Eigen Engine: Deterministic Truth in Enterprise AI via Sovereign Memory and Quantum-Inspired Dynamics

Enterprise deployment of large language models remains critically impaired by probabilistichallucination arising from semantic similarity retrieval. Standard Retrieval-AugmentedGeneration (RAG) systems cannot distinguish between a superseded document and itsauthoritative replacement when the two are semantically near-identical — a failure mode weterm the Similarity Trap. This paper presents the Eigen Engine, a physics-based truthadjudication architecture that models information retrieval as an energy minimization problemsolved via an Ising solver. The system introduces Sovereign Memory — an immutable,hierarchically enforced storage layer that encodes document authority, provenance, and versionstate as physical biases in the energy landscape — and a Conflict Gate mechanism that rendersthe coexistence of conflicting documents physically impossible in the solution space. The resultis a deterministic, auditable, and reproducible truth-retrieval system that transitions enterpriseAI from probabilistic guessing to physics-grounded knowing. We describe the full architecture,energy landscape formulation, conflict resolution via repulsive coupling, authority fields vialocal biases, deployment modes including air-gapped pre-computed sovereignty, andapplications across nine enterprise verticals.