nucleic-purpose-classifier/experiments/sol-high-v2.json

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  "recordedAt": "2026-08-03",
  "sourceCheckoutCommit": "531e9ba301a20dd124062afed47aa412244c407a",
  "status": "no-qualified-sol-high-v2-candidate",
  "summary": "The Sol-high label reset and promotion completed. The selected lite float checkpoint reached 93.7167% frozen accuracy, while the best deep validation checkpoint reached 77.6445%. Neither meets the shipping contract; frozen-set-driven tuning and further deep continuations are stopped.",
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  "reproductionCommands": {
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    "promotion": [
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      "ml/purpose-classifier/rebuild_sol_high.py",
      "promote",
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      "overwrite-all-labels-with-sol-high",
      "--exclude-real-line",
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      "--exclude-real-line",
      "1481"
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    "liteFromPretrained": [
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      "mps",
      "--model",
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      "--dataset-dir",
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      "--epochs",
      "3",
      "--learning-rate",
      "2e-5",
      "--warmup-ratio",
      "0.1",
      "--boundary-weight",
      "1",
      "--early-stopping-patience",
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      "--output-dir",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2",
      "--overwrite-output"
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    "liteFromPretrainedFrozenEvaluation": [
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      "-u",
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      "--device",
      "mps",
      "--model-dir",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2/model",
      "--calibration",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2/calibration.json",
      "--report",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2/frozen-eval.json"
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    "liteBoundaryContinuation": [
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      "--dataset-dir",
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      "--epochs",
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      "--learning-rate",
      "3e-6",
      "--warmup-ratio",
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      "--boundary-weight",
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      "--early-stopping-patience",
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      "--output-dir",
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      "--overwrite-output"
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    "liteBoundaryFrozenEvaluation": [
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      "mps",
      "--model-dir",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2-boundary-cont/model",
      "--calibration",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2-boundary-cont/calibration.json",
      "--report",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2-boundary-cont/frozen-eval.json"
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    "teacherCache": [
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      "mps",
      "--dataset-dir",
      "ml/purpose-classifier/.artifacts/dataset-v1",
      "--model",
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      "--output",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2-teacher.pt",
      "--batch-size",
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      "--progress-steps",
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      "--overwrite-output"
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    "deepBase": [
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      "ml/purpose-classifier/train_deep_mlx.py",
      "--variant",
      "base",
      "--device",
      "metal",
      "--dataset-dir",
      "ml/purpose-classifier/.artifacts/dataset-v1",
      "--epochs",
      "3",
      "--early-stopping-patience",
      "1",
      "--progress-steps",
      "10",
      "--output-dir",
      "ml/purpose-classifier/outputs/purpose-deep-sol-high-v2-base",
      "--overwrite-output"
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    "deepDistilledContinuation": [
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      "ml/purpose-classifier/train_deep_mlx.py",
      "--variant",
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      "--device",
      "metal",
      "--resume-from",
      "ml/purpose-classifier/outputs/purpose-deep-sol-high-v2-base/model",
      "--dataset-dir",
      "ml/purpose-classifier/.artifacts/dataset-v1",
      "--distillation-cache",
      "ml/purpose-classifier/outputs/purpose-lite-sol-high-v2-teacher.pt",
      "--distillation-weight",
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      "--distillation-temperature",
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      "--learning-rate",
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  },
  "decisions": [
    "The missing MiniLM classifier.weight and classifier.bias load report is expected because the pretrained encoder has no task-specific eight-way classifier head; downstream training initializes those two tensors.",
    "Do not tune another lite continuation against the Sol-high v2 frozen set.",
    "Do not QAT or export a Sol-high v2 lite candidate until a float checkpoint first clears the validation and frozen qualification policy without frozen-driven selection.",
    "Do not run another plain or distilled deep continuation, ModernBERT large, or purpose-max from these results.",
    "The previously deployed v1 W8A8 artifact is historical runtime evidence only and is not label-compatible with Sol-high v2."
  ],
  "nextSteps": [
    {
      "order": 1,
      "action": "Implement a validation-only representation diagnostic harness.",
      "details": [
        "Report the exact same 917-record primary and 240-record hard metrics used by the deep trainer.",
        "Cache ModernBERT representations with keys bound to model revision, tokenizer contract, dataset hash, and normalized prompt hash.",
        "Compare the current pretrained prediction-head CLS representation with attention-masked mean pooling.",
        "Fit only a regularized eight-way primary linear probe; exclude secondary, mixed-intent, and difficulty losses."
      ]
    },
    {
      "order": 2,
      "action": "Apply a fail-fast probe gate before another full deep run.",
      "gate": {
        "minimumMeanPoolingGainOverClsPercentagePointsOnEitherOverallOrHard": 3.0,
        "minimumOverallAccuracy": 0.85,
        "perPurposeCollapseAllowed": false
      }
    },
    {
      "order": 3,
      "action": "If the probe passes, add explicit --pooling cls|mean and --primary-only trainer options and run one from-pretrained ModernBERT-base experiment.",
      "selection": "Validation overall and hard primary accuracy only; introduce auxiliary heads only after primary representation viability is established."
    },
    {
      "order": 4,
      "action": "Require a revised deep checkpoint to approach lite on validation before any frozen evaluation.",
      "gate": {
        "maximumPrimaryGapBehindLitePercentagePoints": 2.0,
        "minimumHardGainOverLitePercentagePoints": 3.0,
        "note": "First add the matching validation-hard report to lite so this comparison uses identical records and definitions."
      }
    },
    {
      "order": 5,
      "action": "If ModernBERT pooling probes fail, compare sentence-trained encoder backbones with the same frozen probe protocol or remove the deep tier.",
      "prohibited": "Do not use model size as the next variable and do not run ModernBERT large."
    },
    {
      "order": 6,
      "action": "Improve lite through validation-only error analysis and data work, then reserve a new independent holdout before another candidate cycle.",
      "prohibited": "Do not use the current frozen set for optimizer, data, calibration, or checkpoint selection."
    },
    {
      "order": 7,
      "action": "Run QAT, export, target-runtime parity, latency, energy, and residency only after a float candidate qualifies.",
      "shippingGate": {
        "liteFrozenAccuracy": 0.95,
        "deepFrozenAccuracy": 0.97,
        "deepMinimumHardGainOverLitePercentagePoints": 5.0
      }
    }
  ]
}