@inproceedings{feng-etal-2026-agentswing,
title = "{A}gent{S}wing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents",
author = "Feng, Zhaopeng and
Su, Liangcai and
Zhang, Zhen and
Wang, Xinyu and
Wang, Xiaobin and
Fang, Runnan and
Zhang, Xiaotian and
Chen, Hui and
Zhang, Qi and
Zhou, Joey Tianyi and
Xie, Pengjun and
Qian, Chenxiong and
Hooi, Bryan and
Liu, Zuozhu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.750/",
pages = "15789--15808",
ISBN = "979-8-89176-462-0",
abstract = "As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing context management methods typically commit to a single fixed strategy throughout the entire trajectory. Such static designs may work well in some states, but they cannot adapt as the usefulness and reliability of the accumulated context evolve during long-horizon search. To formalize this challenge, we introduce a probabilistic framework that characterizes long-horizon success through two complementary dimensions: search efficiency and terminal precision. Building on this perspective, we propose AgentSwing, a state-aware adaptive parallel context management routing framework. At each trigger point, AgentSwing expands multiple context-managed branches in parallel and uses lookahead routing to select the most promising continuation. Experiments across diverse benchmarks and agent backbones show that AgentSwing consistently outperforms strong static context management methods, often matching or exceeding their performance with substantially fewer interaction turns while also improving the ultimate performance ceiling of long-horizon web agents. Beyond the empirical gains, the proposed probabilistic framework provides a principled lens for analyzing and designing future context management strategies for long-horizon agents."
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<abstract>As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing context management methods typically commit to a single fixed strategy throughout the entire trajectory. Such static designs may work well in some states, but they cannot adapt as the usefulness and reliability of the accumulated context evolve during long-horizon search. To formalize this challenge, we introduce a probabilistic framework that characterizes long-horizon success through two complementary dimensions: search efficiency and terminal precision. Building on this perspective, we propose AgentSwing, a state-aware adaptive parallel context management routing framework. At each trigger point, AgentSwing expands multiple context-managed branches in parallel and uses lookahead routing to select the most promising continuation. Experiments across diverse benchmarks and agent backbones show that AgentSwing consistently outperforms strong static context management methods, often matching or exceeding their performance with substantially fewer interaction turns while also improving the ultimate performance ceiling of long-horizon web agents. Beyond the empirical gains, the proposed probabilistic framework provides a principled lens for analyzing and designing future context management strategies for long-horizon agents.</abstract>
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%0 Conference Proceedings
%T AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents
%A Feng, Zhaopeng
%A Su, Liangcai
%A Zhang, Zhen
%A Wang, Xinyu
%A Wang, Xiaobin
%A Fang, Runnan
%A Zhang, Xiaotian
%A Chen, Hui
%A Zhang, Qi
%A Zhou, Joey Tianyi
%A Xie, Pengjun
%A Qian, Chenxiong
%A Hooi, Bryan
%A Liu, Zuozhu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F feng-etal-2026-agentswing
%X As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing context management methods typically commit to a single fixed strategy throughout the entire trajectory. Such static designs may work well in some states, but they cannot adapt as the usefulness and reliability of the accumulated context evolve during long-horizon search. To formalize this challenge, we introduce a probabilistic framework that characterizes long-horizon success through two complementary dimensions: search efficiency and terminal precision. Building on this perspective, we propose AgentSwing, a state-aware adaptive parallel context management routing framework. At each trigger point, AgentSwing expands multiple context-managed branches in parallel and uses lookahead routing to select the most promising continuation. Experiments across diverse benchmarks and agent backbones show that AgentSwing consistently outperforms strong static context management methods, often matching or exceeding their performance with substantially fewer interaction turns while also improving the ultimate performance ceiling of long-horizon web agents. Beyond the empirical gains, the proposed probabilistic framework provides a principled lens for analyzing and designing future context management strategies for long-horizon agents.
%U https://aclanthology.org/2026.findings-emnlp.750/
%P 15789-15808
Markdown (Informal)
[AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents](https://aclanthology.org/2026.findings-emnlp.750/) (Feng et al., Findings 2026)
ACL
- Zhaopeng Feng, Liangcai Su, Zhen Zhang, Xinyu Wang, Xiaobin Wang, Runnan Fang, Xiaotian Zhang, Hui Chen, Qi Zhang, Joey Tianyi Zhou, Pengjun Xie, Chenxiong Qian, Bryan Hooi, and Zuozhu Liu. 2026. AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 15789–15808, Budapest, Hungary. Association for Computational Linguistics.