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

Hugging Face Daily Papers Papers
deep-search-agents training-data-synthesis shortcut-resistance supervised-fine-tuning search-agents

Summary

FORT-Searcher introduces a framework for synthesizing shortcut-resistant training data for deep search agents by identifying and mitigating four shortcut risks. The resulting agent, trained via supervised fine-tuning, achieves state-of-the-art performance among comparable open-source search agents.

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.
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Source: https://huggingface.co/papers/2606.12087 Published on Jun 10

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Abstract

A framework for creating shortcut-resistant training data for deep search agents by identifying and mitigating four shortcut risks in data synthesis processes.

Trainingdeep search agentsrequires 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 ashortcut-aware difficulty frameworkand identify four actionableshortcut risks: evidence co-coverage, single-clue selectivity, exposed constants, and prior-knowledge binding. To diagnose their realized effects, we usetrajectory signaturesincluding solving cost, answer hit time, and prior-shortcut rate. Guided by this framework, we introduceFORT, a Framework of Shortcut-Resistant Training-Data Synthesis.FORTconstructs shortcut-resistant training data by controllingshortcut risksacross entity selection, evidence graph construction, question formulation, and adversarial refinement. Experiments show thatFORTinduces longer pre-answer search and fewer shortcut patterns than existing open-source deep search datasets. Using the resulting trajectories, we trainFORT-Searcher withsupervised fine-tuning(SFT) only, and it achieves the best overall performance among comparable-size open-source search agents on challengingdeep search benchmarks. Relevant resources will be made available at https://github.com/RUCAIBox/FORT-Searcher.

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