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
Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation
Xuying Li, Zhuo Li, Yuji Kosuga, Yasuhiro Yoshida, Victor Bian
MageBench: Bridging Large Multimodal Models to Agents
Miaosen Zhang, Qi Dai, Yifan Yang, Jianmin Bao, Dongdong Chen, Kai Qiu, Chong Luo, Xin Geng, Baining Guo
Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation
Yi-Hung Chiu, Ung Hee Lee, Changseob Song, Manaen Hu, Inseung Kang
Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planning
Song Jiang, Da JU, Andrew Cohen, Sasha Mitts, Aaron Foss, Justine T Kao, Xian Li, Yuandong Tian
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
Hang Zhou, Yehui Tang, Haochen Qin, Yujie Yang, Renren Jin, Deyi Xiong, Kai Han, Yunhe Wang