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REVIEW 4 major objections 5 minor 29 references

Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Explicitly encoding time across frames lifts video-language models by up to 4.7 percent and lets them drop 87.5 percent of frames with little loss.

desk verdict Useful compression module and a thorough empirical sweep, but the central 'explicit temporal modeling is critical' claim is confounded by unmatched SFT and train/eval overlap. read the letter →

arxiv 2501.16786 v1 pith:CQ7OWDGL submitted 2025-01-28 cs.CV cs.CL

Yun Li , Zhe Liu , Yajing Kong , Guangrui Li , Jiyuan Zhang , Chao Bian , Feng Liu , Lina Yao
show 1 more author
Zhenbang Sun
This is my paper · ORCID
classification cs.CVcs.CL
keywords videounderstandingmultimodallargelanguagemodelsStackableTemporalEncoderexplicitmodelingframecompressionreasoningquestionansweringLLaVA
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 enters a design debate in video multimodal language models: should temporal relations across frames be left to the LLM to infer implicitly, or should a dedicated module encode them explicitly? It proposes the Stackable Temporal Encoder (STE), a small convolutional layer that runs across frame embeddings before they reach the vision-language projector, and inserts it into two established implicit models, LLaVA-OV and LLaVA-Video. The paper reports that this addition raises average accuracy by 4.7% and 1.5% respectively over six video benchmarks, and that explicit temporal encoding lets the models drop 75-87.5% of frames while matching or nearly matching full-frame accuracy. It also reports gains on every temporal-specific task group it inspected, plus design findings about receptive fields and where temporal learning should live. A sympathetic reader would take the paper as evidence that explicit temporal modeling is not optional but a load-bearing component for video understanding.

What carries the argument

The central object is the Stackable Temporal Encoder (STE), a stackable temporal convolution module. It concatenates frame embeddings along the time axis, divides a dynamic-length video into convolutional units, and uses a sliding window of size 2 and stride 1; choosing the number of output channels sets the output frame count, so a layer can keep frames fixed ((2:2)) or compress them by ratios such as (4:3) and (2:1). Stacking L layers multiplies the compression and widens the temporal receptive field covered by the final window. The module is inserted between the vision encoder and the vision-language projector, so it learns temporal structure in continuous visual space before frames are tokenized into semantic space; this placement is what lets it both improve temporal understanding and absorb frame reduction.

What would settle it

Train the original LLaVA-OV and LLaVA-Video backbones through the same pretraining-plus-SFT protocol on the same five video datasets with STE removed; if the retrained implicit baselines match or exceed the reported 4.7% and 1.5% gains, the attribution to explicit temporal modeling fails. A second check: permute the frame order inside each STE convolution window during pretraining and see whether the gains survive; if they do, the module is not encoding temporal order.

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Extended reading notes

Core claim

The central claim is that a video MLLM that encodes temporal structure explicitly, rather than relying on the LLM decoder to infer it from a bag of frame embeddings, understands video better and can tolerate far more frame compression. The paper's instrument is STE, a stackable convolutional encoder placed after the vision encoder and before the vision-language projector; each layer slides a window of size 2 and stride 1 over frames and can be configured to keep the frame count the same or compress it by any ratio. Instantiated on LLaVA-OV and LLaVA-Video, STE improves average accuracy across PerceptionTest, ActNet-QA, NExT-QA, MLVU, MVBench, and VideoMME by 4.7% and 1.5%, and improves the backbone on all fourteen temporal-related task groups examined. Under compression, LLaVA-OV-STE with 87.5% of frames removed slightly outperforms the 32-frame original, and LLaVA-Video-STE loses only 0.5% at 75% compression; the authors interpret this as explicit temporal encoding compensating for missing tokens. The paper further argues that temporal convolution belongs in visual space rather than semantic space, and that the module works as a lightweight plug-in even without supervised fine-tuning.

Load-bearing premise

The reported gains are attributed to explicit temporal modeling, but the STE-augmented models were trained on five video datasets while the original backbones were not retrained on that same data; if that extra training, rather than the temporal encoder itself, drives the improvement, the central claim collapses.

