PhysWeep: Does a Video Generator Realize the Physics You Ask For?
Not on the map. Why?
Abstract (arXiv)
Image-to-video generators are often credited with absorbing physical dynamics as implicit world models, a claim the community currently checks with plausibility scores that ask whether a clip looks consistent with real-world motion. Plausibility is the wrong test on its own, because a clip can look natural while encoding the wrong value of the governing physical parameter, and no existing benchmark measures this gap directly. PhysWeep closes it with a fixed, label-free audit, treating a frozen generator as a black box, recovering the realized parameter from generated pixels, and reporting how often generation is trackable at all, how far the realized value sits from the requested one, and which, if either, of the literature's two proposed failure mechanisms the data support. A deterministic-simulator positive control confirms every score is exactly checkable. Applied to three open generators across six sweep axes, PhysWeep finds a specific, reproducible, previously undocumented failure. Conditional on producing trackable motion, two of the three generate confident, well-fit dynamics that converge to one of a small number of fixed, wrong values selected by the sampling seed rather than by the request, reproducing across two independent model families, two physical systems, and an independent tracker. It matches neither the prior reversion nor the case-based clamping the literature anticipates, because the reversion target is seed-conditional rather than a single global default, and a leave-one-out selection rule rejects both; the in-range faithfulness slope is statistically indistinguishable from zero wherever a response is estimable at all. A benchmark averaging over seeds would never see this: each sample is confidently locked to a wrong constant, exactly the failure a plausibility score is structurally blind to. We release the protocol, suite, and analysis code.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 31 of its 38 references that are in the map. First posted on arXiv 5 Sep 2026.
Builds on
31 of 38 references.
References · 31
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- 2019PHYRE: A New Benchmark for Physical Reasoningcited by 137
19 more
- 2025Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signalscited by 53
- 2025VLIPP: Towards Physically Plausible Video Generation with Vision and Language Informed Physical Priorcited by 52
- 2025PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Dropcited by 50
- 2024Good Seed Makes a Good Crop: Discovering Secret Seeds in Text-to-Image Diffusion Modelscited by 48
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- 2025LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preferencecited by 16
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- 2026Phantom: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamicscited by 12
- 2026PhyGround: Benchmarking Physical Reasoning in Generative World Modelscited by 12
- 2025PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Groundingcited by 9
- 2026The Invisible Hand of Physics: When Video Diffusion Models Know More Than They Showcited by 8
- 2026Evolution of Video Generative Foundationscited by 7
- 2025Inferring Dynamic Physical Properties from Video Foundation Modelscited by 6
- 2025Objects in Generated Videos Are Slower Than They Appear: Models Suffer Sub-Earth Gravity and Don't Know Galileo's Principle...for nowcited by 5
- 2026PhyCo: Learning Controllable Physical Priors for Generative Motioncited by 5
- 2026Least-Action-Guided Diffusion for Physical Extrapolationcited by 2
- 2026OrthoPhys: Physically Plausible Video Generation with Orthogonal-View Geometry Guidancecited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.
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- 2026World Models' Last Exam in Physicscited by 0
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