NP-Hardness Collapsed: Deterministic Resolution of Spin-Glass Ground States via Information-Geometric Manifolds (N=8-100)

This preprint presents the first large-scale empirical demonstration of a deterministic information-geometric collapse mechanism capable of resolving NP-hard Spin-Glass ground states across the range N=8N = 8N=8 to N=100N = 100N=100. The GCIS-based I-GCO architecture operates without weights, training, stochasticity or iterative optimization. Instead, it collapses high-dimensional state spaces onto a one-dimensional manifold while retaining full informational structure across all layers. Exhaustive verification up to N=24N = 24N=24 confirms exact ground-state solutions; larger systems show invariant collapse geometry, complete information preservation and stable correlation symmetries, suggesting scale-free behavior. Beyond Spin-Glass systems, the results indicate that informational-geometric manifold collapse may represent a computational modality fundamentally distinct from algorithmic search. The mechanism may generalize to broader optimization, inference and constraint-satisfaction domains. Ongoing work aims to scale the architecture further and apply it to real-world industrial and scientific problem classes where classical algorithms or quantum approaches are limited. Key Contributions • Deterministic resolution of NP-hard Spin-Glass ground states for N=8–N=100.• Full information retention across 100 layers with non-local correlation symmetry.• Evidence for geometric collapse onto a one-dimensional attractor manifold.• Empirical behavior incompatible with classical algorithmic or probabilistic methods.• Clear pathway toward scaling and applying the mechanism to practical large-scale problems. The findings motivate further exploration of information-geometric computation as a potential foundation for new classes of efficient, deterministic problem-solving systems.