AI Agent
AI agents are autonomous systems designed to perceive, reason, and act within an environment to achieve specified goals. Current research emphasizes improving agent capabilities through techniques like self-improvement mechanisms (e.g., recursive self-modification), enhanced search algorithms (e.g., Monte Carlo Tree Search), and the integration of large language models (LLMs) for reasoning and tool use. This field is crucial for advancing AI safety and reliability, particularly in addressing challenges like adversarial attacks and ensuring responsible deployment across diverse applications, from traffic modeling to personalized search engines.
Papers
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities
Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei, Lizhen Qu, Gerard de Melo, Xiaojun Chang, Yazhou Ren, Zenglin Xu
Human-AI Coordination via Human-Regularized Search and Learning
Hengyuan Hu, David J Wu, Adam Lerer, Jakob Foerster, Noam Brown