Beyond representational alignment with brain-guided language models for robust reasoning

• URL: http://arxiv.org/abs/2606.11893v1
• Date: Wed, 10 Jun 2026 10:22:49 GMT
• Title: Beyond representational alignment with brain-guided language models for robust reasoning
• Abstract summary: We show that large language models (LLMs) align with neural signals from reasoning-related regions.<n>Using a neural-predictivity metric, we find that LLMs explain a substantial fraction of the explainable variance in reasoning-related regions.<n>We propose a brain-guided framework: we steer model representations along directions induced by the joint structure of model and brain representations.
• Score: 51.87370932573858
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The correspondence between large language models (LLMs) and the neural mechanisms underlying human higher-order cognition remains insufficiently characterized. Given that language and reasoning in the human brain appear dissociable, an open question is whether LLMs align with neural signals from reasoning-related regions and whether such signals can improve them. Here, focusing on deductive reasoning, we show that LLM internal representations are not only partially aligned with task-fMRI activity but can also be directly enhanced by these signals. Using a neural-predictivity metric, we find that LLMs explain a substantial fraction of the explainable variance in reasoning-related regions at the aggregate level, whereas predictivity within specific reasoning types is lower, indicating both alignment and divergence. Building on this, we propose a brain-guided framework: we steer model representations along directions induced by the joint structure of model and brain representations, applying intervention at inference and fine-tuning during training. We demonstrate that task-evoked brain signals can directly enhance LLM reasoning, yielding gains orthogonal to language-only supervision across 10 LLMs (1.5B-72B), with transfer across reasoning types and up to 13\% absolute accuracy gain. Our results advance LLM-brain correspondences from correlation to guidance, establishing a brain-signal-driven pathway toward more robust and cognitively aligned AI.

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