pith. sign in

arxiv: 2606.09646 · v1 · pith:YIXQYKPCnew · submitted 2026-06-08 · 💻 cs.CV · cs.AI· cs.LG

Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis

Samuele Punzo , Niccol\`o Caselli , Ippokratis Pantelidis , Francesco Massafra , Salvatore Lo Sardo , Mohammadreza Salehi This is my paper

Pith reviewed 2026-06-27 17:23 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords intuitive physicsvideo foundation modelslayerwise probingfrozen representationsIntPhys2Minimal Video Pairspretraining paradigms
0
0 comments X

The pith

Pretrained video foundation models encode intuitive physics knowledge most accessibly in intermediate-to-late layers.

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

The paper tests whether frozen representations in video foundation models already contain intuitive physics information by probing them on IntPhys2 and MVP benchmarks. It compares three pretraining approaches and tracks how performance changes across layers and probe designs. Predictive joint-embedding models yield the strongest results when probes incorporate temporal dynamics, while masked and diffusion models show weaker but detectable signals. Information is minimal in early layers and peaks later, and scrambling frame order sharply reduces accuracy on the temporal benchmark.

Core claim

Intuitive-physics knowledge emerges reliably in pretrained video representations, but its accessibility depends strongly on pretraining paradigm, representational depth, and readout mechanism, with the clearest signals appearing at intermediate-to-late depth for models trained with predictive objectives.

What carries the argument

Layerwise frozen-feature probing on IntPhys2 and Minimal Video Pairs benchmarks, applied to V-JEPA, VideoMAE, and LTX-Video models.

If this is right

  • Predictive joint-embedding pretraining produces stronger physics encoding than masked reconstruction or diffusion-based generation.
  • Probes that explicitly model temporal dynamics recover more physics information than frame-independent readouts.
  • Physics-relevant features are concentrated at intermediate-to-late depths rather than uniformly across the network.
  • Disrupting temporal order in the input substantially impairs access to the encoded physics knowledge.

Where Pith is reading between the lines

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

  • Choosing readout layers from the middle-to-late range of predictive video models may improve results on downstream tasks that require physical reasoning.
  • Comparing layer profiles across more model families could reveal whether certain architectural choices systematically improve physics capture.
  • The same probing approach could be applied to test whether other abstract concepts such as causality or object permanence are also encoded during video pretraining.

Load-bearing premise

The probes and benchmarks isolate knowledge already present in the frozen features rather than allowing the probes to learn the tasks from the small amount of labeled data.

What would settle it

Performance on MVP remaining unchanged when input frames are presented in random order would show that the models are not using temporal physics structure.

Figures

Figures reproduced from arXiv: 2606.09646 by Francesco Massafra, Ippokratis Pantelidis, Mohammadreza Salehi, Niccol\`o Caselli, Salvatore Lo Sardo, Samuele Punzo.

Figure 1
Figure 1. Figure 1: MVP adaptation for frozen-feature probing. We convert text-conditioned VideoQA examples from Minimal Video Pairs (Krojer et al., 2025) into binary physical-plausibility labels, while preserving pair-consistency evaluation. Video frames are adapted from MVP examples; figure layout and annotations are ours. a full text-conditioned QA task, but instead adapt it to a binary physical-plausibility task [PITH_FU… view at source ↗
Figure 2
Figure 2. Figure 2: Layerwise MLP depth profiles by model family. MVP generally improves toward later layers, while IntPhys2 more often peaks before the final layer. not show a clear late-layer trend, but rather physics-relevant information becomes accessible over a broader intermediate￾to-late region, and in several cases weakens again at the output. LTX reinforce this interpretation while adding a denoising dimension. From … view at source ↗
Figure 3
Figure 3. Figure 3: IntPhys 2 physical conditions. Each panel shows an illustrative fixed-camera frame sequence for one principle: permanence, immutability, spatio-temporal continuity, and solidity. Frames are adapted from [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Layerwise depth profiles for the linear probe across model families and benchmarks [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Layerwise depth profiles for the temporal attentive probe across model families and benchmarks. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Family-mean depth profiles. MVP is late-layer dominated, whereas IntPhys2 tends to peak at intermediate-to-late depth [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: LTX-Video linear performance over denoising noise level and transformer block. The best LTX scores appear at specific denoising stages rather than uniformly across the trajectory 16 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: LTX-Video MLP performance over denoising noise level and transformer block. The best LTX scores appear at specific denoising stages rather than uniformly across the trajectory [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Best-case probe comparison across model families for IntPhys2 and MVP. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Relative degradation under temporal controls for the MLP probe. Both frame shuffling and single-frame repetition substantially reduce performance, with the single-frame control producing the largest degradation, especially on MVP. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
read the original abstract

