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Experimental approach to math functions
Adds python/spectral_codebook_db.py: sync codebook rows into ene.rrc_predictions on the neon-64gb Postgres (NEON_PG convention from scripts/auto/auto_pipeline.py, default research_stack DB). - Row shape (flat, SQL-typed, Spark-JDBC readable): equation_id, proxy_pred = cluster codeword C0..C8, exact_pred = shape from exact lambda under CURRENT ClassifyN.lean thresholds (1.5/4.0 Q16.16, integer semantics mirrored), matrix_hash = 'charpoly=<c1..c8>;pos10=<base-10 positional hash>' (similarity + injective identity keys), confidence = 1.0 unique fingerprint / 1/k in k-way charpoly collision class; deterministic uuid5 ids so reruns upsert idempotently. - SAFE BY DEFAULT: dry run prints summary + sample SQL and writes nothing; --apply required to insert (psycopg2, with --emit-sql data/spectral_codebook_sync.sql fallback when the driver is absent). --apply has NOT been run; live DB untouched. --verify-schema does a read-only column check; schema verified offline against scripts/auto/ene_schema.sql in tests (live check left to the user per the ask-before-DB-work rule). - spectral_codebook.py gains --sync-db (always dry-run from that entry point). Dry-run counts: 250 rows; C0=35 C1=20 C2=13 C3=79 C4=29 C5=15 C6=22 C7=19 C8=18; Logogram=69 Signal=131 CognitiveLoad=50; 183 rows at confidence 1.0. - docs: 'Neon data layer' section — ENE table map, stale ene.rrc_classifications finding (120 rows with artifact spectral radii 0.3-0.85 predating the exact-eigenvalue fix; recommend re-classification via this codebook), empty landing tables, Spark JDBC snippet, arxiv-pg (podman-exec only) citation layer note. - tests: 8 new dry-run/no-network tests (23 total). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| 6-Documentation/docs/specs | ||
| archive/2026-07-02/docs | ||
| c | ||
| coq | ||
| Core | ||
| cpp | ||
| data | ||
| docs | ||
| exe | ||
| experiments | ||
| extraction | ||
| formal | ||
| fortran | ||
| go | ||
| infra/sigs | ||
| julia | ||
| octave | ||
| python | ||
| qubo | ||
| r | ||
| rust | ||
| scala | ||
| scripts | ||
| signatures | ||
| specs | ||
| tests | ||
| .gitignore | ||
| .sops.yaml | ||
| AGENTS.md | ||
| BREAKGLASS_LOG.md | ||
| CITATION.cff | ||
| ERROR_INVENTORY.md | ||
| lake-manifest.json | ||
| lakefile.lean | ||
| lean-toolchain | ||
| PORTING_MANIFEST.md | ||
| PORTING_MAP.md | ||
| pytest.ini | ||
| README.md | ||
| REBASE_RULES.md | ||
| requirements.txt | ||
| SORRY_PROTOCOL.md | ||
| SORRY_RESOLUTION_S1S3.md | ||
| TRACEABILITY_GRAPH.md | ||
| VERIFICATION_LOG.md | ||
| WORK_LOG.md | ||
SilverSight
A formally verified, hardware-native computation stack for braid topology analysis, eigensolid compression, and cross-domain 1/n-scaling signature mining.
Quick Start
# Verify the entire pipeline (∼4 min)
bash scripts/run_entry_gate.sh
This runs all 4 anti-smuggle layers:
| Layer | Gate | What it proves |
|---|---|---|
| 0 | check_determinism.py |
All artifacts are reproducible (SHA-256 chains, seeded RNG) |
| 1 | lake build |
All 3307 Lean jobs compile (Q16_16 fixed-point, AVM ISA, PIST classifiers) |
| 2 | rrc-emit-fixture |
The manifold receipt emits with 278/278 rows passing alignment |
| 3 | verify_with_sympy.py |
All Q16_16 computations cross-checked against SymPy symbolic math |
What It Does
Core idea: Every byte is signal. Gaps, timing, and absences are the encoding. The compressor encodes everything; the decompressor must reconstruct everything, including the gaps — because the gaps are the compression.
Pipeline
Equation text → tokenizer → 8×8 strand adjacency matrix
↓
PIST spectral classifier
(MatrixN → SpectralN → ClassifyN)
↓
Q16_16Manifold (278 fixture rows)
↓
AVM ISA receipt (JSON)
Key Modules
| Module | Purpose |
|---|---|
MatrixN |
Generic n×n matrix operations (power iteration, Laplacian, A^T A) |
SpectralN |
Spectral profile: eigenvalue, spectral gap, density, Frobenius norm |
ClassifyN |
Spectral-radius → color → shape-name classifier |
BraidStateN |
n-strand braid state, crossStep, eigensolid convergence |
FisherRigidityN |
n-dimensional Fisher-Rao geometric rigidity |
FixedPointBridge |
Q16_16 ↔ Q0_64 quad matrix bridge (zero LSB error) |
FeasibleSet |
QUBO k-hot relaxation with weak monotonicity proofs |
Infrastructure
| Service | Host | Purpose |
|---|---|---|
| AppFloyo Cloud | neon-64gb:8000 | Module dependency dashboard |
| GoTrue | neon-64gb:9999 | JWT auth for API access |
| Authentik | neon-64gb:30001 | SSO provider (OAuth2/OIDC) |
| Homarr | neon-64gb:7575 | Infrastructure dashboard |
| CouchDB | neon-64gb:5984 | Document store |
Verification
# Full formal build
lake build # 3307 jobs, 0 errors
# Generate predictions from equations
python3 python/generate_predictions.py
# Build matrix data
python3 python/build_pist_matrices_250.py
# Build manifold fixture rows
python3 python/build_manifold.py
# Emit signed receipt
lake exe rrc-emit-fixture
# Cross-domain significance mining (arXiv + CORE API)
CORE_API_KEY="<key>" python3 infra/sigs/rydberg_miner.py
python3 scripts/cross_domain_significance.py
Project Structure
formal/ Lean 4 source (truth)
python/ Python I/O shims
scripts/ Anti-smuggle protocol, infrastructure
signatures/ Cross-domain signature data
infra/sigs/ Literature mining tools
specs/ Design documents
License
Apache 2.0