ST-SimDiff: Balancing Spatiotemporal Similarity and Difference for Efficient Video Understanding with MLLMs

Bingjun Luo; Chaoqi Chen; Tony Wang; Xinpeng Ding

arxiv: 2605.22158 · v1 · pith:5NDZNTVFnew · submitted 2026-05-21 · 💻 cs.AI · cs.CV

ST-SimDiff: Balancing Spatiotemporal Similarity and Difference for Efficient Video Understanding with MLLMs

Bingjun Luo , Tony Wang , Chaoqi Chen , Xinpeng Ding This is my paper

Pith reviewed 2026-05-22 06:11 UTC · model grok-4.3

classification 💻 cs.AI cs.CV

keywords video understandingmultimodal large language modelstoken compressionspatio-temporal graphcommunity detectiontemporal differencetraining-free

0 comments

The pith

A training-free method builds a spatio-temporal graph from video tokens and runs parallel selections for similarity and difference to keep both static scenes and key changes with far fewer tokens.

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

The paper tries to cut the computational burden of long videos in multimodal large language models by keeping only a small set of visual tokens that still represent the full content. Existing approaches prune or merge tokens mainly by importance or similarity, but this often drops the turning points where content shifts. The proposed solution first links all tokens into one graph that captures their space and time relations, then applies community detection to collapse similar static parts and separate difference checks to hold onto dynamic events. If the selections work as claimed, models could handle longer videos on the same hardware without losing narrative threads or event details.

Core claim

The central claim is that similarity marks redundancy in unchanging parts of a video while difference marks the important shifts, and these two signals can be extracted together on a single spatio-temporal graph through community detection for the first and direct temporal comparison for the second, yielding a minimal token set that still supports accurate video understanding.

What carries the argument

A spatio-temporal graph connecting visual tokens, processed by parallel similarity-based community detection to compress static content and temporal difference selection to retain change points.

If this is right

Long videos require far fewer visual tokens for processing, directly lowering memory use and inference time.
Both background elements that stay the same and motion events that matter are retained in one pass without extra training steps.
Performance on standard video benchmarks exceeds prior token-reduction techniques while costs fall.
The same token budget can now cover longer sequences or more videos per batch.

Where Pith is reading between the lines

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

The dual-selection idea could transfer to audio or text sequences where both repetition and novelty matter.
Combining the graph with a small amount of learned weighting might further improve selection on noisy inputs.
Experiments on videos with different rates of change would show how sensitive the difference selection is to timing.

Load-bearing premise

The assumption that the graph construction plus the two parallel selections will correctly identify and keep the essential static representatives and dynamic turning points without needing any model training or fine-tuning.

What would settle it

Apply the method to a set of videos containing subtle but critical changes and check whether the selected tokens still allow the model to detect those changes at the same rate as when all original tokens are used.

Figures

Figures reproduced from arXiv: 2605.22158 by Bingjun Luo, Chaoqi Chen, Tony Wang, Xinpeng Ding.

**Figure 2.** Figure 2: The overview framework of ST-SimDiff, which consists of three parts: Spatio-Temporal [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Computational cost comparison between our method and the baseline LLaVA-Video [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

**Figure 4.** Figure 4: Visualization of the ST-SimDiff process. The visualization breaks down the token selec [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

**Figure 5.** Figure 5: Ablation study results for different values of [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗

**Figure 6.** Figure 6: Ablation study results for different values of [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗

read the original abstract

Multimodal Large Language Models (MLLMs) face significant computational overhead when processing long videos due to the massive number of visual tokens required. To improve efficiency, existing methods primarily reduce redundancy by pruning or merging tokens based on importance or similarity. However, these approaches largely overlook a critical dimension of video content, i.e., changes and turning points, and they lack a collaborative model for spatio-temporal relationships. To address this, we propose a new perspective: similarity is for identifying redundancy, while difference is for capturing key events. Based on this, we designed a training-free framework named ST-SimDiff. We first construct a spatio-temporal graph from the visual tokens to uniformly model their complex associations. Subsequently, we employ a parallel dual-selection strategy: 1) similarity-based selection uses community detection to retain representative tokens, compressing static information; 2) temporal difference-based selection precisely locates content-changing points to preserve tokens that capture key dynamic shifts. This allows it to preserve both static and dynamic content with a minimal number of tokens. Extensive experiments show our method significantly outperforms state-of-the-art approaches while substantially reducing computational costs. Our code is available in https://github.com/bingjunluo/ST-SimDiff.

