21 citations · 23 across the 7 of their papers we have counts for
4 papers · 1 filter
PAPERBENCHX: Can AI Reproduce Science Across Disciplines?
Ruoyu Wu, Zengji Tu, Aolong Sun +14
Before AI can be trusted to discover new science, it should be able to reproduce what science already knows. Reproduction provides a rigorous checkpoint for an AI scientist: the ta…
SaaS-Bench: Can Computer-Use Agents Leverage Real-World SaaS to Solve Professional Workflows?
Kean Shi, Zihang Li, Tianyi Ma +13
Computer-Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text-based reasoning toward action execution in more complex environments, such as web browse…
Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents
Ruihan Yang, Fanghua Ye, Xiang We +10
Large language models (LLMs) are increasingly deployed as autonomous agents for multi-turn decision-making tasks. However, current agents typically rely on fixed cognitive patterns…
Safe RLHF: Safe Reinforcement Learning from Human Feedback
Josef Dai, Xuehai Pan, Ruiyang Sun +5
With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tensio…