Editorial extensions

If this is right

  • Video MLLM builders should treat temporal encoding as a first-class component and place it before the projector; the paper argues this yields better temporal understanding than implicit modeling.
  • Frame compression via explicit temporal encoding is a viable efficiency lever: the paper shows 50-87.5% of frames can be dropped with much smaller accuracy loss than simply sampling fewer frames.
  • Task-level results imply that explicit temporal modeling buys specific abilities such as motion recognition, stability prediction, state change, and action order, not a uniform boost.
  • A pre-trained STE can be plugged into a frozen 7B backbone and still compress 75% of frames with less than 2% average drop, suggesting fast deployment without retraining the LLM.
  • Training only on video data drops single-image benchmarks to about 95% of the original while improving multi-image understanding, so video fine-tuning needs image data to avoid image regression.

Reading between the lines

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

  • Our inference: the comparison would be cleaner if the original backbones were retrained under the same five-dataset pretraining and SFT protocol; until that control exists, part of the 4.7% and 1.5% gains could come from extra training rather than from temporal encoding itself.
  • If the placement result generalizes, temporal structure should be enforced before tokenization; a direct test is to move an existing explicit temporal encoder from after the projector to before it and see whether the same visual-space gain appears.
  • A testable extension is to combine STE with a semantic token compressor such as two-token-per-frame encoders; the paper's logic predicts that temporal encoding would recover much of the accuracy those aggressive compressors lose.
  • The tasks that improve most, such as stability prediction and state change, are exactly the ones a single frame cannot answer; this suggests STE is learning inter-frame differences rather than just adding parameters, which a frame-averaging pooling ablation could confirm.
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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 proposes the Stackable Temporal Encoder (STE), a convolutional module inserted between the vision encoder and the vision-language projector of video MLLMs. STE supports arbitrary frame input/output ratios and can be stacked to adjust temporal receptive fields and token compression. The authors integrate STE into LLaVA-OV and LLaVA-Video and compare against implicit temporal modeling baselines, reporting average gains of 4.7% and 1.5% across six video benchmarks, improvements on temporal-specific tasks, favorable compression curves, and ablations on receptive field, learning space, plug-in use, and image-domain impact. They conclude that explicit temporal modeling is critical for video understanding in MLLMs.

Significance. If the comparison were properly matched, this would be a useful empirical contribution: the STE design is flexible and lightweight, the compression analysis is thorough, and the learning-space and dataset ablations (Tables 3 and 7) are informative. The no-SFT plug-in results in Table 4 also provide a practical measure of what a pretrained temporal compressor can preserve. However, the central causal claim currently rests on an under-matched comparison: the STE models receive a two-stage pretraining and SFT protocol on datasets that overlap the evaluation benchmarks, while the original backbones receive no equivalent training. Table 4 shows essentially no full-frame gain without SFT, so the headline gains are not yet attributable to explicit temporal modeling.

major comments (4)
  1. [4.2, 4.3, Table 1] The headline comparison (4.7% and 1.5% gains) is confounded with additional training. The STE models are trained with pretraining (STE only) followed by full-model SFT on five video datasets (Section 4.2), whereas the LLaVA-OV and LLaVA-Video baselines are taken as off-the-shelf checkpoints with no equivalent SFT. No implicit baseline is trained under the same two-stage protocol with STE removed. Table 4 shows that without SFT, STE provides essentially no full-frame improvement (LLaVA-OV 62.3 vs 62.3; LLaVA-Video 66.4 vs 66.0). The large gains in Table 1, for example PerceptionTest rising from 57.1 to 70.1 for LLaVA-OV, could therefore reflect extra data exposure rather than the temporal encoder. The authors should train an implicit control that receives the identical pretraining/SFT protocol with STE disabled or replaced by a non-temporal module, and report that comparison before drawing conclusions about the necessity of explicit temporal modeling.
  2. [4.2, Appendix B, Table 1] Three of the six evaluation benchmarks, namely ActNet-QA, NExT-QA, and PerceptionTest, are part of the SFT training set described in Appendix B. Gains on these benchmarks are expected from SFT on the exact benchmark data and do not establish a temporal-modeling mechanism. For example, LLaVA-OV rises from 57.1 to 70.1 on PerceptionTest after training on PerceptionTest. The remaining held-out benchmarks show substantially smaller gains, so the 4.7% average is inflated by training overlap. The paper should report held-out benchmarks separately and/or remove training-overlap benchmarks from the headline average.
  3. [4.5, Table 3] The learning-space ablation is not a matched comparison. The semantic-space STE operates on projected d-dimensional tokens and has approximately 25.69M parameters, versus about 2.65M for the visual-space STE, as stated in Section 4.5. The visual-space advantage may therefore reflect capacity or optimization differences rather than the intrinsic suitability of visual-space temporal modeling. To support the design claim, the authors should either match parameter counts and optimization budgets or include a capacity-matched semantic-space baseline.
  4. [4.3, Table 2] The compression comparison mixes training protocols. The STE-compressed models are trained with their compression ratios during pretraining and SFT, whereas the sampling-frequency baselines are the same checkpoints evaluated with fewer sampled frames without any retraining for that setting. This is a valid deployment comparison, but it does not isolate the effect of temporal compression versus training for reduced frame counts. The text should be revised to state this distinction explicitly.
minor comments (5)
  1. [Table 4] The baseline row for LLaVA-OV appears to transpose the MLVU and VideoMME (w/o sub) columns: the printed values 56.7 and 65.2 contradict Table 1, which lists MLVU 65.2, MVBench 56.7, and VideoMME (w/o sub) 58.5. The average 62.3 matches the corrected interpretation, so this is a presentation error, but it should be fixed.
  2. [3.2] The output shape expression R^{1×p×((T+k)To,l d)/Tu,l} is ambiguous because the parentheses do not make clear whether the division by Tu,l applies inside or outside the feature dimension. Please clarify the formula and state explicitly how the d-dimensional split reconstructs abstract frames.
  3. [4.1, Tables 1 and 2] Several reported differences are small, for example 57.8 versus 57.9 on MVBench in Table 1, but the paper gives no indication of evaluation variance or significance. At least a statement about the absence of multiple runs and the expected variability of GPT-based evaluation would help calibrate the reader's confidence.
  4. [5 / Introduction] The sentence 'We will release the codes recently' is ungrammatical and should be rephrased, for example 'We will release the code upon publication.'
  5. [1] The claim that implicit models 'process frames independently' is imprecise, since the LLM receives sequential visual tokens and can attend across them. Please rephrase to describe the architectural difference more carefully.

