arXiv:2606. 18425v1 Announce Type: cross Abstract: Scientific workflow management systems (WMS) support scalable and reproducible execution of complex pipelines, but workflow design, implementation, and debugging remain largely manual and require significant expertise.
By Komal Thareja, Hamza Safri, Rajiv Mayani, Anirban Mandal, Ewa Deelman
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
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:2606. 01961v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering.
By Junqi Liu, Salena Song, Yuhan Wang, Jiawei Mao, Hardy Chen, Xiaoke Huang, Tianhao Qi, Pengfei Guo, Yucheng Tang, Yufan He, Can Zhao, Andriy Myronenko, Dong Yang, Daguang Xu, Yuyin Zhou
AgentMercury is a scalable framework that synthesizes executable environments from high‑level business scenarios instead of task‑specific benchmarks. It creates a persistent world with entities, services, tools, and invariants, allowing diverse tasks and interaction trajectories to emerge naturally. The authors generated 4,783 environments across 14 industries and 50 countries, and training reinforcement‑learning agents on them improved performance on enterprise workflows and out‑of‑domain benchmarks, while the construction process itself can be learned to increase authoring success.
By Minbyul Jeong, Chanwoong Yoon
arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.
By Tejas Singh Anand, Yuet Ying Christina Wang, Wanting Jiang, Steve Masson, Tian Zheng, Bingjie Zhou