Mixture of Cognitive Reasoners: Modular Reasoning with Brain-Like Specialization

Human cognitive behavior arises from the interaction of specialized brain networks dedicated to distinct functions, such as language, logic, and social reasoning. Concretely, we partition the layers of a pretrained language model into four expert modules aligned with well-studied cognitive networks in the human brain. 's behavior can be dynamically steered at inference time by routing tokens to particular experts (e.g., favoring social over logical reasoning), enabling fine-grained control over outputs. Taken together, cognitively grounded functional specialization yields models that are both more humanlike and more human-interpretable. Neuroscience research suggests that distinct brain regions support language, reasoning, social cognition, and other cognitive functions (Saxe & Kanwisher, 2003; Kanwisher, 2010; Fedorenko et al., 2024). In contrast, the internal organization of Large Language Models (LLMs) is largely unstructured. While certain units or subnetworks show selective activation (Zhang et al., 2022; 2023; Bayazit et al., 2023; AlKhamissi et al., 2025a; Wang et al., 2025), such specialization is implicit and difficult to interpret or control. Motivated by this discrepancy, we propose a model architecture that explicitly incorporates specialization. On the machine learning (ML) side, such designs hold great potential for improving interpretability and controllability; on the cognitive science side, they provide a framework toward formulating testable computational hypotheses about how the relative contributions of different brain networks support complex behavior. The final training stage of this curriculum uses this now inductively-biased architecture to perform large-scale supervised finetuning.