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

ST-SimDiff offers a clean training-free split that uses community detection on a spatio-temporal graph for static redundancy and temporal differences for events, but the abstract leaves open whether the retained tokens actually match what the MLLM needs for reasoning.

read the letter

The main takeaway is a training-free token compression scheme for long-video MLLMs that builds one spatio-temporal graph and then runs two selections side by side: community detection to keep representative tokens from similar regions, and frame-to-frame difference to hold onto the changing points. That explicit separation of similarity for redundancy and difference for events is the clearest new angle relative to the pruning and merging baselines mentioned in the abstract. The authors report that the approach beats prior methods while cutting token count and compute, and they have put the code on GitHub, which makes the claims easier to check directly. The framework itself is simple enough that someone working on inference efficiency could adapt the graph construction or the selection rules without much overhead. The reported gains on standard video benchmarks suggest the dual path is at least competitive in practice. The soft spots sit in the validation details. The abstract does not describe how the graph edges are defined or how the two branches are balanced, so it remains possible that the communities form around low-level visual cues rather than the semantic content the language model actually uses downstream. The temporal-difference branch could also surface noise or miss gradual but important shifts depending on sampling rate. Without ablations that isolate each component or error analysis on cases where the method drops critical information, the central efficiency claim rests on the final numbers rather than on evidence that the selection preserves task-relevant content. This work is aimed at groups building or deploying video MLLMs who care about token budgets more than about adding another training stage. A reader already experimenting with token reduction would get immediate value from the graph-plus-dual-selection template even if they change the community algorithm or the difference threshold. The paper is coherent on its own terms and ships reproducible code, so it clears the bar for a serious referee. I would send it to review with a request for clearer controls on the selection criteria and their effect on downstream accuracy.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces ST-SimDiff, a training-free framework for reducing visual tokens in MLLMs processing long videos. It first builds a spatio-temporal graph over visual tokens, then applies a parallel dual-selection procedure: similarity-driven community detection to retain representative tokens for static content, and temporal-difference selection to keep tokens at content-changing points. The authors claim this balances redundancy reduction with event preservation, yielding both higher accuracy and lower compute than prior token-pruning or merging methods, with public code.

Significance. If the central claim holds under controlled experiments, the work supplies a lightweight, training-free alternative that explicitly separates static compression from dynamic-event retention. The open-source release and absence of learned parameters are clear strengths for reproducibility. The approach could meaningfully extend MLLM context lengths for video, provided the graph-based selections demonstrably retain query-relevant semantics rather than low-level visual statistics.

major comments (3)

[Abstract and §3] Abstract and §3: The central claim that the dual-selection strategy 'preserves both static and dynamic content with a minimal number of tokens' rests on the untested assumption that community detection on the spatio-temporal graph groups tokens by semantic relevance rather than low-level features (color, texture). No ablation on similarity metrics, no qualitative token inspection, and no comparison against random or uniform baselines are referenced, leaving the load-bearing correctness of the pipeline unsupported.
[Abstract] Abstract: The statement that 'extensive experiments show our method significantly outperforms state-of-the-art approaches' is presented without any quantitative results, dataset names, or effect sizes. Because the soundness of the efficiency and accuracy claims depends on these controls, the absence of even summary numbers in the abstract weakens evaluation of the central contribution.
[§3.2] §3.2: The description of the 'parallel dual-selection strategy' does not specify how the outputs of the community-detection branch and the temporal-difference branch are merged or balanced (e.g., fixed ratio, adaptive threshold, or union). This omission directly affects the title claim of 'balancing' and the assertion that both static and dynamic information are retained without loss.

minor comments (1)

[Abstract] The abstract would be clearer if it briefly named the video benchmarks and the primary evaluation metric (e.g., accuracy on Video-MME or similar).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and indicate where revisions will be incorporated to improve clarity and support for the claims.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3: The central claim that the dual-selection strategy 'preserves both static and dynamic content with a minimal number of tokens' rests on the untested assumption that community detection on the spatio-temporal graph groups tokens by semantic relevance rather than low-level features (color, texture). No ablation on similarity metrics, no qualitative token inspection, and no comparison against random or uniform baselines are referenced, leaving the load-bearing correctness of the pipeline unsupported.

