "native_repaired_actions": 0,
+            "native_recovered_valid_actions": 0,
+            "fps15_repaired_actions": 0,
+            "fps15_recovered_valid_actions": 0,
+            "unresolved_events": 0,
+        }
+        for path in sorted((candidate_root / task / "repair_events").glob("*/*.json")):
+            for event in json.loads(path.read_text()).get("events", []):
+                confidence = str(event["confidence"]).upper()
+                row["events"].setdefault(confidence, 0)
+                row["events"][confidence] += 1
+                row["repaired_frames"] += len(event.get("replaced_frames", []))
+                if confidence in {"MEDIUM", "LOW"} and event.get("decision") != "accept_repair":
+                    row["unresolved_events"] += 1
+        for rate, repaired_key, recovered_key in (
+                ("actions_native", "native_repaired_actions",
+                 "native_recovered_valid_actions"),
+                ("actions_fps15", "fps15_repaired_actions",
+                 "fps15_recovered_valid_actions")):
+            for path in sorted((candidate_root / task / rate).glob("*/*.npz")):
+                with np.load(path, allow_pickle=False) as data:
+                    repaired = np.asarray(data["repaired"], bool)
+                    valid = np.asarray(data["valid"], bool)
+                    row[repaired_key] += int(repaired.sum())
+                    row[recovered_key] += int((repaired & valid).sum())
+        summary["tasks"][task] = row
+    return summary
+
+
+def run_review(candidate_root: Path = DEFAULT_OUTPUT, *, render: bool = False,
+               review_root: Path | None = None,
+               high_sample_rate: float = 0.1,
+               sample_seed: int = 0) -> dict[str, Any]:
+    from twm.scripts.build_pose_review import build_review
+
+    candidate_root = Path(candidate_root).resolve()
+    manifest = _load_pinned_manifest(candidate_root)
+    source_root = Path(manifest.source_root)
+    review_root = (Path(review_root).resolve() if review_root is not None
+                   else candidate_root.parent / "review_package")
+    results = {}
+    for task in manifest.tasks:
+        results[task] = build_review(
+            source_root / task, review_root / task, render=render,
+            candidate_task_root=candidate_root / task,
+            high_sample_rate=high_sample_rate,
+            sample_seed=sample_seed)
+    return results
+
+
+def _parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
+    sub = parser.add_subparsers(dest="command", required=True)
+    audit = sub.add_parser("audit")
+    audit.add_argument("--source", type=Path, default=DEFAULT_SOURCE)
+    audit.add_argument("--task", action="append", choices=TASKS)
+    bench = sub.add_parser("benchmark")
+    bench.add_argument("--max-intervals", type=int, default=300)
+    bench.add_argument("--seed", type=int, default=0)
+    build = sub.add_parser("build")
+    build.add_argument("--branch-review", action="store_true")
+    build.add_argument("--require-loss-evidence", action="store_true")
+    review = sub.add_parser("review")
+    review.add_argument("--render", action="store_true")
+    review.add_argument("--review-root", type=Path)
+    review.add_argument("--high-sample-rate", type=float, default=0.1)
+    review.add_argument("--seed", type=int, default=0)
+    decisions = sub.add_parser("apply-decisions")
+    decisions.add_argument("decisions", type=Path)
+    sub.add_parser("verify")
+    sub.add_parser("summary")
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = _parser().parse_args(argv)
+    if args.command == "audit":
+        result = run_audit(args.source, args.output, args.task or TASKS)
+        print(json.dumps(result.to_dict(), indent=2, sort_keys=True))
+    elif args.command == "benchmark":
+        result = run_benchmarks(
+            args.output, max_intervals=args.max_intervals, seed=args.seed)
+        print(json.dumps({task: report.to_dict()
+                          for task, report in result.items()}, indent=2,
+                         sort_keys=True))
+    elif args.command == "build":
+        result = run_build(args.output, branch_review=args.branch_review,
+                           require_loss_evidence=args.require_loss_evidence)
+        print(f"episodes={len(result.episodes)} manifest={result.manifest_digest}")
+    elif args.command == "review":
+        result = run_review(
+            args.output, render=args.render, review_root=args.review_root,
+            high_sample_rate=args.high_sample_rate, sample_seed=args.seed)
+        print(json.dumps({task: len(value["review_events"])
+                          for task, value in result.items()}, indent=2,
+                         sort_keys=True))
+    elif args.command == "apply-decisions":
+        apply_decisions(args.output, json.loads(args.decisions.read_text()))
+        print(json.dumps(summarize_candidate(args.output), indent=2,
+                         sort_keys=True))
+    elif args.command == "verify":
+        report = run_verify(args.output)
+        print(f"episodes={report.episodes} errors={len(report.errors)}")
+    elif args.command == "summary":
+        print(json.dumps(summarize_candidate(args.output), indent=2,
+                         sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

