Agent System
Agent systems, encompassing autonomous software entities capable of complex actions, aim to improve efficiency and decision-making across diverse fields. Current research emphasizes enhancing agent controllability and safety, often utilizing large language models (LLMs) within multi-agent frameworks employing techniques like chain-of-thought reasoning, hierarchical task delegation, and adversarial training to improve robustness and accuracy. These advancements hold significant potential for automating tasks in areas such as software engineering, materials science, and even scientific research itself, streamlining workflows and accelerating progress.
Papers
Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface
Wenyue Hua, Mengting Wan, Shashank Vadrevu, Ryan Nadel, Yongfeng Zhang, Chi Wang
TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation
Zhiqiang Yuan, Weitong Chen, Hanlin Wang, Kai Yu, Xin Peng, Yiling Lou