From the 1 of 43 papers with an AI index.
42 citations
- Shanghai Artificial Intelligence LaboratoryCN11 papers
- Peking UniversityCN8 papers
- Shanghai Jiao Tong UniversityCN7 papers
- Tsinghua UniversityCN7 papers
- Chinese University of Hong KongHK4 papers
- Fudan UniversityCN4 papers
- Institute of AutomationCN4 papers
- Shandong Institute of AutomationCN4 papers
- University of Chinese Academy of SciencesCN4 papers
- University of Hong KongHK4 papers
- Beihang UniversityCN3 papers
- Chinese Academy of SciencesCN3 papers
7 papers · 1 filter
Understanding Cognition-Induced Risks in Agentic AI Systems
Guanchu Wang, Qinuo Li, Mengnan Du +2
Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated across different domains, their…
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
Yisong Chen, Yifan Gao, Sijing Yu +2
We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engine…
RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning
Jinrui Liu, Bingyan Nie, Boyu Li +4
Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully. Despite the succe…
AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining
Hongjun Ding, Binqi Chen, Jinsheng Huang +6
Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic progr…
SOM: Structured Opponent Modeling for LLM-based Agents via Structural Causal Model
Shiyue Cao, Pei Xu, Likun Yang +3
Accurately predicting opponents' behavior from interactions is a fundamental capability for large language model (LLM)-based agents in multi-agent and game-theoretic environments.…
Repeated Deceptive Path Planning against Learnable Observer
Shiyue Cao, Pei Xu, Likun Yang +6
We study the problem of deceptive path planning (DPP), where an agent aims to conceal its true destination from external observers. While existing work assumes static, non-learning…