Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on costly expert annotations. While recent rubric-based approaches enhance evaluation transparency, they lack systematic quality control, yielding noisy and redundant criteria, failing to mitigate persistent biases (e.g., verbosity, position) in LLM evaluators, and creating a scalability-reliability trade-off. To address these limitations, we propose CDRRM (Contrast-Driven Rubric Reward Model), a framework built on a novel Contrast-then-Synthesis paradigm for high-quality rubric generation and guided preference judgment. CDRRM first conducts multi-dimensional contrastive profiling on preference pairs to identify causal discriminative factors, then synthesizes these insights into compact, context-aware rubrics to guide preference judg- ments. Extensive experiments on three authoritative benchmarks (RewardBench, RMBench, RMB) demonstrate that CDRRM achieves state-of-the-art performance across diverse domains and effectively mitigates aforementioned evaluation biases. Notably, our approach delivers exceptional data efficiency: training the rubric generator on only 3k high-quality samples empowers a frozen pre-trained judge model to outperform fully fine-tuned baselines. This work offers a scalable, interpretable, and data-efficient path for reward modeling.
REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading
School of Computing and Augmented Intelligence, Arizona State University, USA（计算与增强智能学院，亚利桑那州立大学，美国）；Mary Lou Fulton Teachers College, Arizona State University, USA（玛丽·卢·福洛顿教师学院，亚利桑那州立大学，美国）；Department of Computer Science, National Yang Ming Chiao Tung University, TW（国立阳明交通大学计算机科学系，台湾）
Context-Aware Pseudo-Label Scoring for Zero-Shot Video Summarization
Nanyang Technological University（南洋理工大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, San Diego（加州大学圣地亚哥分校）；Texas A&M University（德克萨斯A&M大学）；Stevens Institute of Technology（史蒂文斯理工学院）
EvoRubrics: Dynamic Rubrics as Rewards via Adversarial Co-Evolution for LLM Reinforcement Learning
National Engineering Research Center of Software Engineering, Peking University（北京大学软件工程国家工程研究中心）；School of Computer Science, Peking University（北京大学计算机学院）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）；Peking University Information Technology Institute, Tianjin Binhai（北京大学滨海信息技术研究院）；Research Institute, GRGBanking Equipment Co., Ltd.（广电运通金融电子股份有限公司研究院）
AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Adobe Inc.（Adobe公司）；Department of Computer Science, University of California, Santa Barbara（加州大学圣芭芭拉分校计算机科学系）
Stanford University（斯坦福大学）；Boston University（波士顿大学）；Columbia University（哥伦比亚大学）；University of Toronto（多伦多大学）；Institut Teknologi Bandung（Bandung 工程技术大学）；Monash University Indonesia（墨尔本大学印尼分校）；Capital One
FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models
National University of Singapore（新加坡国立大学）；Xiamen University（厦门大学）；University of Manchester（曼彻斯特大学）；Yunnan University（云南大学）；Fudan University（复旦大学）；Beihang University（北京航空航天大学）；China Telecom Artificial Intelligence Technology(Beijing) Co., Ltd.（中国电信人工智能技术（北京）有限公司）
RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling
New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别新实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The Hong Kong University of Science and Technology（香港科技大学）；Tencent（腾讯）
QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI
AI Lab, Arioobarzan Engineering Team（艾伊罗巴赞工程团队人工智能实验室）；Department of Computer Engineering and Information Technology（计算机工程与信息科技系）；Department of Informatics, Bioengineering, Robotics and Systems Engineering（信息学、生物工程、机器人与系统工程系）；University of Genoa（热那亚大学）
