With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise preferences, numerical scores, and explanatory justifications for assessing model-generated responses. However, existing critics are primarily trained in general visual domains such as captioning or image question answering, leaving physical AI tasks involving perception, causal reasoning, and planning largely underexplored. We introduce PhyCritic, a multimodal critic model optimized for physical AI through a two-stage RLVR pipeline: a physical skill warmup stage that enhances physically oriented perception and reasoning, followed by self-referential critic finetuning, where the critic generates its own prediction as an internal reference before judging candidate responses, improving judgment stability and physical correctness. Across both physical and general-purpose multimodal judge benchmarks, PhyCritic achieves strong performance gains over open-source baselines and, when applied as a policy model, further improves perception and reasoning in physically grounded tasks.
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning
Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiaotong University（上海交通大学）；Nankai University（南开大学）；Shanghai University（上海大学）；Nanyang Technological University（南洋理工大学）；Hong Kong Polytechnic University（香港理工大学）；Tongji University（同济大学）；Nanjing University（南京大学）；University of California, Merced（加州大学梅德福分校）；Chinese University of Hong Kong（香港中文大学）
Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models
University of British Columbia（不列颠哥伦比亚大学）；Zuse School（Zuse学校）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
