aiqbench analysis
This arXiv preprint proposes a inference-time prompting method that has autoregressive image generation models produce intermediate frames before each prediction, aiming to make their physical simulations more stable and interpretable without fine-tuning. The reported results cover simple 2-D physics and real 3-D video, with the authors claiming short-horizon predictions are reasonably accurate. For AI readers, the practical question is whether the stabilization holds beyond the tested domains and how it compares with video world models trained specifically for dynamics.
Chain of Time: In-Context Physical Simulation with Image Generation Models
arXiv:2511.00110v2 Announce Type: replace-cross Abstract: We propose a novel cognitively-inspired prompting method to stabilize and interpret the physical simulation produced by autoregressive image generation models, called Chain-of-Time. Chain-of-Time involves generating a series of intermediate images during a simulation, and it is motivated by mental simulation in humans, as well as in-context reasoning in LLMs. Chain-of-Time is used at inference time, and requires no additional fine-tuning. We apply this method to synthetic and real-world domains, including 2-D graphics simulations and natural 3-D videos. These domains cover various physical dynamics, ranging from rigid-body rolling to harmonic oscillation and pulley systems. We found that Chain-of-Time simulation stabilizes the physics simulated by the state-of-the-art image generation models. Beyond examining performance, we also analyzed the specific states of the world simulated by an image model at each time step, which sheds light on the dynamics underlying these simulations. This analysis reveals insights that are hidden from traditional evaluations of physical reasoning, highlighting cases where image generation models can simulate physical properties such as velocity, gravity, oscillations, and pulley systems. Like humans, image generation models are able to accurately predict future world states over short time periods. By providing a mechanism analogous to working memory, Chain-of-Time enables a model to stitch together a series of short-term predictions into an accurate long-term simulation.
Abstract:We propose a novel cognitively-inspired prompting method to stabilize and interpret the physical simulation produced by autoregressive image generation models, called Chain-of-Time. Chain-of-Time involves generating a series of intermediate images during a simulation, and it is motivated by mental simulation in humans, as well as in-context reasoning in LLMs. Chain-of-Time is used at inference time, and requires no additional fine-tuning. We apply this method to synthetic and real-world domains, including 2-D graphics simulations and natural 3-D videos. These domains cover various physical dynamics, ranging from rigid-body rolling to harmonic oscillation and pulley systems. We found that Chain-of-Time simulation stabilizes the physics simulated by the state-of-the-art image generation models. Beyond examining performance, we also analyzed the specific states of the world simulated by an image model at each time step, which sheds light on the dynamics underlying these simulations. This analysis reveals insights that are hidden from traditional evaluations of physical reasoning, highlighting cases where image generation models can simulate physical properties such as velocity, gravity, oscillations, and pulley systems. Like humans, image generation models are able to accurately predict future world states over short time periods. By providing a mechanism analogous to working memory, Chain-of-Time enables a model to stitch together a series of short-term predictions into an accurate long-term simulation.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2511.00110 [cs.CV]
(or arXiv:2511.00110v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2511.00110 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 309 words
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