Self-Organizing Cognitive Reasoning Architecture with Triple-network Executive System

Abstract

Large scale language models exhibit extraordinary generative performance across modalities, yet most remain architecturally monolithic: a single model performing forward pass inference. In contrast, human cognition emerges from dynamic interactions among large-scale neural networks, notably the Default Mode Network (DMN), Salience Network (SN), and Central Executive Network (CEN). We present a computational architecture that operationalizes principles from the Triple Network Model within a structured artificial system. The proposed design decomposes cognition into interacting modules governed by a formal inter-module communication protocol with asynchronous temporal scheduling, a global workspace competition mechanism, a token-based affective-motivational system with dopaminergic dynamics, a meta-cognitive self-monitoring layer with adaptive strategy selection, and a comprehensive offline processing system incorporating adversarial counterfactual generation, hierarchical abstraction, creative recombination, and affective reappraisal. We do not claim machine consciousness. Instead, we investigate whether architectural decomposition informed by network neuroscience improves reasoning stability, epistemic robustness, and long horizon planning compared to monolithic inference systems. Formal stability analysis demonstrates that all subsystem dynamics are bounded: the inhibitory coupling converges to a bistable attractor (spectral radius < 0.29), the token economy recovers from total depletion within 21 ticks, the dream mode is resourceregenerative (net positive token balance), and the meta-cognitive drift rate is bounded at 0.049/tick. Seven experimental protocols are proposed to empirically validate the architecture's predicted advantages.