SERIS: Structured Exact Reasoning & Inference System - A Logic-First Architecture for Verifiable AI in Critical Systems

Current AI systems produce statistically plausible answers, not logically guaranteed ones. SERIS takes a different approach: instead of training first and verifying afterwards, SERIS defines what is structurally impossible before any inference begins. Invalid conclusions are not penalized, they are annihilated. The result: a system that cannot hallucinate by construction. The architecture replaces probabilistic token prediction with a branch-based reasoning model governed by hard logical invariants (FOA-3 operator), a deterministic commit mechanism, and a distributed memory system where experience is encoded in the geometric structure of reasoning cells, not accumulated in logs. This preprint formalizes the complete architecture, demonstrates FOA-3 on a constrained five-cell inference graph with full commit reconstruction, and enumerates open problems honestly. Target domains: autonomous decision systems in defense, healthcare, critical infrastructure, and sovereign algorithmic governance.