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 performance in balancing accuracy and efficiency. Experiments demonstrate that WebClipper compresses tool-call rounds under excellent performance, providing practical insight into balancing effectiveness and efficiency in web agent design.

翻译：基于网页智能体的深度研究系统在解决复杂信息检索任务方面展现出强大潜力，但其搜索效率仍有待深入探索。我们观察到，当前许多先进的开放源码网页智能体依赖于冗长的工具调用轨迹，这些轨迹常包含循环推理回路和无效分支探索。为解决该问题，我们提出WebClipper框架，通过基于图的剪枝方法压缩网页智能体轨迹。具体而言，我们将智能体的搜索过程建模为状态图，并将轨迹优化问题转化为最小必要有向无环图挖掘问题，从而生成既能保留核心推理逻辑又可消除冗余步骤的剪枝轨迹。基于优化轨迹的持续训练使智能体能够向更高效的搜索模式进化，在提升准确率的同时将工具调用轮次减少约20%。此外，我们提出名为F-AE Score的新评估指标，用以衡量模型在准确性与效率平衡方面的综合性能。实验表明，WebClipper在保持优异性能的前提下显著压缩了工具调用轮次，为网页智能体设计中效果与效率的平衡提供了实践指导。