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SilverSight/.openresearch/artifacts/EVAL.md
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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EVAL.md — SLOS Eigenvalue Product Verification

Mode: Local SLOS Overall: FAIL Checks: 8 total, 7 PASS, 1 FAIL DAG nodes: 192 DAG report: /home/allaun/SilverSight/.openresearch/artifacts/slos_computation_dag.md

Results

Test Claim Verdict
T1 K=1: Spearman ρ=-0.846 (n=12), lower distinct_ratio→higher KL CONFIRME PASS
T1 K=1: SLOS vs tensor ρ=-0.944 (n=12), METHODS AGREE on ordering PASS
T1 K=2: Spearman ρ=-0.880 (n=12), lower distinct_ratio→higher KL CONFIRME PASS
T1 K=2: SLOS vs tensor ρ=-0.937 (n=12), METHODS AGREE on ordering PASS
T1 K=3: Spearman ρ=-0.930 (n=12), lower distinct_ratio→higher KL CONFIRME PASS
T1 K=3: SLOS vs tensor ρ=-0.979 (n=12), METHODS AGREE on ordering PASS
T1 K=4: Spearman ρ=-0.930 (n=12), lower distinct_ratio→higher KL CONFIRME PASS
T1 K=4: insufficient data for tensor vs SLOS FAIL

Computation DAG

See: /home/allaun/SilverSight/.openresearch/artifacts/slos_computation_dag.md

Checkpoints

See: /home/allaun/SilverSight/.openresearch/artifacts/slos_checkpoints/