action_repair_tools/twm/scripts/export_old_motherboard_actions.py

@@ -0,0 +1,240 @@
+"""Export old motherboard parquet with repaired T-row LeRobot actions.
+
+Raw release columns are preserved. Candidate poses and action validity come
+from the provenance-safe mocap repair tree; unresolved events stay invalid.
+"""
+from __future__ import annotations
+
+import argparse
+from datetime import datetime, timezone
+import hashlib
+import json
+import os
+from pathlib import Path
+import tempfile
+from typing import Mapping, Sequence
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+
+TASK = "motherboard"
+DATES = ("2026-05-10", "2026-05-11", "2026-05-19")
+EXPECTED_EPISODES = 32
+SIDES = ("left", "right")
+
+
+def _fixed_float32(values: np.ndarray, width: int) -> pa.Array:
+    flat = pa.array(np.asarray(values, np.float32).reshape(-1), type=pa.float32())
+    return pa.FixedSizeListArray.from_arrays(flat, width)
+
+
+def _append(table: pa.Table, name: str, values: pa.Array) -> pa.Table:
+    if name in table.column_names:
+        raise ValueError(f"refusing to replace existing column {name!r}")
+    return table.append_column(name, values)
+
+
+def _long_transition_mask(events: Sequence[Mapping[str, object]], side: str,
+                          n_rows: int) -> np.ndarray:
+    result = np.zeros(n_rows, dtype=bool)
+    for event in events:
+        if str(event.get("side")) != side:
+            continue
+        start, end = int(event["start"]), int(event["end"])
+        if end - start + 1 <= 60:
+            continue
+        first = max(0, start - 1)
+        last = min(n_rows - 2, end)
+        if last >= first:
+            result[first:last + 1] = True
+    return result
+
+
+def build_action_episode(raw: pa.Table, candidate: pa.Table,
+                         native_left: Mapping[str, np.ndarray],
+                         native_right: Mapping[str, np.ndarray],
+                         events: Sequence[Mapping[str, object]]) -> pa.Table:
+    """Return raw columns plus repaired pose/action columns, aligned to T rows."""
+    if raw.num_rows != candidate.num_rows:
+        raise ValueError("raw and candidate row counts differ")
+    n = raw.num_rows
+    if n < 1:
+        raise ValueError("empty episode")
+    repaired_pose = {}
+    native = {"left": native_left, "right": native_right}
+    for side in SIDES:
+        repaired_pose[side] = np.asarray(
+            candidate[f"sensor_{side}_pose"].to_pylist(), dtype=np.float32)
+        if repaired_pose[side].shape != (n, 7):
+            raise ValueError(f"candidate {side} pose must have shape ({n}, 7)")
+        for key in ("valid", "repaired"):
+            if np.asarray(native[side][key]).shape != (n - 1,):
+                raise ValueError(f"native {side} {key} must have {n - 1} rows")
+
+    target = np.concatenate([
+        np.concatenate([repaired_pose["left"][1:],
+                        repaired_pose["right"][1:]], axis=1),
+        np.concatenate([repaired_pose["left"][-1:],
+                        repaired_pose["right"][-1:]], axis=1),
+    ], axis=0)
+    out = raw
+    for side in SIDES:
+        out = _append(out, f"sensor_{side}_pose_repaired",
+                      _fixed_float32(repaired_pose[side], 7))
+    out = _append(out, "action", _fixed_float32(target, 14))
+
+    for side in SIDES:
+        valid = np.r_[np.asarray(native[side]["valid"], bool), False]
+        repaired = np.r_[np.asarray(native[side]["repaired"], bool), False]
+        pose_conf = np.asarray(
+            candidate[f"pose_{side}_repair_confidence"].to_numpy(),
+            dtype=np.uint8)
+        confidence = np.zeros(n, dtype=np.uint8)
+        if n > 1:
+            confidence[:-1] = np.maximum(pose_conf[:-1], pose_conf[1:])
+        event_overlap = np.zeros(n, dtype=bool)
+        for event in events:
+            if str(event.get("side")) != side:
+                continue
+            start, end = int(event["start"]), int(event["end"])
+            first, last = max(0, start - 1), min(n - 2, end)
+            if last >= first:
+                event_overlap[first:last + 1] = True
+        lost = (~valid) & event_overlap
+        long_lost = _long_transition_mask(events, side, n) & lost
+        out = _append(out, f"action_valid_{side}", pa.array(valid))
+        out = _append(out, f"action_repaired_{side}", pa.array(repaired))
+        out = _append(out, f"action_repair_confidence_{side}",
+                      pa.array(confidence, type=pa.uint8()))
+        out = _append(out, f"action_lost_track_{side}", pa.array(lost))
+        out = _append(out, f"action_long_lost_track_{side}",
+                      pa.array(long_lost))
+    return out
+
+
+def _sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def _load_native(path: Path) -> dict[str, np.ndarray]:
+    with np.load(path, allow_pickle=False) as data:
+        return {name: np.asarray(data[name]) for name in data.files}
+
+
+def _atomic_parquet(table: pa.Table, path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with tempfile.NamedTemporaryFile(dir=path.parent, prefix=f".{path.name}.",
+                                     suffix=".tmp", delete=False) as stream:
+        temp = Path(stream.name)
+    try:
+        pq.write_table(table, temp, compression="zstd")
+        os.replace(temp, path)
+    finally:
+        temp.unlink(missing_ok=True)
+
+
+def _columns_equal(left: pa.ChunkedArray, right: pa.ChunkedArray) -> bool:
+    if left.type != right.type or len(left) != len(right):
+        return False
+    if left.equals(right):
+        return True
+    try:
+        if (pa.types.is_list(left.type) or pa.types.is_large_list(left.type)
+                or pa.types.is_fixed_size_list(left.type)):
+            a = np.asarray(left.to_pylist(), dtype=float)
+            b = np.asarray(right.to_pylist(), dtype=float)
+        elif pa.types.is_floating(left.type):
+            a = np.asarray(left.to_numpy(zero_copy_only=False), dtype=float)
+            b = np.asarray(right.to_numpy(zero_copy_only=False), dtype=float)
+        else:
+            return False
+    except (TypeError, ValueError):
+        return False
+    return bool(np.array_equal(a, b, equal_nan=True))
+
+
+def export_tree(release: Path, candidate_root: Path, output: Path) -> dict:
+    release, candidate_root, output = map(Path, (release, candidate_root, output))
+    sources = [p for date in DATES
+               for p in sorted((release / TASK / "meta" / date).glob("episode_*.parquet"))]
+    if len(sources) != EXPECTED_EPISODES:
+        raise ValueError(f"expected {EXPECTED_EPISODES} episodes, found {len(sources)}")
+    episodes = []
+    totals = {"valid_left": 0, "valid_right": 0,
+              "repaired_left": 0, "repaired_right": 0,
+              "lost_left": 0, "lost_right": 0,
+              "long_left": 0, "long_right": 0}
+    for source in sources:
+        date, name = source.parent.name, source.name
+        candidate = candidate_root / TASK / "meta" / date / name
+        event_path = candidate_root / TASK / "repair_events" / date / f"{source.stem}.json"
+        if not candidate.is_file() or not event_path.is_file():
+            raise FileNotFoundError(f"missing candidate artifacts for {date}/{source.stem}")
+        raw_table, candidate_table = pq.read_table(source), pq.read_table(candidate)
+        event_doc = json.loads(event_path.read_text())
+        events = event_doc.get("events", [])
+        native = {
+            side: _load_native(candidate_root / TASK / "actions_native" / date /
+                               f"{source.stem}_{side}.npz")
+            for side in SIDES
+        }
+        table = build_action_episode(raw_table, candidate_table,
+                                     native["left"], native["right"], events)
+        for field in raw_table.schema:
+            if field.name not in table.column_names or table.schema.field(field.name) != field:
+                raise ValueError(f"original schema changed: {date}/{name}:{field.name}")
+            if not _columns_equal(raw_table[field.name], table[field.name]):
+                raise ValueError(f"original values changed: {date}/{name}:{field.name}")
+        dest = output / "meta" / date / name
+        _atomic_parquet(table, dest)
+        event_dest = output / "action_repair_events" / date / f"{source.stem}.json"
+        event_dest.parent.mkdir(parents=True, exist_ok=True)
+        event_dest.write_bytes(event_path.read_bytes())
+        stats = {}
+        for side in SIDES:
+            for key, col in (("valid", f"action_valid_{side}"),
+                             ("repaired", f"action_repaired_{side}"),
+                             ("lost", f"action_lost_track_{side}"),
+                             ("long", f"action_long_lost_track_{side}")):
+                value = int(np.asarray(table[col].to_numpy()).sum())
+                stats[f"{key}_{side}"] = value
+                totals[f"{key}_{side}"] += value
+        episodes.append({"date": date, "episode": source.stem,
+                         "rows": table.num_rows, "source_sha256": _sha256(source),
+                         "parquet_sha256": _sha256(dest), **stats})
+    manifest = {
+        "schema_version": 1,
+        "created_utc": datetime.now(timezone.utc).isoformat(),
+        "task": TASK,
+        "dates": list(DATES),
+        "episodes": episodes,
+        "totals": totals,
+        "action": "next-frame repaired left pose (7) + right pose (7)",
+        "terminal_action_valid": False,
+        "long_lost_track_min_frames": 61,
+    }
+    (output / "action_update_manifest.json").write_text(
+        json.dumps(manifest, indent=2, sort_keys=True) + "\n")
+    return manifest
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--release", type=Path, required=True)
+    parser.add_argument("--candidate", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    args = parser.parse_args()
+    manifest = export_tree(args.release, args.candidate, args.output)
+    print(f"episodes={len(manifest['episodes'])} errors=0")
+    print(json.dumps(manifest["totals"], sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

