@inproceedings{cao-etal-2026-remember,
title = "Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution",
author = "Cao, Zouying and
Deng, Jiaji and
Yu, Li and
Zhou, Weikang and
Liu, Zhaoyang and
Ding, Bolin and
Zhao, Hai",
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.829/",
pages = "16803--16822",
ISBN = "979-8-89176-395-1",
abstract = "Procedural memory enables large language model (LLM) agents to internalize ``how-to'' knowledge and thus reduce redundant trial-and-error. However, existing frameworks predominantly suffer from a ``passive accumulation'' paradigm, treating memory as a static append-only archive. To bridge the gap between static storage and dynamic reasoning, we propose ReMe (Remember Me, Refine Me), a comprehensive framework for experience-driven agent evolution. ReMe manages the memory lifecycle via three mechanisms: 1) multi-faceted distillation, which extracts fine-grained experiences by recognizing success patterns, analyzing failure triggers and generating comparative insights; 2) context-adaptive reuse, which tailors historical insights to new contexts through scenario-aware indexing; and 3) utility-based refinement, which automatically adds validated memories and prunes outdated ones to maintain a compact, high-quality experience pool. Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system. Crucially, we observe a significant memory-scaling effect: Qwen3-8B equipped with ReMe outperforms larger, memoryless Qwen3-14B, indicating that self-evolving memory provides a computation-efficient path for lifelong learning."
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<abstract>Procedural memory enables large language model (LLM) agents to internalize “how-to” knowledge and thus reduce redundant trial-and-error. However, existing frameworks predominantly suffer from a “passive accumulation” paradigm, treating memory as a static append-only archive. To bridge the gap between static storage and dynamic reasoning, we propose ReMe (Remember Me, Refine Me), a comprehensive framework for experience-driven agent evolution. ReMe manages the memory lifecycle via three mechanisms: 1) multi-faceted distillation, which extracts fine-grained experiences by recognizing success patterns, analyzing failure triggers and generating comparative insights; 2) context-adaptive reuse, which tailors historical insights to new contexts through scenario-aware indexing; and 3) utility-based refinement, which automatically adds validated memories and prunes outdated ones to maintain a compact, high-quality experience pool. Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system. Crucially, we observe a significant memory-scaling effect: Qwen3-8B equipped with ReMe outperforms larger, memoryless Qwen3-14B, indicating that self-evolving memory provides a computation-efficient path for lifelong learning.</abstract>
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%0 Conference Proceedings
%T Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
%A Cao, Zouying
%A Deng, Jiaji
%A Yu, Li
%A Zhou, Weikang
%A Liu, Zhaoyang
%A Ding, Bolin
%A Zhao, Hai
%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 cao-etal-2026-remember
%X Procedural memory enables large language model (LLM) agents to internalize “how-to” knowledge and thus reduce redundant trial-and-error. However, existing frameworks predominantly suffer from a “passive accumulation” paradigm, treating memory as a static append-only archive. To bridge the gap between static storage and dynamic reasoning, we propose ReMe (Remember Me, Refine Me), a comprehensive framework for experience-driven agent evolution. ReMe manages the memory lifecycle via three mechanisms: 1) multi-faceted distillation, which extracts fine-grained experiences by recognizing success patterns, analyzing failure triggers and generating comparative insights; 2) context-adaptive reuse, which tailors historical insights to new contexts through scenario-aware indexing; and 3) utility-based refinement, which automatically adds validated memories and prunes outdated ones to maintain a compact, high-quality experience pool. Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system. Crucially, we observe a significant memory-scaling effect: Qwen3-8B equipped with ReMe outperforms larger, memoryless Qwen3-14B, indicating that self-evolving memory provides a computation-efficient path for lifelong learning.
%U https://aclanthology.org/2026.findings-acl.829/
%P 16803-16822
Markdown (Informal)
[Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/) (Cao et al., Findings 2026)
ACL
- Zouying Cao, Jiaji Deng, Li Yu, Weikang Zhou, Zhaoyang Liu, Bolin Ding, and Hai Zhao. 2026. Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution. In Findings of the Association for Computational Linguistics: ACL 2026, pages 16803–16822, San Diego, California, United States. Association for Computational Linguistics.