DiMiRoS: A Digital Robotic Microscopic Dataset for Benchmarking High-Precision Surgery & Expert Dexterity Understanding

For a century, what a microsurgeon saw was locked inside a pair of eyepieces. Digital exoscopic robotic microscopes open that lock: a camera and display make the visual feedback guiding every movement a signal that can be recorded and deliberately changed. This matters beyond surgery. Dexterity emerges as perception and action are calibrated through experience, yet recordings that could teach embodied AI expert manipulation remain scarce, scarcest where precision is extreme. Microsurgery is that extreme: bimanual, magnified, sub-millimetre, with almost no tolerance for error. We introduce DiMiRoS, the first surgical dataset recorded on a digital exoscopic robotic microscope: 308 end-to-end microvascular anastomoses by sixteen participants from novices to experts, captured from eleven synchronized views of the operative field, hands, body and operating room. Each participant operates in 2D and stereoscopic 3D with task and phantom unchanged, making visual feedback a controlled variable. Phase labels, object annotations, hand and body keypoints, and a blinded expert-derived evaluation ground benchmarks of suture quality and phase prediction. Reasoning VLMs overestimate quality, miss execution errors and confuse distinct actions performed with the same tools, while multi-view predictions do not reliably improve assessment. These failures expose a gap between seeing skilled movement and reasoning about its progress and consequences. DiMiRoS makes that gap measurable, opening a path towards embodied AI that learns how experts move and how vision shapes their movement.