Commit graph

5 commits

Author SHA1 Message Date
83b4f0ce2c feat: Direction B Gerver sofa implementation + CRTSidonN partial fix
Direction B results: Gerver sofa at T=100 produces χ=2 (bipartite),
not reaching χ≥4. Confirms 'unit-distance events are measure-zero.'

CRTSidonN: auto-generated, ~10 remaining structural issues. Design is
correct (natural n-moduli extension of CRT Sidon theorem).
2026-07-04 11:04:14 -05:00
3bfe13ee3b docs: update photonic evidence with exact Q16_16 encoder results
The photonic_sidon_search.py script now uses encoder_q16.py (exact
Q16_16 fixed-point arithmetic) instead of float-based encoding.

This updates the evidence file with slightly different omega values
due to exact arithmetic, but all 18 tests still pass.

EVAL.md was regenerated with the photonic search results.
2026-07-04 02:31:33 -05:00
7bf5a0479d fix(sidon-sofa): tighten unit-distance tolerance from 5% to 0.001%
Critical correction to Direction A results:

Old tolerance: |d - 1| < 0.05 (5%)
New tolerance: |d - 1| < 1e-05 (0.001%)

Impact:
- χ values dropped from 12-24 to 1-2
- Most configurations now feasible (was mostly infeasible)
- Edge counts dropped from 200+ to 0-5

The original 5% tolerance was too loose, counting points as 'unit distance'
when they were actually up to 5% away. This created artificially dense
conflict graphs with high chromatic numbers.

The tighter tolerance reveals the Sidon-Sofa coloring problem is more
tractable than initially thought, with sparse conflict graphs and low
chromatic numbers for most configurations.
2026-07-04 02:06:04 -05:00
12f84c8973 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)
2026-07-04 02:02:50 -05:00
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