Recent deep search agents built on large reasoning models (LRMs) excel at complex question answering by iteratively planning, acting, and gathering evidence, a capability known as search-integrated reasoning. However, mainstream approaches often train this ability using only outcome-based supervision, neglecting the quality of intermediate thoughts and actions. We introduce SRR-Judge, a framework for reliable step-level assessment of reasoning and search actions. Integrated into a modified ReAct-style rate-and-refine workflow, SRR-Judge provides fine-grained guidance for search-integrated reasoning and enables efficient post-training annotation. Using SRR-annotated data, we apply an iterative rejection sampling fine-tuning procedure to enhance the deep search capability of the base agent. Empirically, SRR-Judge delivers more reliable step-level evaluations than much larger models such as DeepSeek-V3.1, with its ratings showing strong correlation with final answer correctness. Moreover, aligning the policy with SRR-Judge annotated trajectories leads to substantial performance gains, yielding over a 10 percent average absolute pass@1 improvement across challenging deep search benchmarks.
Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit
Columbia University（哥伦比亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；The University of Chicago（芝加哥大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；The Hong Kong Polytechnic University（香港理工大学）
Reference-Free Rating of LLM Responses via Latent Information
Technical University of Munich, Munich Center for Machine Learning, MDSI（慕尼黑技术大学，慕尼黑机器学习中心，MDSI）；National Yang Ming Chiao Tung University（阳明交通大学）；Helmholtz Munich（海德堡慕尼黑研究所）；Harvard University（哈佛大学）
Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models
DJCAD, University of Dundee, United Kingdom（邓迪大学DJCAD研究中心，英国）；Hebei Academy of Fine Art, China（河北美术院，中国）；Computer Science, Nanjing University, China（南京大学计算机科学系，中国）；Department of Mechanical Engineering, School of Engineering, University of Birmingham（伯明翰大学工程学院机械工程系）
A Multi-Instance LLM Framework for Rating the Quality of Surgical Feedback
Computing + Mathematical Sciences, California Institute of Technology（加州理工学院计算与数学科学系）；Department of Urology, Cedars-Sinai（塞斯医疗中心泌尿科）；Keck School of Medicine, University of Southern California（美国南加州大学凯克医学院）
