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REVIEW 3 major objections 6 minor 18 references

PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Video MLLMs used for pixel-level visual grounding rely on static appearance rather than motion, and the paper's multi-video probing shows they cannot distinguish true from fake motion or forward from reverse order.

desk verdict A serious probing benchmark, but the main experiment is confounded by the multi-video layout; the central claim needs a control. read the letter →

arxiv 2509.02807 v1 pith:VJGKNGU5 submitted 2025-09-02 cs.CV

Mennatullah Siam This is my paper
classification cs.CV
keywords videomulti-modallargelanguagemodelspixel-levelvisualgroundingreferringsegmentationmotionunderstandingmotion-centricbenchmarkstaticappearancebiastemporalreasoningprobing
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

Video multi-modal large language models that can segment objects in response to language descriptions are expected to reason about what happens over time. This paper argues that they mostly do not: when the same video is shown beside a repeated static frame, or beside the video run backward, model performance drops by roughly half. The underlying reason is that current referring-video-segmentation benchmarks often let a single still frame reveal the answer through object category, location, heading, or color, so a model can succeed without temporal understanding. To test whether motion is actually used, the paper builds MoCentric-Bench, a motion-centric benchmark with four probing setups, and shows that both published methods and simple single-image baselines fail at distinguishing true from fake motion and forward from reverse motion order. Single-image baselines that never look at motion are competitive with or better than prior video methods on standard benchmarks, which reinforces the static-cue explanation.

What carries the argument

The central object is MoCentric-Bench, built around a multi-video layout: a modified video—either a repeated static keyframe for the motion-existence probe, or the video played backward with a correspondingly reversed expression for the motion-order probe—is placed side by side with the original video. The model must ground the referring expression in the correct one of the two streams, which requires understanding that the static side has no motion and that the reversed side's motion contradicts the expression. The benchmark also includes single-frame and fully reversed variants, and the performance gap between these and the multi-video layouts is the measurement the argument rests on.

What would settle it

A decisive check would be to run the same models on the val_u & Single frame layout and record which half of the output mask falls on: the static-keyframe side or the dynamic-video side. A model that genuinely understands motion would mostly segment the dynamic side; if most predicted masks land on the static side, the static-bias conclusion is supported, while a model whose masks consistently land on the dynamic side and whose val_u & Single frame score stays close to its val_u score would falsify the central claim. The same side-selection test can be applied to val_u & Reverse to check wheth

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Extended reading notes

Core claim

On its own terms, the paper establishes that existing referring-video-segmentation methods and benchmarks do not measure motion understanding. Using automatic keyframe selection, it turns each video into a static version by repeating one frame; a motion-referring expression like "the elephant walking to the left" can still be grounded in that static frame. Across all tested models, performance on the multi-video layout—original video beside the repeated keyframe—falls to roughly half the score on the original video, and the same happens when the video and its language expression are reversed. The paper concludes that video MLLMs are biased toward static appearance cues: they segment what the

Load-bearing premise

The paper's conclusion rests on the assumption that when a static repeated keyframe or a reversed video is placed beside the original, the sharp performance drop reflects a failure to use motion, not a difficulty caused by the side-by-side layout itself.

Editorial extensions

If this is right

  • The MeVIS benchmark, and likely similar referring-video-segmentation benchmarks, overstates video MLLMs' temporal ability because a large fraction of its expressions can be resolved from a single frame using category, location, heading, or color.
  • Adding a repeated static keyframe beside the original video cuts measured accuracy by roughly half across all compared methods, so current models are not robust to fake motion.
  • Reversing video and expression also causes large drops, showing that models do not encode the order of actions and cannot tell "walking to the left" from "walking to the right" when the video is flipped.
  • Single-image baselines that first detect a bounding box with an MLLM and then segment with a propagation model are competitive with published video methods, meaning static appearance is sufficient on standard benchmarks.
  • A motion-centric LoRA adaptation of the vision encoder improves MoCentric-Bench scores but leaves a large gap, indicating that simple finetuning on motion-centric data is not enough to make models genuinely motion-aware.

Reading between the lines

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

  • The same multi-video probing could be applied to other dense spatiotemporal tasks such as video instance segmentation, tracking-based grounding, and grounded conversation generation; the static-bias failure likely appears there too.
  • Because models drop sharply when a keyframe is added, gains reported on existing benchmarks may be inflated; future benchmarks should include paired static/dynamic video layouts as a mandatory control.
  • The limited improvement from vision-encoder LoRA suggests the bottleneck may be architectural, such as how video frames are tokenized or how temporal attention is computed, rather than a matter of adding more finetuning data.
  • The automatic keyframe-selection procedure could be repurposed to mine truly motion-dependent expressions: keep only expressions whose ground truth cannot be identified from any single keyframe, then use those for training and evaluation.
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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

3 major / 6 minor

Summary. The paper asks whether video multi-modal LLMs use motion in pixel-level visual grounding. It argues that existing referring-video-segmentation benchmarks can often be solved from a single static frame, and it introduces four probing variants (single frame, reversed video, and two multi-video layouts with a static keyframe or a reversed video paired with the original) to test 'motion existence' and 'motion order'. These variants are assembled into MoCentric-Bench, built from the MeVIS val_u split. The paper also proposes two strong single-image baselines (Qwen2.5-VL + SAM 2.0, with and without automatic keyframe selection), a GPT-4o-based expression reversal procedure, a LoRA adaptation of Sa2VA, and a fine-grained analysis of referring expressions. The central empirical claim is that all tested video MLLMs, including the new single-image baselines, drop by roughly half on the multi-video layout variants and therefore cannot differentiate true from fake motion and do not understand motion order.

