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InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Large multimodal models fail at inductive physical reasoning: a new benchmark shows they cannot infer physics that contradicts what they learned in training, and what little reasoning they do is driven by language, not vision.

desk verdict A solid new benchmark with a robust language-bias finding, but the 'weak inductive reasoning' claim needs calibration against human/oracle baselines. read the letter →

arxiv 2509.12263 v3 pith:OSBUDV5U submitted 2025-09-12 cs.AI cs.LG

Gautam Sreekumar , Vishnu Naresh Boddeti This is my paper
classification cs.AIcs.LG
keywords inductivephysicalreasoninglargemultimodalmodelsvisualquestionansweringbenchmarklanguagebiasmomentumconservationin-contextlearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper introduces InPhyRe, a visual-question-answering benchmark that tests whether large multimodal models (LMMs) can infer physical laws from a few demonstration videos, including collisions that violate universal laws such as momentum conservation. Across 13 open and proprietary models, it finds that LMMs use demonstrations only when those demonstrations confirm what the model already knows; when the demonstrated physics contradicts their parametric knowledge, few-shot accuracy drops sharply. The paper also shows that the limited inductive physical reasoning that does occur is largely language-driven—when exemplars contain only videos, accuracy falls to near chance for several models. The authors conclude that LMMs treat physical laws as fixed rules rather than transferable mathematical models, casting doubt on their trustworthiness in safety-critical applications.

What carries the argument

The central object is InPhyRe, a synthetic benchmark of collision videos whose trajectories are generated by manually overriding a PyBullet simulation at the moment of collision so that they violate laws such as momentum conservation. Scenarios are grouped into irregular (law-violating) and regular (law-abiding) counterparts, and the key metric is the difference between few-shot accuracy in the irregular scenario and the model's best regular-scenario accuracy; a negative value is interpreted as weak inductive physical reasoning. A second experimental manipulation—exemplars with both video and question-answer pairs versus exemplars with video only—isolates the contribution of language and exp

What would settle it

If human participants, given the same images and three exemplars, showed a similar or larger accuracy drop on the irregular scenarios, the gap would reflect task ambiguity or difficulty rather than a model-specific deficit in inductive physical reasoning.

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Extended reading notes

Core claim

InPhyRe is the first visual question-answering benchmark purpose-built to measure inductive physical reasoning in LMMs by confronting them with collision videos that violate real-world physical laws, generated by intervening in a physics simulator. The paper reports three findings from 13 models: (1) LMMs can recite momentum and energy conservation but apply these laws inconsistently even in regular scenarios; (2) when exemplar videos follow laws unseen in training, almost all models show a substantial accuracy drop relative to regular scenarios, indicating weak inductive physical reasoning; and (3) removing question-answer pairs from the exemplars (video-only) cuts accuracy dramatically, sh

Load-bearing premise

The paper assumes that the accuracy gap between irregular and regular scenarios measures inductive physical reasoning—that is, that the regular and irregular versions are matched in all respects except the violated law and that the intended law is uniquely inferable from three exemplars.

Editorial extensions

If this is right

  • In safety-critical settings where novel physics can occur, an LMM cannot be assumed to adapt from demonstrations; its predictions will default to parametric knowledge.
  • Exemplars help LMMs only when they align with the physical laws already encoded in the model's parameters; conflicting demonstrations are not incorporated.
  • The language-bias result implies that standard visual-question-answering accuracy can overstate a model's visual understanding; multimodal evaluation should separate textual and visual contributions.
  • Instruction tuning as currently practiced does not address this gap; the authors suggest simulation-based feedback signals, similar to reinforcement learning from human feedback, as a direction.
  • The same benchmark methodology—impossible or law-violating scenarios—can be applied to other branches of physics beyond mechanics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The authors do not draw this conclusion, but the irregular-versus-regular gap likely conflates inductive reasoning with task difficulty: if the irregular versions are harder for reasons other than the violated law, part of the drop would appear even in a perfectly inductive agent.
  • A testable extension the paper leaves implicit: adding human participants to the same scenarios would calibrate the gap; if humans show a comparable drop, the metric is not measuring model-specific inductive ability.
  • One could also vary exemplar count beyond three and provide explicit textual statements of the law to separate failure of visual perception, rule induction, and rule application.
  • The observation that larger models show larger language bias suggests that scaling up models may worsen, not fix, the reliance on text, which runs counter to the usual assumption that larger models are more robust reasoners.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces InPhyRe, a synthetic video question-answering benchmark designed to measure inductive physical reasoning in large multimodal models (LMMs). Scenarios depict collision events that either follow or violate universal physical laws such as momentum conservation. Models are evaluated zero-shot and few-shot, with exemplars containing either videos plus question-answer pairs or videos only. The main findings are: (1) LMMs have limited and poorly applied parametric knowledge of physical laws; (2) LMMs show weak inductive physical reasoning when exemplars violate the laws encoded in their parameters; and (3) the observed inductive behavior is driven primarily by language, with visual inputs playing little role. The headline metric is the accuracy gap between irregular and regular scenarios, with negative gaps interpreted as poor inductive physical reasoning.

