The paper presents a network-based framework that links cognitive skills, LLM architectures, and datasets to uncover the cognitive patterns of Large Language Models. It identifies module communities within LLMs that exhibit skill distributions partially mirroring distributed cognitive organization seen in avian and small mammalian brains, yet distinct from the focalized specialization of specific biological systems. The study highlights that LLM skill acquisition benefits from dynamic, cross-regional interactions and neural plasticity, suggesting fine-tuning should leverage distributed learning dynamics rather than rigid modular interventions.
By Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao
arXiv:2607. 26179v1 Announce Type: cross Abstract: LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own.
By Chandra Sripada, Richard Lewis
arXiv:2609.01170v1 Announce Type: new
Abstract: Large language models exhibit a modular internal organization that mirrors well-studied functional networks of the human brain, but how this organizati...
By Guangqi Li, Yongxin Li
arXiv:2607. 19973v1 Announce Type: new Abstract: AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech.
By Jaeho Seol
arXiv:2607. 00397v1 Announce Type: cross Abstract: Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognition.
By Zhongxiang Sun, Haolang Lu, Qiang Ma, Qi Li, Qipeng Wang, Liang Pang, Chenyu Liu, Qiankun Li, Hao Sun, Kun Wang, Yi Zeng, Jun Xu, Guoqi Li, Ji-Rong Wen
arXiv:2606. 04025v1 Announce Type: cross Abstract: Dominant programming paradigms inherit an execution model optimised for a bygone era of a single human mind instructing a local machine, leaving contemporary systems burdened with historical path dependencies.
By Philip Sheldrake, Dirk Scheffler
arXiv:2608.29530v1 Announce Type: cross
Abstract: Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modele...
By R. Thomas McCoy, Paul Soulos, Tal Linzen, Paul Smolensky
The paper investigates whether current reasoning models exhibit systematicity—the idea that understanding one concept should extend to closely related variations—by extending rule induction tasks from cognitive science. Using task isomorphisms like recombination and substitution, the authors generate structurally equivalent task variants and test models on them. Results show that while models can solve the original tasks, they frequently fail on these equivalent variants, indicating a lack of systematicity in their reasoning abilities.
By Simon Schug, Brenden M. Lake
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
By Seokhoon Jeong, Mijung Kim, Taehwan Kim
arXiv:2509. 14474v3 Announce Type: replace Abstract: The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance versus replicating human-like cognitive processes.
By Meltem Subasioglu, Nevzat Subasioglu
The study investigates whether language models tailored to specific cognitive domains better align with corresponding brain systems. By prompting and fine‑tuning large language models into six domain experts—sensory, spatial, numerical, reasoning, social, and abstract—the authors find that each expert’s representations more closely match the brain region associated with its domain than other experts. This domain‑specific alignment holds across multiple base models and fMRI datasets, while overall prediction accuracy remains largely unchanged, indicating that regional alignment can be obscured when summarizing across the brain.
By Zhivar Sourati, Mengxuan Helen Wu, Nona Ghazizadeh, Jonas Kaplan, Morteza Dehghani, Samuel A. Nastase
arXiv:2606. 07722v1 Announce Type: new Abstract: This article offers a perspective on the nature of chatbots as genuine conversation partners when discussing problems in relation to their solutions.
By S. F. M. van Vlijmen, H. D. Lethe jr