Weksa/docs/calibration.md

Calibrating the accept rule

weksa loop calibrate measures how the live accept rule behaves on a run's own Validation corpus: how often it accepts, rejects as no gain, or regresses a candidate that should be none of those. It does not reimplement the rule. It reads the answered draws through RunStores::read_draws (the scoring weksa loop relower uses), decides every trial with decide_panel over paired_floor (crates/weksa/src/run.rs), and prints counts. The numbers in docs/targets/interlingua-lowering-map.md ("Recalibration on the grown corpus") come from it. It is a diagnostic: it records nothing, writes no prompts, and never touches Test or Train.

What it counts

The draws are lowerings of one round's Validation packets: the round's own (recorded) lowering R, plus draws you label. P is R and the --prompted draws; the floor size is FLOOR_ARMS_MIN (3), the live rule's.

• Prompted trials: every ordered pair (base, candidate) in P, against every 3-subset of the rest of P as the floor. A repeat of the same lowering should read NoGain; Accepted is a false accept, Regressed a false reject. (R comes from the round, not from relower, so it may not be exchangeable with a repeat; read the pairs that involve it with that in mind.)
• Degraded trials, per --degraded arm: every base in P against every 3-subset of the rest of P, the degraded draw as the candidate. These should read Regressed.
• Candidate trials, pooled over the --candidate arms: the same, tallied Accepted, NoGain, Regressed. This is the rule's power against that arm.
• Noise: the mean panel_noise over every (base, floor) choice.

With six prompted draws including R that is 120 prompted trials, 60 per degraded arm and 240 candidate trials. Trials share draws and are not independent. Each count is printed for all Validation items and for each --class NAME=PREFIX[,PREFIX...] (items whose key starts with any prefix), from the same per-item scores. A class with fewer than two read items in a language has no floor and reads Regressed in every trial; that is printed.

Steps

1. weksa loop open ROOT ... and weksa loop round ROOT record round N and leave its answers in ROOT/round-N/.
2. For each draw label: weksa loop relower ROOT --round N --arm LABEL --lowerer NAME writes the byte-identical lowering prompts to ROOT/relower-N-LABEL/<digest>.prompt and exits 3. Answer each prompt into ROOT/relower-N-LABEL/<digest>.answer.
3. weksa loop calibrate ROOT --round N --prompted p1 --prompted p2 ... [--degraded LABEL]... [--candidate LABEL]... [--class NAME=PREFIX].... At least four --prompted labels are needed (a pair and three floors with R make five draws). It exits 1 and lists unanswered LABEL COUNT for any draw with prompts still unanswered. Labels match [a-z0-9-]{1,32} and differ across the three kinds. It needs the Ollama embedder of the run; a full 60-item, 13-draw calibration is on the order of 1500 embedded texts, so launch it detached with a log.

The scratch packet store is ROOT/calibrate-N/base.cc; delete it at will.

Answer file format

<digest>.answer is one JSON object and nothing else, no code fence: {"text": "..."}, a JSON-escaped string. The answerer reads only the prompt (its first line says what to write) and writes the answer. Every prompt of the draw must be answered.

Briefs per arm

Independence rule: a fresh agent per batch of answers; no batch sees another draw's answers. Only mild reads round-N/*.answer.

• Plain prompted: answer each prompt as its own first line asks; read nothing else in the run root.
• Mild: for each prompt, read the recorded lowering in ROOT/round-N/<digest>.answer and write the same-format answer whose text is that utterance with exactly one meaning detail blurred: change or drop one of tense, polarity, modality or hedge, a referent, a quantity or a predicate's concept (replace it with a near but different word). Keep language, register, length and fluency; the result must read as a natural utterance and must not mention the change. A second mild draw blurs a different detail than the first and is written without reading the first's answers. The wording of the distil-base original is lost, and the dispatch used for growth-base is not on disk, so this is the wording from now on.
• Train-guide: before answering, read the Train sentences of the corpus in the batch's language as a register guide and let it steer word choice; the guide gives phrasing only and each answer must still say exactly what its own packet says. The guide section is the live Train items of that language, built from weksa corpus dump filtered to Train, and is given to the answerer in the brief, never pasted into this document, and never includes Validation or Test text. Read nothing else in the run root.
• Control: a script writes {"text":"Yes."} as the answer to every prompt.