Significance. If properly validated, InPhyRe would be a valuable benchmark for a question that is both scientifically interesting and practically important: whether LMMs can adapt their physical reasoning to novel or counter-physical environments from a few demonstrations. The paper evaluates a diverse cohort of 13 LMMs, uses a reproducible synthetic generation pipeline, and reports clear, structured accuracy tables. The finding that models often fail when exemplars contradict parametric knowledge, and that performance collapses in video-only settings, is suggestive and worth community attention. However, the strength of the conclusions depends on the validity of the regular–irregular gap as a measure of inductive ability, and on the absence of human/oracle calibration. Those issues are currently unresolved.

major comments (4)
  1. [§4.4, Fig. 4] The central metric compares every irregular scenario against the best regular scenario accuracy, but for AMC, Red-LMC, Red-Pass, and CC, the 'corresponding regular' is LMC(Reg), despite these scenarios differing in question type (rotation, color-conditional motion, passing-through, shape/object permanence) and visual properties. A negative gap therefore conflates induction failure with task-specific difficulty. To support the claim that 'a negative value indicates poor inductive physical reasoning,' the paper needs per-scenario matched regular controls or some other calibration that controls for task difficulty.
  2. [§4.4, Table 3] There is no human baseline or oracle calibration for irregular scenarios. The paper asserts in §1 that humans would 'easily adapt' from demonstrations, but this is not tested. Meanwhile, several irregular accuracies are near ceiling (e.g., InternVL3-8B at 94–100% on LMC, Wall, AMC), so the aggregate negative average is not a uniform failure signature. Without a competent-reasoner reference, the absolute irregular accuracies cannot be interpreted as showing weak inductive physical reasoning.
  3. [§4.5, Fig. 5] In the video-only condition, exemplars include videos plus randomly chosen option letters. In the video-text condition, exemplars include correct question-answer pairs. The comparison therefore varies not only the presence of textual information but also whether the labels are informative. A model may perform worse in the video-only condition because the random labels provide no usable signal, not necessarily because it is visually incapable. A control that keeps labels informative but removes the question text, or another design that separates label informativeness from modality, is needed to support Finding 3.
  4. [§4.4, Fig. 4] The reported accuracy differences lack confidence intervals or repeated-seed variability. Many entries are small (e.g., -0.20, +0.35, +0.30), and without uncertainty quantification it is hard to distinguish genuine effects from noise. Since each scenario contains around 2000 samples and 13 models are evaluated, paired bootstrap or stratified sampling would be straightforward and should be reported.
minor comments (5)
  1. [§B.2] The 'zero-shot' setting includes three assistant messages with random option labels. This is a reasonable formatting control, but calling it 'zero-shot' is potentially confusing. Clarify in the main text that random options are used only to enforce a constrained answer format and do not provide task information.
  2. [§4.4 conclusion] The sentence 'Almost all LMMs showed significant deterioration in performance' uses 'significant' in a non-statistical sense. Recommend replacing with 'substantial' or adding a statistical test.
  3. [§3] The claim that InPhyRe is 'the first visual question answering benchmark to measure inductive physical reasoning in LMMs' should be softened to 'to our knowledge,' as the paper does not exhaustively survey all recent benchmarks.
  4. [Fig. 4 and Fig. 5] The heatmap labels and the additional 'Average over LMMs' and 'Average over scenarios' rows are visually dense and difficult to read. Consider a cleaner formatting or a separate table for the averages.
  5. [§E.3] The heading 'AMC (regular)' in E.3 appears to be a misnomer, since AMC is an irregular scenario; the subsection describes open-ended outputs for AMC. Rename for clarity.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline Finding 2 is the regular–irregular accuracy gap relabeled as 'weak inductive physical reasoning'; the conclusion is stipulated by the metric rather than independently established.

