Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web
  Reconnaissance, Tool Generation, and Task Execution
Recent years, multimodal models have made remarkable strides and pave the way
for intelligent browser use agents. However, when solving tasks on real world
webpages in multi-turn, long-horizon trajectories, current agents still suffer
from disordered action sequencing and excessive trial and error during
execution. This paper introduces Recon-Act, a self-evolving multi-agent
framework grounded in Reconnaissance-Action behavioral paradigm. The system
comprises a Reconnaissance Team and an Action Team: the former conducts
comparative analysis and tool generation, while the latter handles intent
decomposition, tool orchestration, and execution. By contrasting the erroneous
trajectories with successful ones, the Reconnaissance Team infers remedies, and
abstracts them into a unified notion of generalized tools, either expressed as
hints or as rule-based codes, and register to the tool archive in real time.
The Action Team reinference the process empowered with these targeting tools,
thus establishing a closed-loop training pipeline of
data-tools-action-feedback. Following the 6 level implementation roadmap
proposed in this work, we have currently reached Level 3 (with limited
human-in-the-loop intervention). Leveraging generalized tools obtained through
reconnaissance, Recon-Act substantially improves adaptability to unseen
websites and solvability on long-horizon tasks, and achieves state-of-the-art
performance on the challenging VisualWebArena dataset.
68d64e2c8ccd91bdd39ffeda
Kaiwen He
68d64e2c8ccd91bdd39ffedb
Zhiwei Wang
68d64e2c8ccd91bdd39ffedc
Chenyi Zhuang
admin_assigned
2025-09-26T16:11:55.014Z
64e847ab5ddcace745b8f5b1
/avatars/89525e66ff2900f86f66a11043a298f9.svg
chenyi zhuang
user
chengle
68d64e2c8ccd91bdd39ffedd
Jinjie Gu
admin_assigned
2025-09-26T16:12:12.398Z
64bbdb049a69e8da48a721b3
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/iqrYveaPTvOs_GbLWiTGu.png
Jinjie Gu
user
dannygjj
Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web
  Reconnaissance, Tool Generation, and Task Execution
Recent years, multimodal models have made remarkable strides and pave the way
for intelligent browser use agents. However, when solving tasks on real world
webpages in multi-turn, long-horizon trajectories, current agents still suffer
from disordered action sequencing and excessive trial and error during
execution. This paper introduces Recon-Act, a self-evolving multi-agent
framework grounded in Reconnaissance-Action behavioral paradigm. The system
comprises a Reconnaissance Team and an Action Team: the former conducts
comparative analysis and tool generation, while the latter handles intent
decomposition, tool orchestration, and execution. By contrasting the erroneous
trajectories with successful ones, the Reconnaissance Team infers remedies, and
abstracts them into a unified notion of generalized tools, either expressed as
hints or as rule-based codes, and register to the tool archive in real time.
The Action Team reinference the process empowered with these targeting tools,
thus establishing a closed-loop training pipeline of
data-tools-action-feedback. Following the 6 level implementation roadmap
proposed in this work, we have currently reached Level 3 (with limited
human-in-the-loop intervention). Leveraging generalized tools obtained through
reconnaissance, Recon-Act substantially improves adaptability to unseen
websites and solvability on long-horizon tasks, and achieves state-of-the-art
performance on the challenging VisualWebArena dataset.
Recon-Act, a self-evolving multi-agent framework, improves adaptability and performance on long-horizon web tasks by generating and utilizing generalized tools through reconnaissance and action teams.
multimodal models intelligent browser use agents Recon-Act Reconnaissance-Action behavioral paradigm Reconnaissance Team Action Team comparative analysis tool generation intent decomposition tool orchestration execution generalized tools hints rule-based codes closed-loop training pipeline VisualWebArena dataset