CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?
Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual generations or compare distributions coarsely over a whole dataset, leaving the fine-grained aleatoric uncertainty of specific phenomena untested. We introduce CaliBench, which scores outcomes in a physically interpretable discrete space - a bin index, a die face, a suit, a colour - rather than a learned feature space such as in FID, so the distance from a known reference distribution is measured directly. We curate outcome spaces whose reference
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- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “CaliBench: Are the Stochastic Dynamics of Video World Models” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Jonathan Sadeghi →
“CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?”
- LinkedLinked via arxiv author · 85%Jenny Seidenschwarz →
“CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?”
- LinkedLinked via arxiv author · 85%Jesse Allardice →
“CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?”
- LinkedLinked via arxiv author · 85%Sirish Srinivasan →
“CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?”
- LinkedLinked via arxiv author · 85%Benjamin Graham →
“CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?”
- LinkedLinked via arxiv author · 85%Jeffrey Hawke →
“CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?”