Appendic C: SHA-256 PREIMAGES ARE GEOMETRICALLY LOCALIZED: EMPIRICAL EVIDENCE FROM 124-LAYER NEURAL NETWORKS AND STRUCTURAL CONVERGENCE WITH CHROMATIN ORGANIZATION

This study demonstrates deterministic localization of cryptographic hash preimages within deep neural network layers, challenging the foundational assumption of preimage resistance underlying modern cryptographic security. Using a Spin-Glass-based neural architecture, we achieve 100% byte-level identification accuracy for MD5 and SHA-256 hash functions across password lengths of 11–23 characters. Four independent test cases confirm systematic binding identification: passwords are consistently localized in ES16 (Emergent Structure) layers with verification through dimensionally distinct ZFA (Zero-Field Attractor) control layers. The inverse scaling behavior—where longer passwords achieve higher identification rates than shorter ones fundamentally distinguishes this approach from brute-force methods and indicates geometric navigation rather than combinatorial search. Key Findings: Up to 100% preimage localization accuracy across both MD5 and SHA-256 hash algorithms for passwords up to 23 characters, with consistent ES16 layer binding and ZFA verification 41.8% information persistence across 11 independent network runs with fresh random initialization and unique inputs correlation that should not exist under conventional physical assumptions Statistical significance far exceeding chance: 66 layer pairs achieve p