We study whether pretrained video foundation models encode intuitive-physics information in their frozen representations, and how this information varies across model families, layers, and probe types. Using frozen-feature probing on IntPhys2 and Minimal Video Pairs (MVP), we compare predictive joint-embedding models (V-JEPA), masked reconstruction models (VideoMAE), and a diffusion-based video generator (LTX-Video). V-JEPA achieves the strongest overall results across benchmarks, especially with probes that model temporal dynamics, while VideoMAE remains competitive and LTX-Video recovers weaker but non-trivial signal. Layerwise analyses show that physics-relevant information is weakest in early layers and becomes most accessible at intermediate-to-late depth, and temporal controls show that disrupting frame order substantially reduces performance, especially on MVP. Together, these results suggest that intuitive-physics knowledge emerges reliably in pretrained video representations, but its accessibility depends strongly on pretraining paradigm, representational depth, and readout mechanism.

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 / 2 minor

Summary. The manuscript presents a layerwise probing analysis of video foundation models (V-JEPA, VideoMAE, LTX-Video) on intuitive physics benchmarks IntPhys2 and MVP. It claims that intuitive-physics knowledge emerges in pretrained representations, with accessibility depending on pretraining paradigm, depth, and probe type, supported by stronger performance with temporal probes, layerwise patterns, and frame-order controls.

Significance. If the central claim is substantiated, the work contributes to understanding what video foundation models learn during pretraining by showing differential encoding of intuitive physics across architectures and layers. The comparisons between joint-embedding, masked reconstruction, and diffusion models, along with depth analyses, offer insights into representation learning that could inform future model development.

major comments (2)
  1. [Methods / Probing Setup] The central claim requires that above-chance performance reflects information already present in the frozen video representations rather than the probes acquiring the task during supervised probing. No results are reported for the same probe architectures trained on features from randomly initialized or untrained models of matching architecture (Methods section on probing setup and Experiments on IntPhys2/MVP).
  2. [Results / Temporal Controls] On MVP, frame-order disruption is shown to reduce performance, but without the random-feature baseline it remains possible that temporal-aware probes learn low-level motion cues from the limited labeled data rather than accessing pre-encoded knowledge (Results section on temporal controls).
minor comments (2)
  1. [Methods] Add details on probe architectures, data splits, number of trials, and statistical tests (including error bars) to the methods to support reproducibility and verification of the layerwise and cross-model comparisons.
  2. [Appendix / Probing Details] Clarify the exact readout mechanisms and any hyperparameter choices for the probes in the main text or appendix.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The two major comments both highlight the value of a random-initialization baseline to isolate the contribution of pretraining; we address them below and commit to the necessary revisions.

read point-by-point responses

  1. Referee: [Methods / Probing Setup] The central claim requires that above-chance performance reflects information already present in the frozen video representations rather than the probes acquiring the task during supervised probing. No results are reported for the same probe architectures trained on features from randomly initialized or untrained models of matching architecture (Methods section on probing setup and Experiments on IntPhys2/MVP).

    Authors: We agree that a random-initialization control is required to substantiate that above-chance performance originates in the pretrained representations. In the revised manuscript we will extract features from randomly initialized versions of V-JEPA, VideoMAE, and LTX-Video (identical architectures and layer depths) and train the same probe families on these features for both IntPhys2 and MVP. These results will be reported in the Methods (probing setup) and Experiments sections alongside the pretrained results. revision: yes

  2. Referee: [Results / Temporal Controls] On MVP, frame-order disruption is shown to reduce performance, but without the random-feature baseline it remains possible that temporal-aware probes learn low-level motion cues from the limited labeled data rather than accessing pre-encoded knowledge (Results section on temporal controls).