action_repair_tools/twm/scripts/propagate_mocap_repairs.py

@@ -0,0 +1,522 @@
+"""Map repaired source poses to every published segment without altering raw data.
+
+Inventory pins remote membership/digests and records a verified coordinate
+conversion. Export keeps every non-action base column, including force columns.
+"""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from html import escape
+import json
+from pathlib import Path
+import re
+from urllib.parse import quote
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+from twm.react_toolbox.frames import convert_poses
+from twm.react_preprocess.mocap_candidate import _write_actions
+from twm.react_preprocess.repaired_actions import native_actions, actions_fps15
+from twm.scripts.export_old_motherboard_actions import (
+    build_action_episode, _atomic_parquet, _columns_equal, _sha256)
+
+
+SIDES = ('left', 'right')
+POSES = tuple(f'sensor_{s}_pose' for s in SIDES)
+
+
+def source_relative_path(remote_path: str | Path) -> Path:
+    parts = Path(remote_path).parts
+    if len(parts) != 5 or parts[0] not in ('data', 'old_data') or parts[2] != 'meta':
+        raise ValueError(f'unsupported remote path: {remote_path}')
+    return Path(*parts[1:-1], re.sub(r'_seg\d+(?=\.parquet$)', '', parts[-1]))
+
+
+def _values(table, column):
+    array = table[column].combine_chunks()
+    if (pa.types.is_list(array.type) or pa.types.is_fixed_size_list(array.type)) and not array.null_count:
+        if len(array) and len(array.values) % len(array) == 0:
+            return array.values.to_numpy(zero_copy_only=False).reshape(len(array), -1)
+    return np.asarray(array.to_pylist())
+
+
+def _coordinate_mode(raw: pa.Table, selected: pa.Table) -> str:
+    if all(np.array_equal(_values(raw, c), _values(selected, c), equal_nan=True)
+           for c in POSES):
+        return 'identity'
+    if all(np.array_equal(_values(raw, c), convert_poses(_values(selected, c)),
+                          equal_nan=True) for c in POSES):
+        return 'yup_to_zup'
+    raise ValueError('sensor pose mismatch: neither identity nor verified Y-up to Z-up')
+
+
+def align_rows(raw: pa.Table, source: pa.Table) -> np.ndarray:
+    """Require unique source IDs, consecutive rows, exact times and known poses."""
+    if raw.num_rows == 0:
+        raise ValueError('empty segment')
+    ids = source['source_h5_frame'].to_pylist()
+    if len(set(ids)) != len(ids):
+        raise ValueError('duplicate source_h5_frame in source')
+    index = {value: i for i, value in enumerate(ids)}
+    try:
+        rows = np.asarray([index[value] for value in raw['source_h5_frame'].to_pylist()],
+                          dtype=np.int64)
+    except KeyError as exc:
+        raise ValueError(f'missing source_h5_frame: {exc}') from exc
+    if np.any(np.diff(rows) != 1):
+        raise ValueError('segment source rows must be contiguous and unique')
+    selected = source.take(pa.array(rows))
+    if not np.array_equal(_values(raw, 'timestamp'), _values(selected, 'timestamp')):
+        raise ValueError('timestamp mismatch')
+    _coordinate_mode(raw, selected)
+    return rows
+
+
+def _derived(name):
+    return (name == 'action' or name.startswith('action_')
+            or name in ('sensor_left_pose_repaired', 'sensor_right_pose_repaired')
+            or name.startswith(('pose_left_', 'pose_right_')))
+
+
+def build_segment(raw: pa.Table, source: pa.Table, candidate: pa.Table,
+                  source_rows: np.ndarray, events: list[dict]):
+    """Return augmented parquet and native/fps15 ActionSeries for each side.
+
+    Recompute transitions from candidate pose validity after selecting the
+    published segment, so neither actions nor 15fps phase cross a cut boundary.
+    """
+    rows = align_rows(raw, source)
+    if not np.array_equal(rows, source_rows):
+        raise ValueError('provided source rows disagree with verified mapping')
+    if candidate.num_rows != source.num_rows:
+        raise ValueError('candidate/source row counts differ')
+    for c in ('source_h5_frame', 'timestamp'):
+        if not _columns_equal(source[c], candidate[c]):
+            raise ValueError(f'candidate/source {c} mismatch')
+    segment = candidate.take(pa.array(rows))
+    mode = _coordinate_mode(raw, source.take(pa.array(rows)))
+    sidecars = {}
+    for side in SIDES:
+        required = [f'pose_{side}_{key}' for key in
+                    ('valid', 'repaired', 'repair_confidence', 'repair_event_id')]
+        for name in required:
+            if name not in segment.column_names:
+                raise ValueError(f'missing candidate provenance: {name}')
+        column = f'sensor_{side}_pose'
+        pose = _values(segment, column)
+        if mode == 'yup_to_zup':
+            pose = convert_poses(pose)
+            segment = segment.set_column(segment.schema.get_field_index(column),
+                                         column, pa.array(pose.tolist()))
+        native = native_actions(pose, _values(segment, required[0]),
+                                _values(segment, required[1]),
+                                _values(segment, required[3]))
+        sidecars[side] = {'native': native, 'fps15': actions_fps15(pose, native)}
+    # Keep original event duration; negative local starts are intentional and
+    # retain long-loss classification for events clipped by the segment edge.
+    local_events = [dict(event, start=int(event['start']) - int(rows[0]),
+                        end=int(event['end']) - int(rows[0]))
+                    for event in events
+                    if int(event['end']) >= rows[0] - 1
+                    and int(event['start']) <= rows[-1] + 1]
+    base = raw.select([name for name in raw.column_names if not _derived(name)])
+    out = build_action_episode(base, segment,
+        {k: getattr(sidecars['left']['native'], k) for k in ('valid', 'repaired')},
+        {k: getattr(sidecars['right']['native'], k) for k in ('valid', 'repaired')},
+        local_events)
+    for side in SIDES:
+        for key in ('valid', 'repaired', 'repair_confidence', 'repair_event_id'):
+            name = f'pose_{side}_{key}'
+            out = out.append_column(name, segment[name])
+        method_name = f'pose_{side}_repair_method'
+        if method_name in segment.column_names:
+            out = out.append_column(method_name, segment[method_name])
+        else:
+            methods = {event['event_id']: event.get('method') for event in events
+                       if event.get('side') == side and 'event_id' in event}
+            values = [methods.get(event_id) for event_id in
+                      segment[f'pose_{side}_repair_event_id'].to_pylist()]
+            out = out.append_column(method_name, pa.array(values, type=pa.string()))
+    for name in base.column_names:
+        if out.schema.field(name) != base.schema.field(name) or not _columns_equal(out[name], base[name]):
+            raise ValueError(f'preserved column changed: {name}')
+    return out, sidecars
+
+
+def segment_reviews(remote_path: str, raw: pa.Table, output: pa.Table,
+                    rows: np.ndarray, events: list[dict], video_files: set[str],
+                    revision: str, include_high_charts: bool = False) -> list[dict]:
+    """Clip review coordinates to the published video, preserving source evidence."""
+    remote = Path(remote_path)
+    capture_times = _values(raw, 'timestamp').astype(float)
+    capture_times -= capture_times[0]
+    # Published videos contain one frame per parquet row at constant 30 fps.
+    # Capture timestamps include acquisition stalls and are not media time.
