Rui Zhang

Discovering Effective Agent Memory Designs through Program Evolution

Xinheng Ding, Seyyed Saeid Cheshmi, Ziyang Zhang, et al.

Kimi K3: Open Frontier Intelligence

Kimi Team, Tongtong Bai, Yifan Bai, et al.

Physics Transformer: Tailoring Transformer for General PDE Prediction

Guoze Sun, Rui Zhang, Jiankai Tang, et al.

ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning

Binglin Zhou, Peng Shi, Ryo Kamoi, et al.

SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model

Xiaoxin Lu, Ranran Haoran Zhang, Rui Zhang

When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise

Philip Wootaek Shin, Ajay Narayanan Sridhar, Sivani Devarapalli, et al.

AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning

Chongxiao Li, Pengwei Jin, Di Huang, et al.

Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding

Mu Nan, Muquan Yu, Weijian Mai, et al.

QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization

Changxin Ke, Rui Zhang, Jiaming Guo, et al.

AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science

An Luo, Jin Du, Xun Xian, et al.

AgentDS is a benchmark that tests how well AI agents and humans working together can solve real-world data science problems across industries like healthcare, retail, and manufacturing. The research found that while AI agents alone perform poorly on these domain-specific tasks, human-AI collaboration produces the best results, showing that human expertise remains crucial even as AI becomes more advanced.