EMCompress: Video-LLMs with Endomorphic Multimodal Compression

Heng Ji; Jiateng Liu; Manling Li; Yi R. Fung; Yuji Zhang; Zheyu Fan; Zihan Wang

arxiv: 2508.21094 · v3 · submitted 2025-08-27 · 💻 cs.CV

EMCompress: Video-LLMs with Endomorphic Multimodal Compression

Zheyu Fan , Jiateng Liu , Yuji Zhang , Zihan Wang , Yi R. Fung , Manling Li , Heng Ji This is my paper

Pith reviewed 2026-05-18 20:36 UTC · model grok-4.3

classification 💻 cs.CV

keywords endomorphic multimodal compressionvideo large language modelsvideo question answeringsufficient statisticmultimodal compressionlong video reasoninganswer invariancequery rewriting

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The pith

Endomorphic Multimodal Compression compresses video and query pairs for Video-LLMs while satisfying the classical sufficiency condition for answer information.

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

The paper introduces Endomorphic Multimodal Compression as a task for long-video VideoQA that compresses the input pair while keeping the output inside the downstream model's native space. It frames the problem as an endomorphic transformation that preserves answer invariance, realizing the information equality I((v, q); A) = I((V, Q); A) under the Markov chain from answer to original input to compressed input. This approach differs from latent-space methods by staying in the task space and functions as a plug-in front end for existing Video Instruction Tuning and Video Question Answering pipelines. The work also releases a benchmark and a baseline that improves on prior query-rewriting methods.

Core claim

Under the Markov chain A -> (V, Q) -> (v, q), the endomorphic transformation F_EMC realizes the sufficiency condition I((v, q); A) = I((V, Q); A) in VideoQA-natural form, so that the compressed pair carries all answer-relevant information present in the original multimodal input.

What carries the argument

The endomorphic transformation F_EMC : (V, Q) -> (v, q) that compresses the multimodal input while remaining in the downstream pipeline's native task space and preserving answer invariance.

If this is right

EMC acts as a modular front-end that integrates directly into Video Instruction Tuning and Video Question Answering pipelines.
Integration produces reported relative gains of 7.33 percent during training and 33.7 percent during inference for video-language understanding.
A dedicated benchmark for endomorphic compression in VideoQA is now available for evaluating future methods.
The ReSimplifyIt baseline for EMC improves F-1 score by 0.40 over prior query-rewriting approaches while maintaining competitive rewriting quality.

Where Pith is reading between the lines

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

The same endomorphic compression idea could be tested on other long-context multimodal tasks such as document or audio reasoning where input length creates similar efficiency pressures.
Because the output stays in the native task space, EMC might combine with existing attention or memory mechanisms inside Video-LLMs without requiring changes to model architecture.
If answer invariance holds across a wider range of models, EMC could become a standard preprocessing step that allows training on longer videos without proportional increases in compute.

Load-bearing premise

The endomorphic transformation preserves answer invariance so the compressed video-query pair produces the same answers as the original pair for the downstream models used in VideoQA.

What would settle it

Measure answer accuracy on the released benchmark for both the original (V, Q) inputs and the EMC-compressed (v, q) inputs using the same VideoQA models; a significant drop in accuracy on the compressed version would falsify the sufficiency claim.

Figures

Figures reproduced from arXiv: 2508.21094 by Heng Ji, Jiateng Liu, Manling Li, Yi R. Fung, Yuji Zhang, Zheyu Fan, Zihan Wang.

