Promotes the proven attention set-encoder smoke test (PR #4,
tape_train_smoke.rs) into a reusable rtx-transformers layer for Supervised
Memory Training. The teacher maps a window of past tokens to a fixed-size
memory M via embed → self-attention → residual → mean-pool → memory
projection, with a decoder head supervised by predict-the-future MSE so that
M becomes a sufficient statistic of the past.
- Trains end-to-end on Autodiff<CpuBackend> (the gradient-correct real backend
from PR #3), with a self-contained deterministic host-side Adam.
- Time-parallel by construction (one window → one memory, no recurrence to
unroll) — this is the oracle whose trajectory the recurrent Mamba updater is
later behaviorally cloned against, so the recurrent net never needs BPTT.
- Exposes named_params/set_named_params so the caller (omni-think's
PredictiveStateTeacher facade) owns safetensors persistence + BLAKE3 sealing.
- Adds rtx-backend + rtx-backend-cpu deps (the tape needs a concrete backend).
Tests: teacher trains (loss >5x drop), encode is deterministic + fixed-size,
params round-trip. fmt + clippy(-D warnings) clean on the new module.
Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
