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.

翻译：训练深度搜索智能体需要可验证的问题，其答案在通过搜索获取足够证据前保持不可知状态。现有合成方法常通过丰富图结构来增加表面难度，但结构复杂性本身无法保证实际搜索难度：预期搜索过程可能通过更简单的识别路径崩塌。我们以捷径感知难度框架形式化了这一差距，并识别出四种可操作的捷径风险：证据共覆盖、单线索选择性、暴露常量及先验知识绑定。为诊断其实际影响，我们使用包含求解成本、答案命中时间和先验捷径率的轨迹特征。在该框架指导下，我们提出FORT——一种抗捷径训练数据合成框架。FORT通过控制实体选择、证据图构建、问题生成及对抗性精炼过程中的捷径风险，构建抗捷径训练数据。实验表明，与现有开源深度搜索数据集相比，FORT诱导了更长的答案前搜索路径及更少的捷径模式。利用所得轨迹，我们仅通过监督微调训练FORT-Searcher，其在具有挑战性的深度搜索基准测试中达到了可比规模开源搜索智能体的最佳综合性能。相关资源将在https://github.com/RUCAIBox/FORT-Searcher 开放获取。