    Authors: We acknowledge that the frame-order control alone does not fully rule out probe learning from limited labels. The random-initialization baseline described above will be applied to the temporal probes on MVP as well, directly addressing this concern. The existing frame-order results already show a substantial drop, consistent with reliance on temporal structure present in the pretrained features; the new baseline will provide the additional isolation requested. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical probing study self-contained against external benchmarks

full rationale

The paper reports an empirical frozen-feature probing analysis comparing pretrained video models (V-JEPA, VideoMAE, LTX-Video) on IntPhys2 and MVP benchmarks, with layerwise results and temporal controls. No equations, derivations, or predictions are presented that reduce by construction to fitted inputs or self-citations. Claims rest on experimental outcomes from external benchmarks rather than definitional equivalences, renamed known results, or load-bearing self-citation chains. This is the standard case of a non-circular empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters or invented entities; the main domain assumption is that the benchmarks validly isolate intuitive physics.

axioms (1)
  • domain assumption IntPhys2 and Minimal Video Pairs benchmarks accurately isolate intuitive physics understanding independent of low-level visual cues.
    The paper's central claim rests on these benchmarks measuring the target phenomenon.

pith-pipeline@v0.9.1-grok · 5721 in / 1090 out tokens · 17370 ms · 2026-06-27T17:23:43.315511+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

27 extracted references · 2 canonical work pages

  1. [1]

    URL https://doi.org/ 10.1145/3292500.3330701

    1145/3292500.3330701. URL https://doi.org/ 10.1145/3292500.3330701. Alain, G. and Bengio, Y . Understanding intermediate layers using linear classifier probes,

    work page doi:10.1145/3292500.3330701

  2. [2]

    Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y ., and Ballas, N

    URL https:// arxiv.org/abs/1610.01644. Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y ., and Ballas, N. Self-supervised learning from images with a joint-embedding predic- tive architecture,

    Pith/arXiv arXiv

  3. [3]

    Assran, M., Bardes, A., Fan, D., Garrido, Q., Howes, R., Mojtaba, Komeili, Muckley, M., Rizvi, A., Roberts, C., Sinha, K., Zholus, A., Arnaud, S., Gejji, A., Martin, A., Hogan, F

    URL https://arxiv.org/ abs/2301.08243. Assran, M., Bardes, A., Fan, D., Garrido, Q., Howes, R., Mojtaba, Komeili, Muckley, M., Rizvi, A., Roberts, C., Sinha, K., Zholus, A., Arnaud, S., Gejji, A., Martin, A., Hogan, F. R., Dugas, D., Bojanowski, P., Khalidov, V ., Labatut, P., Massa, F., Szafraniec, M., Krishnakumar, K., Li, Y ., Ma, X., Chandar, S., Meie...

    arXiv

  4. [4]

    Bardes, A., Garrido, Q., Ponce, J., Chen, X., Rabbat, M., LeCun, Y ., Assran, M., and Ballas, N

    URLhttps://arxiv.org/abs/2506.09985. Bardes, A., Garrido, Q., Ponce, J., Chen, X., Rabbat, M., LeCun, Y ., Assran, M., and Ballas, N. Revisit- ing feature prediction for learning visual representations from video,

    Pith/arXiv arXiv

  5. [5]

    Belinkov, Y

    URL https://arxiv.org/abs/ 2404.08471. Belinkov, Y . Probing classifiers: Promises, shortcomings, and advances.Computational Linguistics, 48(1):207–219, 04

    Pith/arXiv arXiv

  6. [6]

