AgentFold: Long-Horizon Web Agents with Proactive Context Management
Authors: name: Rui Ye; name: Zhongwang Zhang; name: Kuan Li; name: Huifeng Yin; name: Zhengwei Tao; name: Yida Zhao; name: Liangcai Su; name: Liwen Zhang; name: Zile Qiao; name: Xinyu Wang; name: Pengjun Xie; name: Fei Huang; name: Siheng Chen; name: Jingren Zhou; name: Yong Jiang
Published: 2025-10-28 17:51:50

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 deep consolidations
to abstract away entire multi-step sub-tasks. The results on prominent
benchmarks are striking: with simple supervised fine-tuning (without continual
pre-training or RL), our AgentFold-30B-A3B agent achieves 36.2% on BrowseComp
and 47.3% on BrowseComp-ZH. Notably, this performance not only surpasses or
matches open-source models of a dramatically larger scale, such as the
DeepSeek-V3.1-671B-A37B, but also surpasses leading proprietary agents like
OpenAI's o4-mini.