The transition from symbolic manipulation to science-grade reasoning represents a pivotal frontier for Large Language Models (LLMs), with physics serving as the critical test anchor for binding abstract logic to physical reality. Physics demands that a model maintain physical consistency with the laws governing the universe, a task that fundamentally requires multimodal perception to ground abstract logic in reality. At the Olympiad level, diagrams are often constitutive rather than illustrative, containing essential constraints, such as boundary conditions and spatial symmetries, that are absent from the text. To bridge this visual-logical gap, we introduce P1-VL, a family of open-source vision-language models engineered for advanced scientific reasoning. Our method harmonizes Curriculum Reinforcement Learning, which employs progressive difficulty expansion to stabilize post-training, with Agentic Augmentation, enabling iterative self-verification at inference. Evaluated on HiPhO, a rigorous benchmark of 13 exams from 2024-2025, our flagship P1-VL-235B-A22B becomes the first open-source Vision-Language Model (VLM) to secure 12 gold medals and achieves the state-of-the-art performance in the open-source models. Our agent-augmented system achieves the No.2 overall rank globally, trailing only Gemini-3-Pro. Beyond physics, P1-VL demonstrates remarkable scientific reasoning capacity and generalizability, establishing significant leads over base models in STEM benchmarks. By open-sourcing P1-VL, we provide a foundational step toward general-purpose physical intelligence to better align visual perceptions with abstract physical laws for machine scientific discovery.
OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Huawei Technologies（华为技术有限公司）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Beijing International Center for Mathematical Research, Peking University（北京国际数学研究中心）
Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（深圳市大数据研究院，香港中文大学（深圳））；Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau（澳门大学智慧城市物联网国家重点实验室）
University of Gotenburg（哥德堡大学）；Dublin Institute of Technology（都柏林理工学院）
Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.
Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications
School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；George Mason University Department of Computer Science（乔治·梅奥大学计算机科学系）
Rethinking AI Literacy Education in Higher Education: Bridging Risk Perception and Responsible Adoption
School of Professional Studies, Clark University（克拉克大学专业研究学院）；Cardiff School of Technologies, Cardiff Metropolitan University（卡迪夫 Metropolitan 大学技术学院）；Harvard Medical School（哈佛医学院）
Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；ETH Zurich（苏黎世联邦理工学院）；ETH AI Center（苏黎世联邦理工学院人工智能中心）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Yinwang Intelligent Technology Co., Ltd.（云网智能技术有限公司）；Peking University and Beijing International Center for Mathematical Research（北京大学和北京国际数学研究中心）
Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
Nanjing University（南京大学）；Tencent Youtu Lab（腾讯优图实验室）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；University of Washington（华盛顿大学）；The Chinese University of Hong Kong（香港中文大学）；Australian National University（澳大利亚国立大学）
Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN
Sun Yat-sen University, China（中山大学，中国）；Zhejiang University, China（浙江大学，中国）；University of Exeter, U.K.（埃克塞特大学，英国）；Singapore University of Technology and Design, Singapore（新加坡科技设计大学，新加坡）；Purple Mountain Laboratories, Nanjing, China（紫金山实验室，南京，中国）
Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference
School of AI, Shanghai Jiao Tong University（上海交通大学人工智能学院）；University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；BAAI（北京人工智能研究院）
