@inproceedings{ma-etal-2026-deserves,
title = "What Deserves Memory: Adaptive Memory Distillation for {LLM} Agents",
author = "Ma, Wenquan and
Nan, Jiayan and
Wu, WenLong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1607/",
doi = "10.18653/v1/2026.acl-long.1607",
pages = "34789--34812",
ISBN = "979-8-89176-390-6",
abstract = "Memory systems for LLM agents struggle to determine what information deserves retention. Existing approaches rely on predefined heuristics such as importance scores, emotional tags, or factual templates, encoding designer intuition rather than learning from the data itself. Inspired by cognitive ideas, we propose \textbf{Nemori}, an adaptive memory distillation framework that casts the assessment of the experience{'}s future utility as a matter of predictability. Specifically, Nemori comprises two cascading modules: Episodic Memory Integration transforms raw interactions into coherent narratives, and Semantic Knowledge Distillation extracts insights via prediction error. Centering on distillation, the framework remains agnostic to downstream management. Extensive experiments confirm that Nemori achieves strong performance, efficiency, and storage reduction. Our work suggests that observing the intrinsic properties of interaction sequences offers a viable, data-driven alternative to heuristic-based memory design."
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<abstract>Memory systems for LLM agents struggle to determine what information deserves retention. Existing approaches rely on predefined heuristics such as importance scores, emotional tags, or factual templates, encoding designer intuition rather than learning from the data itself. Inspired by cognitive ideas, we propose Nemori, an adaptive memory distillation framework that casts the assessment of the experience’s future utility as a matter of predictability. Specifically, Nemori comprises two cascading modules: Episodic Memory Integration transforms raw interactions into coherent narratives, and Semantic Knowledge Distillation extracts insights via prediction error. Centering on distillation, the framework remains agnostic to downstream management. Extensive experiments confirm that Nemori achieves strong performance, efficiency, and storage reduction. Our work suggests that observing the intrinsic properties of interaction sequences offers a viable, data-driven alternative to heuristic-based memory design.</abstract>
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%0 Conference Proceedings
%T What Deserves Memory: Adaptive Memory Distillation for LLM Agents
%A Ma, Wenquan
%A Nan, Jiayan
%A Wu, WenLong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ma-etal-2026-deserves
%X Memory systems for LLM agents struggle to determine what information deserves retention. Existing approaches rely on predefined heuristics such as importance scores, emotional tags, or factual templates, encoding designer intuition rather than learning from the data itself. Inspired by cognitive ideas, we propose Nemori, an adaptive memory distillation framework that casts the assessment of the experience’s future utility as a matter of predictability. Specifically, Nemori comprises two cascading modules: Episodic Memory Integration transforms raw interactions into coherent narratives, and Semantic Knowledge Distillation extracts insights via prediction error. Centering on distillation, the framework remains agnostic to downstream management. Extensive experiments confirm that Nemori achieves strong performance, efficiency, and storage reduction. Our work suggests that observing the intrinsic properties of interaction sequences offers a viable, data-driven alternative to heuristic-based memory design.
%R 10.18653/v1/2026.acl-long.1607
%U https://aclanthology.org/2026.acl-long.1607/
%U https://doi.org/10.18653/v1/2026.acl-long.1607
%P 34789-34812
Markdown (Informal)
[What Deserves Memory: Adaptive Memory Distillation for LLM Agents](https://aclanthology.org/2026.acl-long.1607/) (Ma et al., ACL 2026)
ACL
- Wenquan Ma, Jiayan Nan, and WenLong Wu. 2026. What Deserves Memory: Adaptive Memory Distillation for LLM Agents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 34789–34812, San Diego, California, United States. Association for Computational Linguistics.