# nous-fleet-safety

Galatea fleet & interpretability-driven safety integration (Phase 177.10), in
PyTorch, CPU-runnable. Composes the continual-learning and interpretability libs
into fleet-level safety gates.

## Components

- **Fleet continual update** (`fleet_update.py`, 177.10.1.1) — a monotonic-
  improvement harness that runs a federated round (Phase 85.20.1.3 seam) through
  privacy replay + EWC, then accepts the rollout only if backward transfer
  `BWT ≥ 0` on certified skills; otherwise it rolls back to the last certified
  checkpoint. Certified baselines advance on accept.
- **Continual-evaluation watchdog** (`watchdog.py`, 177.10.1.3) — re-runs the
  regression suite after each update and freezes it if any safety-critical skill
  regresses beyond tolerance (BWT < −1 %; a looser bound for non-safety skills),
  naming the violations.
- **Interpretability-integrated eval** (`interp_eval.py`, 177.10.2.2) — adds
  circuit-stability (edge-set Jaccard) and SAE feature-drift (unmatched-cosine)
  metrics that gate promotion, so internal-structure regressions block a release
  even when behavioural scores pass.
- **Red-team interp tooling** (`red_team.py`, 177.10.2.3) — root-causes an
  elicited failure to its top contributing SAE features
  (`activation · decoder·failure_dir`) and the circuit edges they participate
  in, in seconds.

## Tests

`python3 -m pytest tests/ -q` — 15 tests: accept/rollback + advancing baseline,
safety-critical freeze vs benign pass + per-skill BWT, Jaccard stability +
feature-drift (zero on identical/permuted, bounded on rotated) + structure-gated
promotion, and failure root-cause ranking + edge mapping.

## Not locally actionable

177.10.2.1 (per-checkpoint SAE safety report) needs real trained SAEs on real
checkpoints; 177.10.2.4 (wiring SAE features into Kuan Yin precognition, Phase
85.5.2.4) needs that phase — both left unchecked. The fleet dashboard
(177.10.1.2) is delivered separately in `@nous/interp-dashboard`.