@inproceedings{fang-etal-2026-memp,
title = "Memp: Exploring Agent Procedural Memory",
author = "Fang, Runnan and
Liang, Yuan and
Wang, Xiaobin and
Wu, Jialong and
Qiao, Shuofei and
Xie, Pengjun and
Huang, Fei and
Chen, Huajun and
Zhang, Ningyu",
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.866/",
pages = "17490--17502",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We propose a procedural-memory repository that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions. Coupled with a dynamic regimen that continuously updates, corrects, and deprecates its contents, this repository evolves in lockstep with new experience. Empirical evaluation on TravelPlanner and Alfworld shows that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks. Moreover, procedural memory built from a stronger model retains its value: migrating the procedural memory to a weaker model yields substantial performance gains."
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<abstract>Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We propose a procedural-memory repository that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions. Coupled with a dynamic regimen that continuously updates, corrects, and deprecates its contents, this repository evolves in lockstep with new experience. Empirical evaluation on TravelPlanner and Alfworld shows that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks. Moreover, procedural memory built from a stronger model retains its value: migrating the procedural memory to a weaker model yields substantial performance gains.</abstract>
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%0 Conference Proceedings
%T Memp: Exploring Agent Procedural Memory
%A Fang, Runnan
%A Liang, Yuan
%A Wang, Xiaobin
%A Wu, Jialong
%A Qiao, Shuofei
%A Xie, Pengjun
%A Huang, Fei
%A Chen, Huajun
%A Zhang, Ningyu
%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 fang-etal-2026-memp
%X Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We propose a procedural-memory repository that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions. Coupled with a dynamic regimen that continuously updates, corrects, and deprecates its contents, this repository evolves in lockstep with new experience. Empirical evaluation on TravelPlanner and Alfworld shows that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks. Moreover, procedural memory built from a stronger model retains its value: migrating the procedural memory to a weaker model yields substantial performance gains.
%U https://aclanthology.org/2026.findings-acl.866/
%P 17490-17502
Markdown (Informal)
[Memp: Exploring Agent Procedural Memory](https://aclanthology.org/2026.findings-acl.866/) (Fang et al., Findings 2026)
ACL
- Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. 2026. Memp: Exploring Agent Procedural Memory. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17490–17502, San Diego, California, United States. Association for Computational Linguistics.