Circularity Check

0 steps flagged · score 2.0 of 10

No derivation-level circularity; the comparison is empirical, though the headline gain conflates STE with an unmatched SFT pass, which is an attribution weakness rather than a circular reduction.

full rationale

The paper is an empirical study rather than an analytic derivation: the claim that explicit temporal modeling helps is supported by benchmark measurements (Tables 1–4), not by an equation that defines the predicted quantity in terms of the fitted inputs. I checked the specific circularity patterns. There is no self-definitional identity, no self-citation chain carrying the argument (the LLaVA backbones, LLaVA-Hound, and LLaVA-Video-178K are external resources), and no imported uniqueness theorem. The closest concern is that Table 1's reported 4.7%/1.5% gains compare STE models after a two-stage pretrain plus SFT protocol (Section 3.3) against untouched LLaVA-OV/LLaVA-Video checkpoints, while three of the six evaluation benchmarks (ActNet-QA, NExT-QA, PerceptionTest) are also used as SFT training datasets (Section 4.2 and Appendix B). The paper's own plug-in results (Table 4) show no full-frame gain without SFT (LLaVA-OV 62.3 vs 62.3; LLaVA-Video 66.4 vs 66.0), so the headline improvement is not isolated to the temporal encoder. This is a genuine experimental-confound and attribution weakness, but it is not a case where a prediction is equivalent to its input by construction or where a fitted parameter is renamed as a prediction. Under the stated rules, that does not constitute circularity. Score 2 reflects the mild selection/attribution concern without treating it as derivation-level circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central comparison relies on the assumption that any observed gains come from temporal encoding rather than from the extra training data and procedure used for STE models. It also assumes that evaluating on datasets used for training is a valid measure of understanding. These assumptions are not tested.

free parameters (3)
  • Sliding window size Tw and stride Ts = Tw=2, Ts=1
    Set identically for all STE layers in Appendix A; chosen for small padding, not derived.
  • Number of STE layers and frame I/O ratio (Tu:To) = 1-4 layers; ratios (2:2), (4:3), (2:1)
    Swept across experiments; headline results use 3 layers of (2:2), a configuration selected based on evaluation performance.
  • Learning rates and batch sizes = Pretrain 1e-3, SFT 1e-5 (vision encoder 2e-6); batch size 1-2
    Adopted from LLaVA training recipes, not tuned or justified in the paper.
assumptions (5)
  • standard math The output channel count formula Cl = To,l*d/N correctly realizes the desired frame I/O ratio
    Assumed in Section 3.2 to guarantee shape compatibility across layers.
  • domain assumption Local convolution over frame embeddings in visual space is a suitable inductive bias for temporal relations
    The entire method assumes convolutional sliding windows preserve and improve temporal understanding (Section 3.2).
  • domain assumption Backbones LLaVA-OV and LLaVA-Video are representative implicit temporal models
    The study's conclusions about implicit vs explicit modeling rely on two 7B LLaVA variants; other architectures may differ (Section 4.1).
  • ad hoc to paper Evaluation benchmarks can be used for both training and evaluation without invalidating comparisons
    The paper does not discuss the overlap between training datasets (Appendix B) and evaluation benchmarks, yet reports gains on those benchmarks.
  • ad hoc to paper Additional supervised fine-tuning does not differentially affect the comparison
    No implicit baseline is fine-tuned on the same five video datasets, so the comparison assumes extra training is not the cause of improvements (Sections 4.2-4.3).

