As Large Language Models increasingly automate complex, long-horizon tasks such as \emph{vibe coding}, a supervision gap has emerged. While models excel at execution, users often struggle to guide them effectively due to insufficient domain expertise, the difficulty of articulating precise intent, and the inability to reliably validate complex outputs. It presents a critical challenge in scalable oversight: enabling humans to responsibly steer AI systems on tasks that surpass their own ability to specify or verify. To tackle this, we propose Scalable Interactive Oversight, a framework that decomposes complex intent into a recursive tree of manageable decisions to amplify human supervision. Rather than relying on open-ended prompting, our system elicits low-burden feedback at each node and recursively aggregates these signals into precise global guidance. Validated in web development task, our framework enables non-experts to produce expert-level Product Requirement Documents, achieving a 54\% improvement in alignment. Crucially, we demonstrate that this framework can be optimized via Reinforcement Learning using only online user feedback, offering a practical pathway for maintaining human control as AI scales.
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究所）；Chongqing School, University of Chinese Academy of Sciences（中国科学院大学重庆学院）；The First Affiliated Hospital, University of Science and Technology of China（中国科学技术大学第一附属医院）；City University of Hong Kong（香港城市大学）
X-SYS: A Reference Architecture for Interactive Explanation Systems
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD – 柏林学习与数据基础研究所）；Technical University Dublin（都柏林理工大学）
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Georgia State University（佐治亚州立大学）；Computer Science Lab, SRI（SRI计算机科学实验室）；Army Cyber Institute（陆军网络学院）；United States Military Academy（美国军事学院）；University of Florida（佛罗里达大学）
From Attribution to Action: A Human-Centered Application of Activation Steering
Fraunhofer Heinrich-Hertz-Institut（弗劳恩霍夫 Heinrich-Hertz 研究所）；Technische Universität Berlin（柏林技术大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systems
Chalmers University of Technology（楚德斯技术大学）；University of Gothenburg（哥德堡大学）；Carnegie Mellon University（卡内基梅隆大学）；RISE Research Institutes of Sweden（瑞典RISE研究机构）
