This preprint introduces SERIS (Structured Exact Reasoning & Inference System), a novel AI architecture grounded in formal logic rather than statistical inference. SERIS replaces probabilistic token prediction with a branch-based reasoning model governed by hard logical invariants, a deterministic commit mechanism, and a distributed memory architecture in which experience is incarnated in the geometric structure of reasoning cells rather than accumulated in logs. The paper formalizes the Field of Attention Operator (FOA) in three variants, with particular focus on FOA-3 (Strict Interference), which annihilates logically invalid branches prior to scoring, rendering hallucination structurally impossible rather than merely unlikely. The architecture is grounded in the Sphaera substrate and the Resonant Geminae model, in which reasoning dimensions evolve through threshold-based structural transitions and inter-cell signal propagation is non-directional and resonance-driven. A constrained five-cell inference graph demonstration with full commit reconstruction is provided. Open problems are enumerated with precision, including the critical path item: the formal specification of the inter-geminae signal language σ(G,t). Target domains include autonomous decision systems in defense, medical diagnosis, critical infrastructure, and sovereign algorithmic governance.
Cédric Abunar (Sun,) studied this question.