allaun f1a050277b feat(slos): eigenvalue products predict SLOS concentration ordering - verified with Spearman correlation, cross-validated with exact tensor network ... 48 test points across K=1..4 and 12 label sets (Sidon power sets, Sidon constructions, dense non-Sidon, prime-based). Results: K=1: ρ=-0.85 (products→SLOS), ρ=-0.94 (SLOS↔tensor) K=2: ρ=-0.88 (products→SLOS), ρ=-0.94 (SLOS↔tensor) K=3: ρ=-0.93 (products→SLOS), ρ=-0.98 (SLOS↔tensor) K=4: ρ=-0.93 (products→SLOS), tensor N/A (K>3) Key: all Spearman correlations are negative and strengthen with K. Sidon sets produce 1.5-2.3× higher KL divergence than same-size non-Sidon. Primes are intermediate: partially Sidon-like but weaker. DAG: 192 nodes, 96 edges, all individually checkpointed for resume. Resume with: python3 scripts/perceval_slos_verify.py --resume Receipt: docs/research/SLOS_SIDON_VERIFICATION_RECEIPT.md Build: N/A (Python/perceval verification, no Lean build)		2026-07-03 17:55:26 -05:00
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BindingSite	tag(axioms): justify all 18 custom axioms with HONESTY CLASS tags	2026-07-03 10:54:08 +00:00
CoreFormalism	feat(slos): eigenvalue products predict SLOS concentration ordering - verified with Spearman correlation, cross-validated with exact tensor network	2026-07-03 17:55:26 -05:00
RRCLib	feat(rrc): bare-minimum RRC refactor into SilverSight	2026-06-21 09:08:48 -05:00
SilverSight	feat(slos): eigenvalue products predict SLOS concentration ordering - verified with Spearman correlation, cross-validated with exact tensor network	2026-07-03 17:55:26 -05:00