S-AI-Recursive: Bio-Inspired Sparse AI Cuts Parameters to 10M with Hormonal Reasoning

In a new preprint, Said Slaoui formalizes S-AI-Recursive, a sparse AI architecture that treats reasoning as a dynamical system governed by hormonal feedback rather than a single feed-forward pass. The Recursive Reasoning Cycle (RRC) introduces two antagonistic hormones: Clarifine, which signals convergence toward a stable state, and Confusionin, which detects remaining uncertainty. Their closed-loop regulation drives iterative state refinement until a Lyapunov-stable cognitive equilibrium is reached. The framework includes a full mathematical foundation—Lyapunov stability proofs, entropic contraction theorems, hormonal stopping criteria with finite-time guarantees, Euler-Maruyama discretization, and recursive engram memory with warm-start acceleration. This is built upon the author's prior S-AI foundational framework and the hormonal-probabilistic unification doctrine.

The experimental validation on the SAI-UT+ testbench shows that S-AI-Recursive achieves competitive reasoning performance on abstract and symbolic benchmarks using fewer than 10 million parameters—dramatically smaller than typical large language models. The core insight is temporal parsimony: iterative cognitive depth substitutes for architectural width, enabling energy-frugal reasoning. The paper (51 pages, no figures) is a preprint that positions the architecture as a potential path toward more efficient AI systems that introspect and self-correct without massive compute. While still theoretical, the approach could inspire new directions in sparse modeling and neuro-symbolic reasoning.