In this paper, we introduce the problem of denoting and deriving the complexity of workflows (plans, schedules) in collaborative, planner-assisted settings where humans and agents are trying to jointly solve a task. The interactions -- and hence the workflows that connect the human and the agents -- may differ according to the domain and the kind of agents. We adapt insights from prior work in human-agent teaming and workflow analysis to suggest metrics for workflow complexity. The main motivation behind this work is to highlight metrics for human comprehensibility of plans and schedules. The planning community has seen its fair share of work on the synthesis of plans that take diversity into account -- what value do such plans hold if their generation is not guided at least in part by metrics that reflect the ease of engaging with and using those plans?
ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；The University of Sydney（悉尼大学）；The Chinese University of Hong Kong（香港中文大学）
A Human-AI Collaborative Workflow for Mathematical Discovery: A Case Study in Grover-Compatible Riemannian Optimization
Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）；Yau Mathematical Sciences Center, Tsinghua University（清华大学尤伊数学科学中心）
Collaborative Agent Reasoning Engineering (CARE): A Three-Party Design Methodology for Systematically Engineering AI Agents with Subject Matter Experts, Developers, and Helper Agents
EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems
Sun Yat-Sen University（中山大学）；Zhejiang University（浙江大学）；ShanghaiTech University（上海理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
DeepWisdom（深度智慧）；AI Initiative, King Abdullah University of Science and Technology（阿卜杜拉国王科技大学人工智能计划）；Xiamen University（厦门大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanjing University（南京大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California, Berkeley（加州大学伯克利分校）；The Swiss AI Lab IDSIA/USI/SUPSI（瑞士IDSIA/USI/SUPSI人工智能实验室）
Complexity and Scale in AI-Assisted Workflow Management: A Federated Learning Case Study
RENCI, University of North Carolina at Chapel Hill, NC, USA（RENCI，北卡罗来纳大学教堂山分校）；Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA（信息科学研究所，南加州大学马里纳德尔雷耶斯分校）
MAIA: A Collaborative Medical AI Platform for Integrated Healthcare Innovation
Department of Biomedical Engineering and Health Systems, KTH, Royal Institute of Technology（生物医学工程与健康系统系，皇家理工学院）；Department of Clinical Sciences, Intervention and Engineering, Karolinska Institutet（临床科学、干预与工程系，卡罗林斯卡研究所）；Division of Medical Radiation Physics, Department of Physics, Stockholm University（医学放射物理 division，物理系，斯德哥尔摩大学）；Department of Oncology-Pathology, Karolinska Institutet（肿瘤学-病理学系，卡罗林斯卡研究所）；Department of Radiology, Karolinska University Hospital（放射学系，卡罗林斯卡大学医院）；Department of Molecular Medicine and Surgery, Karolinska Institutet（分子医学与外科系，卡罗林斯卡研究所）；Department of Neurobiology, Care Sciences and Society, Karolinska Institutet（神经生物学、护理科学与社会系，卡罗林斯卡研究所）
本文提出开源协作平台 MAIA，基于 Kubernetes 集成数据、建模、标注、部署与临床反馈工具，以推动医学影像 AI 从研究到临床的可复现转化。
