Comments17 pages (11 main + appendices), 7 figures. Accepted at the North East AI Agents Day 2026 workshop, Jane Street, New York City. Workshop: https://ne-agents-day.github.io/
Human-AI Agent Interaction as a Neuroplastic Training Environment
作为神经可塑性训练环境的人类与人工智能代理交互
Eranga Bandara, Ross Gore, Asanga Gunaratna, Ravi Mukkamala, Nihal Siriwardanagea, Gihan Siriwardanagea, Sachini Rajapakse, Isurunima Kularathna, Pramoda Karunarathna, Chalani Rajapakse, Sachin Shetty, Christopher K. Rhea, Ng Wee Keong, Kasun De Zoysa, Amin Hass, Shaifali Kaushik, Wathsala Herath, Preston Samuel, Anita H. Clayton, Atmaram Yarlagadd
机构
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Old Dominion University(奥多明尼昂大学)
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AI Motion Labs(人工智能运动实验室)
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Nanyang Technological University(南洋理工大学)
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University of Colombo(科伦坡大学)
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Accenture Technology Labs(埃森哲技术实验室)
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GSI Scandinavia AB(GSI斯堪的纳维亚公司)
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Lithuanian University of Health Sciences(立陶宛健康科学大学)
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Department of Psychiatry and Neurobehavioral Sciences, University of Virginia School of Medicine(弗吉尼亚大学医学院精神病学和神经行为科学系)
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Blanchfield Army Community Hospital(布兰奇菲尔德陆军社区医院)
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McDonald Army Health Center(麦克唐纳陆军健康中心)
CommentsPreprint v1 (pre-launch). The system described launches in Q3 2026; a revised version will add post-launch deployment measurements under the pre-registered protocol in Section 5
A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO
一种将遗留工作流提升为智能体BPM的过程驾驭框架:在CUGA FLO中的设计与实现
Fabiana Fournier, Lior Limonad
机构
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IBM SIL, Israel(IBM 研究实验室,以色列)
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Department of Information Systems, Faculty of Computer & Information Sciences, University of Haifa(海法大学计算机与信息科学学院信息系统系)
Comments8 pages, 1 figure, 4 tables. Evaluated on a production 15-node compute fleet with 114 real task traces. Code available at https://aispark.airlive.com/joe-hackathon/
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
EMBL AI Librarian:面向AI智能体的生命科学知识层
Luigi Sigillo, Matteo Silvestri, Francesco Tabaro, Rajat Bhatnagar, Syed Irtaza Mubashar, Matt Jeffryes, Daljit Nijjer, Vittorio Perera, Ola Spjuth, Julio Saez-Rodriguez, Melissa Harrison, Fabio Petroni
Comments9 pages, 4 figures. Accepted to ACM SIGKDD 2026 Workshop: Agentic AI for Scientific and Societal Advances (SciSoc Agents and LLMs). Describes an agentic AI platform for scientific software engineering with governed multi-cloud inference, structured multiagent workflows, and domain-aware coding support (cs.SE, cs.MA, cs.AI)
A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
用于合成约束多目标先导化合物优化的模块化智能体框架
Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland, Matthew Hart, Steini Davidsson, Angelos Angelopoulos, Ron Alterovitz, Marcello DeLuca, Alexander Tropsha
Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis
为面向科学的智能体AI赋予分析严谨性:用于神经影像数据分析的Brain Researcher平台
Zijiao Chen, Nicholas Lu, Xinhui Li, Jocelyn A. Ricard, Ce Ju, Huan H. Wang, Christian Kindermann, Jeanette A. Mumford, Steven Dillmann, James Kent, Alejandro de la Vega, Sanmi Koyejo, Vince D. Calhoun, Joshua W. Buckholtz, Juan Helen Zhou, Steffen Bollmann, Russell A. Poldrack
机构
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Stanford University(斯坦福大学)
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Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)(三机构神经成像与数据科学转化研究中心(TReNDS))
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Georgia State University(佐治亚州立大学)
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Georgia Institute of Technology(佐治亚理工学院)
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Emory University(埃默里大学)
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Inria(法国国家信息与自动化研究所)
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CEA(法国原子能和替代能源委员会)
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Université(大学)