AI RESEARCH
PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding
arXiv CS.CL
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ArXi:2506.17310v3 Announce Type: replace-cross While Large Language Models (LLMs) nstrate strong performance across domains, their long-context capabilities are limited by transient neural activations causing information decay and unstructured feed-forward network (FFN) weights leading to semantic fragmentation. Inspired by the brain's working memory and cortical modularity, we propose PaceLLM, featuring two innovations: (1) a Persistent Activity (PA) Mechanism that mimics prefrontal cortex (PFC) neurons' persistent firing by.