Surgical video question answering requires multi-step reasoning across semantic, spatial, and temporal dimensions. Existing methods architecturally compress videos into discrete token representations and couple visual perception with reasoning. This approach fragments continuous spatial-temporal relationships and has been shown to restrict multi-step reasoning capabilities. We introduce a reinforcement learning (RL) framework that trains large language models (LLMs) to decouple perception from reasoning by operating over digital twin representations constructed from surgical foundation models. Additionally, we introduce hierarchical representations across frame, temporal window, and procedure levels with probabilistic uncertainty estimates. Finally, we propose a novel reward that combines format validation with accuracy assessment through clinical plausibility evaluation and uncertainty-aware calibration for training. To demonstrate the capabilities of this approach, we introduce REAL-Colon-Reason, a colonoscopic benchmark with 2000 question-answer pairs across three complexity levels. We achieve state-of-the-art performance on REAL-Colon-Reason and two existing surgical VideoQA benchmarks REAL-Colon-VQA and EndoVis18-VQA.

翻译：手术视频问答需要在语义、空间和时间维度上进行多步推理。现有方法在架构上将视频压缩为离散的令牌表示，并将视觉感知与推理耦合。这种方法割裂了连续的时空关系，已被证明会限制多步推理能力。我们提出一种强化学习框架，训练大语言模型通过操作基于手术基础模型构建的数字孪生表示，将感知与推理解耦。此外，我们引入跨帧、时间窗口和手术步骤层次的层次化表示，并附有概率不确定性估计。最后，我们提出一种新颖的奖励函数，通过临床合理性评估和不确定性感知校准，将格式验证与准确性评估相结合进行训练。为展示该方法的能力，我们提出REAL-Colon-Reason——一个包含2000个跨越三个复杂度等级的问答对的结肠镜基准测试。我们在REAL-Colon-Reason以及两个现有手术视频问答基准测试REAL-Colon-VQA和EndoVis18-VQA上取得了最先进性能。