Bowen Zhou

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement

Yi Duan, Ying Liu, Zirui Tang, et al.

Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning

Kai Chen, Jifeng Ding, Ning Ding, et al.

Intern-S2-Preview: Scientific Agentic Foundation Model

Lei Bai, Jiaqi Cao, Chiyu Chen, et al.

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

Junlin Yang, Che Jiang, Yu Fu, et al.

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

Chen Tang, Yizhou Wang, Jianyu Wu, et al.

A Definition and Roadmap for World Models

Xinyuan Chen, Haoyu Guo, Shi Guo, et al.

Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

Lei Bai, Zongsheng Cao, Yang Chen, et al.

NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?

Yuru Wang, Lejun Cheng, Yuxin Zuo, et al.

Post-Trained MoE Can Skip Half Experts via Self-Distillation

Xingtai Lv, Li Sheng, Kaiyan Zhang, et al.

OS-Themis: A Scalable Critic Framework for Generalist GUI Rewards

Zehao Li, Zhenyu Wu, Yibo Zhao, et al.

OS-Themis is a new framework that helps train AI agents to better interact with graphical user interfaces (like phone apps) by providing more reliable feedback on whether the agent is performing tasks correctly. Instead of using a single judge, it breaks down agent actions into verifiable milestones and cross-checks the evidence before making a decision, similar to how a court system works. When tested on smartphone tasks, this approach improved performance by about 10% during training and 7% when filtering practice data.