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Cite this review

Pith. "Pith review of Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding." pith.science (2026). https://pith.science/paper/CQ7OWDGL

@misc{pith2026250116786,
  author       = {Pith},
  title        = {Pith review of: Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CQ7OWDGL}},
  note         = {Machine review of arXiv:2501.16786}
}
read the original abstract

Applying Multimodal Large Language Models (MLLMs) to video understanding presents significant challenges due to the need to model temporal relations across frames. Existing approaches adopt either implicit temporal modeling, relying solely on the LLM decoder, or explicit temporal modeling, employing auxiliary temporal encoders. To investigate this debate between the two paradigms, we propose the Stackable Temporal Encoder (STE). STE enables flexible explicit temporal modeling with adjustable temporal receptive fields and token compression ratios. Using STE, we systematically compare implicit and explicit temporal modeling across dimensions such as overall performance, token compression effectiveness, and temporal-specific understanding. We also explore STE's design considerations and broader impacts as a plug-in module and in image modalities. Our findings emphasize the critical role of explicit temporal modeling, providing actionable insights to advance video MLLMs.

Figures

Figures reproduced from arXiv: 2501.16786 by the authors.

Figure 1
Figure 1. (Left): Explicit temporal modeling may enhance tempo￾ral understanding compared to implicit temporal modeling. (Right): Performance on temporal-related tasks of LLaVA-OV with (labeled as STE) or without explicit temporal modeling across six bench￾marks (arc colors indicate different benchmarks). which is essential for tasks such as temporal reasoning. Recently, increasing attention has been paid to improving video u… view at source ↗
Figure 2
Figure 2. (Left) Overview of our model for processing video inputs. (Right) Schematic diagram of the temporal encoder, comprising 2-layer STE modules that encode every four frames into one abstract frame through stacking two layers of 50% frame compression. The video, with dynamic length, is divided into convolutional units, and the STE is designed to handle diverse Input/Output (I/O) frame ratios based on these units. Tu,l, … view at source ↗
Figure 3
Figure 3. Performance when varying frame compressions: sam￾pling frequency reduction vs. frame compression (STE), showing accuracy differences relative to backbones with 32 input frames. we stack STE layers with varied frame I/O ratios (Tu : To) for explicit temporal modeling or reduce the backbone’s sampling frequency to lower input frame counts for implicit temporal modeling. Results for different frame compres￾sions (e.g.,… view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Task-level performance on benchmarks. LLaVA-OV-STE and LLaVA-Video-STE refer to LLaVA-OV-STE-3-(2:2) and LLaVA￾Video-STE-3-(2:2), respectively.) A. Experiment Setting details For all STE layers, we use unified hyperparameters for the sliding window size and stride, set…
Figure 6
Figure 6. Figure 6: ActNet-QA case study. LLaVA-Video-STE-1/2/3-(2:2) represents LLaVA-Video-STE using 1/2/3 layers of (2:2). QA items and 17,024 multiple-choice QA items. PerceptionTest (Patr ˘ aucean et al. ˘ , 2023): A dataset targeting fundamental perceptual understanding of videos, c…

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    URL https://docs.anthropic.com/en/docs/ about-claude/models. Accessed: 2024-12-18. Ataallah, K., Shen, X., Abdelrahman, E., Sleiman, E., Zhu, D., Ding, J., and Elhoseiny, M. Minigpt4-video: Advancing multimodal llms for video understanding with interleaved visual-textual token...

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    Video-llava: Learning united visual represen- tation by alignment before projection

    9 Explicit Temporal Modeling in Multimodal Large Language Models Lin, B., Ye, Y ., Zhu, B., Cui, J., Ning, M., Jin, P., and Yuan, L. Video-llava: Learning united visual represen- tation by alignment before projection. arXiv preprint arXiv:2311.10122,

    arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.