  1. self definitional [§4.1 Evaluation tools; §4.4 How strong is inductive physical reasoning in LMMs?; §4.4 Conclusion]
    "Inductive physical reasoning in LMMs is evaluated by comparing their few-shot performances in regular and irregular scenarios. ... A negative value indicates poor inductive physical reasoning in that scenario. ... Conclusion: LMMs demonstrate only weak inductive physical reasoning when exemplars violate parametric knowledge."

    The central construct is not independently measured: 'weak inductive physical reasoning' is operationalized as a negative gap between irregular and regular 3-shot accuracy, and the headline discovery restates that same gap. Thus 'LMMs struggle in inductive physical reasoning' follows by definition from the metric, not from an external standard. No human or oracle baseline calibrates the gap, and for AMC, Red-LMC, Red-Pass, and CC the comparison uses LMC(Reg) even though the questions concern rotation, color-conditional motion, pass-through, and shape change rather than the same velocity-change task. A negative gap can therefore reflect task difficulty or question mismatch rather than an induction deficit, but the paper's conclusion adopts the metric's interpretation directly.

full rationale

The paper is mostly transparent: all absolute accuracies are reported in Tables 1, 3, and 4, and the irregular/regular comparison is explicitly stated. However, the headline Finding 2 is an interpretive label attached to the gap metric rather than an independent discovery. The metric defines 'weak inductive physical reasoning' as a negative gap, so the conclusion is baked into the operationalization. This is a partial circularity: the benchmark would produce the same 'finding' for any model that performed worse on irregular than regular scenarios, regardless of why. The language-bias finding in §4.5 is a separate controlled comparison and is less circular, and the qualitative AMC analysis in §E.3 is an independent check for one scenario. The paper would be strengthened by an oracle/human baseline and by matching question types across regular/irregular comparisons. Given that the raw accuracy tables remain informative, the circularity is partial, not total.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

This is an empirical benchmark paper, not a derivation, so the ledger records the evaluation choices and unvalidated assumptions that the headline conclusions rest on. Key choices are 3 exemplars, 8 sampled frames, CLIP-L retrieval, and random options in the video-only condition. The axioms concern training-data coverage, matched difficulty between regular and irregular scenarios, uniqueness of the inferred law, and standard physics for regular scenes.

free parameters (4)
  • Exemplar count = 3
    All headline results use 3-shot exemplars; Sec. 5 varies 1 to 3, but the main figures and conclusions depend on the 3-shot choice.
  • Frames sampled per video = 8 of 240
    Sec. A.1 samples 8 frames per video; this determines whether the motion and the physical anomaly are visually discernible, directly affecting the video-only condition.
  • NNER retrieval feature space = CLIP-L cosine distance, top-k unspecified
    Sec. 5 uses CLIP-L features for nearest-neighbor exemplar retrieval; the negative result in Fig. 7 is tied to this retriever choice.
  • Random options in video-only exemplars = Random letters A-D
    Sec. B.2 inserts random answer letters into video-only exemplars to force option-only outputs; this may add noise and affects the language-bias comparison.
assumptions (4)
  • domain assumption Physically impossible scenarios are less likely to have been seen during training, so performance on them isolates inductive ability rather than memorized priors.
    Stated in Sec. 3: the benchmark uses 'physically impossible scenarios that are less likely to have been observed by LMMs during training.' The models' training data are closed, so this premise is unverifiable.
  • domain assumption The accuracy gap between irregular and regular scenarios is a valid measure of inductive physical reasoning.
    Sec. 4.4 assumes 'a negative value indicates poor inductive physical reasoning.' This requires that regular and irregular versions are matched in difficulty except for the violated law, which is not calibrated.
  • domain assumption Three exemplar videos (or their text labels) uniquely determine the intended violated law.
    The ground-truth answer in irregular scenarios is defined by the intended law, but any finite set of demonstrations is consistent with many alternative laws; no simplicity prior or human baseline is supplied.
  • standard math Standard conservation laws correctly describe the regular scenarios.
    Regular scenario ground truth relies on standard elastic collision momentum and energy conservation, which is appropriate background physics.

