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Experimental approach to math functions
5-minute per-shot limit on Quandela cloud. Script handles this with:
1. RECOVERABLE DAG: each computation step is a DAG node
- Checkpointed to disk after each node
- If a shot times out, resume from last checkpoint with --resume
- The DAG records HOW SLOS computes (the path, not just the result)
- This is informative: the computation structure IS data
2. NODE TYPES:
- eigenvalue_products: cheap (O(n^k)), always runs
- slos_circuit: circuit built, about to sample
- slos: the actual SLOS simulation (5-min limit)
- compare: eigenvalue products vs SLOS output
3. EDGE TYPES:
- products → compare (comparison depends on products)
- slos → compare (comparison depends on SLOS)
4. CHECKPOINTS:
- Each node saved to .openresearch/artifacts/slos_checkpoints/node_<id>.json
- Full DAG state saved to slos_computation_dag.json
- --resume flag loads DAG state and skips already-computed nodes
5. DAG REPORT:
- slos_computation_dag.md: human-readable report of all nodes
- Records: what was computed, when, how long, what it found
- The computation path itself is data about how SLOS processes
the Sidon structure
Usage:
# Local
python3 scripts/perceval_slos_verify.py
# Quandela cloud (5-min/shot limit)
PERCEVAL_TOKEN='token' python3 scripts/perceval_slos_verify.py --cloud
# Resume after timeout
python3 scripts/perceval_slos_verify.py --resume
Tests:
- T1: Sidon vs non-Sidon at K=2 and K=3
- 4 test cases × 2 photon numbers = 8 SLOS shots
- Each shot: ~5 min on cloud (or seconds local)
- Total cloud time: ~40 min (8 shots)
- DAG records the exact computation path for each shot
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| 6-Documentation/docs/specs | ||
| archive | ||
| c | ||
| coq | ||
| Core | ||
| cpp | ||
| data | ||
| docs | ||
| exe | ||
| experiments | ||
| external | ||
| extraction | ||
| formal | ||
| fortran | ||
| go | ||
| infra/sigs | ||
| julia | ||
| octave | ||
| python | ||
| qubo | ||
| r | ||
| rust | ||
| scala | ||
| scripts | ||
| signatures | ||
| tests | ||
| .gitignore | ||
| .gitmodules | ||
| .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