Lab2Sim: Turning Real-World Experiment Videos into Interactive Virtual Experiments

Science experiment videos demonstrate how experiments are performed, but offer no direct interaction for learners. Turning them into virtual experiments requires not only reproducing the observed apparatus and state changes, but also enforcing the preconditions governing each operation. We introduce Lab2Sim, a benchmark of 218 science experiment videos with evidence-grounded specifications and 11,881 rubric items. To address this task, we propose EduVideo2Code, which extracts experimental procedures hierarchically, encodes them as explicit state machines to guide code generation, and refines the resulting pages through execution-based verification. Experiments show that existing multimodal models readily generate runnable pages but struggle to faithfully reconstruct experimental apparatus. EduVideo2Code consistently improves all four evaluation dimensions across two backbone models, with apparatus fidelity gains of 24.15 points.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-11-02
DOI
https://doi.org/10.48550/arxiv.2611.02928
Primary Topic
Multimodal Machine Learning Applications
Type
preprint
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preprint

Lab2Sim: Turning Real-World Experiment Videos into Interactive Virtual Experiments

Xusen Hei, Jinyu Yang, Jiali Chen, Yi Cai
arXiv (Cornell University)
Multimodal Machine Learning Applications
preprint

Lab2Sim: Turning Real-World Experiment Videos into Interactive Virtual Experiments

Xusen Hei, Jinyu Yang, Jiali Chen, Yi Cai
preprint en

Abstract

Science experiment videos demonstrate how experiments are performed, but offer no direct interaction for learners. Turning them into virtual experiments requires not only reproducing the observed apparatus and state changes, but also enforcing the preconditions governing each operation. We introduce Lab2Sim, a benchmark of 218 science experiment videos with evidence-grounded specifications and 11,881 rubric items. To address this task, we propose EduVideo2Code, which extracts experimental procedures hierarchically, encodes them as explicit state machines to guide code generation, and refines the resulting pages through execution-based verification. Experiments show that existing multimodal models readily generate runnable pages but struggle to faithfully reconstruct experimental apparatus. EduVideo2Code consistently improves all four evaluation dimensions across two backbone models, with apparatus fidelity gains of 24.15 points.

arXiv (Cornell University)
Multimodal Machine Learning Applications
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