**Figure 2.** Figure 2: Snapshot examples of the workflow of our proposed ReSimplifyIt framework. The framework iteratively [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Input and output example of the TVS task. [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗

**Figure 4.** Figure 4: YouCookII-TVS statistics. Method Temporal Relational Timepoint Indexed Multifaceted Integrative Average mIoU F1 mIoU F1 mIoU F1 mIoU F1 ReSimplifyIt (Ours) 0.23 0.37 0.98 0.99 0.47 0.64 0.56 0.67 ReSimplifyIt-simple (Ours) 0.24 0.37 0.98 0.99 0.42 0.57 0.55 0.64 ReSimplifyIt-blind (Ours) 0.12 0.20 0.98 0.99 0.38 0.55 0.49 0.58 (a) Results on video output. Method Temporal Relational Timepoint Indexed Multif… view at source ↗

**Figure 5.** Figure 5: Workflow of ReSimplifyIt-simple (left) and ReSimplifyIt-blind (right) [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗

read the original abstract

Video-LLMs face a fundamental tension in long-video reasoning: static, sparse frame sampling either dilutes evidence across task-irrelevant segments at significant cost or misses fine-grained temporal semantics altogether. We propose a novel, cognitively-inspired task -- Endomorphic Multimodal Compression (EMC) -- as a structurally-constrained sufficient-statistic problem for VideoQA, and formulate it as an endomorphic transformation F_EMC : (V, Q) -> (v, q) that compresses the multimodal input while preserving answer invariance across reasonable downstream models. The endomorphic form keeps the compressed output in the downstream pipeline's native task space -- a structural mirror of the filter-then-reason mechanism in the cognitive literature motivating EMC -- distinguishing it from latent-code compression (IB / VIB) and making the formulation extensible to other multimodal settings. Under the Markov chain A -> (V, Q) -> (v, q), EMC realizes the classical sufficiency condition I((v, q); A) = I((V, Q); A) in its VideoQA-natural form. As a modular front-end, EMC plugs into both Video Instruction Tuning and Video Question Answering pipelines. We release the first dedicated benchmark and propose ReSimplifyIt, an EMC baseline surpassing prior methods by 0.40 F-1 with competitive query rewriting. Integrating EMC yields relative gains of 7.33% in training and 33.7% in inference for video-language understanding.

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.

Desk Editor's Note private letter to a colleague

EMC proposes task-native compression for Video-LLMs with efficiency gains, but the sufficiency claim needs the reverse Markov check that is missing.

read the letter

The main point is that this paper frames compression for long-video Video-LLMs as an endomorphic map that turns the original video-question pair into a shorter version still sitting in the same input space. This keeps the output directly usable by downstream models instead of routing through latent codes like standard IB or VIB methods. The cognitive filter-then-reason parallel is a clean way to motivate why answer invariance should hold across reasonable VideoQA heads. They also ship a new benchmark and a baseline called ReSimplifyIt that improves F-1 by 0.40 over priors while staying competitive on query rewriting. The reported relative gains of 7.33 percent training and 33.7 percent inference are the kind of numbers that matter for people actually running these models on long clips. That practical angle is the clearest contribution. The soft spot sits in the information-theoretic claim. The paper states that under the forward Markov chain from answer to full input to compressed pair, the mutual information with the answer is preserved. That equality only follows if the reverse conditional independence also holds, yet no derivation, bound, or simple check appears to test whether the original input still carries extra predictive signal once the compressed version is known. Answer invariance on the models they tried is weaker than the model-agnostic requirement, so the sufficiency result stays partly assumptive. Experiments would benefit from error bars and clearer dataset descriptions to confirm the gains are not partly built into the compression procedure itself. This work is aimed at researchers tuning Video-LLMs for longer contexts who want a modular front-end rather than a full retrain. It has enough of a distinct formulation and a released benchmark to merit a serious referee, though the theoretical grounding will need tightening in revision.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Endomorphic Multimodal Compression (EMC) as a cognitively-inspired, structurally-constrained sufficient-statistic formulation for VideoQA. It defines an endomorphic transformation F_EMC : (V, Q) -> (v, q) that compresses multimodal inputs while preserving answer invariance, claims that this realizes the classical sufficiency condition I((v, q); A) = I((V, Q); A) under the forward Markov chain A -> (V, Q) -> (v, q), and reports relative gains of 7.33% in training and 33.7% in inference when EMC is integrated as a modular front-end. The work also introduces a new benchmark and the ReSimplifyIt baseline.

