arXiv:2608. 08605v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on Large Language Models (LLMs) are proliferating rapidly, but their heterogeneous execution traces provide no common basis for evaluation across methods.
By Guo Chen, Ziwen Li, Reed Li, Yu Lu, Haibo Shi, Bingbing Xu, Junjie Huang
arXiv:2608. 06196v1 Announce Type: new Abstract: Agents backed by large skill libraries must decide which skills to load and in what order.
By Indivara Kolluru, Nathan Sportsman
arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.
By Chuanjie Wu, Zhishang Xiang, Yunbo Tang, Zerui Chen, Qinggang Zhang, Jinsong Su
OOPMAS introduces a training‑free framework that generates both the agent set and the coordination workflow at the granularity of individual queries. Agents are defined as object‑oriented class definitions with dedicated roles, tools, and persistent state, while workflows are expressed as executable main functions over these agent objects. A dynamic skill library accumulates structured lessons from execution feedback across optimization rounds, enabling in‑context improvement without any gradient updates or fine‑tuning, and achieves 89.6% accuracy on a mixed‑task benchmark, outperforming the strongest baseline by 18.1 percentage points.
By Qi Cheng, Shengyu Chen, Wei Cheng, Yiqun Xie, Xiaowei Jia, Haoyu Wang, Haifeng Chen
arXiv:2608. 09934v1 Announce Type: cross Abstract: Large language model (LLM) agents improve task performance by decomposing problems into role-specialized behaviors.
By Vitalii Belov, Artyom Sosedka, Andrey Sakhovskiy, Elizaveta Kovtun, Artyom Boyarskikh, Semen Budennyy
Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question answering. However, existing methods for automatin...