Agent Smith
Research on "Agent Smith" (a placeholder name, as the provided papers don't refer to a specific entity named Agent Smith) focuses on developing autonomous AI agents capable of complex reasoning and interaction within various environments, leveraging large language models (LLMs) as their core decision-making component. Current research emphasizes improving agent capabilities through techniques like knowledge graph integration, multi-agent collaboration, and the incorporation of error-correction mechanisms, often within specialized frameworks designed for specific tasks (e.g., medical question answering, social simulation, or software engineering). This work is significant for advancing AI capabilities in complex domains and improving the reliability and safety of autonomous systems, with potential applications ranging from scientific research to healthcare and industrial automation.
Papers
Learning Embeddings for Sequential Tasks Using Population of Agents
Mridul Mahajan, Georgios Tzannetos, Goran Radanovic, Adish Singla
User Behavior Simulation with Large Language Model based Agents
Lei Wang, Jingsen Zhang, Hao Yang, Zhiyuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang, Ji-Rong Wen