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arxiv: 2606.01538 · v2 · pith:XS26HYVBnew · submitted 2026-06-01 · 💻 cs.GR · cs.CV· cs.LG

MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics

\v{Z}iga Kova\v{c}i\v{c} , Kevin Ellis This is my paper

Pith reviewed 2026-06-28 12:17 UTC · model grok-4.3

classification 💻 cs.GR cs.CVcs.LG
keywords material point methodcode generationvideo diffusionphysical dynamics inferenceextrapolation2D simulation datasetMPM simulationsdynamics from video
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The pith

Code generation models synthesize stable MPM simulations and extrapolate physical dynamics forward in time more reliably than video diffusion models, which better recover geometry but produce implausible physics.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a dataset of 2D material point method simulations that include deformable objects, fluids, kinetic objects, and emitters. It compares code generation models against video diffusion models on the tasks of inferring dynamics from video and extrapolating them, while varying how much physical side information each model receives. The comparison shows code generation can automatically produce MPM code yet has trouble recovering physical parameters from visuals, but it maintains physical and temporal consistency in forward predictions. Video diffusion recovers geometric structure more readily from the same inputs yet generates extrapolations that violate physical laws.

Core claim

By constructing a dataset of 2D MPM physical simulations and evaluating code generation versus video diffusion models with varying amounts of side information, the work demonstrates that code generation can automatically synthesize MPM simulations and achieves more physically and temporally stable extrapolations, despite difficulties in inferring physical parameters from visuals, while video diffusion identifies geometric properties more effectively but generates physically implausible results.

What carries the argument

The assembled 2D Material Point Method dataset of simulations covering deformable objects, fluids, kinetic objects, and emitters, used as a controlled testbed to contrast code generation and video diffusion by controlling the quantity of physical side information.

If this is right

  • Code generation can serve as a route to automatic creation of executable physical simulators from visual data.
  • Video diffusion models remain limited for tasks that require long-horizon physical consistency.
  • Strengths of the two approaches are complementary, suggesting possible hybrid pipelines for inference and prediction.
  • The dataset provides a benchmark for measuring how side information affects physical parameter recovery versus geometric recovery.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending the same comparison to three-dimensional MPM simulations could test whether the observed trade-off between stability and geometric fidelity persists in higher dimensions.
  • Applying the models to real-world video footage instead of synthetic MPM renders might expose additional failure modes not captured by the current dataset.
  • The stability advantage of code generation could make it preferable for control or planning applications that require reliable forward simulation over many steps.

Load-bearing premise

Varying the amount of physically relevant side information on the assembled 2D MPM dataset is sufficient to identify and contrast the strengths and weaknesses of code generation and video diffusion approaches.

What would settle it

An experiment in which video diffusion models produce extrapolations with equal or greater physical and temporal stability than code generation models when both receive identical side information.

Figures

Figures reproduced from arXiv: 2606.01538 by Kevin Ellis, \v{Z}iga Kova\v{c}i\v{c}.

Figure 1
Figure 1. Figure 1: MPMWorlds dataset overview. The dataset includes diverse materials and physical interactions. Each entry contains the simulator code, the scene configuration, and the resulting video. In total we contribute the following: 1. A dataset of 2D physical simulations covering fluids, deformable objects, rigid bodies, emitters, ‘motorized’ objects such as pinwheels and conveyor belts, and more. Each simulation in… view at source ↗
Figure 2
Figure 2. Figure 2: Reconstruction and extrapolation pipelines. Models must predict a full video sequence (vˆ = ˆv≤t ∥ vˆ>t) from an initial observation (v≤t). Top: The VLM synthesizes a simulation program that executes from t = 0, explicitly reconstructing the input (blue) alongside the future extrapolation (pink). Bottom: The VDM operates in pixel space, using the input strictly as conditioning to generate future frames. wh… view at source ↗
Figure 3
Figure 3. Figure 3: Dataset statistics for MPMWorlds across base scenes. (a) Distribution of number of object per scene. (b) Distribution of dynamic-body material types across the dataset. (c) Distribution of object types illustrating the diversity of interaction mechanisms present in the dataset. We further expand the dataset by prompting the LLM to modify previously generated scenes through changes such as adding or removin… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of VLM- and VDM-based extrapolation across input conditions and evaluation [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: VDM failure modes in long-horizon physical extrapolation. Left: In an elastoplastic scene, the VLM correctly maintains rigid object permanence and trajectory. The VDM suffers from object collapse, causing the bouncing block to fade and vanish during extrapolation. Right: Subjected to high-energy kinematic colliders, the VLM preserves complex fluid volume and splashing dynamics, whereas the VDM prediction f… view at source ↗
Figure 5
Figure 5. Figure 5: Performance change af [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Average normalized perfor￾mance by material family. Zero de￾notes each model-family mean across materials. Which materials are most challenging for each model class? [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Moving-average W-MAE over continuation frames. VDM motion error grows faster with pre￾diction horizon than VLM error. How do failure modes evolve over time? [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: VLM sensitivity to spatial inputs and compositional scaling. Left: When explicit positional coordinates are withheld, the VLM struggles with visual state estimation, hallucinating an incorrect obstacle layout (red boxes) that alters the physical trajectory. The VDM, relying directly on pixels, preserves the geometry. Right: When provided with full configurations, the VLM scales seamlessly to compositional … view at source ↗
Figure 10
Figure 10. Figure 10: Failure modes in object permanence and material inference. Left: In an elastoplastic scene, the VLM correctly maintains rigid object trajectories, while the VDM suffers from severe object collapse, causing the bouncing green block to disappear during extrapolation. Right: When explicit material properties are withheld from the input prompt, the VLM sometimes struggles with material inference from frames, … view at source ↗
Figure 11
Figure 11. Figure 11: Contrasting failure modes in complex dynamics and spatial reasoning. Left: In a scene featuring high-energy multi-material interactions (a kinematic pinwheel, fluid, and a rigid block), the VLM accurately preserves object permanence and fluid volume. The VDM suffers from temporal object collapse, causing the rigid block to unphysically dissipate into the surrounding fluid during extrapolation (red boxes).… view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of VLM- and VDM-based extrapolation across input conditions and [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Cumulative collapse rate over extrapolated frames. VDM rollouts collapse earlier and [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
read the original abstract

