@inproceedings{wan-etal-2026-inference,
    title = "Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification",
    author = "Wan, Yuxuan  and
      Fang, Tianqing  and
      LI, Zaitang  and
      Huo, Yintong  and
      Wang, Wenxuan  and
      Mi, Haitao  and
      Yu, Dong  and
      Lyu, Michael R.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1243/",
    pages = "24822--24835",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving.While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: test-time self-evolving the agent{'}s ability by iteratively verifying the policy model{'}s outputs, guided by meticulously crafted rubrics. This approach gives rise to an inference-time scaling of verification, wherein an agent self-improves at test time by evaluating its generated answers to produce iterative feedback and refinements without any additional training. We derive the rubrics based on an automatically constructed DRA Failure Taxonomy, which systematically classifies agent failures into five major categories and thirteen sub-categories. We present DeepVerifier, a rubrics-based outcome reward verifier that leverages the asymmetry of verification and outperforms vanilla agent-as-judge and LLM judge baselines by 12{\%}{--}48{\%} in meta-evaluation F1 score. To enable practical test-time self-evolution, DeepVerifier integrates as a plug-and-play module during test-time inference. The verifier produces detailed rubric-based feedback, which is fed back to the agent for iterative bootstrapping{---}refining responses without additional training. This test-time scaling delivers 8{\%}{--}11{\%} accuracy gains on challenging subsets of GAIA and XBench-DeepResearch when powered by capable closed-source LLMs. Finally, to support open-source advancement, we release DeepVerifier-4K, a curated supervised fine-tuning dataset of 4,646 high-quality agent steps focused on DRA verification. These examples emphasize reflection and self-critique, enabling open models to develop robust verification capabilities."
}