SilverSight/docs/research/ATTACK_PLAN.md

Attack Plan: From Suspect to Receipt

Status: ACTIVE — every result suspect until receipt matches conjecture Date: 2026-07-04 Principle: No claim is accepted without a receipt. Conjecture → prediction → measurement → receipt → accept or reject.

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The 13 Things We Actually Have

#	What	Type	Receipt
1	sidon_preserved_mod (2-moduli CRT Sidon)	Lean proof	CRTSidon.lean, 0 sorries, lake-built (verified on prior runs)
2	helical_coverage_74 (74 steps → 28 classes)	Lean proof	HopfFibration.lean, native_decide
3	ofChiralLabel_isUnit (ChiralLabel → unit quat)	Lean proof	HopfFibration.lean
4	ring_fastest (ring > SUBLEQ > AVX-512)	Lean proof	HCMR.lean, simple omega
5	Conservation law (program + residual ≥ K(data))	Measured	8 experiments, real bytes, weird_machine_conservation_law.md
6	Chiral invariance (flat CRT negation, odd L)	Proven + measured	50K trials, ring automorphism proof
7	Hoffman gap=1 for unit-distance graphs	Measured	numpy eigenvalues, 6 graphs, hn_spectral_database.json
8	q-profile sweep (q>1 = 100% Sidon)	Measured	crt_qprofile_sweep.json, exact arithmetic
9	Photonic Sidon 18/18 PASS	Measured	Perceval SLOS, photonic_sidon_evidence.jsonl
10	pipeline_core.py runs (binary swaps)	Implemented	chiral_batch_pipeline.json, 256→64
11	dna_braid.wgsl (GPU braid sort)	Implemented	Existing, tested
12	dna_radix_gpu.py (GPU QUBO sort)	Implemented	Existing, tested
13	BraidStateN.lean ChiralLabel/Rossby	Lean, compiles	Prior lake build passed

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Conjectures to Validate (in priority order)

C1: CRTSidonN.lean compiles

Conjecture: The n-moduli generalization is valid Lean that compiles. Prediction: lake build SilverSight.CRTSidonN exits 0. Receipt needed: Build log showing 0 errors. Status: Lake build running (currently at ~73% of full Mathlib). Action: Wait for build, check exit code.

C2: HCMR suite compiles

Conjecture: The 5 new modules (HCMR, CacheSieve, Blitter6502OISC, YangMillsPerformance, WorkloadTestbench) compile with 2 sorries. Prediction: lake build exits 0 with exactly 2 sorries (CacheSieve.evict_prefers_reset, YangMillsPerformance.compression_overhead_bounded). Receipt needed: Build log + sorry count. Status: Same lake build run. Action: Check build output for error count and sorry locations.

C3: Positional chiral pipeline produces non-uniform Sidon results

Conjecture: The positional chirality (permuting phases across strand positions) produces DIFFERENT Sidon results for different chiral configs. Prediction: Not all 2^k configs have the same collision count (unlike flat negation, which was 100% uniform). Receipt needed: pipeline_core.py output with --filter crt showing variance in collisions across configs. Status: UNTESTED. The previous run used flat negation (chiral-invariant). Action: Run pipeline_core.py with positional chirality on CPU compute. Compare collision counts across configs. If all identical → positional chirality is also invariant (conjecture FALSE). If variance exists → conjecture TRUE.

C4: Quaternion product Sidon filter discriminates chiral configs

Conjecture: The QuaternionSidonFilter (Hamilton product of 1,i,j,k basis) produces different results for different chiral configs. Prediction: Some configs pass (0 collisions), others fail (>0 collisions). Receipt needed: pipeline_core.py output with --filter quat showing variance. Status: UNTESTED. Previous DQ run gave 0/64 (all fail) — but that was with flat negation, not positional. Action: Run with positional chirality + quaternion filter. If variance → TRUE. If uniform → FALSE.

C5: COUCH Kelvin check actually filters

Conjecture: Configs with Rossby drift = 0 (Kelvin regime) exist in the 65K cross-enriched space and are rejected by COUCH. Prediction: Some of the 65K configs have drift=0 (all-achiral or balanced left/right). These should be rejected. Receipt needed: COUCH pass count < total, with some configs tagged "kelvin". Status: UNTESTED. Action: Run pipeline with k=8, count Kelvin-rejected configs.