+    times = np.arange(raw.num_rows, dtype=float) / 30.
+    records = []
+    for event in events:
+        first, last = int(event['start']), int(event['end'])
+        if last < rows[0] or first > rows[-1]:
+            continue
+        lo, hi = max(0, first - int(rows[0])), min(len(rows) - 1, last - int(rows[0]))
+        side = event['side']
+        event_id = event.get('event_id', f'{side}:{first}-{last}')
+        # Video filenames are included only when present in the frozen HF listing.
+        links = {}
+        for view in ('view_middle', f'wrist_{side}', 'view_left', 'view_right'):
+            video = str(Path(*remote.parts[:2], 'videos', remote.parts[3],
+                             remote.stem, view + '.mp4'))
+            if video in video_files:
+                links[view] = (f'https://huggingface.co/datasets/yxma/React/resolve/'
+                               f'{quote(revision or "main", safe="")}/{quote(video, safe="/")}'
+                               f'#t={max(0., times[lo] - 1):.3f},{times[hi] + 1:.3f}')
+        record = dict(event, qualified_event_id=f'{remote_path}::{event_id}',
+                      remote_path=remote_path, source_start_row=first,
+                      source_end_row=last, local_start_row=lo, local_end_row=hi,
+                      local_start_seconds=float(times[lo]), local_end_seconds=float(times[hi]),
+                      capture_start_seconds=float(capture_times[lo]),
+                      capture_end_seconds=float(capture_times[hi]),
+                      video_time_basis='local_row / 30 fps',
+                      video_links=links)
+        if include_high_charts or str(event.get('confidence', '')).upper() != 'HIGH':
+            a, b = max(0, lo - 30), min(len(rows), hi + 31)
+            sample = np.unique(np.r_[np.linspace(a, b - 1, min(160, b - a), dtype=int), lo, hi])
+            chart = {'seconds': times[sample].tolist(), 'sampled': b - a > 160}
+            for label, column in [('raw', f'sensor_{side}_pose'),
+                                  ('repaired', f'sensor_{side}_pose_repaired')]:
+                context_start = max(0, a - 1)
+                pose = _values((output if label == 'repaired' else raw).slice(
+                    context_start, b - context_start), column).astype(float)
+                # Angular displacement between adjacent native frames; no unwrap
+                # or interpolation is invented for missing/zero quaternions.
+                q = pose[:, 3:]
+                norm = np.linalg.norm(q, axis=1)
+                usable = np.isfinite(q).all(axis=1) & (norm > 1e-12)
+                unit = np.full(q.shape, np.nan)
+                unit[usable] = q[usable] / norm[usable, None]
+                angle = np.r_[np.nan, np.degrees(2 * np.arccos(np.clip(
+                    np.abs(np.sum(unit[1:] * unit[:-1], axis=1)), 0, 1)))]
+                data = np.column_stack((pose[:, :3] * 1000., angle))[sample - context_start]
+                chart[label] = [[float(v) if np.isfinite(v) else None for v in row] for row in data]
+            record['chart'] = chart
+        records.append(record)
+    return records
+
+
+def _json_safe(value):
+    """Represent unavailable nonfinite evidence as JSON null, never as a number."""
+    if isinstance(value, float) and not np.isfinite(value):
+        return None
+    if isinstance(value, dict):
+        return {key: _json_safe(item) for key, item in value.items()}
+    if isinstance(value, (list, tuple)):
+        return [_json_safe(item) for item in value]
+    return value
+
+
+def _review_svg(chart: dict) -> str:
+    times = np.asarray(chart['seconds'])
+    left, width, height = 70., 740., 64.
+    x = left + width * (times - times[0]) / max(1e-9, times[-1] - times[0])
+    parts = ['<svg viewBox="0 0 850 340" role="img" aria-label="Raw and repaired pose traces">']
+    for k, title in enumerate(('x (mm)', 'y (mm)', 'z (mm)', 'step (deg)')):
+        arrays = [np.asarray(chart[label], dtype=float)[:, k] for label in ('raw', 'repaired')]
+        finite = np.concatenate([a[np.isfinite(a)] for a in arrays])
+        top = 10. + k * 78.
+        parts.append(f'<text x="0" y="{top + 30}">{title}</text>')
+        if not len(finite):
+            parts.append(f'<text x="80" y="{top + 30}">Missing pose</text>')
+            continue
+        low, high = float(finite.min()), float(finite.max())
+        span = max(1e-6, high - low)
+        for values, color in zip(arrays, ('#bd3342', '#147cbb')):
+            y = top + height - (values - low) / span * height
+            # Separate path pieces at missing values instead of bridging gaps.
+            points, active = [], False
+            for px, py in zip(x, y):
+                if np.isfinite(py):
+                    points.append(f'{"L" if active else "M"}{px:.1f},{py:.1f}')
+                    active = True
+                else:
+                    active = False
+            parts.append(f'<path d="{" ".join(points)}" fill="none" stroke="{color}" stroke-width="1.4"/>')
+        parts.append(f'<text x="815" y="{top + 15}" font-size="9">{high:.2f}</text>')
+        parts.append(f'<text x="815" y="{top + 63}" font-size="9">{low:.2f}</text>')
+    parts.append(f'<text x="70" y="335">{times[0]:.2f}s</text><text x="745" y="335">{times[-1]:.2f}s</text></svg>')
+    return ''.join(parts)
+
+
+def choose_review_samples(records: list[dict], count: int = 25) -> list[dict]:
+    """Prefer 20 algorithmic proposals + 5 other events, balancing all scopes.
+
+    One source event appears at most once even if it overlaps several segments.
+    Selection uses the final candidate event records, never candidate labels from
+    an earlier build. A shortfall is reported by the exported selection manifest.
+    """
+    unique = {}
+    for record in records:
+        source = source_relative_path(record['remote_path'])
+        scope = Path(record['remote_path']).parts[0]
+        key = (scope, str(source), record.get('event_id', record['qualified_event_id']))
+        prior = unique.get(key)
+        if prior is None or record['local_end_row'] - record['local_start_row'] > prior['local_end_row'] - prior['local_start_row']:
+            unique[key] = record
+    def features(record):
+        length = record['source_end_row'] - record['source_start_row'] + 1
+        duration = 'short_1_4' if length <= 4 else 'flicker_5_20' if length <= 20 else 'medium_21_60' if length <= 60 else 'long_over_60'
+        return ('/'.join(Path(record['remote_path']).parts[:2]), duration, record.get('kind', 'unknown'))
+    selected, used = [], Counter()
+    def take(pool, n):
+        pool = list(pool)
+        for _ in range(min(n, len(pool))):
+            def score(record):
+                group, duration, kind = features(record)
+                ambiguity = 'ambiguous' in str(record.get('method', '')) or 'incompatible' in str(record.get('evidence', {}))
+                return (100 / (1 + used[('scope', group)]) +
+                        20 / (1 + used[('duration', duration)]) +
+                        10 / (1 + used[('kind', kind)]) +
+                        (15 if ambiguity and not used[('ambiguous', True)] else 0))
+            choice = max(sorted(pool, key=lambda r: r['qualified_event_id']), key=score)
+            pool.remove(choice)
+            selected.append(choice)
+            group, duration, kind = features(choice)
+            for name, value in [('scope', group), ('duration', duration), ('kind', kind)]:
+                used[(name, value)] += 1
+            if 'ambiguous' in str(choice.get('method', '')) or 'incompatible' in str(choice.get('evidence', {})):
