We present the AI Cosmologist, an agentic system designed to automate cosmological/astronomical data analysis and machine learning research workflows. This implements a complete pipeline from idea generation to experimental evaluation and research dissemination, mimicking the scientific process typically performed by human researchers. The system employs specialized agents for planning, coding, execution, analysis, and synthesis that work together to develop novel approaches. Unlike traditional auto machine-learning systems, the AI Cosmologist generates diverse implementation strategies, writes complete code, handles execution errors, analyzes results, and synthesizes new approaches based on experimental outcomes. We demonstrate the AI Cosmologist capabilities across several machine learning tasks, showing how it can successfully explore solution spaces, iterate based on experimental results, and combine successful elements from different approaches. Our results indicate that agentic systems can automate portions of the research process, potentially accelerating scientific discovery. The code and experimental data used in this paper are available on GitHub at https://github.com/adammoss/aicosmologist. Example papers included in the appendix demonstrate the system's capability to autonomously produce complete scientific publications, starting from only the dataset and task description
A Sober Look at Agentic Misalignment in Automated Workflows
University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Utah（犹他大学）；University of Notre Dame（圣约翰大学）；George Washington University（乔治华盛顿大学）
TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging
Dept. of Computer Science and Engineering, University of Notre Dame（内布拉斯加大学达灵顿分校计算机科学与工程系）；Dept. of Computer Science, The University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德谷分校计算机科学系）
WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港理工大学电子与计算机工程系）
RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows
Pennsylvania State University（宾夕法尼亚州立大学）；City University of New York（纽约市立大学）；New York University（纽约大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Autodesk（欧特克公司）；Carnegie Mellon University（卡内基梅隆大学）
AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Beijing University of Posts and Telecommunications（北京邮电大学）；Guangxi Transportation Science and Technology Group Co., Ltd.（广西交通科技集团有限公司）；Fudan University（复旦大学）
Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation
Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory（太平洋西北国家实验室物理与计算科学部门）；Department of Chemical Engineering, University of Washington（华盛顿大学化学工程系）
HyEvo: Self-Evolving Hybrid Agentic Workflows for Efficient Reasoning
East China Normal University（华东师范大学）；Beihang University（北京航空航天大学）；Shanghai University of International Business and Economics（上海对外经贸大学）；Fudan University（复旦大学）
EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection
Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology（脑科学与认知科学系，麻省理工学院）；Dept. of Neuroscience, University of California, Berkeley（神经科学系，加州大学伯克利分校）；Dept. of Neurology and Neurologic Sciences, Stanford University（神经病学与神经科学系，斯坦福大学）；Weill Institute of Neurology and Neurosciences, University of California, San Francisco（Weill神经医学研究所，加州大学旧金山分校）；University of California, San Francisco（加州大学旧金山分校）
