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REVIEW 5 major objections 5 minor 1 cited by

Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Current multimodal large language models lack genuine visual temporal understanding: on the new TemporalVQA benchmark, GPT-4o lands at chance for event ordering and below humans for time-lapse estimation.

desk verdict Useful benchmark and a sensible consistency metric, but the paper's categorical 'No!' is not supported by its own numbers: the chance baseline is 25%, not 50%, and GPT-4o hits 65.3%. read the letter →

arxiv 2501.10674 v2 pith:B2QY6IFK submitted 2025-01-18 cs.CV cs.CL

Mohamed Fazli Imam , Chenyang Lyu , Alham Fikri Aji This is my paper · ORCID
classification cs.CVcs.CL
keywords multimodallargelanguagemodelsvisualtemporalunderstandingorderreasoningtime-lapseestimationbenchmarkevaluationconsistentaccuracyGPT-4oVQA
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 sets out to show that current multimodal large language models (MLLMs) do not genuinely understand visual time, even though they handle many static image tasks well. It introduces TemporalVQA, a benchmark with two tasks: deciding which of two images shows an earlier event, and estimating how much time elapsed between two images. On the first task, the best tested model, GPT-4o, reaches 49.1% consistent accuracy, indistinguishable from random guessing, while humans average 90.3%; on the second, GPT-4o reaches 70.0% versus 83.3% for humans, and open-source models drop much lower. The authors argue that the failure patterns—strong sensitivity to image layout, valid reasoning flipping to invalid when the image order is swapped, and hallucinated details—show that models lean on superficial cues instead of temporal understanding. This matters because applications such as video analysis, navigation, and time-sensitive decisions depend on exactly this ability.

What carries the argument

The load-bearing mechanism is the TemporalVQA benchmark itself, built from image pairs that a human can order unambiguously. For temporal order, each pair is presented in three layouts (vertical, horizontal, separate) and in both left-right and top-bottom orders under two prompts; the consistent-accuracy metric requires identical correct answers across the two presentation orders, which prevents position bias or random guessing from masquerading as temporal skill. For time-lapse estimation, pairs are labeled into six coarse time spans from seconds to years, with options rephrased to keep adjacent categories distinct. The design deliberately strips out watermarks, timestamps, and other explicit time cues, and pairs are filtered by two annotators for clear temporal logic. The benchmark's role in the argument is to make the failure observable and measurable: if a model cannot answer consistently when the same evidence is shown in reversed order, the paper concludes the model lacks a stable temporal representation.

What would settle it

A decisive check would be to run a new group of, say, twenty annotators over the same 720 ordered pairs and 125 time-lapse pairs: if agreement with the original labels falls well below 95%, or if a model trained or prompted to ignore layout (for example by presenting mirrored and randomly cropped pairs) suddenly exceeds 70% consistent accuracy on the order task, the claim that MLLMs lack temporal understanding would be weakened.

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Extended reading notes

Core claim

The central discovery is a negative result stated directly in the title: when asked to order two images by event time or to estimate the elapsed time between them, current MLLMs perform far below humans and, on ordering, near chance once guessing is controlled for. The control is the paper's consistent-accuracy metric: each image pair is shown in both orders, and a prediction counts only if the model answers correctly in both presentations. Under that metric GPT-4o scores 49.1% on temporal order and 70.0% on time-lapse estimation, against human scores of 90.3% and 83.3%; Gemini-1.5-Pro scores 63.2% on time-lapse, and open-source models such as LLaVA-NeXT fall far lower on ordering. Qualitative analysis of GPT-4o's reasoning shows it often produces valid reasoning for one presentation order and then invalid reasoning or hallucinated details when the same pair is reversed, which the paper takes as evidence that model outputs are driven by order- and layout-sensitive surface cues rather than a stable representation of event time.

Load-bearing premise

The load-bearing premise is that the benchmark's labels are correct and unambiguous and that the tasks genuinely require temporal reasoning; the paper checks labels with only three human participants and does not report how often annotators agreed, so if the labels are biased, the low model scores would indict the questions rather than the models.

Editorial extensions

If this is right

  • If the claim holds, current MLLMs cannot be safely relied on for visual temporal judgments in applications such as video summarization, surveillance review, or autonomous navigation without human verification.
  • The 49.1% consistent-accuracy result implies that standard single-presentation evaluations can overstate temporal ability by letting position bias and lucky guessing masquerade as understanding.
  • Because open-source models score far below 10% in several temporal-ordering conditions, the gap is not a small engineering issue but a basic capability missing from the current generation.
  • The benchmark gives a reusable test: any future MLLM claiming temporal understanding can be measured against the same two tasks and compared with the reported human baselines.

