docs: name the statistical identity — Bayesian detective, frequentist court

The two halves of the system run on opposite statistical philosophies, on
purpose, and until now the docs never said so. The optimizer is a Bayesian
detective: playbook prior, ledger evidence, policy ranking, explain
feedback — belief-updating that decides what to try next. The trust layer
is a frequentist court: equivalence gates, per-machine calibrated error
rates (A/A false-positive bound, injected-regression power bound), and a
CI-lower-bound accept rule with no prior anywhere on the accept path —
because its audience is a hostile reviewer who shares none of your priors.

The load-bearing edge is the return path: evidence flows from court to
detective (ledger rows + explain); authority never flows the other way.
Invariant, now stated in four places: priors steer where we look; they
never touch what we conclude. A wrong prior costs compute — each wasted
attempt honestly ledgered. A prior in the verdict would cost a false
claim, the one unrecoverable failure for a project whose product is trust.

- thesis.md: full section with the two-box diagram incl. the return edge,
  in docket order (equivalence before measurement); notes that estimated
  payoff is a belief and dollar figures are minted only by the mechanical
  report from accepted rows, and that even the learning layer bets on
  Wilson LOWER bounds — the detective is a pessimist too.
- README: the one-breath version, linking the full argument.
- SPEC §1: dated note mapping each half to its sections.
- CLAUDE.md: the mental model, one paragraph under Mission.

Framing credit: emerged from a design conversation on whether peltier is
"Bayesian engineering" — it is exactly half of one.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

CLAUDE.md

@@ -6,6 +6,8 @@
 
 An agent that profiles real binaries and services, isolates hot paths, proposes optimizations, and accepts a change **only** when (a) behavioral-equivalence gates pass and (b) the benchmark improvement is statistically significant on trusted infrastructure. Output: verified performance deltas translated into ROI reports nobody can dispute.
 
+Mental model: **the optimizer is a Bayesian detective; the trust layer is a frequentist court.** The detective (agent + playbook prior + policy ranking + ledger evidence + explain feedback) chooses what to try; the court (gates + calibrated bench + CI-lower-bound rule) decides what is true, with measured error rates and no priors on the accept path. Evidence flows back; authority never does. Priors steer where we look — they never touch what we conclude.
+
 Full engineering spec: `SPEC.md`. Read it before starting any phase.
 
 ## Non-negotiables (re-read every session)