Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
Existing surgical video question answering methods compress videos into discrete tokens and couple perception with reasoning, limiting multi-step reasoning. This paper introduces a reinforcement learning framework that trains LLMs to operate over digital twin representations, decoupling perception from reasoning. It introduces hierarchical representations and a novel reward, and presents the REAL-Colon-Reason benchmark, achieving state-of-the-art performance on multiple benchmarks.
[2606.17279] Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
[Submitted on 15 Jun 2026]
Title:Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
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Abstract: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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.17279 [cs.CV]
(or arXiv:2606.17279v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.17279
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yiqing Shen [view email] [v1] Mon, 15 Jun 2026 20:40:45 UTC (193 KB)
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