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About this paper
Yuxuan Wan, Tianqing Fang, Zaitang Li, Yintong Huo, Wenxuan Wang, Haitao Mi, Dong Yu, Michael R. Lyu
DeepVerifier introduces a framework for enhancing Deep Research Agents (DRAs) by enabling them to self-improve through iterative, rubric-guided verification during test-time inference. This approach yielded accuracy gains of 8% to 12.22% on the GAIA dataset for Claude-3.7-Sonnet and improved open-source models like Qwen3-8B by 5.5% in reflective capabilities.
Key insights
- Verifying an answer's correctness is often easier than generating it from scratch, allowing for a decomposition of complex verification tasks into simpler, externally verifiable sub-questions.
- A systematic and fine-grained classification of agent failures (DRA failure taxonomy) is essential for providing discriminative and actionable feedback, which is more effective than generic textual critiques.
- Iterative, rubric-guided feedback during inference can significantly improve agent reliability and performance without requiring additional training of the base model, demonstrating an effective paradigm for agent self-evolution.
Summary by alphaXiv.
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