LivePhys：通过扫描即播放框架将静态物理问题转化为交互式模拟
LivePhys: Transforming Static Physics Problems into Interactive Simulations via a Scan-to-Play Framework
AI总结：
研究针对教科书中静态物理问题学习的高认知负担，提出LivePhys框架，通过多模态处理构建中间表示，用大语言模型推理并由物理引擎执行生成交互式模拟，显著提升模拟性能并降低学习者认知负担。
AI中文摘要：
教科书中的物理问题通常以静态图表和简短文字描述呈现，这要求学习者通过心理可视化推断动态物理行为，增加认知负担并限制准确心理模型的形成。本文提出LivePhys框架，通过将静态物理问题转化为可执行的交互式模拟，实现力学学习的扫描即播放范式。该框架将多模态感知与物理感知推理及确定性模拟解耦，通过文本提取、几何分割和跨模态基础构建结构化的物理感知中间表示，再用多模态大语言模型作为推理控制器，由物理引擎执行生成模拟。评估结果表明LivePhys在模拟可执行性、空间准确性和交互保真度上显著优于通用多模态模型，用户研究显示与LivePhys生成的模拟交互可降低学习者的认知负担。
英文摘要：
Physics problems in textbooks are typically presented as static diagrams accompanied by brief textual descriptions, requiring learners to infer dynamic physical behaviors through mental visualization. This process often imposes high cognitive demands and limits learners' ability to form accurate mental models. In this paper, we present \textbf{LivePhys}, a framework that enables a \emph{Scan-to-Play} paradigm for mechanics learning by transforming static textbook physics problems into executable, interactive simulations. LivePhys decouples multimodal perception from physics-aware reasoning and deterministic simulation. Given a problem diagram and its accompanying text, LivePhys performs text extraction, geometric segmentation, and cross-modal grounding to construct a structured, physics-aware intermediate representation. A multimodal large language model is then used as a reasoning controller to infer entities, parameters, and constraints, which are executed by a physics engine to generate spatially consistent and interactive simulations that allow learners to explore and manipulate problem conditions dynamically. Our evaluation results show that LivePhys significantly outperforms general-purpose multimodal models in simulation executability, spatial accuracy, and interaction fidelity. In addition, a user study demonstrates that interacting with LivePhys-generated simulations reduces learners' perceived cognitive load compared to static textbook materials.