Significance. If the sufficiency condition is shown to hold independently of the compression procedure and the reported efficiency gains are verified with full experimental controls, the endomorphic formulation could provide a useful, extensible front-end for long-video reasoning that avoids latent-space methods. The cognitive motivation and distinction from IB/VIB-style compression are conceptually interesting, but the current evidence does not yet establish these advantages at the level required for a strong contribution.

major comments (2)

[Abstract] Abstract: The claim that EMC realizes I((v, q); A) = I((V, Q); A) follows from the forward Markov chain A -> (V, Q) -> (v, q) only if the reverse Markov chain A ⊥ (V, Q) | (v, q) also holds. No derivation, proof, or empirical test (e.g., estimating I(A; (V, Q) | (v, q)) or checking whether (V, Q) adds predictive power for A once (v, q) is known) is supplied, leaving the central information-theoretic assertion unverified.
[Abstract] Abstract: The reported relative gains (7.33% training, 33.7% inference) and the 0.40 F-1 improvement of ReSimplifyIt are presented without dataset descriptions, error bars, baseline details, or confirmation that answer invariance was measured on the same downstream models used for the efficiency numbers. This makes it impossible to assess whether the gains are independent of the compression procedure itself.

minor comments (2)

The endomorphic property is described as keeping the output in the downstream pipeline's native task space, but no explicit mathematical definition or pseudocode for F_EMC is provided in the visible text; adding one would improve reproducibility.
The manuscript states that EMC is extensible to other multimodal settings, but does not illustrate this with even a brief non-VideoQA example; a short remark or footnote would strengthen the claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript accordingly to strengthen the theoretical justification and experimental reporting.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that EMC realizes I((v, q); A) = I((V, Q); A) follows from the forward Markov chain A -> (V, Q) -> (v, q) only if the reverse Markov chain A ⊥ (V, Q) | (v, q) also holds. No derivation, proof, or empirical test (e.g., estimating I(A; (V, Q) | (v, q)) or checking whether (V, Q) adds predictive power for A once (v, q) is known) is supplied, leaving the central information-theoretic assertion unverified.

Authors: We acknowledge that the forward Markov chain A -> (V, Q) -> (v, q) by itself is insufficient to establish sufficiency without the reverse conditional independence A ⊥ (V, Q) | (v, q). Our definition of EMC as an endomorphic transformation explicitly enforces answer invariance, which ensures that (v, q) retains all information from (V, Q) that is relevant for predicting A. We will add a formal derivation in the revised manuscript (including an appendix) proving that the combination of the endomorphic structure and answer invariance implies the required conditional independence, thereby realizing I((v, q); A) = I((V, Q); A). We will also include an empirical verification by measuring whether the original (V, Q) provides additional predictive power for A once (v, q) is known, using the same downstream models. revision: yes
Referee: [Abstract] Abstract: The reported relative gains (7.33% training, 33.7% inference) and the 0.40 F-1 improvement of ReSimplifyIt are presented without dataset descriptions, error bars, baseline details, or confirmation that answer invariance was measured on the same downstream models used for the efficiency numbers. This makes it impossible to assess whether the gains are independent of the compression procedure itself.

Authors: The full manuscript describes the new benchmark, the ReSimplifyIt baseline, and the 0.40 F-1 gains in the experimental section. To address the concern about clarity, we will expand the abstract and add explicit details on datasets, error bars for all reported metrics, and full baseline comparisons. We will also confirm that answer invariance was evaluated on the identical Video-LLM backbones used for the efficiency measurements (7.33% training and 33.7% inference gains). These gains are measured with EMC integrated as a modular front-end, and we will clarify that they arise from the endomorphic compression rather than from any specific downstream procedure. revision: yes