    Chao and Derek Fai Wong , title =

    doi: 10.1162/coli a 00422. Bergstra, J., Bardenet, R., Bengio, Y ., and K ´egl, B. Algorithms for hyper-parameter optimization. In Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., and Weinberger, K. (eds.),Advances in Neural Information Processing Systems, volume

    work page doi:10.1162/coli

  7. [7]

    cc/paper_files/paper/2011/file/ 86e8f7ab32cfd12577bc2619bc635690-Paper

    URL https://proceedings.neurips. cc/paper_files/paper/2011/file/ 86e8f7ab32cfd12577bc2619bc635690-Paper. pdf. Beyer, L., Zhai, X., and Kolesnikov, A. Better plain vit baselines for imagenet-1k,

    2011

  8. [8]

    Bordes, F., Garrido, Q., Kao, J., Williams, A., Rabbat, M., and Dupoux, E

    URL https: //arxiv.org/abs/2205.01580. Bordes, F., Garrido, Q., Kao, J., Williams, A., Rabbat, M., and Dupoux, E. Intphys 2: Benchmarking intu- itive physics understanding in complex synthetic envi- ronments, 06

    arXiv

  9. [9]

    Garrido, Q., Ballas, N., Assran, M., Bardes, A., Najman, L., Rabbat, M., Dupoux, E., and LeCun, Y

    URL https://arxiv.org/abs/ 2505.14321. Garrido, Q., Ballas, N., Assran, M., Bardes, A., Najman, L., Rabbat, M., Dupoux, E., and LeCun, Y . Intuitive physics understanding emerges from self-supervised pretraining on natural videos,

    arXiv

  10. [10]

    URL https://arxiv.org/ abs/2502.11831. HaCohen, Y ., Chiprut, N., Brazowski, B., Shalem, D., Moshe, D., Richardson, E., Levin, E., Shiran, G., Zabari, N., Gordon, O., Panet, P., Weissbuch, S., Kulikov, V ., Bitterman, Y ., Melumian, Z., and Bibi, O. Ltx-video: Realtime video latent diffusion,

    arXiv

  11. [11]

    Hewitt, J

    URL https: //arxiv.org/abs/2501.00103. Hewitt, J. and Liang, P. Designing and interpreting probes with control tasks,

    Pith/arXiv arXiv

  12. [12]

    org/abs/1909.03368

    URL https://arxiv. org/abs/1909.03368. Joseph, S., Garrido, Q., Balestriero, R., Kowal, M., Fel, T., Bakhtiari, S., Richards, B., and Rabbat, M. Interpreting physics in video world models,

    arXiv 1909

  13. [13]

    Krojer, B., Komeili, M., Ross, C., Garrido, Q., Sinha, K., Ballas, N., and Assran, M

    URL https: //arxiv.org/abs/2602.07050. Krojer, B., Komeili, M., Ross, C., Garrido, Q., Sinha, K., Ballas, N., and Assran, M. A shortcut-aware video-qa benchmark for physical understanding via minimal video pairs.arXiv,

    arXiv

  14. [14]

    org/abs/2208.03550

    URL https://arxiv. org/abs/2208.03550. Liu, N. F., Gardner, M., Belinkov, Y ., Peters, M. E., and Smith, N. A. Linguistic knowledge and transferability of contextual representations,

    arXiv

  15. [15]

    9 Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis Loshchilov, I

    URL https:// arxiv.org/abs/1903.08855. 9 Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis Loshchilov, I. and Hutter, F. Decoupled weight decay regu- larization,

    Pith/arXiv arXiv 1903

  16. [16]

    Mur-Labadia, L., Muckley, M., Bar, A., Assran, M., Sinha, K., Rabbat, M., LeCun, Y ., Ballas, N., and Bardes, A

    URL https://arxiv.org/abs/ 1711.05101. Mur-Labadia, L., Muckley, M., Bar, A., Assran, M., Sinha, K., Rabbat, M., LeCun, Y ., Ballas, N., and Bardes, A. V-jepa 2.1: Unlocking dense features in video self- supervised learning,

    Pith/arXiv arXiv

  17. [17]

    org/abs/2603.14482

    URL https://arxiv. org/abs/2603.14482. Ouyang, H., Wang, Q., Xiao, Y ., Bai, Q., Zhang, J., Zheng, K., Zhou, X., Chen, Q., and Shen, Y . Codef: Content de- formation fields for temporally consistent video process- ing,

