Title: DNON: A Brain-Inspired Architecture for Multi-Domain Reasoning with Specialized Neural Modules
Primary area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
Keywords: Neural orchestration, Information geometry, Brain-inspired architecture, Multi-tier memory, Lyapunov stability, Modular language models

Abstract: Dynamic Neural Orchestration Networks (DNON) is introduced as a brain-inspired architecture that composes specialized language models via information-theoretic routing. DNON comprises four modules — Perception, Memory (short-term, long-term, deep subconscious), Reasoning, and Executive — whose interactions are dynamically regulated by mutual-information-based routing on information manifolds. The framework provides a principled path for modular cognition and offers Lyapunov-style convergence guarantees under reasonable assumptions. The implementation leverages frozen foundation models (Claude for closed-source experiments and Mistral Large for open-source deployments) while training the routing mechanisms, information-geometric parameters, and memory dynamics through gradient-based optimization of mutual information objectives. Empirically, DNON is evaluated across four reasoning benchmarks using stratified 5-fold validation (seed 50) and demonstrates strong accuracy while reducing end-to-end inference cost compared to baselines and retrieval-augmented methods. Ablation studies confirm the importance of the three-tier memory: removing STM, LTM, or DSM degrades performance substantially. DNON thus combines theoretical rigor and practical gains for multi-domain reasoning with modular LLMs.

TL;DR: DNON orchestrates specialized language models through information-theoretic routing on Riemannian manifolds, providing both theoretical convergence guarantees and improved performance on reasoning benchmarks.