activity
20232026
most citedDARWIN Series: Domain Specific Large Language Models for Natural Science

33 citations · 33 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Beyond Task Completion: Training Capable and Safe Computer-Use Agents

Zeyu Kang, Zhenyun Yin, Yang Zhang +5

Computer-use agents (CUAs) have made rapid progress in completing complex tasks through graphical user interfaces, yet post-training centered on task success alone does not induce…

cs.AI2026

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence

Xinquan Chen, Zhenyun Yin, Shan He +38

As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment intera…

cs.AI2025

SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law

Shanghai AI Lab, :, Yicheng Bao +115

We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…

cs.AI2025

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints

Zhenyun Yin, Shujie Wang, Xuhong Wang +2

Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose \textbf{Deliberative Searcher}, the firs…

cs.CL2023★ 33 cited

DARWIN Series: Domain Specific Large Language Models for Natural Science

Tong Xie, Yuwei Wan, Wei Huang +9

Emerging tools bring forth fresh approaches to work, and the field of natural science is no different. In natural science, traditional manual, serial, and labour-intensive work is…