Reading between the lines

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

  • The authors leave implicit that adopting their reversed-order consistent-accuracy protocol in other image and video benchmarks would likely lower reported accuracies for most current MLLMs, since order sensitivity is shown here to be widespread.
  • A weaker interpretation—that models have some temporal ability that positional priors mask—remains open; a testable follow-up would be to train or prompt models against layout bias and see whether consistent accuracy rises.
  • A next experiment the paper does not run is fine-tuning an open-source model on ordered frame pairs and re-testing; if consistent accuracy stays near chance, the architectural claim would be strengthened.
  • Because only three human participants validated the labels, a larger human replication would directly test whether the ground truth is as unambiguous as the paper assumes.
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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

5 major / 5 minor

Summary. The paper introduces TemporalVQA, a benchmark with two tasks: temporal order understanding from pairs of images and time-lapse estimation between images. The authors evaluate eight multimodal LLMs, including GPT-4o and Gemini-1.5-Pro, and report that all models perform poorly on temporal order understanding when scored by a 'consistent accuracy' metric that requires correct answers on both swapped presentations, and that they are well below human accuracy on time-lapse estimation. The paper further analyzes GPT-4o's reasoning, finding order sensitivity, invalid reasoning, and hallucination. The central claim, stated in the title and abstract, is that current MLLMs cannot do visual temporal understanding and reasoning.

Significance. If its conclusions were fully supported, this paper would be a valuable negative result for the MLLM evaluation literature. The benchmark's design has real strengths: the swapped-presentation consistent-accuracy metric is a sensible way to control for positional bias, the dataset is released publicly, and the qualitative case study in Section 4.5 provides concrete evidence of order-dependent reasoning failures. The human evaluation, though small, offers an external anchor. However, the paper's headline claim is contradicted by its own best result, and several methodological details need clarification. The defensible contribution is a benchmark showing that current MLLMs have partial, brittle, layout-sensitive temporal reasoning that is far below human performance; the categorical 'No' is not supported by the evidence.

major comments (5)
  1. [Introduction and Section 4.2] The claim that GPT-4o's 49.1% 'is equivalent to random guessing' is numerically wrong under the paper's own metric. Consistent accuracy, as defined in Section 4.2, requires joint correctness on both swapped presentations. For a model guessing uniformly at random in each presentation, the probability of being correct on both is 0.5 × 0.5 = 0.25, not 0.5. The best GPT-4o result in Table 3 is 65.3% (separate multi-image input with Prompt 2), which is far above this chance level. A model with a fixed positional bias but no temporal understanding would score near 0% on joint correctness. The data therefore support 'partial and brittle temporal judgment,' not 'No.' Please state the chance baseline explicitly and revise the Introduction and abstract accordingly.
  2. [Abstract, Figure 1, and Table 3] The headline number 49.1% average consistent accuracy for GPT-4o on temporal order understanding is not reproducible from Table 3. Averaging the six GPT-4o consistent-accuracy entries (vertical and horizontal single-image, and separate multi-image, with prompts P1 and P2) gives 43.8%, not 49.1%. Averaging only the three P2 conditions gives 52.4%, and averaging only the two single-image P2 conditions gives 46.0%. The paper must specify exactly which conditions are averaged to obtain 49.1%, and the figure and abstract should be corrected if the number is an error.
  3. [Section 4.2 and Table 6] The definition of consistent accuracy is ambiguous and potentially contradictory. The text says 'the model must provide identical answers for both orders,' but because the correct answer swaps sides when the image order is reversed, identical literal answers (e.g., repeatedly saying 'left') cannot be correct in both presentations. The reported numbers, such as the 65.3% 'consistently correctly classified' in Table 6, only make sense if 'consistent' means giving the same temporal judgment (e.g., 'the first image' in one order and 'the second image' in the swapped order) rather than the same surface answer. Please define the scoring procedure precisely, including how open-ended responses and 'true/false' prompt answers are mapped.
  4. [Sections 3.3, 4.3, and 4.4] The reliability of the benchmark labels is asserted but not quantified. The authors report two annotators for dataset construction and a survey of three human participants, but no inter-annotator agreement measure is given, and no per-participant results or error bars are reported. The statement that 'there was no single case where all participants made an incorrect prediction' is not a substitute for an agreement statistic. Please report Cohen's kappa (or equivalent) for the annotation stage and per-participant accuracy with confidence intervals for the human evaluation, since the benchmark's external validity depends on the labels being unambiguous.
  5. [Section 5 (Conclusion)] The conclusion that current MLLMs 'primarily rely on superficial visual cues rather than genuine temporal comprehension' is stronger than the evidence. The 65.3% consistent accuracy in the multi-image P2 setting means GPT-4o often changes its answer correctly when the image order is swapped, which is not compatible with a purely superficial-cue account. The evidence supports the more nuanced conclusion that models are highly sensitive to layout, prompt, and input order, and that their reasoning is sometimes invalid or hallucinated, but it does not support the categorical denial of temporal understanding. Please rephrase the conclusion and title to match the actual findings.
minor comments (5)
  1. [Section 4.3] The claim that models like LLaVA-NeXT and Qwen-VL show performance 'dropping under alternate prompts' is not consistent with Table 3 in all conditions: Qwen-VL improves from 0.6% to 23.9% consistent accuracy under P2 in the separate multi-image setting, and from 5.8% to 19.2% in the vertical layout. Please qualify this statement.
  2. [Table 4] The header 'A verage Acc.' contains a spacing typo; it should read 'Average Acc.'
  3. [Prompt #3 in Section 3.4] The multiple-choice options have several gaps (e.g., between 15 seconds and 2 minutes, between 15 minutes and 1 hour, and between 12 months and 3 years). This may be intentional to reduce ambiguity, but the paper should state this design choice explicitly and explain how the human annotators mapped the six label categories onto these options.
  4. [Section 4.2] The phrase 'effectively doubling the data size' in Section 3.1 should be clarified, because the consistent-accuracy metric uses both swapped presentations jointly, so the 720 total image pairs are not independent samples for that metric.
  5. [Figure 1] The figure legend states that the Temporal Order Understanding accuracy is 'averaged consistent accuracy across all different layouts,' but the value shown for GPT-4o (49.1%) does not match the average of the six consistent-accuracy entries in Table 3. Please reconcile the figure with the table.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: TemporalVQA is a self-contained empirical evaluation; its conclusions rest on benchmark validity and human comparison, not on fitting or self-citation.

