WebShaper: Agentically Data Synthesizing via Information-Seeking
  Formalization
The advent of Large Language Model (LLM)-powered agents has revolutionized
artificial intelligence by enabling solutions to complex, open-ended tasks
through web-based information-seeking (IS) capabilities. The scarcity of
high-quality training data has limited the development of IS agents. Existing
approaches typically adopt an information-driven paradigm that first collects
web data and then generates questions based on the retrieval. However, this may
lead to inconsistency between information structure and reasoning structure,
question and answer. To mitigate, we propose a formalization-driven IS data
synthesis framework WebShaper to construct a dataset. WebShaper systematically
formalizes IS tasks through set theory. Central to the formalization is the
concept of Knowledge Projections (KP), which enables precise control over
reasoning structure by KP operation compositions. During synthesis, we begin by
creating seed tasks, then use a multi-step expansion process. At each step, an
agentic Expander expands the current formal question more complex with
retrieval and validation tools based on our formalization. We train our model
on the synthesized dataset. Experiment results demonstrate that WebShaper
achieves state-of-the-art performance among open-sourced IS agents on GAIA and
WebWalkerQA benchmarks.
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Zhengwei Tao
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Jialong Wu
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2025-07-22T14:08:42.255Z
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Jialong Wu
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Wenbiao Yin
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Wenbiao Yin
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NLPblue
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Junkai Zhang
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Baixuan Li
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Baixuan Li
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MuBai2001
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Haiyang Shen
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Shen HaiYang
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seaforestshen
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Kuan Li
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Liwen Zhang
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Xinyu Wang
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2025-07-22T14:09:31.152Z
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Xinyu Wang
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XinyuWang
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Yong Jiang
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Pengjun Xie
admin_assigned
2025-07-22T14:09:37.774Z
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pengjun xie
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xpjandy
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Fei Huang
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Jingren Zhou
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2025-07-22T14:09:49.501Z
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Zhou
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Jingren
WebShaper: Agentically Data Synthesizing via Information-Seeking
  Formalization
The advent of Large Language Model (LLM)-powered agents has revolutionized
artificial intelligence by enabling solutions to complex, open-ended tasks
through web-based information-seeking (IS) capabilities. The scarcity of
high-quality training data has limited the development of IS agents. Existing
approaches typically adopt an information-driven paradigm that first collects
web data and then generates questions based on the retrieval. However, this may
lead to inconsistency between information structure and reasoning structure,
question and answer. To mitigate, we propose a formalization-driven IS data
synthesis framework WebShaper to construct a dataset. WebShaper systematically
formalizes IS tasks through set theory. Central to the formalization is the
concept of Knowledge Projections (KP), which enables precise control over
reasoning structure by KP operation compositions. During synthesis, we begin by
creating seed tasks, then use a multi-step expansion process. At each step, an
agentic Expander expands the current formal question more complex with
retrieval and validation tools based on our formalization. We train our model
on the synthesized dataset. Experiment results demonstrate that WebShaper
achieves state-of-the-art performance among open-sourced IS agents on GAIA and
WebWalkerQA benchmarks.
WebShaper, a formalization-driven framework, synthesizes information-seeking datasets using set theory and Knowledge Projections to enhance reasoning structure and achieve top performance in open-sourced benchmarks.
Large Language Model (LLM) information-seeking (IS) agents formalization-driven IS data synthesis set theory Knowledge Projections (KP) agentic Expander GAIA WebWalkerQA benchmarks