发表机构
ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MIVAIS是双层研究平台，通过标准化人机交互等基础设施与自动记录多模态数据的研究环境，降低混合驱动视觉分析应用的开发与评估门槛，已复现相关系统并获专家验证。
AI 中文摘要
混合驱动视觉分析（VA）系统通过将人类直觉与软件智能体及其机器智能相结合，为人类用户赋能。然而，这类系统的开发与严格评估仍受限于工程开销：开发者需实现复杂的底层状态同步以管理异步智能体行为，研究者则难以捕获研究和评估人机协作所需的多模态溯源数据。我们提出MIVAIS，这是一个双层研究平台，旨在抽象混合驱动VA的结构复杂性。首先，它提供标准化人机交互、状态同步及智能体间通信的计算基础设施；其次，它提供声明式研究环境，自动记录多模态人机遥测数据，包括应用/系统状态、屏幕截图、音频及其他传感器数据，支持无缝的原位用户研究和会话后分析。我们通过复现Podium、Voyager 2和ProactiveVA这三个最先进系统，对基础设施进行技术验证；此外，我们通过与HCI和VA研究者开展专家案例研究，评估该框架的表达能力与效率，证明MIVAIS可有效降低智能协同自适应界面的原型开发与评估门槛。
英文摘要
Mixed-initiative Visual Analytics (VA) systems empower human users by interleaving human intuition with software agents and their machine intelligence. However, the development and rigorous evaluation of such systems remain constrained by engineering overhead. Developers must, e.g., implement complex, low-level state synchronization to manage asynchronous agent behaviors, while researchers struggle to capture the multimodal provenance required to study and evaluate human-AI collaboration. We present MIVAIS, a dual-layered research platform designed to abstract the structural complexities of mixed-initiative VA. First, it contributes a computational Infrastructure that standardizes human-software agent interaction, state synchronization, and communication between the agents. Second, it provides a declarative Study Environment that automatically logs multimodal human-AI telemetry - including application/system state, screen capture, audio, and additional sensor data - enabling seamless, in-situ user studies and post-session analysis. We technically validate our infrastructure by replicating three state-of-the-art systems (Podium, Voyager 2, and ProactiveVA). Furthermore, we evaluate the framework's expressiveness and efficiency through expert case studies with HCI and VA researchers, demonstrating how MIVAIS effectively lowers the barrier to prototyping and evaluating intelligent, co-adaptive interfaces.
