arXiv AI August 7, 2026 By Maryam Rahimi, Mohammad Reza Daliri, Yadollah Yaghoobzadeh

Explanations of Large Language Models Explain Language Representations in the Brain

arXiv:2502. 14671v4 Announce Type: replace-cross Abstract: Large Language Model (LLM) representations are known to align with brain activity during language processing, but it remains unclear what drives this alignment.

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arXiv Computation and Language

Sep 11

Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts

The study examined whether brain-language model alignment reflects shared computational mechanisms or merely stable lexical‑semantic correspondences. Using whole‑brain encoding across Mandarin, English, and French, transformer representations predicted activity in a distributed network that overlapped across languages and remained stable across layers. Contextual embeddings and measures of prediction or compression did not outperform static lexical embeddings, suggesting that alignment is robust but not informative about shared computational processes.

By Ni Yang, Rui He, Philipp Homan, Iris Sommer, Davide Staub, Wolfram Hinzen

llms rag safety

arXiv AI

Aug 11

Topographic Constraints Shape Brain-Like Component Structure in Auditory Models

arXiv:2509. 24039v2 Announce Type: replace-cross Abstract: If topography is a fundamental feature of the brain, it should influence both how neurons are arranged in space (i.

By Haider Al-Tahan, Mayukh Deb, Jenelle Feather, N. Apurva Ratan Murty

safety

arXiv AI

Aug 11

Large Language Models Align with the Human Brain during Creative Thinking

arXiv:2604. 03480v2 Announce Type: replace-cross Abstract: Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine.

By Mete Ismayilzada, Simone A. Luchini, Abdulkadir Gokce, Badr AlKhamissi, Antoine Bosselut, Antonio Laverghetta Jr., Lonneke van der Plas, Roger E. Beaty

llms safety

arXiv AI

Jun 11

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

arXiv:2606. 11893v1 Announce Type: cross Abstract: The correspondence between large language models (LLMs) and the neural mechanisms underlying human higher-order cognition remains insufficiently characterized.

By Mingqing Xiao, Kai Du, Zhouchen Lin

llms fine-tuning safety

arXiv Machine Learning

Aug 7

MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis

arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.

By Yuanhao Chen, Peter Chin

llms rag benchmarks safety

arXiv AI

Aug 7

Layer-wise Positional Bias in Short-Context Language Modeling

arXiv:2601. 04098v2 Announce Type: replace-cross Abstract: Transformer language models systematically prefer tokens at specific input positions regardless of semantic relevance---a phenomenon known as positional bias.

By Maryam Rahimi, Mahdi Nouri, Yadollah Yaghoobzadeh

llms safety

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Related stories

Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts

Topographic Constraints Shape Brain-Like Component Structure in Auditory Models

Large Language Models Align with the Human Brain during Creative Thinking

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

MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis

Layer-wise Positional Bias in Short-Context Language Modeling