# External data layout

No records, identifiers, labels, audio, images, or videos are distributed here.
Paths below are input/output contracts for running the source with separately
provided assets. Paths stored inside JSONL records must resolve on the machine
running the code. JSONL means one JSON object per line.

## Raw source trajectories

`datasets/source_trajectories/{static,sound}/{train,validation}.jsonl`:

Each row contains `id`, `episode_id`, `step`, `task`, `split`, `scene`, `clip_id`,
`instruction`, `pose` (integer yaw and pitch), `panorama`, and `answer`.
Sound rows also contain `audio_path`. Static rows use `source: "custom"` and
`task: "HOS"`. Sound task names are `sound_class`, `duration`, and `order`.
The source answer is an integer `rotate` or current-pose `submit` action enclosed
in one `<answer>...</answer>` block. Step values must be consecutive.

## Benchmark identity and annotations

`datasets/omini_360_benchmark_v0_1/`:

- `public/{train,validation,test}.jsonl`: instruction records; test records
  include `id`, `task_type`, `instruction`, `initial_pose`.
- `private/episode_map.jsonl`: maps `id` to `scene`, `clip_id`, and `task_id`.
- `private/static_labels/hos_{train,validation}.jsonl`: `task_id`, `split`,
  `yaw`, `pitch`, and `bbox` for the HOS geometric expert.

`datasets/hstar_full_test_v1/<task_id>/` contains exactly one panorama PNG/JPEG
and an `annotation.json` list. The first annotation supplies `yaw` and `pitch`
intervals used by the static evaluator.

`datasets/omini_sound_search_v1/`:

- `public/{train,validation,test}.jsonl`: `id`, `task`, `instruction`,
  `initial_pose`, with source identity fields as needed by preparation.
- `private/labels.jsonl`: `id`, `scene`, `clip_id`, `split`, `panorama`,
  `audio_path`, `target_index`, and `candidates`.
- Each candidate contains `center: [yaw,pitch]`, pixel `bbox: [x1,y1,x2,y2]`,
  `noun`, and a `dry_audio` path for constructing auxiliary mixtures.

The sound scorer uses 512x2048 panorama annotation coordinates. Audio must
supply at least ten seconds of two-channel finite samples. Exact test counts
are HOS=4536, sound_class=716, duration=1076, order=1076. Ground-truth labels
are for geometric supervision and scoring; the policy message builder never
accepts labels.

## Prepared policy data

`workspace/fourtask_v2/data/` contains `train.jsonl`, `validation.jsonl`,
`train_episodes.jsonl`, `validation_episodes.jsonl`, and `audit.json`.
These are generated by `prepare_data.py`; they are not included in the archive.

Policy rows contain `id`, `episode_id`, `task`, `split`, `scene`, `clip_id`,
`instruction`, `pose`, `observations`, `history_actions`, `audio_path`,
`audio_end_s`, `reference`, `dynamic`, `answers`, `supervision_kind`, and
`recovery_prefix_steps`. An observation is a panorama path and a camera pose.
Rows use one to three observations and at most two historical actions.

Episode rows contain `id`, `task`, `split`, `scene`, `clip_id`, `instruction`,
`initial_pose`, `panorama`, `audio_path`, and `label`. The integrity manifest
records SHA256 for policy and episode files and requires the scene/clip
split-disjointness flag. Do not fabricate audit manifests to bypass these
checks; use the builders with matching source records.

## Auxiliary data

`workspace/fourtask_v3_audio_realign/data/` contains `aux_train.jsonl`,
`aux_validation.jsonl`, generated waveforms, and their audit manifests.
Each row contains source identity, paired candidates, a temporal query,
`target_index`, onset/duration metadata, and the emitted waveform SHA256.
The builder requires at least 100 accepted training clips and 16 validation
clips. It verifies emitted energy windows and rejects unsuitable recordings.