+                used[('ambiguous', True)] += 1
+    def proposed(record):
+        evidence = record.get('evidence', {})
+        return (str(record.get('confidence', '')).upper() == 'HIGH'
+                or str(evidence.get('prior_confidence', record.get('prior_confidence', ''))).upper() == 'HIGH'
+                or evidence.get('independent_loss_evidence') is False)
+    high = [r for r in unique.values() if proposed(r)]
+    other = [r for r in unique.values() if not proposed(r)]
+    take(high, max(0, count - 5))
+    take(other, min(5, count))
+    selected_ids = {r['qualified_event_id'] for r in selected}
+    take([r for r in unique.values() if r['qualified_event_id'] not in selected_ids], count - len(selected))
+    return [dict(record, human_verification_status='UNVERIFIED',
+                 sample_group='algorithmic_proposal' if proposed(record) else 'other_uncertain')
+            for record in selected]
+
+
+def review_html(records: list[dict], *, include_high: bool = False,
+                title: str = 'Mocap event review') -> str:
+    review = [r for r in records if include_high or str(r.get('confidence', '')).upper() != 'HIGH']
+    parts = ['<!doctype html><html lang="en"><meta charset="utf-8">',
+             '<title>Mocap event review</title><style>body{font:15px system-ui;max-width:1100px;margin:30px auto;padding:0 16px}details{border:1px solid #ccc;margin:10px 0;padding:12px}summary{cursor:pointer;overflow-wrap:anywhere}svg{width:100%;max-width:900px}a{margin-right:15px}pre{white-space:pre-wrap}</style>',
+             f'<h1>{escape(title)}</h1><p>{len(review)} {"selected" if include_high else "non-HIGH"} event occurrences across published segments. ',
+             'Raw traces are red; repaired traces are blue. Missing values are gaps. ',
+             'Each chart includes one second of context; long intervals are sampled. ',
+             'Video links and charts use segment-local frame / 30 fps; capture-time offsets are listed separately. No approval is implied. ',
+             'The measured camera footage is unchanged; before/after repair is shown in the red/blue pose charts. ',
+             'These examples support human verification and are not proof that every jump is tracking loss. ',
+             'Check visible hand/object motion against the measured jump, the surrounding frames, and the repair.</p>']
+    for record in review:
+        eid = escape(record['qualified_event_id'])
+        confidence = escape(str(record.get('confidence', 'UNKNOWN')))
+        method = escape(str(record.get('method', 'unavailable')))
+        parts.append(f'<details data-clip-start="{max(0., record["local_start_seconds"] - 1):.3f}" '
+                     f'data-clip-end="{record["local_end_seconds"] + 1:.3f}"><summary>{eid} — {confidence} — {method} — '
+                     f'{record["local_start_seconds"]:.3f}–{record["local_end_seconds"]:.3f}s'
+                     f'{" — UNVERIFIED" if include_high else ""}</summary>')
+        parts.append('<p>' + ''.join(f'<a href="{escape(url, quote=True)}" target="_blank" rel="noopener">{escape(view)}</a>'
+                                     for view, url in record['video_links'].items()) + '</p>')
+        if not record['video_links']:
+            parts.append('<p>No matching video in the frozen repository inventory.</p>')
+        if include_high:
+            if record['video_links']:
+                parts.append('<p><button type="button" onclick="playSample(this)">Play synced</button> '
+                             '<button type="button" onclick="pauseSample(this)">Pause</button> '
+                             '<span class="play-status" aria-live="polite"></span></p>')
+            for view, url in record['video_links'].items():
+                if view == 'view_middle' or view.startswith('wrist_'):
+                    parts.append(f'<p>{escape(view)}</p><video controls preload="none" width="480" src="{escape(url, quote=True)}"></video>')
+        if 'chart' in record:
+            parts.append(_review_svg(record['chart']))
+        parts.append('<pre>' + escape(json.dumps({key: record.get(key) for key in
+            ('source_start_row', 'source_end_row', 'local_start_row', 'local_end_row', 'capture_start_seconds', 'capture_end_seconds', 'video_time_basis', 'kind', 'evidence')}, indent=2)) + '</pre></details>')
+    if include_high:
+        parts.append('''<script>
+let reviewPlayToken = 0;
+function pauseSample(button) {
+  reviewPlayToken++;
+  button.closest('details').querySelectorAll('video').forEach(v => v.pause());
+}
+async function playSample(button) {
+  const token = ++reviewPlayToken;
+  const box = button.closest('details');
+  const videos = Array.from(box.querySelectorAll('video'));
+  const status = box.querySelector('.play-status');
+  const start = Number(box.dataset.clipStart), end = Number(box.dataset.clipEnd);
+  document.querySelectorAll('video').forEach(v => v.pause());
+  status.textContent = 'Loading video metadata…';
+  try {
+    await Promise.all(videos.map(v => v.readyState >= 1 ? Promise.resolve() : new Promise((resolve, reject) => {
+      v.addEventListener('loadedmetadata', resolve, {once:true});
+      v.addEventListener('error', () => reject(new Error('Video unavailable; use the direct link.')), {once:true});
+      v.load();
+    })));
+    if (token !== reviewPlayToken) return;
+    videos.forEach(v => {v.currentTime = Math.min(start, Math.max(0, v.duration - 0.05));});
+    const master = videos[0];
+    master.ontimeupdate = () => {
+      if (master.currentTime >= end) {videos.forEach(v => v.pause()); return;}
+      if (!master.paused) videos.slice(1).forEach(v => {
+        if (Math.abs(v.currentTime - master.currentTime) > 0.15) v.currentTime = master.currentTime;
+      });
+    };
+    videos.forEach(v => {v.onended = () => videos.forEach(other => other.pause());});
+    await Promise.all(videos.map(v => v.play()));
+    status.textContent = 'Playing the same segment interval.';
+  } catch (error) {
+    videos.forEach(v => v.pause());
+    status.textContent = error.message + ' Click Play synced again if playback was blocked.';
+  }
+}
+</script>''')
+    return ''.join(parts) + '</html>'
+
+
+def inventory(base_root: Path, source_root: Path, validation_source: Path,
+              revision: str = '') -> dict:
+    entries, errors = [], []
+    cache = {}
+    for path in sorted(base_root.glob('*/*/meta/*/*.parquet')):
+        relative = path.relative_to(base_root)
+        source_relative = source_relative_path(relative)
+        root = validation_source if relative.parts[1] == 'validation' else source_root
+        source_path = root / source_relative
+        entry = {'remote_path': str(relative), 'base_sha256': _sha256(path),
+                 'source_path': str(source_path), 'candidate_relative': str(source_relative)}
+        try:
+            raw = pq.read_table(path)
+            if source_path not in cache:
+                cache[source_path] = pq.read_table(source_path)
+            source = cache[source_path]
+            rows = align_rows(raw, source)
+            entry.update(rows=len(rows), start_row=int(rows[0]), end_row=int(rows[-1]),
+                         coordinate_mode=_coordinate_mode(raw, source.take(pa.array(rows))),
+                         source_sha256=_sha256(source_path))
+            entries.append(entry)
+        except (ValueError, FileNotFoundError, KeyError) as exc:
+            errors.append(dict(entry, error=str(exc)))
+    return {'schema_version': 1, 'revision': revision, 'base_root': str(base_root),
