MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning

• URL: http://arxiv.org/abs/2601.18204v1
• Date: Mon, 26 Jan 2026 06:39:27 GMT
• Title: MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning
• Authors: Juexiang Ye, Xue Li, Xinyu Yang, Chengkai Huang, Lanshun Nie, Lina Yao, Dechen Zhan,
• Abstract summary: We propose a unified memory framework that consolidates long-term agent experiences into three interconnected components.<n>MemWeaver employs a dual-channel retrieval strategy that jointly retrieves structured knowledge and supporting evidence to construct compact yet information-dense contexts.
• Score: 26.119505362626338
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language model-based agents operating in long-horizon interactions require memory systems that support temporal consistency, multi-hop reasoning, and evidence-grounded reuse across sessions. Existing approaches largely rely on unstructured retrieval or coarse abstractions, which often lead to temporal conflicts, brittle reasoning, and limited traceability. We propose MemWeaver, a unified memory framework that consolidates long-term agent experiences into three interconnected components: a temporally grounded graph memory for structured relational reasoning, an experience memory that abstracts recurring interaction patterns from repeated observations, and a passage memory that preserves original textual evidence. MemWeaver employs a dual-channel retrieval strategy that jointly retrieves structured knowledge and supporting evidence to construct compact yet information-dense contexts for reasoning. Experiments on the LoCoMo benchmark demonstrate that MemWeaver substantially improves multi-hop and temporal reasoning accuracy while reducing input context length by over 95\% compared to long-context baselines.

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