Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
Researchers have developed a new framework using reinforcement learning to train large language models for surgical video question answering. This approach decouples visual perception from reasoning by operating over digital twin representations derived from surgical foundation models. The system also incorporates hierarchical representations and a novel reward mechanism that combines format validation with clinical plausibility and uncertainty-aware calibration. AI