TiMem introduces a Temporal Memory Tree that consolidates conversational history into hierarchical persona representations, reaching 75.30% on LoCoMo and 76.88% on LongMemEval-S while cutting recalled length by 52%.
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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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TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents
TiMem introduces a Temporal Memory Tree that consolidates conversational history into hierarchical persona representations, reaching 75.30% on LoCoMo and 76.88% on LongMemEval-S while cutting recalled length by 52%.
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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.