FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents

arXiv:2606.12087v2 Announce Type: replace Abstract: Training deep-search agents requires verifiable questions that demand substantial evidence acquisition, yet structurally complex tasks may still admit cheap identifying routes when clues are overly selective, co-covered by the same evidence, directly searchable, or resolved from solver priors. We formalize this gap with a shortcut-aware difficulty framework that distinguishes intended construction complexity from identifying routes available through retrieval. Guided by this framework, we introduce FORT, a Framework of Shortcut-Resistant Training-Data Synthesis, which targets four shortcut risks across entity selection, evidence graph construction, question formulation, and adversarial refinement. We evaluate realized search behavior with solver-conditioned trajectory diagnostics rather than graph complexity or search length alone. Compared with existing open-source deep-search datasets, FORT shows later answer exposure, higher retrieval effort, lower single-clue selectivity, greater evidence dispersion, and higher realized pre-answer dependency costs. Using the resulting trajectories, we train FORT-Searcher with supervised fine-tuning alone. Across model scales, FORT-Searcher consistently improves over its backbones, achieving the strongest overall performance among similarly sized agents. Relevant resources will be made available at https://github.com/RUCAIBox/FORT-Searcher.

Abstract:Training deep-search agents requires verifiable questions that demand substantial evidence acquisition, yet structurally complex tasks may still admit cheap identifying routes when clues are overly selective, co-covered by the same evidence, directly searchable, or resolved from solver priors. We formalize this gap with a shortcut-aware difficulty framework that distinguishes intended construction complexity from identifying routes available through retrieval. Guided by this framework, we introduce FORT, a Framework of Shortcut-Resistant Training-Data Synthesis, which targets four shortcut risks across entity selection, evidence graph construction, question formulation, and adversarial refinement. We evaluate realized search behavior with solver-conditioned trajectory diagnostics rather than graph complexity or search length alone. Compared with existing open-source deep-search datasets, FORT shows later answer exposure, higher retrieval effort, lower single-clue selectivity, greater evidence dispersion, and higher realized pre-answer dependency costs. Using the resulting trajectories, we train FORT-Searcher with supervised fine-tuning alone. Across model scales, FORT-Searcher consistently improves over its backbones, achieving the strongest overall performance among similarly sized agents. Relevant resources will be made available at this https URL.

Computation and Language (cs.CL)

arXiv:2606.12087 [cs.CL]

(or arXiv:2606.12087v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.12087 Focus to learn more arXiv-issued DOI via DataCite

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FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents

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