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

Human intelligence emerges from the interaction of specialized brain networks, each dedicated to distinct cognitive functions such as language processing, logical reasoning, social understanding, and memory retrieval. Inspired by this biological observation, we introduce the Mixture of Cognitive Reasoners (MiCRo) architecture and training paradigm: a modular transformer-based language model with a training curriculum that encourages the emergence of functional specialization among different modules. Inspired by studies in neuroscience, we partition the layers of a pretrained transformer model into four expert modules, each corresponding to a well-studied cognitive brain network. Our Brain-Like model has three key benefits over the state of the art: First, the specialized experts are highly interpretable and functionally critical, where removing a module significantly impairs performance on domain-relevant benchmarks. Second, our model outperforms comparable baselines that lack specialization on seven reasoning benchmarks. And third, the model's behavior can be steered at inference time by selectively emphasizing certain expert modules (e.g., favoring social over logical reasoning), enabling fine-grained control over the style of its response. Our findings suggest that biologically inspired inductive biases involved in human cognition lead to significant modeling gains in interpretability, performance, and controllability.

Rational Metareasoning for Large Language Models

Being prompted to engage in reasoning has emerged as a core technique for using large language models (LLMs), deploying additional inference-time compute to improve task performance. However, as LLMs increase in both size and adoption, inference costs are correspondingly becoming increasingly burdensome. How, then, might we optimize reasoning's cost-performance tradeoff? This work introduces a novel approach based on computational models of metareasoning used in cognitive science, training LLMs to selectively use intermediate reasoning steps only when necessary. We first develop a reward function that incorporates the Value of Computation by penalizing unnecessary reasoning, then use this reward function with Expert Iteration to train the LLM. Compared to few-shot chain-of-thought prompting and STaR, our method significantly reduces inference costs (20-37\% fewer tokens generated across three models) while maintaining task performance across diverse datasets.