+            'counts': dict(Counter('/'.join(Path(e['remote_path']).parts[:2]) for e in entries)),
+            'episodes': entries, 'errors': errors}
+
+
+def export_inventory(manifest: dict, candidate_root: Path, validation_candidate: Path,
+                     output_root: Path, base_root: Path | None = None,
+                     video_inventory: dict | None = None) -> dict:
+    if manifest['errors']:
+        raise ValueError('inventory has unresolved mapping errors')
+    base_root = Path(base_root or manifest['base_root'])
+    if output_root.resolve() == base_root.resolve():
+        raise ValueError('output must differ from frozen base')
+    records, cache, review_records = [], {}, []
+    video_inventory = video_inventory or {}
+    video_files = set(video_inventory.get('files', []))
+    for entry in manifest['episodes']:
+        relative = Path(entry['remote_path'])
+        base_path = base_root / relative
+        # A refreshed base is allowed only after the caller freezes a new
+        # inventory. This prevents silently overwriting concurrently changed force.
+        if _sha256(base_path) != entry['base_sha256']:
+            raise ValueError(f'base digest changed: {relative}; regenerate inventory')
+        source_path = Path(entry['source_path'])
+        if _sha256(source_path) != entry['source_sha256']:
+            raise ValueError(f'source digest changed: {source_path}')
+        root = validation_candidate if relative.parts[1] == 'validation' else candidate_root
+        candidate_path = root / entry['candidate_relative']
+        if candidate_path not in cache:
+            task, _, date, filename = Path(entry['candidate_relative']).parts
+            event_path = root / task / 'repair_events' / date / (Path(filename).stem + '.json')
+            cache[candidate_path] = (pq.read_table(source_path), pq.read_table(candidate_path),
+                                    json.loads(event_path.read_text())['events'])
+        source, candidate, events = cache[candidate_path]
+        raw = pq.read_table(base_path)
+        rows = np.arange(entry['start_row'], entry['end_row'] + 1)
+        out, sidecars = build_segment(raw, source, candidate, rows, events)
+        dest = output_root / relative
+        _atomic_parquet(out, dest)
+        for side, rates in sidecars.items():
+            for rate, series in rates.items():
+                side_path = output_root / relative.parts[0] / relative.parts[1] / ('actions_' + rate) / relative.parts[3] / (relative.stem + '_' + side + '.npz')
+                _write_actions(side_path, series)
+        reviews = segment_reviews(str(relative), raw, out, rows, events, video_files,
+                                  video_inventory.get('revision', manifest.get('revision', 'main')))
+        review_records.extend(reviews)
+        review_path = output_root / relative.parts[0] / relative.parts[1] / 'action_repair_events' / relative.parts[3] / (relative.stem + '.json')
+        review_path.parent.mkdir(parents=True, exist_ok=True)
+        review_path.write_text(json.dumps(_json_safe({'schema_version': 1, 'remote_path': str(relative),
+                                          'source_path': str(source_path), 'events': reviews}), indent=2, allow_nan=False) + '\n')
+        records.append(dict(entry, output_sha256=_sha256(dest),
+                            valid_left=int(sum(out['action_valid_left'].to_pylist())),
+                            valid_right=int(sum(out['action_valid_right'].to_pylist()))))
+    result = dict(manifest, episodes=records, output_root=str(output_root))
+    result['review_event_occurrences'] = len(review_records)
+    result['non_high_review_occurrences'] = sum(str(r.get('confidence', '')).upper() != 'HIGH' for r in review_records)
+    (output_root / 'action_review.html').write_text(review_html(review_records))
+    samples = choose_review_samples(review_records)
+    output_cache = {}
+    for i, sample in enumerate(samples):
+        if 'chart' in sample:
+            continue
+        relative = sample['remote_path']
+        if relative not in output_cache:
+            output_cache[relative] = pq.read_table(output_root / relative)
+        table = output_cache[relative]
+        entry = next(e for e in manifest['episodes'] if e['remote_path'] == relative)
+        rows = np.arange(entry['start_row'], entry['end_row'] + 1)
+        samples[i] = segment_reviews(relative, table, table, rows, [sample], video_files,
+                                     video_inventory.get('revision', manifest.get('revision', 'main')),
+                                     include_high_charts=True)[0]
+    sample_manifest = {'requested_count': 25, 'actual_count': len(samples),
+                       'high_count': sum(str(s.get('confidence', '')).upper() == 'HIGH' for s in samples),
+                       'sample_group_counts': dict(Counter(s.get('sample_group', 'unknown') for s in samples)),
+                       'scope_counts': dict(Counter('/'.join(Path(s['remote_path']).parts[:2]) for s in samples)),
+                       'selection': 'Prefer 20 algorithmic proposals (HIGH/prior HIGH or explicitly lacking independent loss evidence) and 5 other uncertain; balance scopes, durations, kinds; deduplicate source events. All require human verification.',
+                       'limitation': 'Human verification examples, not proof that every jump is tracking loss.',
+                       'events': samples}
+    result['sample_review_count'] = len(samples)
+    (output_root / 'action_sample25.json').write_text(json.dumps(_json_safe(sample_manifest), indent=2, allow_nan=False) + '\n')
+    (output_root / 'action_sample25.html').write_text(review_html(samples, include_high=True,
+        title=f'{len(samples)} examples for human verification'))
+    (output_root / 'action_publication_manifest.json').write_text(json.dumps(result, indent=2) + '\n')
+    return result
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('command', choices=('inventory', 'export'))
+    parser.add_argument('--base', type=Path, required=True)
+    parser.add_argument('--source', type=Path, required=True)
+    parser.add_argument('--validation-source', type=Path, required=True)
+    parser.add_argument('--inventory', type=Path, required=True)
+    parser.add_argument('--revision', default='')
+    parser.add_argument('--candidate', type=Path)
+    parser.add_argument('--validation-candidate', type=Path)
+    parser.add_argument('--output', type=Path)
+    parser.add_argument('--remote-videos', type=Path, help='Frozen JSON with revision and MP4 files list')
+    args = parser.parse_args()
+    if args.command == 'inventory':
+        result = inventory(args.base, args.source, args.validation_source, args.revision)
+        args.inventory.parent.mkdir(parents=True, exist_ok=True)
+        args.inventory.write_text(json.dumps(result, indent=2) + '\n')
+    else:
+        if not all((args.candidate, args.validation_candidate, args.output)):
+            parser.error('export requires --candidate --validation-candidate --output')
+        result = export_inventory(json.loads(args.inventory.read_text()), args.candidate,
+                                  args.validation_candidate, args.output, args.base,
+                                  json.loads(args.remote_videos.read_text()) if args.remote_videos else None)
+    print(json.dumps({'episodes': len(result['episodes']), 'counts': result['counts'],
+                      'errors': result['errors']}, indent=2))
+    return bool(result['errors'])
+
+
+if __name__ == '__main__':
+    raise SystemExit(main())

