SurgEye: Instruction Tuning-Free Perception-Reasoning Framework for Surgical Scene Understanding

Qing Xu; Xiechen Shen; Xianjian He; Guqiao Liang; Zixiao Chai; Jiale Qin; Yifan Chen; Siyi Li; Tian Yuan; Yiwei Li; Ziheng Meng; Zhen Chen

arXiv · 2611.04012

SurgEye: Instruction Tuning-Free Perception-Reasoning Framework for Surgical Scene Understanding

Qing Xu·Xiechen Shen·Xianjian He·Guqiao Liang·Zixiao Chai·Jiale Qin·Yifan Chen·Siyi Li·Tian Yuan·Yiwei Li·Ziheng Meng·Zhen Chen

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Abstract

Surgical scene understanding is pivotal for computer-assisted intervention, yet demands compositional reasoning across instruments, anatomy, and workflow. Existing surgical vision-language models rely on instruction tuning with large-scale question-answer pairs, entailing laborious data curation and costly retraining. To bridge this gap, we propose SurgEye, a perception-reasoning framework that grounds reasoning in a vision foundation model without instruction tuning. Specifically, we devise Routed Multi-Task Perception (RMP) that routes the vision foundation model through task-specific pathways learned from public perception labels, producing spatial and temporal evidence. We further introduce Agentic Compositional Reasoning (ACR) that employs a frozen large language model to coordinate RMP specialists and temporal memory as callable tools, composing evidence across tasks and time into grounded answers. Moreover, we construct SurgEye-Bench spanning eight surgical procedures, pairing each question with perception labels for verifiable evaluation. Extensive experiments demonstrate that SurgEye outperforms state-of-the-arts in surgical reasoning. The source code will be available.

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Qing Xu, Xiechen Shen, Xianjian He, Guqiao Liang, Zixiao Chai, Jiale Qin, Yifan Chen, Siyi Li, Tian Yuan, Yiwei Li, Ziheng Meng, Zhen Chen. 2026-11-02. SurgEye: Instruction Tuning-Free Perception-Reasoning Framework for Surgical Scene Understanding. https://arxiv.org/abs/2611.04012

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