The paper introduces InFlowOp, a label‑free optimization framework that assigns costs to each decision in a multi‑agent workflow, balancing agent competence against execution time. It determines task granularity and agent assignment before execution and corrects faults during execution using the same cost metric. The authors also present Braid, a benchmark for multi‑agent coordination, and show that InFlowOp outperforms single‑agent baselines by up to 11.97% across various domains.
By Xuehang Guo, Haoyu Wang, Shengyu Chen, Zach Chen, Wei Cheng, Qingyun Wang, Haifeng Chen
arXiv:2607. 18979v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at multi-step reasoning, yet current parallel reasoning approaches often fail to distinguish the contributions of individual reasoning paths.
By Wentao Zhang, Haoyu Zhang, Xinke Jiang, Yuxuan Cheng, Yuhan Pan, Miao Li, Zhipeng Qiao, Tao Feng, Zhen Tao, Dengji Zhao
arXiv:2511. 10687v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) in multi-agent systems (MAS) have shown promise for complex tasks, yet current training methods lack principled ways to connect system-level evaluation with agent- and message-level learning.
By Chih-Hsuan (Bella), Yang, Tanwi Mallick, Le Chen, Krishnan Raghavan, Amal Gueroudji, Ian T. Foster, Rajeev Thakur
arXiv:2608.25992v2 Announce Type: replace
Abstract: Multi-agent large language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative re...
By Songyuan Li, Ahmed M. Abdelmoniem, Shiqiang Wang
The paper examines when multi‑agent collaboration is beneficial versus single‑agent approaches. It finds that collaboration yields systematic advantages mainly in long‑horizon tasks with sparse dependencies, while single agents perform better in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight multi‑agent mechanism that models collaboration as a dynamically evolving graph, and show that it balances context efficiency and task performance without always improving outcomes as more agents are added.
By Yishuo Yuan, Yibo Wu, Yihan Zhang, Minyuan Sun, Shenliang Li, Xinkai Ma, Yifan Li, Jiaheng Liu
arXiv:2608. 13173v1 Announce Type: new Abstract: Agent skills are crucial external instructions that enable language agents to execute long procedural tasks such as coding or document processing.
By Chang Liu, Yuqi Zhang, Yiman Zhong, Boyi Liu, Hengjun Wang, Shuyue Wei
arXiv:2606. 03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks.
By Taiyu Zhu, Yifan Wu, Weilin Jin, Ying Li, Gang Huang
arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.
By Mingzhou Fan, Siyuan Xu, Mingxuan Yuan
arXiv:2608.22152v1 Announce Type: new
Abstract: Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than ac...
By Weixiang Sun, Zehong Wang, Hong Huang, Colby Nelson, Yanfang Ye
arXiv:2608. 02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks.
By Zian Zhai, Xingyu Tan, Gaowang Zou, Xiaoyang Wang, Wenjie Zhang
The paper introduces a text-based, multi-agent version of the board game Clue to test multi-step deductive reasoning in large language models (LLMs). Six LLM-based agents (GPT‑4o‑mini and Gemini‑2.5‑Flash) play turn‑based games, and a tool‑augmented approach uses a structured possibility matrix to convert implicit game state into explicit remaining possibilities, thereby offloading memory and deductive constraints from the agents. The study compares this tool‑augmented method against a baseline to assess its impact on reasoning quality and task success in a strategic reasoning environment.
By Rebecca Ansell, Autumn Toney-Wails
arXiv:2610.01026v1 Announce Type: cross
Abstract: Tackling complex real-world tasks can exceed the capabilities of a single large language model (LLM), motivating the use of multi-agent workflows tha...
By Xuehang Guo, Haoyu Wang, Haifeng Chen, Yangyi Chen, Zhenhailong Wang, Qingyun Wang