@inproceedings{yang-etal-2026-towards,
title = "Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement",
author = "Yang, Cheng and
Yang, Xuemeng and
Wen, Licheng and
Fu, Daocheng and
Mei, Jianbiao and
Wu, Rong and
Cai, Pinlong and
Shen, Yufan and
Deng, Nianchen and
Xu, Jia and
Shi, Botian and
Qiao, Yu and
Li, Haifeng",
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.1522/",
pages = "30424--30451",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models often struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. To address this, we propose MUSE, a framework that enables iterative self-improvement through a hierarchical Memory Module. MUSE organizes cross-domain insights to facilitate the orchestration of long-horizon workflows. The core of our approach is an autonomous post-execution critique mechanism: after completing each sub-task, the system analyzes its operational logs and distills raw execution data into structured, reusable knowledge. This allows the agent to evolve dynamically rather than relying on fixed parameters. Evaluated on the rigorous TAC productivity benchmark, MUSE achieves new state-of-the-art results, significantly outperforming previous methods using only the streamlined Gemini-2.5 Flash model. Our analysis demonstrates that MUSE{'}s performance scales with the accumulation of insights and exhibits strong cross-task transferability, marking a key step toward autonomous systems capable of lifelong learning in professional environments. Demo videos can be found in our supplementary materials."
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<abstract>Large Language Models often struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. To address this, we propose MUSE, a framework that enables iterative self-improvement through a hierarchical Memory Module. MUSE organizes cross-domain insights to facilitate the orchestration of long-horizon workflows. The core of our approach is an autonomous post-execution critique mechanism: after completing each sub-task, the system analyzes its operational logs and distills raw execution data into structured, reusable knowledge. This allows the agent to evolve dynamically rather than relying on fixed parameters. Evaluated on the rigorous TAC productivity benchmark, MUSE achieves new state-of-the-art results, significantly outperforming previous methods using only the streamlined Gemini-2.5 Flash model. Our analysis demonstrates that MUSE’s performance scales with the accumulation of insights and exhibits strong cross-task transferability, marking a key step toward autonomous systems capable of lifelong learning in professional environments. Demo videos can be found in our supplementary materials.</abstract>
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%0 Conference Proceedings
%T Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement
%A Yang, Cheng
%A Yang, Xuemeng
%A Wen, Licheng
%A Fu, Daocheng
%A Mei, Jianbiao
%A Wu, Rong
%A Cai, Pinlong
%A Shen, Yufan
%A Deng, Nianchen
%A Xu, Jia
%A Shi, Botian
%A Qiao, Yu
%A Li, Haifeng
%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 yang-etal-2026-towards
%X Large Language Models often struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. To address this, we propose MUSE, a framework that enables iterative self-improvement through a hierarchical Memory Module. MUSE organizes cross-domain insights to facilitate the orchestration of long-horizon workflows. The core of our approach is an autonomous post-execution critique mechanism: after completing each sub-task, the system analyzes its operational logs and distills raw execution data into structured, reusable knowledge. This allows the agent to evolve dynamically rather than relying on fixed parameters. Evaluated on the rigorous TAC productivity benchmark, MUSE achieves new state-of-the-art results, significantly outperforming previous methods using only the streamlined Gemini-2.5 Flash model. Our analysis demonstrates that MUSE’s performance scales with the accumulation of insights and exhibits strong cross-task transferability, marking a key step toward autonomous systems capable of lifelong learning in professional environments. Demo videos can be found in our supplementary materials.
%U https://aclanthology.org/2026.findings-acl.1522/
%P 30424-30451
Markdown (Informal)
[Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement](https://aclanthology.org/2026.findings-acl.1522/) (Yang et al., Findings 2026)
ACL
- Cheng Yang, Xuemeng Yang, Licheng Wen, Daocheng Fu, Jianbiao Mei, Rong Wu, Pinlong Cai, Yufan Shen, Nianchen Deng, Jia Xu, Botian Shi, Yu Qiao, and Haifeng Li. 2026. Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement. In Findings of the Association for Computational Linguistics: ACL 2026, pages 30424–30451, San Diego, California, United States. Association for Computational Linguistics.