AI搜索的捷径陷阱：如何让模型真正学会搜索
训练AI搜索时，一个常见问题是：模型会走捷径，而不是真正搜索。比如，它可能只凭一个线索就猜出答案，或者依赖训练数据中的固定模式。这篇论文系统性地识别了四种捷径风险，并设计了一套数据合成框架来规避它们。他们生成的训练数据迫使模型必须完成完整搜索才能找到答案，训练出的搜索模型在复杂任务上表现更好。虽然这不是你明天就能用的工具，但它揭示了AI搜索能力提升的关键——不是堆数据，而是堵住捷径。
📄 原文摘要(英文)
Training deep search agents requires verifiable questions whose answers remain unavailable until sufficient evidence has been acquired through search. Existing synthesis methods often increase apparent difficulty by enriching graph structures, but structural complexity alone does not guarantee realized search difficulty: the intended search process can collapse through a cheaper identifying route. We formalize this gap with a shortcut-aware difficulty framework and identify four actionable shortcut risks: evidence co-coverage, single-clue selectivity, exposed constants, and prior-knowledge binding. To diagnose their realized effects, we use trajectory signatures including solving cost, answer hit time, and prior-shortcut rate. Guided by this framework, we introduce FORT, a Framework of Shortcut-Resistant Training-Data Synthesis. FORT constructs shortcut-resistant training data by controlling shortcut risks across entity selection, evidence graph construction, question formulation, and adversarial refinement. Experiments show that FORT induces longer pre-answer search and fewer shortcut patterns than existing open-source deep search datasets. Using the resulting trajectories, we train FORT-Searcher with supervised fine-tuning (SFT) only, and it achieves the best overall performance among comparable-size open-source search agents on challenging deep search benchmarks. Relevant resources will be made available at https://github.com/RUCAIBox/FORT-Searcher.