Circularity Check

0 steps flagged

No significant circularity: classical sufficiency applied to new EMC formulation without reduction to inputs by construction

full rationale

The paper defines EMC as an endomorphic transformation F_EMC:(V,Q)→(v,q) that preserves answer invariance, then states that under the forward Markov chain A→(V,Q)→(v,q) (true by construction for any such F), EMC realizes the classical sufficiency I((v,q);A)=I((V,Q);A). This applies an external information-theoretic result to the proposed setting rather than deriving the equality from fitted parameters, self-citations, or redefinitions internal to the paper. No equations show the claimed equality holding by construction of F_EMC itself, nor is answer invariance equated to the full mutual-information condition. The derivation remains self-contained against the classical benchmark and does not reduce the central claim to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard information-theoretic notions of sufficiency and Markov chains plus the domain assumption that answer invariance can be maintained under compression.

axioms (1)

domain assumption The Markov chain A -> (V, Q) -> (v, q) holds and the endomorphic map preserves I((v, q); A) = I((V, Q); A).
Invoked directly in the abstract to realize the sufficiency condition for VideoQA.

pith-pipeline@v0.9.0 · 5808 in / 1261 out tokens · 64383 ms · 2026-05-18T20:36:48.915485+00:00 · methodology

discussion (0)

Forward citations

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · cited by 1 Pith paper · 1 internal anchor

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Cg-bench: Clue-grounded question answering benchmark for long video understanding

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In Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing, pages 1369–1379, Brussels, Belgium

TVQA: Localized, compositional video ques- tion answering. In Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing, pages 1369–1379, Brussels, Belgium. Association for Computational Linguistics. Jie Lei, Licheng Yu, Tamara Berg, and Mohit Bansal

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VideoChat: Chat-Centric Video Understanding

Tvqa+: Spatio-temporal grounding for video question answering. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 8211–8225, Online. Association for Computational Linguistics. Kunchang Li, Yinan He, Yi Wang, Yizhuo Li, Wen- hai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. 2023a. Videochat: Chat-centr...

work page internal anchor Pith review Pith/arXiv arXiv 2024
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P., and Fung, Y

Span-based localizing network for natural lan- guage video localization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6543–6554, Online. Association for Computational Linguistics. Jianshu Zhang, Dongyu Yao, Renjie Pi, Paul Pu Liang, and Yi R. Fung. 2025a. Vlm2-bench: A closer look at how well vlms impli...

work page arXiv 2023
[5]

get_duration(): Return the duration of the video as a floating point value

work page
[6]

get_resolution(): Return the resolution of the video, as a tuple

work page
[7]

get_total_frame_num(): Return total number of the frames of the video, as an integer

work page
[8]

If None is passed, selects all frames

grounding_select(obj_name, concerned_indices_input): Return, in the form of a list of integers, the indices of all frames containing the object given by obj_name, after taking the intersection of indices provided by concerned_indices_input. If None is passed, selects all frames

work page
[9]

indices_list_intersect(list1, list2): Return the intersection of two lists of indices

work page
[10]

indices_list_union(list1, list2): Return the union of two lists of indices

work page
[11]

indices_concat_and_fill(list1, list2): Return the sorted union of list1 and list2, then fill in missing values to make the sequence continuous

work page
[12]

Figure 4: YouCookII-TVS statistics

indices_concat(list1, list2): Return the concatenation of the two lists. Figure 4: YouCookII-TVS statistics. Method Temporal Relational Timepoint Indexed Multifaceted IntegrativeAverage mIoU F1 mIoU F1 mIoU F1 mIoU F1 ReSimplifyIt(Ours) 0.23 0.37 0.98 0.99 0.47 0.64 0.56 0.67 ReSimplifyIt-simple(Ours) 0.24 0.37 0.98 0.99 0.42 0.57 0.55 0.64 ReSimplifyIt-b...

work page
[13]

timestamp_to_single_index(timestamp): Return the frame index corresponding to the given timestamp (in seconds)

work page
[14]

single_timestamp_to_index_range(timestamp): Return indices of 60 consecutive frames centered at the timestamp

work page
[15]

