@inproceedings{ye-etal-2026-memweaver,
title = "{M}em{W}eaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning",
author = "Ye, Juexiang and
Li, Xue and
Xinyu, Yang and
Huang, Chengkai and
Nie, Lanshun and
Yao, Lina and
Zhan, Dechen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.630/",
pages = "12928--12956",
ISBN = "979-8-89176-395-1",
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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<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.</abstract>
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%0 Conference Proceedings
%T MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning
%A Ye, Juexiang
%A Li, Xue
%A Xinyu, Yang
%A Huang, Chengkai
%A Nie, Lanshun
%A Yao, Lina
%A Zhan, Dechen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ye-etal-2026-memweaver
%X 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.
%U https://aclanthology.org/2026.findings-acl.630/
%P 12928-12956
Markdown (Informal)
[MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning](https://aclanthology.org/2026.findings-acl.630/) (Ye et al., Findings 2026)
ACL
- Juexiang Ye, Xue Li, Yang Xinyu, Chengkai Huang, Lanshun Nie, Lina Yao, and Dechen Zhan. 2026. MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12928–12956, San Diego, California, United States. Association for Computational Linguistics.