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12f84c8973
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feat: agent computation results — 16 QRNG runs, Hoffman bound, v3 sweep, CMYK fix
Agent outputs from the 9-agent parallel run:
CMYKColoringCore.lean:
- Restored §3 section header (accidentally deleted during native_decide cleanup)
- Proof uses dec_trivial per AGENTS.md §5 (no native_decide, no sorries)
- All 8 sections (§1-§8) verified present
Computation scripts:
- hn_hoffman_bound.py: Hadwiger-Nelson Hoffman spectral bound
- sidon_sofa_coloring_v3.py: Fine q-value sweep + n=34 extension
- mcp_worker.py: MCP autoproof worker process
Artifacts (16 QRNG-seeded runs):
- sidon_sofa_coloring_v2_qrng_*.json (16 files, 106KB each)
- sidon_sofa_coloring_v2.json (base run)
- sidon_sofa_coloring_v2_cupfox.json (CupFox variant)
- hn_hoffman_bound.json (Hoffman bound results)
- EVAL_cupfox.md (evaluation document)
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2026-07-04 02:02:50 -05:00 |
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f1a050277b
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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)
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2026-07-03 17:55:26 -05:00 |
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