To study the ability to infer physical dynamics from videos and extrapolate them forward in time, we assemble a dataset of 2D Material Point Method (MPM) physical simulations covering rich physical phenomena such as deformable objects, fluids, kinetic objects, and emitters. We study code generation and video diffusion approaches on this dataset, identifying their strengths and weaknesses by varying the amount of physically relevant side information. The code generation model, beyond giving a working demonstration of automatic synthesis of MPM simulations, reveals that such an approach struggles with inferring physical parameters from visual input, but relative to video diffusion, produces physically and temporally stable extrapolations forward in time, while the video diffusion model more strongly identifies geometric properties from visual input but produces physically implausible extrapolations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper assembles a dataset of 2D Material Point Method (MPM) simulations covering deformable objects, fluids, kinetic objects, and emitters. It compares code generation and video diffusion models for inferring physical dynamics from videos and extrapolating forward in time, by varying the amount of physically relevant side information. The central claim is that code generation struggles to infer physical parameters from visual input but produces physically and temporally stable extrapolations relative to video diffusion, while video diffusion better identifies geometric properties but yields physically implausible extrapolations.

Significance. If the qualitative contrasts can be substantiated with quantitative metrics and controlled ablations, the assembled MPM dataset and the demonstration of automatic MPM code synthesis would be useful contributions for benchmarking physical inference methods. The work highlights potential complementary strengths between code-based and video-based generative approaches for simulation tasks.

major comments (2)
  1. [Abstract] Abstract: the central claims rest on qualitative observations of model behaviors without any reported quantitative metrics (e.g., parameter recovery error, temporal stability scores against held-out MPM ground truth, or geometric fidelity measures), dataset scale, or evaluation protocols, which are load-bearing for assessing the claimed differences in inference and extrapolation performance.
  2. [Evaluation] Evaluation section (implied by the side-information variation protocol): no ablation tables or explicit definitions of the side-information schedule are provided to demonstrate that the observed model contrasts are attributable to the information variation rather than training regime, prompting, or unstated criteria, undermining isolation of the claimed strengths and weaknesses.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We agree that quantitative metrics and explicit ablations are needed to substantiate the claims and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims rest on qualitative observations of model behaviors without any reported quantitative metrics (e.g., parameter recovery error, temporal stability scores against held-out MPM ground truth, or geometric fidelity measures), dataset scale, or evaluation protocols, which are load-bearing for assessing the claimed differences in inference and extrapolation performance.

    Authors: We agree that the central claims would be strengthened by quantitative support. In the revised manuscript we will add quantitative metrics (parameter recovery error, temporal stability scores, and geometric fidelity measures computed on held-out MPM ground truth), report the full dataset scale, and describe the evaluation protocols in detail. revision: yes

  2. Referee: [Evaluation] Evaluation section (implied by the side-information variation protocol): no ablation tables or explicit definitions of the side-information schedule are provided to demonstrate that the observed model contrasts are attributable to the information variation rather than training regime, prompting, or unstated criteria, undermining isolation of the claimed strengths and weaknesses.

