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
A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops
Kamer Ali Yuksel, Hassan Sawaf
KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis
Kaiwen Zuo, Yirui Jiang, Fan Mo, Pietro Lio
Cocoa: Co-Planning and Co-Execution with AI Agents
K. J. Kevin Feng, Kevin Pu, Matt Latzke, Tal August, Pao Siangliulue, Jonathan Bragg, Daniel S. Weld, Amy X. Zhang, Joseph Chee Chang
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System
Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen
How to Correctly do Semantic Backpropagation on Language-based Agentic Systems
Wenyi Wang, Hisham A. Alyahya, Dylan R. Ashley, Oleg Serikov, Dmitrii Khizbullin, Francesco Faccio, Jürgen Schmidhuber
WiS Platform: Enhancing Evaluation of LLM-Based Multi-Agent Systems Through Game-Based Analysis
Chengwei Hu, Jianhui Zheng, Yancheng He, Hangyu Guo, Junguang Jiang, Han Zhu, Kai Sun, Yuning Jiang, Wenbo Su, Bo Zheng