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[2602.12852] WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning
Summary
WebClipper introduces a novel framework for optimizing web agent trajectories through graph-based pruning, enhancing search efficiency and accuracy in information-seeking tasks.
Why It Matters
As web agents become increasingly integral to information retrieval, improving their efficiency is crucial. WebClipper's approach not only reduces unnecessary tool calls but also enhances performance, making it relevant for developers and researchers focused on AI and machine learning advancements.
Key Takeaways
- WebClipper optimizes web agent trajectories using graph-based pruning.
- The framework reduces tool-call rounds by approximately 20% while improving accuracy.
- Introduces the F-AE Score metric for evaluating model performance.
- Focuses on balancing effectiveness and efficiency in web agent design.
- Provides practical insights for future developments in AI-driven information retrieval.
Computer Science > Artificial Intelligence arXiv:2602.12852 (cs) [Submitted on 13 Feb 2026] Title:WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning Authors:Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Duolin Sun, Meixiu Long, Yihan Jiao, Zhehao Tan, Jian Wang, Peng Wei, Jinjie Gu View a PDF of the paper titled WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning, by Junjie Wang and 11 other authors View PDF HTML (experimental) Abstract:Deep Research systems based on web agents have shown strong potential in solving complex information-seeking tasks, yet their search efficiency remains underexplored. We observe that many state-of-the-art open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and exploration of unproductive branches. To address this, we propose WebClipper, a framework that compresses web agent trajectories via graph-based pruning. Concretely, we model the agent's search process as a state graph and cast trajectory optimization as a minimum-necessary Directed Acyclic Graph (DAG) mining problem, yielding pruned trajectories that preserve essential reasoning while eliminating redundant steps. Continued training on these refined trajectories enables the agent to evolve toward more efficient search patterns and reduces tool-call rounds by about 20% while improving accuracy. Furthermore, we introduce a new metric called F-AE Score to measure the model's overall pe...