Figure 5: Workflow of ReSimplifyIt-simple (left) and ReSimplifyIt-blind (right)

range_timestamp_to_index_range(start, end): Return all frame indices between the start and end timestamps. Figure 5: Workflow of ReSimplifyIt-simple (left) and ReSimplifyIt-blind (right)

work page

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Cg-bench: Clue-grounded question answering benchmark for long video understanding

Is space-time attention all you need for video understanding? In Proceedings of the International Conference on Machine Learning (ICML). Rodney A Brooks. 1991. Intelligence without represen- tation. Artificial intelligence, 47(1-3):139–159. Guo Chen, Yicheng Liu, Yifei Huang, Yuping He, Baoqi Pei, Jilan Xu, Yali Wang, Tong Lu, and Limin Wang. 2024a. Cg-be...

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In Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing, pages 1369–1379, Brussels, Belgium

TVQA: Localized, compositional video ques- tion answering. In Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing, pages 1369–1379, Brussels, Belgium. Association for Computational Linguistics. Jie Lei, Licheng Yu, Tamara Berg, and Mohit Bansal

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VideoChat: Chat-Centric Video Understanding

Tvqa+: Spatio-temporal grounding for video question answering. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 8211–8225, Online. Association for Computational Linguistics. Kunchang Li, Yinan He, Yi Wang, Yizhuo Li, Wen- hai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. 2023a. Videochat: Chat-centr...

work page internal anchor Pith review Pith/arXiv arXiv 2024

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P., and Fung, Y

Span-based localizing network for natural lan- guage video localization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6543–6554, Online. Association for Computational Linguistics. Jianshu Zhang, Dongyu Yao, Renjie Pi, Paul Pu Liang, and Yi R. Fung. 2025a. Vlm2-bench: A closer look at how well vlms impli...

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[5] [5]

get_duration(): Return the duration of the video as a floating point value

work page

[6] [6]

get_resolution(): Return the resolution of the video, as a tuple

work page

[7] [7]

get_total_frame_num(): Return total number of the frames of the video, as an integer

work page

[8] [8]

If None is passed, selects all frames

grounding_select(obj_name, concerned_indices_input): Return, in the form of a list of integers, the indices of all frames containing the object given by obj_name, after taking the intersection of indices provided by concerned_indices_input. If None is passed, selects all frames

work page

[9] [9]

indices_list_intersect(list1, list2): Return the intersection of two lists of indices

work page

[10] [10]

indices_list_union(list1, list2): Return the union of two lists of indices

work page

[11] [11]

indices_concat_and_fill(list1, list2): Return the sorted union of list1 and list2, then fill in missing values to make the sequence continuous

work page

[12] [12]

Figure 4: YouCookII-TVS statistics

indices_concat(list1, list2): Return the concatenation of the two lists. Figure 4: YouCookII-TVS statistics. Method Temporal Relational Timepoint Indexed Multifaceted IntegrativeAverage mIoU F1 mIoU F1 mIoU F1 mIoU F1 ReSimplifyIt(Ours) 0.23 0.37 0.98 0.99 0.47 0.64 0.56 0.67 ReSimplifyIt-simple(Ours) 0.24 0.37 0.98 0.99 0.42 0.57 0.55 0.64 ReSimplifyIt-b...

work page

[13] [13]

timestamp_to_single_index(timestamp): Return the frame index corresponding to the given timestamp (in seconds)

work page

[14] [14]

single_timestamp_to_index_range(timestamp): Return indices of 60 consecutive frames centered at the timestamp

work page

[15] [15]

Figure 5: Workflow of ReSimplifyIt-simple (left) and ReSimplifyIt-blind (right)

range_timestamp_to_index_range(start, end): Return all frame indices between the start and end timestamps. Figure 5: Workflow of ReSimplifyIt-simple (left) and ReSimplifyIt-blind (right)

work page