PaceLLM introduces brain-inspired persistent activity and cortical expert clustering to mitigate contextual decay and semantic fragmentation in LLMs, reporting 6% gains on LongBench multi-document QA and 12.5-17.5% on Infinite-Bench while reaching 200K-token NIAH performance.
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PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding
PaceLLM introduces brain-inspired persistent activity and cortical expert clustering to mitigate contextual decay and semantic fragmentation in LLMs, reporting 6% gains on LongBench multi-document QA and 12.5-17.5% on Infinite-Bench while reaching 200K-token NIAH performance.