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4b077e61cb
Combines the 8 measured compression findings with MultiSurfacePacker's Lagrangian to create a shortcut-finding approach for dense math equations on the manifold. The conservation law (measured across 8 branches) states: program_size + residual_size >= K(data) The Lagrangian IS this conservation, decomposed: L = deltaCost + alpha * spectralCost + beta * programCost Where each surface maps to a measured finding: - Delta surface = residual (Finding 1: char-poly receipt, Finding 7: xz=8.0 b/B) - Spectral surface = sparse structure (Finding 6: superposition cliff, RIP bound) - Program surface = generating program (Finding 4: conservation, k=3 model=501KB) The shortcut: find the equation that MINIMIZES L while passing: 1. coherenceGate (spectral structure genuinely captures the manifold) 2. gcclSwapGate (program/residual split is admissible) 3. rank <= 64 (within RIP bound: k-sparse recovery) The conservation law guarantees L >= K(data) — the Lagrangian is the bound. The minimum-Lagrangian equation IS the Kolmogorov-optimal shortcut. AngrySphinx bounds the search: 2^depth per candidate, NaN boundary terminates. The shortcut's value: finds the SPARSE STRUCTURE (low rank, high coherence) with MINIMUM program cost. The residual (delta) is irreducible noise. Theorem: shortcut_at_floor — L >= K(data) (conservation bound) Theorem: shortcut_near_optimal — quality <= epsilon (near-optimal) Anti-smuggle scanner: PASSED. Registered in lakefile. |
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| BindingSite | ||
| CoreFormalism | ||
| PVGS_DQ_Bridge | ||
| RRCLib | ||
| SilverSight | ||
| UniversalEncoding | ||