New ruvector module: anonymized ProfileStore (one-way SHA-256 hashed tags,
safe-session scores only), deterministic exact kNN, cohort warm-start (a new
person's optimizer seeded from the k nearest responders as down-weighted GP
pseudo-observations), physiological drift detection (Welford centroid with
stimulus-input fields masked out of the distance), and deterministic k-means
response clustering.
Honesty guarantees, asserted in tests: cohort priors carry >=25x the
real-observation noise, are excluded from the EI incumbent, the audit log,
and the clinician report — borrowed expectations never masquerade as this
person's measured response. The GP gains per-observation noise; the real
path is arithmetically unchanged (pinned witness 13cb164c... preserved).
Governor wiring: seed_from_cohort, export_anonymized_profile, per-session
drift_status. Integration tests: cohort warm-start beats the cold 40 Hz
prior for a detuned subject; collapsed physiology flags Drifted.
Crate: 75 tests + 1 doctest. Workspace gate: 2,876 passed, 0 failed.
Benches: kNN/500 profiles ~15us, warm-start ~16us; no regression on
existing paths (recommend ~15us, calibration sweep ~111us).
https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
BayesianOptimizer::recommend evaluated Expected Improvement at every 0.1 Hz
grid candidate, and each predict() rebuilt the kernel matrix and re-ran
Cholesky — ~82 factorizations per call — though K and alpha=K^-1 y depend
only on the observations, not the query point. Fit the GP once (GpFit:
cached L + alpha, lower-triangle-only K build) and reuse it across the grid.
Bit-identical arithmetic: the pinned deterministic witness and all 64 tests
are unchanged; pure work elimination. Measured (criterion, paired):
gamma_bayesian_recommend 105us -> 19us (-81%); gamma_calibration_sweep
466us -> 135us (-71%).
https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
ruv