AgentFold: Long-Horizon Web Agents with Proactive Context Management
Rui Ye, Zhongwang Zhang, Kuan Li, Huifeng Yin, Zhengwei Tao, Yida Zhao, Liangcai Su, Liwen Zhang, Zile Qiao, Xinyu Wang, Pengjun Xie, Fei Huang, Siheng Chen, Jingren Zhou, Yong Jiang

TL;DR
AgentFold introduces a proactive context management approach for long-horizon web agents, dynamically consolidating history at multiple scales to improve performance on information-seeking tasks without extensive retraining.
Contribution
It proposes a novel paradigm where agents actively sculpt their context through folding operations, enhancing long-term memory and task performance compared to existing methods.
Findings
Achieves 36.2% on BrowseComp benchmark.
Surpasses larger open-source models and proprietary agents in performance.
Operates effectively with simple supervised fine-tuning without RL.
Abstract
LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that fixedly summarize the full history at each step risk the irreversible loss of critical details. Addressing these, we introduce AgentFold, a novel agent paradigm centered on proactive context management, inspired by the human cognitive process of retrospective consolidation. AgentFold treats its context as a dynamic cognitive workspace to be actively sculpted, rather than a passive log to be filled. At each step, it learns to execute a `folding' operation, which manages its historical trajectory at multiple scales: it can perform granular condensations to preserve vital, fine-grained details, or…
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