Significance. If the central claim survives scrutiny, this is a valuable negative result: current video MLLMs used for pixel-level grounding rely primarily on static appearance cues, and existing benchmarks do not measure motion understanding. The paper also makes useful methodological contributions: automatic keyframe selection via coarse video grounding, automatic motion-expression reversal with manual filtering, and strong single-image baselines that are competitive with recent video MLLMs. The expression-property analysis is a nice addition. However, the main evidence is currently underdetermined because the multi-video layout changes several factors at once, and the missing control means the observed drops cannot be unambiguously attributed to motion understanding. Code and data are promised but not shipped, and no error bars are reported for the adapted model.

major comments (3)
  1. [§3.2 and §4.3, Table 3] The central conclusion—that models 'cannot differentiate true from fake motion and do not understand the motion order'—rests on the drops from val_u to val_u & Single frame and from Reverse to val_u & Reverse. But the multi-video layout introduces confounds beyond the absence/reversal of motion: side-by-side video inputs, an instruction that does not name which panel contains the target, reduced per-video resolution, and a duplicated static keyframe acting as an appearance-level distracter. A model could score lower simply because it is unfamiliar with the layout or because segmenting both panels is a reasonable response to an ambiguous prompt. Appendix B validates only the Single-frame layout against background-prediction bias, not the multi-video layout. Please add a control that keeps the layout fixed while removing the motion confound—e.g., val_u & Dynamic Distracter (original video
  2. [§4.1 and §A, evaluation protocol] It is not specified how predictions in the multi-video layouts are scored. The paper does not state whether the predicted mask is matched against the ground truth in the original panel only, whether segmenting the static-keyframe/distractor panel is penalized, or how masks that cover both panels are handled. Figure 1 mentions 'correct and deceiving ground-truth segmentations', but the metric and matching rule are not defined. Since Table 3 is the principal quantitative evidence, this protocol needs to be stated precisely. Relatedly, the Single-frame variant is measured with an unusual background IoU (JBg), and Appendix B itself notes that this metric can reward all-background predictors; this is exactly why the missing multi-video protocol is load-bearing.
  3. [§4.3 and §A, adaptation claims] The adapted Sa2VA⋆ is reported as the average of two runs without variance or error bars, and several reported differences are small (e.g., the val_u & Reverse J&F values of 37.0 vs 42.0 for Sa2VA and Sa2VA⋆, and the claimed ~1% narrowing of the multi-video gap). Moreover, the adaptation is trained on synthetic MeVIS data generated with the same keyframe-selection and multi-video-layout procedure used in the evaluation, so its improvement on MoCentric-Bench partly reflects train/eval distribution matching. To support the claim of state-of-the-art motion-centric adaptation, please report per-run results and variance, and show that the adapted model generalizes to settings that do not share the exact probing layout.
minor comments (6)
  1. [Footnote 2] Please clarify which version is under review. The footnote states that the arXiv version results differ from the NeurIPS submission; the tables and text need to be consistent with a single clearly identified version.
  2. [§3.2 and Table 3] The naming of the four variants is confusing: 'Single frame', 'val_u & Single frame', 'Reverse', and 'val_u & Reverse'. A compact table defining each layout and which panel(s) contain the target would improve readability.
  3. [§4.3] Typo: 'thorugh' should be 'through'.
  4. [Figures 1 and 3] Color coding for correct/deceiving segmentations is inconsistent: Figure 1 says red/blue, Figure 3 says green. Please align the captions and figures.
  5. [Table 5] Small typos: 'aircraft' appears in both the forward and reverse rows; please proofread the expression examples.
  6. [Abstract / Reproducibility] The paper promises code and datasets, but no code or data are currently available. A reproducibility statement with a link or supplementary material would be helpful, especially since the benchmark is synthetic and central to the conclusions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical benchmark result and the derivation chain is self-contained.