full rationale

The paper is an empirical benchmark study with no fitted parameters, no derivation chain, and no first-principles claims. The 'consistent accuracy' metric is a post-hoc scoring rule applied to model predictions, not a quantity constructed from the labels it claims to predict. The benchmark labels come from manual annotation with independent review, and the paper reports human performance as an external reference point (90.3% and 83.3% on the two tasks). The conclusion that MLLMs lack robust temporal understanding is an interpretation of low model accuracy, not an equivalence forced by definition. The skeptic's concern about the correct chance baseline (25% under joint consistent accuracy rather than 50%) and the paper's unsupported 'equivalent to random guessing' statement are correctness issues, not circularity. No load-bearing self-citation or ansatz-smuggling appears in the manuscript. Therefore the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The benchmark construction assumes the annotated labels are reliable, the consistent-accuracy metric is a valid probe of temporal reasoning, and the three-person human evaluation is a trustworthy gold standard. The paper does not report inter-annotator agreement or statistical significance for these choices.

assumptions (3)
  • domain assumption Ground-truth labels are correct and unambiguous.
    The paper relies on manual filtering by two annotators to ensure each image pair has a clear temporal order and time-lapse label. If some labels are wrong or ambiguous, the measured accuracies would be biased. Section 3.3.
  • domain assumption The consistent accuracy metric accurately reflects temporal reasoning.
    The central metric requires identical predictions under swapped order, but a model could be consistently wrong yet score low on consistency; the metric is presented as a valid measure without formal validation. Section 4.2.
  • domain assumption Human evaluation with three participants provides a reliable gold standard.
    Human accuracy is used to validate the dataset, but only three participants were used and no inter-annotator agreement is reported. Section 4.3.

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Cite this review

Pith. "Pith review of Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!." pith.science (2026). https://pith.science/paper/B2QY6IFK

@misc{pith2026250110674,
  author       = {Pith},
  title        = {Pith review of: Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B2QY6IFK}},
  note         = {Machine review of arXiv:2501.10674}
}
read the original abstract

Multimodal Large Language Models (MLLMs) have achieved significant advancements in tasks like Visual Question Answering (VQA) by leveraging foundational Large Language Models (LLMs). However, their abilities in specific areas such as visual temporal understanding, which is crucial for comprehending real-world dynamics, remain underexplored. To address this, we propose a challenging evaluation benchmark named TemporalVQA, consisting of two parts: 1) Temporal Order Understanding and 2) Time-lapse Estimation. The first part requires MLLMs to determine the sequence of events by analyzing temporally consecutive video frames. The second part presents image pairs with varying time differences, framed as multiple-choice questions, asking MLLMs to estimate the time-lapse between images with options ranging from seconds to years. Our evaluations of advanced MLLMs, including models like GPT-4o and Gemini-1.5-Pro, reveal significant challenges: GPT-4o achieved only 49.1% average consistent accuracy in temporal order task and 70% in time-lapse estimation, with open-source models performing even poorly. These findings underscore the limitations of current MLLMs in visual temporal understanding and reasoning, highlighting the need for further improvements for their temporal capability. Our dataset can be found at https://huggingface.co/datasets/fazliimam/temporal-vqa.

Figures

Figures reproduced from arXiv: 2501.10674 by the authors.

Figure 1
Figure 1. Performance comparison across Tempo￾ralVQA tasks. The plot shows the accuracy (%) of different models on Temporal Order Understanding (or￾ange) and Timelapse Estimation (green). The accuracy shown for Temporal Order Understanding is averaged consistent accuracy across all different layouts. areas such as temporal understanding and reason￾ing have not been thoroughly explored (Huang and Zhang, 2024). Temporal underst… view at source ↗
Figure 2
Figure 2. An introductory diagram illustrating the task setup for the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Some qualitative cases illustrating the output predictions from GPT4o. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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