[2608.13567] Modular Cognitive Architecture Emerges in Large Language Models

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arXiv:2608.13567 (cs)

[Submitted on 27 Jun 2026]

Title:Modular Cognitive Architecture Emerges in Large Language Models

Authors:Pengrui Han, Jacob Andreas, Evelina Fedorenko, Andrea Gregor de Varda<br>View a PDF of the paper titled Modular Cognitive Architecture Emerges in Large Language Models, by Pengrui Han and 3 other authors

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Abstract:The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning about the physical world. Is this modular organization a fundamental principle of how intelligent systems must be built, or an evolutionary accident specific to biological brains? Here, we test whether a similar organization emerges in Large Language Models--another class of intelligent systems created through a very different optimization process. Using circuit analyses across N=46 tasks spanning four cognitive domains (language, formal reasoning, social reasoning, physical reasoning), we find that LLMs develop a modular architecture that mirrors the human brain: tasks drawing on the same network in humans recruit overlapping neurons in LLMs, whereas tasks drawing on different networks recruit distinct neurons. The convergent emergence of modularity in brains and neural networks suggests that it may be a fundamental property of intelligent systems.

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Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:<br>arXiv:2608.13567 [cs.AI]

(or<br>arXiv:2608.13567v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.13567

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arXiv-issued DOI via DataCite

Submission history<br>From: Pengrui Han [view email]<br>[v1]<br>Sat, 27 Jun 2026 01:56:22 UTC (6,479 KB)

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