    Authors: We will add explicit definitions of each side-information schedule (including the exact prompts and inputs used at each level) together with ablation tables that vary only the amount of side information while holding training regime and prompting fixed. These additions will isolate the contribution of the information schedule to the observed differences. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical comparison on newly assembled dataset

full rationale

The paper assembles a new 2D MPM simulation dataset and reports an empirical comparison of code-generation versus video-diffusion models under varying side information. No equations, fitted parameters renamed as predictions, self-definitional steps, or load-bearing self-citations appear in the provided text. The central claims rest on observed model behaviors against held-out simulations rather than any reduction of outputs to inputs by construction. This is a standard empirical study whose results are independent of the input descriptions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5669 in / 1001 out tokens · 27885 ms · 2026-06-28T12:17:46.010905+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 6 internal anchors

  1. [1]

    Phyre: A new benchmark for physical reasoning.arXiv:1908.05656,

    Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, and Ross Girshick. Phyre: A new benchmark for physical reasoning.arXiv:1908.05656,

    work page arXiv 1908

  2. [2]

    VideoPhy: Evaluating Physical Commonsense for Video Generation

    Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai-Wei Chang, and Aditya Grover. Videophy: Evaluating physical commonsense for video generation.arXiv preprint arXiv:2406.03520,

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    Videophy-2: A challenging action-centric physical commonsense evaluation in video generation

    Hritik Bansal, Clark Peng, Yonatan Bitton, Roman Goldenberg, Aditya Grover, and Kai-Wei Chang. Videophy-2: A challenging action-centric physical commonsense evaluation in video generation. arXiv preprint arXiv:2503.06800,

    work page arXiv

  4. [4]

    Physion: Evaluating physical prediction from vision in humans and machines.arXiv preprint arXiv:2106.08261,

    Daniel M Bear, Elias Wang, Damian Mrowca, Felix J Binder, Hsiao-Yu Fish Tung, RT Pramod, Cameron Holdaway, Sirui Tao, Kevin Smith, Fan-Yun Sun, et al. Physion: Evaluating physical prediction from vision in humans and machines.arXiv preprint arXiv:2106.08261,

    work page arXiv

  5. [5]

    "PhyWorldBench": A Comprehensive Evaluation of Physical Realism in Text-to-Video Models

    Jing Gu, Xian Liu, Yu Zeng, Ashwin Nagarajan, Fangrui Zhu, Daniel Hong, Yue Fan, Qianqi Yan, Kaiwen Zhou, Ming-Yu Liu, et al. " phyworldbench": A comprehensive evaluation of physical realism in text-to-video models.arXiv preprint arXiv:2507.13428,

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    World Models

    David Ha and Jürgen Schmidhuber. World models.arXiv preprint arXiv:1803.10122, 2(3):440,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    How Far is Video Generation from World Model: A Physical Law Perspective

    Bingyi Kang, Yang Yue, Rui Lu, Zhijie Lin, Yang Zhao, Kaixin Wang, Gao Huang, and Jiashi Feng. How far is video generation from world model: A physical law perspective.arXiv preprint arXiv:2411.02385,

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    doi: 10.1145/2897824.2925906

    ISSN 0730-0301. doi: 10.1145/2897824.2925906. URL https://doi.org/10.1145/ 2897824.2925906. Minchen Li, Chenfanfu Jiang, Zhaofeng Luo, Wenxin Du, Chang Yu, Žiga Kova ˇciˇc, and Tianyi Xie.Physics-Based Simulation. March

    work page doi:10.1145/2897824.2925906

  9. [9]

    URL https: //doi.org/10.5281/zenodo.20597655

    doi: 10.5281/zenodo.20597655. URL https: //doi.org/10.5281/zenodo.20597655. Open-source online book. Live version available at https://phys-sim-book.github.io/. Shiqian Li, Kewen Wu, Chi Zhang, and Yixin Zhu. I-PHYRE: Interactive physical reasoning. InThe Twelfth International Conference on Learning Representations,

    work page doi:10.5281/zenodo.20597655

  10. [10]

    Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation

    Fanqing Meng, Jiaqi Liao, Xinyu Tan, Wenqi Shao, Quanfeng Lu, Kaipeng Zhang, Yu Cheng, Dianqi Li, Yu Qiao, and Ping Luo. Towards world simulator: Crafting physical commonsense-based benchmark for video generation.arXiv preprint arXiv:2410.05363,

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    Phybench: Holistic evaluation of physical perception and reasoning in large language models.arXiv preprint arXiv:2504.16074,

    Shi Qiu, Shaoyang Guo, Zhuo-Yang Song, Yunbo Sun, Zeyu Cai, Jiashen Wei, Tianyu Luo, Yixuan Yin, Haoxu Zhang, Yi Hu, et al. Phybench: Holistic evaluation of physical perception and reasoning in large language models.arXiv preprint arXiv:2504.16074,

    work page arXiv

  12. [12]

    PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement

    Tianyidan Xie, Peiyu Wang, Yuyi Qian, Yuxuan Wang, Rui Ma, Ying Tai, Song Wu, Qian Wang, Lanjun Wang, and Zili Yi. Physcodebench: Benchmarking physics-aware symbolic simulation of 3d scenes via self-corrective multi-agent refinement.arXiv preprint arXiv:2604.23580,

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Likephys: Evaluating intuitive physics understanding in video diffusion models via likelihood preference.arXiv preprint arXiv:2510.11512,

    Jianhao Yuan, Fabio Pizzati, Francesco Pinto, Lars Kunze, Ivan Laptev, Paul Newman, Philip Torr, and Daniele De Martini. Likephys: Evaluating intuitive physics understanding in video diffusion models via likelihood preference.arXiv preprint arXiv:2510.11512,

    work page arXiv