# Committed vectors

The vectors the reference implementation's tests decode, and the conformance corpora
under `wai/*-conformance/` and the cross-architecture goldens are built on. A vector
changes only through its generator (CONTRIBUTING, *Vectors*).

**Provenance.** Each directory's row names the generator that writes it and the
generator's input. `tests/corpus_provenance.rs` requires a row for every directory.

| directory | provenance |
|---|---|
| `generate/` | Generated: `tools/wai_generate_model.py` writes the spectral model from an integer sine table and a word list. |
| `int_audio/` | Generated: `tools/wai_int_audio_export.py` trains the codec on a signal it generates from seed 1 and codes another signal from the same seed; it reads no audio file ([`int_audio/README.md`](int_audio/README.md)). |
| `int_cond/` | Generated: `tools/wai_int_cond_export.py` (seed 20261109) trains the conditional video codec on seeded procedural clips it draws itself (panning gratings and moving shapes), quantises it after training, and codes a held-out procedural clip from seed 20261110 with the integer networks; it reads no image or video file. |
| `int_hyper/` | Generated. `tiny_*`: `tools/wai_int_hyper_tiny_vector.py` draws the tiny conditional codec's integer weights from a seeded generator; no image. `leaves_*`: `tools/wai_int_hyper_train.py` trains a set (seed 20261110) on the dead-leaves pictures `tools/wai_int_synth_real.py` generates from that seed, streams 1 to 4096, and codes stream 0 with it through `tools/wai_sota_int_synth_export.py`; it reads no image file. `leaves_set.json` records the set, its seeds and the commit it was trained at, and `python3 -I tools/wai_int_hyper_train.py check --own wai-rs/tests/vectors/int_hyper leaves` reads it. `natural_*`: `tools/wai_int_hyper_train.py` trains a set (seed 20261112) on the `train` pictures of `research/training-pictures/natural-1.json`, openly licensed pictures that are **not** generated and are not in the repository (each one's licence, credit, URL, retrieval time and SHA-256 are in that manifest; decisions D60), and codes stream 0 of the dead-leaves pictures of that seed with it, so the committed bitstream and decode are of a generated picture. `natural_set.json` records the set, and `python3 -I tools/wai_int_hyper_train.py check --open wai-rs/tests/vectors/int_hyper natural` reads it. |
| `int_mlicpp/` | Generated: `tools/wai_int_mlicpp_export.py` (seed 20260929) codes an integer pattern of ramps, an xor texture and a disc with seeded weights; `tools/wai_int_mlicpp_set_form.py` writes its set-form twin. |
| `int_mlicpp_natural/` | Trained: `tools/wai_int_mlicpp_train.py` (seed 20261112) trains a set on the `train` pictures of `research/training-pictures/natural-1.json` (openly licensed, not generated, not in the repository; decisions D60), exports it through `tools/wai_int_mlicpp_export.py`'s integer path and codes stream 0 of the dead-leaves pictures of that seed with it, so the committed payload and decode are of a generated picture; `tools/wai_int_mlicpp_set_form.py` writes its set-form twin. `natural_set.json` records the set, and `python3 -I tools/wai_int_mlicpp_train.py check --open wai-rs/tests/vectors/int_mlicpp_natural natural` reads it. |
| `int_splat/` | Generated: `tools/wai_int_splat_export.py` trains on a seeded procedural cloud. |
| `int_video/` | `int_video.*` is generated by `tools/wai_int_video_encode.py` (a disc over a gradient), and `video_nk.*` by `tools/wai_int_video_keyframe_encode.py` (the `int_hyper` tiny keyframe, panned). `video_real.*` is **not** generated: `tools/wai_int_video_real_encode.py` pans over a third-party picture that was not made for this repository and has no recorded licence for redistribution. It is to be replaced by generated content. |
| `int_world/` | Generated: `tools/wai_int_world_export.py` trains on a seeded procedural world. |
| `int_world2/` | Generated: `tools/wai_int_world2_export.py` trains on seeded procedural backgrounds. |
| `jpegai/` | `test256.bin` is the output of the ISO/IEC 6048-1 reference encoder (BSD-3) for a 256×256 generated test image of gradients and blocks ([`jpegai/README.md`](jpegai/README.md)). |
| `neural_audio/` | Generated: `tools/wai_neural_audio_vectors.py` writes, from fixed dyadic parameters and SplitMix64 codes (seeds 1 to 4), a small decoder with each model's geometry, an envelope of seeded codes and the decode it computes itself ([`neural_audio/README.md`](neural_audio/README.md)). No audio, no model weights. |
| `staged_bounds/` | Constructed here for the bound tests: grey frames from a residual stream of zeros, and envelopes whose capability is refinement-only or companion-only ([`staged_bounds/README.md`](staged_bounds/README.md)). No image. |