@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://preview.aclanthology.org/ingest-acl/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."
}