FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents
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 re...
| Authors | Jia Deng, Yimeng Chen, Xiaoqing Xiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 June 2026 |
| Last updated at source | 12 October 2026 |
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