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Cite this review

Pith. "Pith review of InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning." pith.science (2026). https://pith.science/paper/OSBUDV5U

@misc{pith2026250912263,
  author       = {Pith},
  title        = {Pith review of: InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OSBUDV5U}},
  note         = {Machine review of arXiv:2509.12263}
}
read the original abstract

Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge. It allows LMMs to answer physical reasoning queries, such as the outcome of a potential collision event from visual input. However, since parametric knowledge includes only the physical laws seen during training, it is insufficient for reasoning in inference scenarios that follow physical laws unseen during training. In such novel physical environments, humans could adapt their physical reasoning based on provided demonstrations. This inductive physical reasoning ability is indispensable for LMMs if they are to replace human agents in safety-critical applications. Despite its importance, existing visual benchmarks do not evaluate inductive physical reasoning and only consider the parametric knowledge in LMMs. To this end, we propose InPhyRe, the first visual question answering benchmark to measure inductive physical reasoning in LMMs. InPhyRe evaluates LMMs' ability to predict the outcome of collision events in algorithmically generated synthetic videos. By inspecting over 13 open-source and proprietary LMMs, InPhyRe informs us that (1) LMMs struggle to apply their limited parametric knowledge about universal physical laws to reasoning, (2) inductive physical reasoning in LMMs is weak when the physical laws underlying inference scenarios were unseen during training, and (3) inductive physical reasoning in LMMs suffers from language bias and may ignore the visual inputs, questioning the trustworthiness of LMMs regarding visual inputs.

Figures

Figures reproduced from arXiv: 2509.12263 by the authors.

Figure 1
Figure 1. (Left) A large multimodal model (LMM) is asked to predict the change in vertical velocity of an object colliding with a vertical wall. The model will output “possibility 2” if it uses its parametric knowledge that encodes the universal physical laws (in this case, the momentum conservation principle). However, parametric knowledge would be insufficient if the collision event violated the physical laws encoded in the… view at source ↗
Figure 2
Figure 2. INPHYRE comprises videos (“visual inputs”) of collision events that violate a real-world physical law (“violation”). LMMs must predict state changes in objects due to the collisions, while accounting for the violated physical law (“task”). The videos are grouped into “scenarios”, which are further grouped into three categories based on the nature of physical law they violate. Arrows indicate object motion and are no… view at source ↗
Figure 3
Figure 3. To generate a video, we pass the initial object state to PyBullet [ [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Difference in 3-shot accuracy of LMMs be [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Difference in 3-shot accuracy in irregular sce [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Effect of number of exemplars on the accuracy. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Change in accuracy when NNER is used to find exemplars instead of random sampling. Effect of exemplar retrieval method: The choice of re￾trieved samples has been shown to affect the performance of LLMs [43, 31, 18, 42, 54, 63]. In this section, we verify if this propos…
Figure 8
Figure 8. Figure 8: Regular scenario where linear momentum conservation is followed – [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 11
Figure 11. Figure 11: Irregular scenario where linear momentum conservation is violated – [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Irregular scenario where angular momentum conservation is violated – [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 14
Figure 14. Figure 14: Irregular scenario where only red-colored objects violate linear momentum conservation – [PITH_FULL_IMAGE:figures/full_fig_p016_14.png]
Figure 15
Figure 15. Figure 15: Irregular scenario where red-colored objects can pass through other objects – [PITH_FULL_IMAGE:figures/full_fig_p016_15.png]
Figure 17
Figure 17. Figure 17: Irregular scenario where the colliding object assumes the hue and the shape of the other [PITH_FULL_IMAGE:figures/full_fig_p016_17.png]
Figure 18
Figure 18. Figure 18: Effect of weight quantization on inductive physical reasoning billidlih iifhf [PITH_FULL_IMAGE:figures/full_fig_p021_18.png]

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Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.