3 citations · 3 across the 9 of their papers we have counts for
10 papers
CauScientist: Teaching LLMs to Respect Data for Causal Discovery
Bo Peng, Sirui Chen, Lei Xu +1
Causal discovery is fundamental to scientific understanding and reliable decision-making. Existing approaches face critical limitations: purely data-driven methods suffer from stat…
CauSight: Learning to Supersense for Visual Causal Discovery
Yize Zhang, Meiqi Chen, Sirui Chen +4
Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal di…
DEPO: Dual-Efficiency Preference Optimization for LLM Agents
Sirui Chen, Mengshi Zhao, Lei Xu +5
Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes…
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…
Synthesis by Design: Controlled Data Generation via Structural Guidance
Lei Xu, Sirui Chen, Yuxuan Huang +1
Mathematical reasoning remains challenging for LLMs due to complex logic and the need for precise computation. Existing methods enhance LLM reasoning by synthesizing datasets throu…
Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks
Sirui Chen, Shuqin Ma, Shu Yu +3
Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency. As large language m…