WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling
  Info-Rich Seeking
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.
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Zhengwei Tao
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Haiyang Shen
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Baixuan Li
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Wenbiao Yin
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Jialong Wu
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2026-01-09T08:37:28.205Z
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Jialong Wu
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Kuan Li
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Zhongwang Zhang
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Huifeng Yin
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Rui Ye
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2026-03-17T08:32:18.792Z
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RuiYe
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Liwen Zhang
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Xinyu Wang
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2025-10-30T14:42:22.648Z
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Wang Xinyu
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Pengjun Xie
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Jingren Zhou
69017d55646208eac0d1f40d
Yong Jiang
WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling
  Info-Rich Seeking
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.
WebLeaper framework improves information seeking efficiency and effectiveness by constructing high-coverage tasks and generating efficient solution trajectories using tree-structured reasoning and curated Wikipedia tables.
Large Language Model (LLM) information seeking (IS) search efficiency target entities tree-structured reasoning WebLeaper Basic Union Reverse-Union training trajectories BrowserComp GAIA xbench-DeepSearch WideSearch Seal-0