Add mode2_v1 schema — 73 pre-sliced clean segments from 27 source episodes (104.7 min, bad intervals excluded by construction). New ReactSegmentDataset + demo. No bad_frames.json filtering needed at the dataloader; data is constructively clean. mode1_v1 stays for backward compatibility. See segments.json + new README section.

ReactWindowDataset: prominent contact-rich window count + stats attribute (ds.stats) + per-filter rejection breakdown in the summary print. Demo: write H.264 MP4 per picked window (was just static PNG); --no_mp4 to skip; --mp4_fps to tune.

ReactWindowDataset: default min_motion_fraction 0.5 -> 0.25 (a window passes if >=25 % of frames per active sensor are above the speed threshold).

ReactWindowDataset: default min_motion_mps 0.03 -> 0.01 (30 mm/s -> 10 mm/s). Recipes + README + demo + CLI updated.

Audit batch 1: post-trim numbers in docs/figures.md (#3); tasks.json 2026-05-11 note + trim_offsets lookup (#5, #8); bad_frames.json trim_offsets header (#8); dataloader default which_sensors='both' (#10); new freeze_intervals.json with per-event diagnostic signals (#12)

examples/: upgraded ReactWindowDataset (adds motion filter to catch operator-paused windows the bad_frames check cant); canonical self-contained demo_react_window.py (no twm dependency); new examples/README.md with four-filter explanation and recipes per use case

Bake ot_loss intervals into bad_frames.json (47 events, rotation-aware detector); dataloader skips them; docs/quality + caveats updated; data_quality_breakdown #3 renamed sensor_at_rest → ot_loss

Add example dataloader: ReactWindowDataset for short contact-rich window sampling + demo (static grid + GIF) with sensor-frame overlay