action_repair_tools/twm/scripts/publish_action_repairs.py

@@ -0,0 +1,133 @@
+"""Merge concurrently rebuilt force into staged actions, then publish explicitly.
+
+The force base must be the verified export of the immediately preceding HF
+commit. A parent-commit guard prevents replacing a newer remote update.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+from twm.scripts.export_old_motherboard_actions import (
+    _atomic_parquet, _columns_equal, _sha256)
+
+
+def _derived(name: str) -> bool:
+    return (name == 'action' or name.startswith(('action_', 'pose_left_', 'pose_right_'))
+            or name in ('sensor_left_pose_repaired', 'sensor_right_pose_repaired'))
+
+
+def merge_force_table(action: pa.Table, force: pa.Table) -> pa.Table:
+    if action.num_rows != force.num_rows:
+        raise ValueError('force/action row counts differ')
+    # Check every raw field, not only row count; old action fields in the force
+    # base are intentionally ignored, since these are what this update replaces.
+    for name in force.column_names:
+        if name.startswith('force_') or _derived(name):
+            continue
+        if name not in action.column_names or not _columns_equal(action[name], force[name]):
+            raise ValueError(f'force/action source mismatch: {name}')
+    result = action
+    for name in force.column_names:
+        if not name.startswith('force_'):
+            continue
+        field = force.schema.field(name)
+        if name in result.column_names:
+            result = result.set_column(result.schema.get_field_index(name), field, force[name])
+        else:
+            result = result.append_column(field, force[name])
+    metadata = dict(action.schema.metadata or {})
+    for key, value in (force.schema.metadata or {}).items():
+        if key.startswith(b'twm.force'):
+            metadata[key] = value
+    return result.replace_schema_metadata(metadata)
+
+
+def merge_force_tree(root: Path, force_root: Path, force_revision: str) -> dict:
+    path = root / 'action_publication_manifest.json'
+    manifest = json.loads(path.read_text())
+    records = {entry['remote_path']: entry for entry in manifest['episodes']}
+    sources = sorted(force_root.glob('*/meta/*/*.parquet'))
+    if len(sources) != 36:
+        raise ValueError(f'expected36 force episodes, found {len(sources)}')
+    receipt = json.loads((force_root / 'force_commit.json').read_text())
+    if receipt.get('revision') != force_revision:
+        raise ValueError('force export receipt revision mismatch')
+    expected_hashes = receipt.get('parquet_sha256', {})
+    if len(expected_hashes) != 36:
+        raise ValueError('force receipt must bind36 uploaded parquets')
+    for source in sources:
+        if expected_hashes.get(str(source.relative_to(force_root))) != _sha256(source):
+            raise ValueError(f'force export differs from uploaded content: {source}')
+    for source in sources:
+        relative = Path('old_data') / source.relative_to(force_root)
+        if str(relative) not in records:
+            raise ValueError(f'missing staged action: {relative}')
+        dest = root / relative
+        merged = merge_force_table(pq.read_table(dest), pq.read_table(source))
+        _atomic_parquet(merged, dest)
+        records[str(relative)]['output_sha256'] = _sha256(dest)
+    manifest['force_revision'] = force_revision
+    manifest['force_merged_episodes'] = len(sources)
+    path.write_text(json.dumps(manifest, indent=2) + '\n')
+    return manifest
+
+
+def validate_publication_manifest(manifest: dict, expected_revision: str) -> None:
+    if manifest.get('force_revision') != expected_revision:
+        raise ValueError('force revision must equal the immediately preceding HF commit')
+    if manifest.get('force_merged_episodes') != 36:
+        raise ValueError('publication requires36 merged force episodes')
+
+
+def publish(root: Path, expected_revision: str, repo: str = 'yxma/React'):
+    from huggingface_hub import HfApi, CommitOperationAdd
+    api = HfApi()
+    actual = api.repo_info(repo, repo_type='dataset').sha
+    if actual != expected_revision:
+        raise ValueError(f'remote changed: expected {expected_revision}, got {actual}')
+    manifest = json.loads((root / 'action_publication_manifest.json').read_text())
+    validate_publication_manifest(manifest, expected_revision)
+    for entry in manifest['episodes']:
+        if _sha256(root / entry['remote_path']) != entry['output_sha256']:
+            raise ValueError(f'staged parquet changed: {entry["remote_path"]}')
+    files = sorted(p for p in root.rglob('*') if p.is_file())
+    top_files = {'action_publication_manifest.json', 'action_sample25.json',
+                 'action_sample25.html', 'action_review.html',
+                 'ACTION_REPAIR_20260918.md', 'sample25_verification.json'}
+    directories = {'data', 'old_data', 'action_repair_tools', 'repair_audit'}
+    suffixes = {'.parquet', '.npz', '.json', '.html', '.md', '.py', '.txt', '.toml'}
+    for file in files:
+        relative = file.relative_to(root)
+        if (file.suffix not in suffixes or
+                (len(relative.parts) == 1 and relative.name not in top_files) or
+                (len(relative.parts) > 1 and relative.parts[0] not in directories)):
+            raise ValueError(f'unexpected publication artifact: {relative}')
+    operations = [CommitOperationAdd(path_in_repo=str(p.relative_to(root)),
+                                     path_or_fileobj=str(p)) for p in files]
+    result = api.create_commit(repo, repo_type='dataset', operations=operations,
+        commit_message='Repair mocap actions across all tasks, old_data and validation; retain V8 force',
+        parent_commit=expected_revision, num_threads=8)
+    print(result, flush=True)
+    return result
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--root', type=Path, required=True)
+    parser.add_argument('--force-root', type=Path)
+    parser.add_argument('--parent', required=True)
+    parser.add_argument('--publish', action='store_true')
+    args = parser.parse_args()
+    if args.force_root:
+        merge_force_tree(args.root, args.force_root, args.parent)
+    if args.publish:
+        publish(args.root, args.parent)
+
+
+if __name__ == '__main__':
+    main()

action_repair_tools/twm/scripts/review_mocap_branches.py

@@ -0,0 +1,82 @@
+"""Write separate, never-training-valid branch hypotheses for human review."""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import pyarrow.parquet as pq
+
+from twm.react_preprocess.mocap_branch import find_branch_candidates
+from twm.scripts.build_mocap_repair_candidates import _atomic_json
+
+
+def write_branch_review(candidate_root, source_root, output_root, *, manifest_digest):
+    candidate_root, source_root, output_root = map(Path, (candidate_root, source_root, output_root))
+    for protected in (candidate_root.resolve(), source_root.resolve()):
+        if output_root.resolve().is_relative_to(protected) or protected.is_relative_to(output_root.resolve()):
+            raise ValueError('review output must be separate from source and primary candidates')
+    proposals = []
+    unique_events = set()
+    gaps = {}
+    for event_path in sorted(candidate_root.glob('*/repair_events/*/*.json')):
+        record = json.loads(event_path.read_text())
+        unresolved = [e for e in record['events'] if e['confidence'] != 'HIGH']
+        if not unresolved:
+            continue
+        task = record['task']
+        if task not in gaps:
+            path = source_root/task/'pose_gaps.json'
+            gaps[task] = json.loads(path.read_text()) if path.exists() else {}
+        source_path = source_root/record['source_path']
+        columns = [f'sensor_{side}_pose' for side in sorted({e['side'] for e in unresolved})]
+        table = pq.read_table(source_path, columns=columns, use_threads=False)
+        for side in sorted({e['side'] for e in unresolved}):
+            raw = np.asarray(table[f'sensor_{side}_pose'].to_pylist(), dtype=float)
+            known = gaps[task].get(record['date']+'/'+record['episode'], {}).get(side, [])
+            for proposal in find_branch_candidates(raw, known):
+                events = [e for e in unresolved if e['side'] == side and
+                          e['start'] <= proposal.end+1 and e['end'] >= proposal.start-1]
+                if not events:
+                    continue
+                path = Path(task)/record['date']/f"{record['episode']}_{side}_{proposal.start}_{proposal.end}.json"
+                overlap_ids = [e['event_id'] for e in events]
+                unique_events.update((str(event_path), eid) for eid in overlap_ids)
+                payload = {
+                    'schema_version': 1, 'manifest_digest': manifest_digest,
+                    'source_path': record['source_path'], 'source_sha256': record.get('source_sha256'),
+                    'task': task, 'date': record['date'], 'episode': record['episode'], 'side': side,
+                    'start': proposal.start, 'end': proposal.end,
+                    'confidence': 'MEDIUM', 'training_valid': False,
+                    'requires_independent_branch_identity_evidence': True,
+                    'overlapping_event_ids': overlap_ids, 'evidence': proposal.evidence,
+                    'branch_rotation_xyzw': proposal.branch_rotation_xyzw.tolist(),
+                    'branch_translation_m': proposal.branch_translation_m.tolist(),
+                    'proposed_pose': proposal.pose.tolist(),
+                }
+                _atomic_json(output_root/path, payload)
+                proposals.append({'path': str(path), 'task': task, 'side': side,
+                                  'start': proposal.start, 'end': proposal.end,
+                                  'matching_return_pairs': proposal.evidence['matching_return_pairs']})
+    report = {'schema_version': 1, 'manifest_digest': manifest_digest,
+              'proposal_count': len(proposals), 'overlapping_unresolved_events': len(unique_events),
+              'training_valid': False, 'requires_independent_branch_identity_evidence': True,
+              'proposals': proposals}
+    _atomic_json(output_root/'index.json', report)
+    return report
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--candidate-root', type=Path, required=True)
+    parser.add_argument('--output-root', type=Path, required=True)
+    args = parser.parse_args()
+    manifest = json.loads((args.candidate_root/'input_manifest.json').read_text())
+    report = write_branch_review(args.candidate_root, manifest['source_root'], args.output_root,
+                                manifest_digest=manifest['digest'])
+    print(json.dumps({k:v for k,v in report.items() if k != 'proposals'}), flush=True)
+
+
+if __name__ == '__main__':
+    main()