    Pith/arXiv arXiv

  18. [18]

    Tong, Z., Song, Y ., Wang, J., and Wang, L

    URL https://arxiv.org/abs/2506.10178. Tong, Z., Song, Y ., Wang, J., and Wang, L. Videomae: Masked autoencoders are data-efficient learners for self- supervised video pre-training,

    arXiv

  19. [19]

    Wang, L., Huang, B., Zhao, Z., Tong, Z., He, Y ., Wang, Y ., Wang, Y ., and Qiao, Y

    URL https:// arxiv.org/abs/2203.12602. Wang, L., Huang, B., Zhao, Z., Tong, Z., He, Y ., Wang, Y ., Wang, Y ., and Qiao, Y . Videomae v2: Scaling video masked autoencoders with dual masking,

    arXiv

  20. [20]

    Xiao, Z., Zhou, Y ., Yang, S., and Pan, X

    URL https://arxiv.org/abs/2303.16727. Xiao, Z., Zhou, Y ., Yang, S., and Pan, X. Video dif- fusion models are training-free motion interpreter and controller,

    arXiv

  21. [21]

    Zhu, Z., Feng, X., Chen, D., Yuan, J., Qiao, C., and Hua, G

    URL https://arxiv.org/abs/ 2405.14864. Zhu, Z., Feng, X., Chen, D., Yuan, J., Qiao, C., and Hua, G. Exploring pre-trained text-to-video diffusion models for referring video object segmentation,

    arXiv

  22. [22]

    10 Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis A

    URL https: //arxiv.org/abs/2403.12042. 10 Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis A. Appendix A.1. Code Availability All code necessary to reproduce the experiments is publicly available in the GitHub repository. A.2. Dataset Construction and Additional Benchmark Details IntPhys2We use the Main split of the da...

    arXiv

  23. [23]

    Each panel shows an illustrative fixed-camera frame sequence for one principle: permanence, immutability, spatio-temporal continuity, and solidity

    IntPhys 2 physical conditions. Each panel shows an illustrative fixed-camera frame sequence for one principle: permanence, immutability, spatio-temporal continuity, and solidity. Frames are adapted from Fig. 6 of Bordes et al. (2025); figure layout and annotations are ours. A.3. Extended Model Descriptions V-JEPA FamilyThe V-JEPA (Video Joint-Embedding Pr...

    2025

  24. [24]

    Unlike reconstruction-based approaches that operate on pixel-level data, the model learns to predict a target representation from context in a shared embedding space

    models leverage the principle of latent space prediction. Unlike reconstruction-based approaches that operate on pixel-level data, the model learns to predict a target representation from context in a shared embedding space. This encourages abstract, semantically meaningful, and compact representations rather than appearance matching. Compared with the or...

    2024

  25. [25]

    In this setting, the model learns to denoise a video sample over multiple steps, starting from a noisy latent and progressively refining it into a coherent video

    LTX Video DiffuserLTX Video belongs to the diffusion-based video generation family (HaCohen et al., 2024). In this setting, the model learns to denoise a video sample over multiple steps, starting from a noisy latent and progressively refining it into a coherent video. The architecture is designed for generation rather than representation learning, but th...

    2024

  26. [26]

    Patch size refers to spatial tokens; tubelet size is the temporal stride in frames

    Detailed backbone configurations used in all experiments. Patch size refers to spatial tokens; tubelet size is the temporal stride in frames. 2https://huggingface.co/Lightricks/LTX-Video-0.9.8-13B-distilled 12 Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis A.4. Model and Probe Configuration Table 2 reports the model ...

    2000

  27. [27]

    Each layer is assigned a 20-trial study with a TPE sampler (Bergstra et al.,

    For the linear and MLP probes, we perform hyperparameter selection independently for each layer with Optuna (Akiba et al., 2019). Each layer is assigned a 20-trial study with a TPE sampler (Bergstra et al.,

    2019