@inproceedings{pei-etal-2026-eco,
title = "{ECO}-Mem: An Event-Centric Organization Framework for Visibility-Controlled Long-Term Memory",
author = "Pei, Xing and
Song, Dandan and
Wu, Zhijing and
Tian, Yuhang and
Yang, Jun and
Ma, Yitian and
Li, Yading and
Li, Xudong and
Ma, Huipeng and
Xu, Yan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1817/",
pages = "38825--38843",
ISBN = "979-8-89176-462-0",
abstract = "Large language models have achieved strong single-turn language understanding and generation abilities, but long-term agent applications require models to handle dynamic interaction history beyond an isolated inference call. Existing agent memory systems address this need through external storage and retrieval and have achieved promising results. However, many existing frameworks rely on full storage or highly structured memory representations, which can increase cost and noise while disrupting event coherence. Inspired by cognitive views of event organization and adaptive forgetting, we introduce ECO-Mem, an event-centric organization framework for visibility-controlled long-term memory. ECO-Mem segments dialogue into source-linked event episodes and connects them through typed event relations, representing memory as linked events rather than isolated facts. It implements forgetting as non-destructive visibility control instead of destructive deletion. At query time, it traverses the event-relation graph to identify relevant hidden support and reactivates it only when the source evidence confirms the query context. Experiments on LoCoMo and RealTalk validate ECO-Mem{'}s effectiveness and efficiency for long-term agent memory."
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<abstract>Large language models have achieved strong single-turn language understanding and generation abilities, but long-term agent applications require models to handle dynamic interaction history beyond an isolated inference call. Existing agent memory systems address this need through external storage and retrieval and have achieved promising results. However, many existing frameworks rely on full storage or highly structured memory representations, which can increase cost and noise while disrupting event coherence. Inspired by cognitive views of event organization and adaptive forgetting, we introduce ECO-Mem, an event-centric organization framework for visibility-controlled long-term memory. ECO-Mem segments dialogue into source-linked event episodes and connects them through typed event relations, representing memory as linked events rather than isolated facts. It implements forgetting as non-destructive visibility control instead of destructive deletion. At query time, it traverses the event-relation graph to identify relevant hidden support and reactivates it only when the source evidence confirms the query context. Experiments on LoCoMo and RealTalk validate ECO-Mem’s effectiveness and efficiency for long-term agent memory.</abstract>
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%0 Conference Proceedings
%T ECO-Mem: An Event-Centric Organization Framework for Visibility-Controlled Long-Term Memory
%A Pei, Xing
%A Song, Dandan
%A Wu, Zhijing
%A Tian, Yuhang
%A Yang, Jun
%A Ma, Yitian
%A Li, Yading
%A Li, Xudong
%A Ma, Huipeng
%A Xu, Yan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F pei-etal-2026-eco
%X Large language models have achieved strong single-turn language understanding and generation abilities, but long-term agent applications require models to handle dynamic interaction history beyond an isolated inference call. Existing agent memory systems address this need through external storage and retrieval and have achieved promising results. However, many existing frameworks rely on full storage or highly structured memory representations, which can increase cost and noise while disrupting event coherence. Inspired by cognitive views of event organization and adaptive forgetting, we introduce ECO-Mem, an event-centric organization framework for visibility-controlled long-term memory. ECO-Mem segments dialogue into source-linked event episodes and connects them through typed event relations, representing memory as linked events rather than isolated facts. It implements forgetting as non-destructive visibility control instead of destructive deletion. At query time, it traverses the event-relation graph to identify relevant hidden support and reactivates it only when the source evidence confirms the query context. Experiments on LoCoMo and RealTalk validate ECO-Mem’s effectiveness and efficiency for long-term agent memory.
%U https://aclanthology.org/2026.findings-emnlp.1817/
%P 38825-38843
Markdown (Informal)
[ECO-Mem: An Event-Centric Organization Framework for Visibility-Controlled Long-Term Memory](https://aclanthology.org/2026.findings-emnlp.1817/) (Pei et al., Findings 2026)
ACL
- Xing Pei, Dandan Song, Zhijing Wu, Yuhang Tian, Jun Yang, Yitian Ma, Yading Li, Xudong Li, Huipeng Ma, and Yan Xu. 2026. ECO-Mem: An Event-Centric Organization Framework for Visibility-Controlled Long-Term Memory. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 38825–38843, Budapest, Hungary. Association for Computational Linguistics.