Replace the `Tensor::randn` stubs in occworld-candle's VQVAE encoder
(`encode_occupancy`) and decoder (`decode_to_logits`) with a real,
deterministic, input-dependent convolutional forward pass. Previously
`predict()` emitted trajectory waypoints + confidence that were a function
of RANDOM NOISE, independent of the input and silently presented as model
output — the exact "AI slop" the project must eliminate.
occworld-candle:
- New `cnn.rs`: `Encoder2D` (3× Conv2d + GELU, interpolate2d to pin the
token grid) and `Decoder2D` (upsample_nearest2d + Conv2d + 1×1 head).
Both are deterministic functions of the input — same input → identical
output; different input → different output. No randn in any forward path.
- Deterministic weight init (`det_fill`, seeded xorshift64*) across all
`dummy()` constructors (encoder/decoder, VQ codebook, quant-convs,
transformer), so untrained engines are bit-for-bit reproducible.
- `InferenceOutput.weights_trained: bool` — honest disclosure flag. `false`
for `dummy()` (real but untrained net), `true` only after `load()` reads a
real checkpoint. Priors are always from the real forward pass, never faked.
- VQ codebook + quant/post-quant convs kept and wired encoder→VQ→decoder.
- Centerpiece tests in `tests/predict_honesty.rs` (input-dependence,
run-to-run + cross-engine determinism, untrained flag). All three FAIL on
the old randn stub (verified by temporarily reinstating randn).
pointcloud:
- Optimize `to_gaussian_splats` hot path: 9 separate `.iter().sum()` passes
per voxel → 2 fused accumulation passes. Bit-identical output.
- `benches/splats_bench.rs` (criterion) measures old 9-pass vs new 2-pass
with a parity guard. ~1.3× faster on representative cloud sizes.
- Confirmed: no `randn`/placeholder in any claimed production path. The
remaining synthetic generators (`send_test_frames`, `demo_depth_cloud`)
and honestly-flagged heuristics (`heuristic_pose_from_amplitude`,
luminance pseudo-depth fallback) are explicitly disclosed, not faked output.
DATA-GATED: a trained checkpoint. An untrained-but-real net is the honest
deliverable; accuracy is flagged via `weights_trained`, never claimed.
Tests: occworld 16 unit + 3 integration + 2 doc, pointcloud 18 — all pass
(CPU `Device::Cpu`; CUDA feature is GPU-gated and untouched).
Co-Authored-By: claude-flow <ruv@ruv.net>
ruv