{
  "architecture": "ExactBriefRead (V10) trained standalone: exact relation lookup, learned comparison",
  "steps": 6000,
  "batch_size": 64,
  "learning_rate": 0.002,
  "schedule": "cosine",
  "seeds": [
    731,
    732
  ],
  "training_data": "sampled numbers only: ratio 0.05 to 50, scale 0.05 to 3000, 0 to 4 unrelated relations, random names",
  "question_wordings": [
    [
      "Does the measured ratio fall within the requested band?",
      "Choose how to adjust the ratio to satisfy the requested band."
    ],
    [
      "Does the measured heading to body font-size ratio fall within the requested experimental policy band?",
      "Choose how to adjust the heading to body font-size ratio to satisfy the requested policy band."
    ],
    [
      "Does the measured body line-height to font-size ratio fall within the requested experimental policy band?",
      "Choose how to adjust the body line-height to font-size ratio to satisfy the requested policy band."
    ],
    [
      "Does the measured heading-body gap to next-group gap ratio fall within the requested experimental policy band?",
      "Choose how to adjust the heading-body gap to next-group gap ratio to satisfy the requested policy band."
    ]
  ],
  "edge_bands": [
    [
      1.1,
      1.45
    ],
    [
      2.0,
      3.0
    ],
    [
      0.15,
      0.6
    ],
    [
      4.5,
      21.0
    ],
    [
      1.0,
      1.15
    ],
    [
      45.0,
      75.0
    ],
    [
      0.9,
      1.1
    ],
    [
      0.02,
      0.08
    ]
  ],
  "gates": {
    "edge_error_beyond_2_percent": 0.0,
    "native_validation_accuracy": 1.0
  },
  "selection": "Among eligible checkpoints, lowest summed edge error over the four wordings",
  "sources": {
    "loupe_v2/train_read_v2.py": "86f1d40063e486caebb3bce3831376b64773131bc53d445c3fd070c996d45976",
    "loupe_v2/read_model.py": "92327a4c6bb92cbf6804d5319dc5abf383d1f594264eef8821a338f13f420dbd",
    "loupe_v2/train_read.py": "fb100e1c8f47ded2cea933dcd07d3bb4ec806b3fa7449669fa27fdb53f2da748",
    "loupe_v2/encode_text.py": "19cf51d94f3795964cbac4d5ab1781364f7ce8c29cf702cf38149d4e49be15c3",
    "website_design_model_v10.py": "01dc9ad7399226c0d4ca5fdcb6062918a9b377c34693ba0e9a50e98ce7437f9a"
  },
  "text_cache_sha256": "67a5d033580b8508e58214f51101597714d24502ba35b2b0c650d15045128481",
  "paid_compute": false
}