Brandon Schneider
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6c55cac0a9
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feat(pist): receipt canonicalization v2 with structural math features
- Parses equation names into operators, variables, AST metrics, proof metrics
- Richer canonical hash → more distinct crossing matrices
- Separation ratio improved: 1.007 → 1.051 (within/between class distance)
- CognitiveLoadField accuracy: 44.4% → 50.0%
- Fold/cusp confusion (CLF → SRC): 9/18 → 3/18 (major improvement)
- 26/26 unique matrix hashes maintained
- Receipt format: v1 → v2 (parse_equation + build_proof_metrics)
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2026-05-26 01:55:09 -05:00 |
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Brandon Schneider
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c7eed520f9
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feat(pist): validation + calibration harness
- pist_train.py: leave-one-out nearest-centroid calibration (22 feature dims)
- Validation: 26 equations, 26 unique matrix hashes, 26 unique canonical hashes
- 38.5% LOOCV accuracy vs 25% random baseline — spectral signal confirmed
- CognitiveLoadField: 44.4% (8/18), SignalShapedRouteCompiler: 33.3% (2/6)
- Separation ratio 1.007 — centroids overlap heavily (fold/cusp are adjacent in ADE)
- Feature diversity confirmed: 21/22 features carry variance
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2026-05-26 01:49:21 -05:00 |
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Brandon Schneider
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96be7cdb97
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feat(pist): exact eigendecomposition, matrix diagnostics, 26-equation validation
- pist-decompose: convergence proxy + symmetric/Laplacian/SVD spectrum
- Crossing matrix now hash-derived (Q0_2), unique per equation
- Validation: 26/26 unique matrices, 26/26 unique canonical hashes
- Spectral features: rank(5), density(10), entropy(26), gap(26)
- Classifier rules need labeled training data
- pist_classify.py: full pipeline wrapper
- validate_rrc_predictions.py: batch runner with diagnostics
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2026-05-26 01:20:30 -05:00 |
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