### Inter-frame video, categorical/VQ images, and real-model validation

- **`wai.neural.dcvc_rt` / `wai.neural.dcvc_fm` — inter-frame video decoder
  implemented** (`neural/video_dcvc.rs`). "Requires a GPU" [name removed 2026-09-30] was a *speed* claim:
  inter-frame decode is a frame loop that propagates a reference feature, and
  ONNX Runtime runs it on CPU. The per-frame entropy decode reuses the
  CompressAI-validated hyperprior path; the temporal net is sink-supplied.
  Validated end-to-end (3 inter-frames, feature propagation confirmed).
- **`wai.neural.glc` — categorical / VQ decoder implemented** (`neural/glc.rs`).
  Not the Gaussian-conditional family: codebook indices decoded via a
  per-position softmax → `pmf_to_quantized_cdf` (bit-exact with CompressAI's
  C++, unit-tested) → a bypass-free categorical rANS over a checkerboard → VQ
  synthesis. Surfaced that categorical entropy is the most determinism-sensitive
  path — a per-position CDF flips on ~1e-5 logit drift where Gaussian scale
  buckets don't.
- **Real-model validation** beyond synthetic stand-ins. SNAC's genuine
  `snac_24khz` now exports (`tools/wai_snac_export.py`: zero-noise so the decode
  is deterministic at all + a reshape-free snake) and `wai-rs` decodes it to
  ~9e-7 of the torch reference. A real PTQ-trained, activation-calibrated
  `int_synth` model decodes **bit-identically** (max|Δ|=0) to its integer-forward
  reference (`tools/wai_int_synth_train.py`; also fixed a zero-gradient
  `torch.floor` bug in the QAT trainer).