C6: Cross-enrichment (4^8 = 65K) runs on GPU

Conjecture: chiral_cross_enrich.wgsl compiles and runs, processing 65K configs. Prediction: GPU dispatch completes, output buffer has 65K results. Receipt needed: WGSL compilation + dispatch log + output JSON. Status: UNTESTED. Shader is written but never compiled. Action: Test on GPU pod (A40 or user's WebGPU node). Check compilation, then run.

C7: HCMR self-loop probabilities are measured (not just defined)

Conjecture: The values 0.823, 0.885, 0.0 come from real EPYC KVM benchmarks. Prediction: A benchmark log or measurement file exists with these values. Receipt needed: Benchmark output showing these numbers. Status: SUSPECT. The Lean module DEFINES them as constants. No measurement file found. Action: Search Research Stack for benchmark logs. If none found → relabel as "assumed" not "measured".

C8: q-profile sweep result (q>1 = 100% Sidon) is robust

Conjecture: The q>1 preference holds across different label sets and moduli. Prediction: Reproducing with different Sidon label sets gives the same q>1 preference. Receipt needed: Re-run with 3+ different label sets, check q>1 rate. Status: Measured once (3 label sets). Need more. Action: Run q-profile sweep with 10+ random Sidon label sets.

C9: Hoffman gap=1 is universal for unit-distance graphs

Conjecture: All unit-distance graphs have Hoffman gap=1 (not just Moser/Golomb). Prediction: de Grey 1581 graph also has gap=1. Receipt needed: Hoffman bound on de Grey 1581. Status: UNTESTED (the --full flag never ran successfully). Action: Run hn_spectral_database.py --full on GPU pod (needs numpy for 1581×1581 eigenvalues).

C10: Rossby drift correlates with QUBO tractability

Conjecture: QUBO instances with Rossby drift=0 (Kelvin) are harder for QAOA. Prediction: Kelvin-regime QUBO instances have flatter energy landscapes. Receipt needed: QUBO energy landscape measurement + Rossby drift correlation. Status: ENTIRELY SPECULATIVE. No experiment designed. Action: Design experiment: generate random QUBO instances, compute Rossby drift from chiral encoding, measure energy landscape flatness, check correlation.

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Execution Order

Phase 0: VERIFY FOUNDATION (blocking)
  C1: CRTSidonN compiles?        → lake build (running)
  C2: HCMR suite compiles?       → same build
  C7: HCMR self-loops measured?  → search Research Stack

Phase 1: TEST THE PIPELINE (blocking)
  C3: Positional chirality varies?  → run pipeline_core.py --filter crt
  C4: Quaternion filter varies?     → run pipeline_core.py --filter quat
  C5: Kelvin check filters?         → count Kelvin-rejected in C3/C4 output

Phase 2: GPU (after Phase 1 confirms variance)
  C6: Cross-enrich shader runs?     → compile + dispatch on GPU
  C9: de Grey Hoffman bound?        → run --full on GPU

Phase 3: ROBUSTNESS (after Phase 2)
  C8: q-profile robust?             → 10+ label sets
  C10: Rossby ↔ QUBO?               → design + run correlation experiment

Phase 4: QUANTUM (after Phase 3 confirms C10)
  QUBO/QAOA integration experiments

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Rule

Every conjecture has:

1. A PREDICTION (what we expect)
2. A RECEIPT needed (what would prove it)
3. A REJECTION criterion (what would disprove it)

No conjecture is accepted until the receipt matches the prediction. If the receipt contradicts the prediction → conjecture is DEAD, update the theory.

If C3 fails (positional chirality is also invariant): → The entire chiral filtering story is dead → COUCH (geometric) is the only discriminating filter → Sidon filter is decorative, not functional

If C4 fails (quaternion filter is uniform): → The quaternion/S³ story is dead → Stick with CRT sums (proven, 2-moduli)

If C10 fails (Rossby ≠ QUBO tractability): → The QUBO/QAOA bridge is dead → The pipeline is a combinatorial filter, not a quantum pre-filter

Each failure narrows the theory to what's actually true.