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Brandon Schneider e7525fb6f4 feat(pist): canary batch — 42 real Lean theorems through full pipeline 
- 42/42 unique matrix hashes (100%)
- 42/42 unique canonical hashes (no collisions)
- 42/42 unique spectral gaps (full diversity)
- Rank estimate: 5 distinct values, range [4, 8]
- Laplacian zero count: 3 distinct values, range [1, 3]
- 1 outlier: omega_double classified as CadForceProbeReceipt (rank=4)
- Classifier still collapses to LogogramProjection for rank>=5

Conclusion: spectral features are diverse. Classifier thresholds need training, not hand-tuning.
2026-05-26 02:09:08 -05:00
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artifacts Expand devcontainer with full Python stack, add MCP servers (Notion/AWS), strengthen Lean theorems 2026-05-19 01:52:14 -05:00
data feat: eigensolid convergence proof + QC flagging tool + full pass/fail review 2026-05-14 00:04:08 -05:00
examples Add adversarial duals 16D anchor pack 2026-05-17 15:47:38 -05:00
schemas Add adversarial duals receipt schema 2026-05-17 15:11:34 -05:00
pist_canary_receipts.jsonl feat(pist): canary batch — 42 real Lean theorems through full pipeline 2026-05-26 02:09:08 -05:00
pist_canary_report.json feat(pist): canary batch — 42 real Lean theorems through full pipeline 2026-05-26 02:09:08 -05:00
pist_canary_results.jsonl feat(pist): canary batch — 42 real Lean theorems through full pipeline 2026-05-26 02:09:08 -05:00
rrc_pist_exact_validation.json feat(pist): receipt canonicalization v2 with structural math features 2026-05-26 01:55:09 -05:00
rrc_pist_feature_vectors.jsonl feat(pist): receipt canonicalization v2 with structural math features 2026-05-26 01:55:09 -05:00
rrc_pist_training_report.json feat(pist): receipt canonicalization v2 with structural math features 2026-05-26 01:55:09 -05:00