Large Language Model (LLM)-based agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables autonomous reasoning and decision-making. While prior research has largely focused on improving retrieval depth, we observe that current IS agents often suffer from low search efficiency, which in turn constrains overall performance. A key factor underlying this inefficiency is the sparsity of target entities in training tasks, which limits opportunities for agents to learn and generalize efficient search behaviors. To address these challenges, we propose WebLeaper, a framework for constructing high-coverage IS tasks and generating efficient solution trajectories. We formulate IS as a tree-structured reasoning problem, enabling a substantially larger set of target entities to be embedded within a constrained context. Leveraging curated Wikipedia tables, we propose three variants for synthesizing IS tasks, Basic, Union, and Reverse-Union, to systematically increase both IS efficiency and efficacy. Finally, we curate training trajectories by retaining only those that are simultaneously accurate and efficient, ensuring that the model is optimized for both correctness and search performance. Extensive experiments on both basic and comprehensive settings, conducted on five IS benchmarks, BrowserComp, GAIA, xbench-DeepSearch, WideSearch, and Seal-0, demonstrate that our method consistently achieves improvements in both effectiveness and efficiency over strong baselines.

翻译：基于大语言模型（LLM）的智能体已成为解决开放式问题的一种变革性方法，其中信息搜索（IS）作为核心能力，支撑着自主推理与决策。尽管先前研究主要集中于提升检索深度，但我们观察到当前的信息搜索智能体常受限于较低的搜索效率，进而制约整体性能。这种低效的关键原因在于训练任务中目标实体的稀疏性，限制了智能体学习和泛化高效搜索行为的机会。为应对这些挑战，我们提出了WebLeaper框架，用于构建高覆盖度的信息搜索任务并生成高效的解决方案轨迹。我们将信息搜索建模为树状结构推理问题，使得在有限上下文中嵌入更大规模的目标实体成为可能。利用精心整理的维基百科表格，我们提出了三种合成信息搜索任务的变体——基础型、联合型与反向联合型，以系统性地提升信息搜索的效率和效能。最后，我们通过仅保留同时具备准确性和高效性的训练轨迹来构建训练数据，确保模型在正确性和搜索性能上均得到优化。在五个信息搜索基准测试（BrowserComp、GAIA、xbench-DeepSearch、WideSearch和Seal-0）上进行的基础与综合设置实验表明，我们的方法在效果和效率上均持续优于现有强基线模型。