full rationale

The paper's main conclusion—that video MLLMs and single-image baselines cannot differentiate true from fake motion and do not understand motion order—is an empirical observation from MoCentric-Bench (Table 3), not a derivation from equations or from fitted parameters. The probing techniques (single frame, reverse, multi-video layouts) are constructed from MeVIS and external generators (Qwen2.5-VL, GPT-4o), and the evaluation compares models on held-out val_u splits. The strong single-image baselines are competitive with prior methods for independent reasons, and the paper explicitly frames them as baselines rather than as predictions. The motion-centric adaptation (Sa2VA*) is supervised on synthetic data generated with the same probing procedure used for evaluation, which limits what its MoCentric-Bench improvement proves, but the paper does not present that improvement as evidence for the central motion-failure claim; the failure claim is based on models that were not trained on those synthetic variants. Self-citations (PixFoundation, Kowal et al.) are contextual and not load-bearing: no uniqueness theorem or benchmark-construction premise is imported from the author's prior work. The paper acknowledges a limitation of the single-frame variant in Appendix B (background-bias preference) and moves to the multi-video layout for that reason. The remaining concern—that the multi-video layout may introduce side-by-side presentation or instruction-ambiguity confounds—is a validity/correctness risk about the benchmark, not a circularity in which an output is equivalent to an input by construction. Therefore, no enumerated circular step is present.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new theoretical entities or fitted constants. Its benchmark is a composite of MeVIS with synthetic modifications. The main assumptions are about the validity of the probe constructions and the quality of the underlying data.

free parameters (1)
  • False-positive threshold = 2%
    Used to split referring expressions into Motion and Static groups in Sec. 4.3; a hand-chosen cutoff, not fitted to data.
assumptions (4)
  • domain assumption MeVIS ground-truth segmentation annotations are accurate and reliable.
    The benchmark variants are derived from MeVIS val_u and train splits; errors in MeVIS would propagate to MoCentric-Bench.
  • domain assumption Qwen2.5-VL keyframe selection identifies a frame whose static cues approximate the motion expression.
    This is the foundation of the motion existence probe; the paper provides qualitative examples but no quantitative validation that the keyframe is sufficient.
  • domain assumption GPT-4o-generated reverse expressions correctly describe the reversed video.
    Used in the motion order probe; manual inspection only discarded ambiguous cases and did not independently verify correctness.
  • domain assumption J&F metrics remain valid for evaluating multi-video layouts where ground truth applies to one side of the layout.
    The paper does not discuss whether standard J&F assumptions hold for the side-by-side layout, which could conflate spatial confusion with motion ignorance.

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Cite this review

Pith. "Pith review of PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?." pith.science (2026). https://pith.science/paper/VJGKNGU5

@misc{pith2026250902807,
  author       = {Pith},
  title        = {Pith review of: PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VJGKNGU5}},
  note         = {Machine review of arXiv:2509.02807}
}
read the original abstract

Multi-modal large language models (MLLMs) have shown impressive generalization across tasks using images and text modalities. While their extension to video has enabled tasks such as video question answering and video captioning, their pixel-level visual grounding abilities are less studied. In this work, we raise the pertinent question of whether motion is used in pixel-level visual grounding and whether video MLLMs can segment objects based on natural language expressions describing their motion patterns. We identify the shortcomings in the current benchmarks, where we show that a single frame can often suffice for capturing the motion referring expression without any temporal reasoning. To address this, we introduce four motion-centric probing techniques, particularly designed for the visual grounding task, to study video MLLMs' ability to identify true motion from a fake one and their ability to grasp the motion order. Consequently, we provide a motion-centric benchmark, MoCentric-Bench. It ensures that video MLLMs are evaluated towards leveraging the interaction between motion and language rather than being dominated by static appearance cues emphasized in existing visual grounding datasets. We further establish strong single-image baselines that are on par with or outperform prior methods. Finally, we explore simple motion-centric adaptation techniques that provide state-of-the-art performance on our MoCentric-Bench. Our motion-centric benchmark, evaluation and findings challenge future models to improve dense spatiotemporal grounding and pixel-level understanding within videos. Code and datasets will be made publicly available at https://github.com/MSiam/PixFoundation-2.0.git.

Figures

Figures reproduced from arXiv: 2509.02807 by the authors.

Figure 1
Figure 1. Our major research question: “Do video MLLMs use motion in visual grounding?”. Left: Shortcomings of the current motion referring segmentation benchmark, where the referring expression can be captured through appearance information conveyed from a single frame only without motion, e.g., using location and heading. Right: Our motion-centric evaluation emphasizes the use of motion information in visual grounding withi… view at source ↗
Figure 2
Figure 2. Qualitative analysis of our proposed automatic keyframe selection from five examples that [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. MoCentric-Bench: The mechanism for creating our motion-centric benchmark. Top: We rely on an automatic keyframe selection to identify the best static frame (i.e., keyframe) to convey the motion referring expression and use it to deceive video-MLLMs as fake motion. The output modified video is the repeated keyframe and can be paired with the original video in various layouts to deceive their grounding mechanisms. We … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative analysis comparing the original MeVIS val_u set performance vs. the motion￾centric one that incorporates an additional static keyframe to the video to confuse video MLLMs, val_u & Single frame. Predictions of our strongest baseline, Qwen2.5-VL + SAM 2.0† ar…
Figure 5
Figure 5. Figure 5: Qualitative ablation on MoCentric-Bench comparing our strongest baseline, Qwen2.5-VL + SAM 2.0 †, our adapted motion-centric model, Sa2VA⋆ and concurrent work from VideoGLAMM. Top three rows: multi-video layout w/ Reverse, expression is “bear landing from a jump backwa…
Figure 6
Figure 6. Figure 6: Fine-grained analysis on the properties of the two major groups of referring expressions [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

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