# nous-probes

Probing & representation analysis for interpretability (Phase 177.4), in PyTorch,
CPU-runnable. Operates on activation tensors from `@nous/interp-core`.

## Components

- **Linear probing** (`linear.py`, 177.4.1.1) — multinomial L2-regularised
  logistic regression trained to convergence with L-BFGS, K-fold cross-validation,
  percentile bootstrap CIs on accuracy, and per-layer / per-position sweeps to
  locate where a property becomes linearly decodable.
- **Representation similarity** (`similarity.py`, 177.4.1.2) — RSA (RDMs +
  Spearman correlation of their upper triangles) and CKA (linear + RBF-kernel,
  HSIC-normalised) for comparing checkpoints, model families, and model↔brain
  (NSD / Algonauts) representations.
- **TCAV** (`tcav.py`, 177.4.1.3) — concept activation vectors (unit normal of a
  concept-vs-random linear classifier), directional-derivative sensitivity, the
  TCAV score, statistical TCAV against random directions, and the causal concept
  effect (inject `α·CAV`, measure the real output change).
- **Emergent-capability probes** (`emergent.py`, 177.4.1.4) — track a probe's
  cross-validated accuracy across training checkpoints, flag the emergence
  checkpoint (threshold crossing) and the largest accuracy jump, for scaling-law
  capability-onset analysis.

## Tests

`python3 -m pytest tests/ -q` — 16 tests: probe recovers separable classes /
stays at chance on noise under strong L2, bootstrap CI brackets accuracy,
layer/position sweeps prefer the informative layer/position; RSA identity +
ordering, CKA orthogonal/scale invariance and low value for independent reps;
CAV recovers the concept axis, TCAV score high/low/chance by target alignment,
causal effect matches the analytic linear head; and emergence detection at the
decodability transition.