@inproceedings{yao-etal-2026-arc,
title = "{ARC}: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents",
author = "Yao, Yilun and
Huang, Shan and
Dai, Elsie and
Tan, Zhewen and
Duan, Zhenyu and
Jia, Shousheng and
Jiang, Yanbing and
Yang, Tong",
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.930/",
pages = "18644--18659",
ISBN = "979-8-89176-395-1",
abstract = "Large language models are increasingly deployed as research agents for deep search and long-horizon information seeking, yet their performance often degrades as interaction histories grow. This degradation, known as context rot, reflects a failure to maintain coherent and task-relevant internal states over extended reasoning horizons. Existing approaches primarily manage context through raw accumulation or passive summarization, treating it as a static artifact and allowing early errors or misplaced emphasis to persist. Motivated by this perspective, we propose ARC, which is the first framework to systematically formulate context management as an active, reflection-driven process that treats context as a dynamic internal reasoning state during execution. ARC operationalizes this view through reflection-driven monitoring and revision, allowing agents to actively reorganize their working context when misalignment or degradation is detected. Experiments on challenging long-horizon information-seeking benchmarks show that ARC consistently outperforms passive context compression methods, achieving up to an 11{\%} absolute improvement in accuracy on BrowseComp-ZH with Qwen2.5-32B-Instruct."
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<abstract>Large language models are increasingly deployed as research agents for deep search and long-horizon information seeking, yet their performance often degrades as interaction histories grow. This degradation, known as context rot, reflects a failure to maintain coherent and task-relevant internal states over extended reasoning horizons. Existing approaches primarily manage context through raw accumulation or passive summarization, treating it as a static artifact and allowing early errors or misplaced emphasis to persist. Motivated by this perspective, we propose ARC, which is the first framework to systematically formulate context management as an active, reflection-driven process that treats context as a dynamic internal reasoning state during execution. ARC operationalizes this view through reflection-driven monitoring and revision, allowing agents to actively reorganize their working context when misalignment or degradation is detected. Experiments on challenging long-horizon information-seeking benchmarks show that ARC consistently outperforms passive context compression methods, achieving up to an 11% absolute improvement in accuracy on BrowseComp-ZH with Qwen2.5-32B-Instruct.</abstract>
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%0 Conference Proceedings
%T ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents
%A Yao, Yilun
%A Huang, Shan
%A Dai, Elsie
%A Tan, Zhewen
%A Duan, Zhenyu
%A Jia, Shousheng
%A Jiang, Yanbing
%A Yang, Tong
%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 yao-etal-2026-arc
%X Large language models are increasingly deployed as research agents for deep search and long-horizon information seeking, yet their performance often degrades as interaction histories grow. This degradation, known as context rot, reflects a failure to maintain coherent and task-relevant internal states over extended reasoning horizons. Existing approaches primarily manage context through raw accumulation or passive summarization, treating it as a static artifact and allowing early errors or misplaced emphasis to persist. Motivated by this perspective, we propose ARC, which is the first framework to systematically formulate context management as an active, reflection-driven process that treats context as a dynamic internal reasoning state during execution. ARC operationalizes this view through reflection-driven monitoring and revision, allowing agents to actively reorganize their working context when misalignment or degradation is detected. Experiments on challenging long-horizon information-seeking benchmarks show that ARC consistently outperforms passive context compression methods, achieving up to an 11% absolute improvement in accuracy on BrowseComp-ZH with Qwen2.5-32B-Instruct.
%U https://aclanthology.org/2026.findings-acl.930/
%P 18644-18659
Markdown (Informal)
[ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents](https://aclanthology.org/2026.findings-acl.930/) (Yao et al., Findings 2026)
ACL
- Yilun Yao, Shan Huang, Elsie Dai, Zhewen Tan, Zhenyu Duan, Shousheng Jia, Yanbing Jiang, and Tong Yang. 2026. ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 18644–18659, San Diego, California, United States. Association for Computational Linguistics.