A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning
This study investigates whether large language models (LLMs) exhibit human brain–like mechanisms of information integration and how such mechanisms influence learning and behavior. Drawing on information decomposition theory, the authors conduct cross-architectural analyses, ablation studies, and comparisons between reinforcement learning and supervised fine-tuning. They reveal, for the first time, a high-cooperativity information processing core in intermediate layers of LLMs, whose organizational pattern closely resembles that of the human brain and emerges spontaneously during training. Ablating this cooperative region significantly impairs model performance, while targeted fine-tuning of this region yields substantially greater improvements than fine-tuning redundant regions, confirming its critical role in intelligent behavior. These findings further inform a novel, efficient fine-tuning strategy centered on cooperative neural substrates.