{
  "frozen_utc": "2026-09-20T22:57:24.349399+00:00",
  "purpose": "Full raw-MNIST numerical qualification and complete spatial costs for seeded PCANet grid port",
  "target_correct": 9900,
  "full_test_accuracy_seen": false,
  "selection": "Hyperparameters follow submitted K100 k8 learner; source-based seed/empty-cluster/epsilon corrections and stable primitive exp were made before any full test accuracy was read. No accuracy-based tuning.",
  "numeric_executor": "Generic ordered FP32 IL; independent images and classes use private CPU validation scratch. The scored grid schedule remains globally serialized.",
  "source_comparison": "Remaining differences are FP32 covariance/reductions, subspace eigensolvers and explicit log/exp/sqrt/inverse primitives.",
  "sources": {
    "program.json.gz": "9cbcedf472a2258faedb1ebadbf07207aa2f623ace8b7577c29db6a7acf5b223",
    "verify_candidate.py": "8ee05ea85bd8c06e5502e0206b3aef1d683f94870c3443482d723e63fe3601c9",
    "place_candidate.py": "7b80562589f2f749b6b812d31c890570151664c5e0c00c5b1f24d158bca4e312",
    "score_candidate.py": "2fefa0fdb12d74b9a48391832c0225afe6ce6654eb5d690346981e1f1a65f8bd",
    "parallel_compiler.py": "c332df7eea49d5980d847c2c8ad13f5376f6b6f48763fd35c33059b671b686fb",
    "execute_candidate.py": "c4102fb6aa93ecf7cd7c6b3c7075e45217893dbc3582116a6df94a6b1de95e8a",
    "build_seeded.py": "fcf77954a7999c7def5c71038ebe1a05b690886783b69cdfad259456eed637a2",
    "build_candidate.py": "4fe1270c3a28afaa560b37f7b3e905f099d42b427981bd751b08dc23e7ee64e4",
    "shared/il_compiler.py": "3485a6ef248892f2da76e510937342f676f6f3ddc24fb00ded36d344c45677b6",
    "shared/model_ir.py": "cc039abc33a731a471c4caf9e35d9c1799ea5246501ae9bea962406a91bcf910",
    "shared/score.py": "41b47384ec525718a4546f98f5ad25a4d82dd8bdfb97a762e451476cae29eaf8",
    "shared/affine.py": "8ac38bf9fccbef87ba05c4dd6a4e5d9901dceae4e71baad0ddd5794c22298d45",
    "imported/fuse.py": "c014b3773b3ba59e038cbcbeb67ccf25ddf7b3f86e23a38c16968d5a06e3a1e1",
    "imported/bag_ir.py": "f9dd0e83d22d720f6b4dbeab0f30179ce9abaaa095c6d6f5f0e7779fd1225767",
    "imported/pcanet_ir.py": "adf64a92d26ffed18218e631751d8d1c9e6a9d5fbfee8f3bd6be02172bc169b0"
  }
}