About this episode

This episode discusses the FORT-Searcher framework for synthesizing shortcut-resistant search tasks to improve training for deep search agents.

🤗 Upvotes: 71 | cs.CL Authors: Jia Deng, Yimeng Chen, Xiaoqing Xiang, Ziyang Zeng, Shuo Tang, Wayne Xin Zhao, Feng Chang, Chuan Hao, Yuan Wei, Ran Tao, Bryan Dai, Ji-Rong Wen Title: FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents Arxiv: http://arxiv.org/abs/2606.12087v1 Abstract: 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…

Topics covered

• deep search agents
• training data synthesis
• shortcut-resistant tasks
• search difficulty
• evidence graph construction

Keywords

• FORT-Searcher
• shortcut-resistant
• training data
• search tasks
• deep learning

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