data/motherboard/action_repair_events/2026-09-11/episode_000.json

@@ -0,0 +1,6 @@
+{
+  "schema_version": 1,
+  "remote_path": "data/motherboard/meta/2026-09-11/episode_000.parquet",
+  "source_path": "/media/yxma/Disk1/twm/release/motherboard/meta/2026-09-11/episode_000.parquet",
+  "events": []
+}

data/motherboard/action_repair_events/2026-09-11/episode_001_seg00.json

@@ -0,0 +1,6 @@
+{
+  "schema_version": 1,
+  "remote_path": "data/motherboard/meta/2026-09-11/episode_001_seg00.parquet",
+  "source_path": "/media/yxma/Disk1/twm/release/motherboard/meta/2026-09-11/episode_001.parquet",
+  "events": []
+}

data/motherboard/action_repair_events/2026-09-11/episode_001_seg01.json

@@ -0,0 +1,6 @@
+{
+  "schema_version": 1,
+  "remote_path": "data/motherboard/meta/2026-09-11/episode_001_seg01.parquet",
+  "source_path": "/media/yxma/Disk1/twm/release/motherboard/meta/2026-09-11/episode_001.parquet",
+  "events": []
+}

data/motherboard/action_repair_events/2026-09-11/episode_002_seg00.json

@@ -0,0 +1,6 @@
+{
+  "schema_version": 1,
+  "remote_path": "data/motherboard/meta/2026-09-11/episode_002_seg00.parquet",
+  "source_path": "/media/yxma/Disk1/twm/release/motherboard/meta/2026-09-11/episode_002.parquet",
+  "events": []
+}

data/motherboard/action_repair_events/2026-09-11/episode_003_seg00.json

@@ -0,0 +1,879 @@
+{
+  "schema_version": 1,
+  "remote_path": "data/motherboard/meta/2026-09-11/episode_003_seg00.parquet",
+  "source_path": "/media/yxma/Disk1/twm/release/motherboard/meta/2026-09-11/episode_003.parquet",
+  "events": [
+    {
+      "confidence": "MEDIUM",
+      "end": 6920,
+      "event_id": "right:6919-6920:branch_discontinuity",
+      "evidence": {
+        "anchor_rotation_deg": 47.800997433372316,
+        "anchor_translation_mm": 63.09745055264558,
+        "branch_unambiguous": true,
+        "duration_frames": 2,
+        "endpoint_return_consistent": false,
+        "fit_converged": true,
+        "fit_iterations": 0,
+        "left_anchor": 6918,
+        "left_context_frames": 15,
+        "observed_inlier_frames": 2,
+        "physical": true,
+        "prediction_rotation_max_deg": 15.933665811124106,
+        "prediction_translation_max_mm": 21.032483517548528,
+        "reconstructed_max_step_rotation_deg": 18.02254004629295,
+        "reconstructed_max_step_translation_mm": 23.619626470526953,
+        "replaced_frames": 0,
+        "right_anchor": 6921,
+        "right_context_frames": 15,
+        "rotation_inlier_threshold_deg": 10.882346383786663,
+        "rotation_residual_p95_deg": 3.8708448926461196,
+        "translation_inlier_threshold_mm": 12.887418899887527,
+        "translation_residual_p95_mm": 8.0939798311343
+      },
+      "kind": "branch_discontinuity",
+      "left_context_end": 6918,
+      "method": "endpoint_se3",
+      "replaced_frames": [],
+      "right_context_start": 6921,
+      "seed_frames": [
+        6919,
+        6920
+      ],
+      "side": "right",
+      "start": 6919,
+      "qualified_event_id": "data/motherboard/meta/2026-09-11/episode_003_seg00.parquet::right:6919-6920:branch_discontinuity",
+      "remote_path": "data/motherboard/meta/2026-09-11/episode_003_seg00.parquet",
+      "source_start_row": 6919,
+      "source_end_row": 6920,
+      "local_start_row": 6701,
+      "local_end_row": 6702,
+      "local_start_seconds": 223.36666666666667,
+      "local_end_seconds": 223.4,
+      "video_links": {
+        "view_middle": "https://huggingface.co/datasets/yxma/React/resolve/396b72f02535b653bb07fac4cd1383c46872b5b3/data/motherboard/videos/2026-09-11/episode_003_seg00/view_middle.mp4#t=222.367,224.400",
+        "wrist_right": "https://huggingface.co/datasets/yxma/React/resolve/396b72f02535b653bb07fac4cd1383c46872b5b3/data/motherboard/videos/2026-09-11/episode_003_seg00/wrist_right.mp4#t=222.367,224.400",
+        "view_left": "https://huggingface.co/datasets/yxma/React/resolve/396b72f02535b653bb07fac4cd1383c46872b5b3/data/motherboard/videos/2026-09-11/episode_003_seg00/view_left.mp4#t=222.367,224.400",
+        "view_right": "https://huggingface.co/datasets/yxma/React/resolve/396b72f02535b653bb07fac4cd1383c46872b5b3/data/motherboard/videos/2026-09-11/episode_003_seg00/view_right.mp4#t=222.367,224.400"
+      },
+      "chart": {
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data/motherboard/action_repair_events/2026-09-11/episode_004_seg01.json

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data/motherboard/action_repair_events/2026-09-11/episode_006_seg01.json

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data/motherboard/action_repair_events/2026-09-11/episode_007_seg02.json

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data/motherboard/action_repair_events/2026-09-11/episode_007_seg03.json

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data/motherboard/action_repair_events/2026-09-11/episode_008_seg00.json

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