TAHI方法让人机交互数据成为缩小专业差距的信号，适应后的模型不仅个人任务成功率提升，还能跨用户泛化。
研究人员提出TAHI方法，通过人机交互实现测试时适应。该方法在写作和视觉创作两个领域对30名用户进行适应，在600个任务中使单独任务成功率提高4.5-20.9%。进化评分模块作为可扩展标注工具，比单独使用大模型或人类多捕获16.0-22.3%的失败案例。
Efficient Test-Time Adaptation through Human-AI Interaction
AI agents are trained on population-scale data to encode broad capabilities spanning those of many practitioners. Yet the artifacts they produce rarely meet the personal bar professionals need to stake their reputation on. On realistic, open-ended tasks where success criteria are heterogeneous and insufficiently documented, individual expertise lives precisely in the elevation and departure from the average. In practice, iterative human-agent interaction surfaces criteria that users cannot fully specify up front, yet apply repeatedly across tasks. We argue this cross-session interaction data is a rich, underused signal for closing the gap to individual expertise. In this work, we propose test-time adaptation through human-agent interaction (TAHI), which integrates these signals into agent context and weights, and crystallizes each user's training and evaluation criteria via an evolving rubric module. We adapt agents to 30 individuals in two high-utility domains, writing and visual creation, on a total of 600 tasks. Our agents improve solo task success by 4.5-20.9% within only tens of tasks. Meanwhile, our evolving rubric module serves as a scalable annotation tool, creating evaluation rubrics that catch 16.0-22.3% more failures than those from LMs or humans alone. While agents are adapted towards individuals, we show these personalized agents also produce improvements in success of up to 8.8% that generalize across users.