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
HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
Xuhui Zhou, Hyunwoo Kim, Faeze Brahman, Liwei Jiang, Hao Zhu, Ximing Lu, Frank Xu, Bill Yuchen Lin, Yejin Choi, Niloofar Mireshghallah, Ronan Le Bras, Maarten Sap
Analyzing Probabilistic Methods for Evaluating Agent Capabilities
Axel Højmark, Govind Pimpale, Arjun Panickssery, Marius Hobbhahn, Jérémy Scheurer
MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents
Ming Zhu, Yi Zhou
Automated test generation to evaluate tool-augmented LLMs as conversational AI agents
Samuel Arcadinho, David Aparicio, Mariana Almeida
AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents
Zhe Su, Xuhui Zhou, Sanketh Rangreji, Anubha Kabra, Julia Mendelsohn, Faeze Brahman, Maarten Sap
Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task
Shao Zhang, Xihuai Wang, Wenhao Zhang, Yongshan Chen, Landi Gao, Dakuo Wang, Weinan Zhang, Xinbing Wang, Ying Wen