{
 "application": {
  "by_head_key": {
   "boolean/state4": [
    "boolean",
    "multilabel"
   ],
   "ordered/choiceS": "ordered",
   "single/choiceR": "single"
  },
  "choice": {
   "branches": "|R single choice (single/choiceR), |S ordered choice (ordered/choiceS)",
   "confidence": "the listed top-1 probability",
   "expression": "probabilities = softmax(x[:n] / T)",
   "note": "slice to the n listed options first, then divide by T. Reserved slots are never scored.",
   "rule": "SLICE THEN TEMPER"
  },
  "granularity": "per answer type. The merged yes/no head serves two types, so by_head_key maps it to both and the served type selects the scalar.",
  "idempotence": "apply exactly once; the returned probability and the listed top-1 derive from the same tempered read.",
  "note": "Nouls and choices are tempered DIFFERENTLY. Implement exactly as written.",
  "noul": {
   "branches": "yes/no and every multi-label candidate (head_key boolean/state4, the merged head)",
   "confidence": "max(P(yes), 1 - P(yes))",
   "expression": "z = x[0] - logsumexp(x[1:]);  P(yes) = sigmoid(z / T)",
   "note": "x is the full four-letter logit vector. Do NOT compute softmax(x / T)[0].",
   "rule": "COLLAPSE THEN TEMPER"
  }
 },
 "fit": {
  "calibration_file_sha256": "ea069d224501af950caaae6914d6fdf14ce6d4d2bda566c41549b1e8c30a6222",
  "decision": "served at T = 1.0 for every type: the fitted scalars did not improve held-out calibration (test ECE worse in 8 of 10 type x modality cells; n-weighted 0.0212 fitted vs 0.0199 unscaled)",
  "modality": "image rows use the same per-type scalar (no modality key in serving)",
  "scored_heads_note": "scores were produced with a heads file whose two extra (entity/multilabel) slots were never read; its other 16 arrays are byte-identical to the shipped heads file, so the served logits are the fitted logits",
  "scored_heads_sha256": "96ea51416bbeb32d991b7b38d7f0c22ff3e82539c8910284c4fb1961f2869ace",
  "source": "real development documents (held out from test), this model's BF16 scores, one NLL-minimising scalar per answer type"
 },
 "fitted_on_model": {
  "adapter_sha256": "d122466d430a058bb6457d919f811160e97fbd20149f4f24ca455c5d83e360a0",
  "trained_heads_sha256": "f766d752d7768a419a9657155cf27f042834d9de29392cf7470d8725130e67ab"
 },
 "frozen": true,
 "models": {
  "boolean": {
   "applied": false,
   "fit_units": 192,
   "fitted_temperature": 0.8175095705097734,
   "head_key": "boolean/state4",
   "kind": "noul",
   "task": "boolean",
   "temperature": 1.0,
   "unit": "question"
  },
  "multilabel": {
   "applied": false,
   "fit_units": 1862,
   "fitted_temperature": 0.842297230286191,
   "head_key": "boolean/state4",
   "kind": "noul",
   "task": "multilabel",
   "temperature": 1.0,
   "unit": "candidate noul"
  },
  "ordered": {
   "applied": false,
   "fit_units": 134,
   "fitted_temperature": 1.2561869742268443,
   "head_key": "ordered/choiceS",
   "kind": "choice",
   "task": "ordered",
   "temperature": 1.0,
   "unit": "question"
  },
  "single": {
   "applied": false,
   "fit_units": 134,
   "fitted_temperature": 1.107722547236206,
   "head_key": "single/choiceR",
   "kind": "choice",
   "task": "single",
   "temperature": 1.0,
   "unit": "question"
  }
 },
 "schema": "solomon-readout-temperature-v3",
 "sha256": "945bad449b7f5ffc88e597277d632fbab81c3c8729e22c8babd3f4a45fe1378b"
}