AgentFold: Long-Horizon Web Agents with Proactive Context Management
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.
69017ce1646208eac0d1f3d6
Rui Ye
claimed_verified
2026-03-17T08:32:22.413Z
63ecd42a60ff4b318ad1ef47
/avatars/c05001fa9c41f713d1fe31d11212faae.svg
RuiYe
ruiye-sjtu
user
ruiye-sjtu
69017ce1646208eac0d1f3d7
Zhongwang Zhang
69017ce1646208eac0d1f3d8
Kuan Li
69017ce1646208eac0d1f3d9
Huifeng Yin
69017ce1646208eac0d1f3da
Zhengwei Tao
69017ce1646208eac0d1f3db
Yida Zhao
claimed_verified
2025-10-29T12:45:01.706Z
66e4019518a1920fb7ca19d7
/avatars/4f8be2f3244239510235c2ec6e8d38a6.svg
Yida Zhao
zhaoyd
user
zhaoyd
69017ce1646208eac0d1f3dc
Liangcai Su
claimed_verified
2025-10-29T12:44:38.182Z
677f945ea82c316db164a180
/avatars/50ec99d971564944de3b1d9c17d50cfd.svg
Liangcai Su
HKU-Liangcai
user
HKU-Liangcai
69017ce1646208eac0d1f3dd
Liwen Zhang
69017ce1646208eac0d1f3de
Zile Qiao
69017ce1646208eac0d1f3df
Xinyu Wang
claimed_verified
2025-10-30T14:42:25.508Z
64b73f9317570fdff9b0d1c4
/avatars/62124ae3e929b53f99f37e97226a877d.svg
Wang Xinyu
oriuta
user
oriuta
69017ce1646208eac0d1f3e0
Pengjun Xie
69017ce1646208eac0d1f3e1
Fei Huang
69017ce1646208eac0d1f3e2
Siheng Chen
69017ce1646208eac0d1f3e3
Jingren Zhou
69017ce1646208eac0d1f3e4
Yong Jiang
AgentFold: Long-Horizon Web Agents with Proactive Context Management
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.
AgentFold, a novel proactive context management paradigm, enhances long-horizon task performance through dynamic context folding, achieving superior results on benchmarks compared to larger models and proprietary agents.
ReAct-based agents context saturation context management cognitive workspace folding operation granular condensations deep consolidations BrowseComp BrowseComp-ZH DeepSeek-V3.1-671B-A37B OpenAI's o4-mini