@inproceedings{lan-etal-2026-ema,
title = "{EMA}: An Episodic Memory Agent for Efficient and Selective Memory",
author = "Lan, Hongyi and
Song, Jiaqi and
Zhong, Zhengjia and
Li, Hui and
Liu, Hong and
Lin, Xianming and
Ji, Rongrong",
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.250/",
pages = "5088--5102",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) demonstrate strong generation and reasoning abilities, but they still face challenges in long-term memory retention and multi-turn conversational consistency. Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency. Inspired by the episodic memory mechanism in human cognition, we abstract conversational context into Episodic Memory Units (EMUs). We then propose a comprehensive framework, Episodic Memory Agent (EMA), along with a filtering decision module called MemDecider. Specifically, EMA organizes and retrieves EMUs to support response generation, while MemDecider filters information to reduce noise and improve overall performance. Experiments on two widely-used benchmarks show that EMA maintains competitive performance, and integrating MemDecider into other methods reduces their token consumption by an average of 11.48{\%} while effectively improving the overall performance. Code is available at https://github.com/Hongyi4221/EMA."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lan-etal-2026-ema">
<titleInfo>
<title>EMA: An Episodic Memory Agent for Efficient and Selective Memory</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hongyi</namePart>
<namePart type="family">Lan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiaqi</namePart>
<namePart type="family">Song</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhengjia</namePart>
<namePart type="family">Zhong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hui</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hong</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianming</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rongrong</namePart>
<namePart type="family">Ji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Large Language Models (LLMs) demonstrate strong generation and reasoning abilities, but they still face challenges in long-term memory retention and multi-turn conversational consistency. Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency. Inspired by the episodic memory mechanism in human cognition, we abstract conversational context into Episodic Memory Units (EMUs). We then propose a comprehensive framework, Episodic Memory Agent (EMA), along with a filtering decision module called MemDecider. Specifically, EMA organizes and retrieves EMUs to support response generation, while MemDecider filters information to reduce noise and improve overall performance. Experiments on two widely-used benchmarks show that EMA maintains competitive performance, and integrating MemDecider into other methods reduces their token consumption by an average of 11.48% while effectively improving the overall performance. Code is available at https://github.com/Hongyi4221/EMA.</abstract>
<identifier type="citekey">lan-etal-2026-ema</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.250/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>5088</start>
<end>5102</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T EMA: An Episodic Memory Agent for Efficient and Selective Memory
%A Lan, Hongyi
%A Song, Jiaqi
%A Zhong, Zhengjia
%A Li, Hui
%A Liu, Hong
%A Lin, Xianming
%A Ji, Rongrong
%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 lan-etal-2026-ema
%X Large Language Models (LLMs) demonstrate strong generation and reasoning abilities, but they still face challenges in long-term memory retention and multi-turn conversational consistency. Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency. Inspired by the episodic memory mechanism in human cognition, we abstract conversational context into Episodic Memory Units (EMUs). We then propose a comprehensive framework, Episodic Memory Agent (EMA), along with a filtering decision module called MemDecider. Specifically, EMA organizes and retrieves EMUs to support response generation, while MemDecider filters information to reduce noise and improve overall performance. Experiments on two widely-used benchmarks show that EMA maintains competitive performance, and integrating MemDecider into other methods reduces their token consumption by an average of 11.48% while effectively improving the overall performance. Code is available at https://github.com/Hongyi4221/EMA.
%U https://aclanthology.org/2026.findings-acl.250/
%P 5088-5102
Markdown (Informal)
[EMA: An Episodic Memory Agent for Efficient and Selective Memory](https://aclanthology.org/2026.findings-acl.250/) (Lan et al., Findings 2026)
ACL
- Hongyi Lan, Jiaqi Song, Zhengjia Zhong, Hui Li, Hong Liu, Xianming Lin, and Rongrong Ji. 2026. EMA: An Episodic Memory Agent for Efficient and Selective Memory. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5088–5102, San Diego, California, United States. Association for Computational Linguistics.