Authors: We acknowledge the concern regarding the lack of explicit validation that community detection operates on semantic rather than low-level features. The spatio-temporal graph is constructed from features produced by the MLLM's vision encoder, which are trained to encode semantic content. Community detection then groups tokens with high similarity to retain representatives for static content. To strengthen this, we will add an ablation comparing alternative similarity metrics, include qualitative visualizations of retained tokens, and report comparisons against random and uniform selection baselines in the revised experiments section. revision: yes
Referee: [Abstract] Abstract: The statement that 'extensive experiments show our method significantly outperforms state-of-the-art approaches' is presented without any quantitative results, dataset names, or effect sizes. Because the soundness of the efficiency and accuracy claims depends on these controls, the absence of even summary numbers in the abstract weakens evaluation of the central contribution.

Authors: We agree that the abstract would benefit from concrete quantitative highlights. In the revised manuscript we will update the abstract to include summary results such as accuracy gains on Video-MME and similar benchmarks along with token reduction percentages relative to prior methods. revision: yes
Referee: [§3.2] §3.2: The description of the 'parallel dual-selection strategy' does not specify how the outputs of the community-detection branch and the temporal-difference branch are merged or balanced (e.g., fixed ratio, adaptive threshold, or union). This omission directly affects the title claim of 'balancing' and the assertion that both static and dynamic information are retained without loss.

Authors: We thank the referee for noting this omission. The two branches run in parallel and their outputs are combined via union while respecting a total token budget that is allocated between the branches according to a fixed ratio (adjustable by video duration). We will revise §3.2 to explicitly describe this merging and balancing procedure. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in the training-free heuristic pipeline

full rationale

The paper presents a method that constructs a spatio-temporal graph from visual tokens and applies standard community detection for similarity-based compression alongside temporal difference selection for dynamic content. No equations or derivations are shown that reduce any output quantity to fitted parameters, self-defined quantities, or prior self-citations by construction. The approach relies on external graph algorithms and selection heuristics whose behavior is independent of the target MLLM performance metric, making the central claim empirically testable rather than tautological.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The framework rests on domain assumptions about graph modeling and the utility of similarity versus difference, with likely hyperparameters for selection balance; no new entities are postulated.

free parameters (1)

selection thresholds or community parameters
Parameters controlling the balance between similarity-based and difference-based token retention or the granularity of community detection.

axioms (2)

domain assumption Spatio-temporal graph uniformly models complex associations between visual tokens
Stated as the first construction step to handle associations.
domain assumption Similarity identifies redundancy while difference captures key events
Core design perspective given in the abstract as the basis for the dual-selection strategy.

pith-pipeline@v0.9.0 · 5756 in / 1242 out tokens · 47771 ms · 2026-05-22T06:11:28.527040+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We first construct a spatio-temporal graph from the visual tokens... similarity-based selection uses community detection to retain representative tokens... temporal difference-based selection precisely locates content-changing points
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

The weight of any edge w(vi, vj) is defined by the cosine similarity... When the similarity between corresponding tokens of adjacent frames drops sharply, we consider it a turning point

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 7 internal anchors

[1]

GPT-4 Technical Report

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774,

work page internal anchor Pith review Pith/arXiv arXiv
[2]

Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding, local- ization, text reading, and beyond.arXiv preprint arXiv:2308.12966, 1(8),

work page internal anchor Pith review Pith/arXiv arXiv
[3]

Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report.arXiv preprint arXiv:2502.13923,

work page internal anchor Pith review Pith/arXiv arXiv
[4]

Fast unfolding of communities in large networks.Journal of statistical mechanics: theory and experiment, 2008 (10):P10008,

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks.Journal of statistical mechanics: theory and experiment, 2008 (10):P10008,

work page 2008
[5]

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evalua- tion benchmark of multi-modal llms in video analysis.arXiv preprint arXiv:2405.21075, 2024a. Tianyu Fu, Tengxuan Liu, Qinghao Han, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefe...

work page internal anchor Pith review Pith/arXiv arXiv
[6]

LLaVA-OneVision: Easy Visual Task Transfer

doi: 10.1038/s42256-025-01153-0. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer.arXiv preprint arXiv:2408.03326,

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1038/s42256-025-01153-0
[7]

NVILA: Efficient Frontier Visual Language Models

Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, pp. 26296–26306, 2024a. Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava-next: Improved reasoning, ocr, and world know...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[8]

Llava-prumerge: Adaptive token reduction for efficient large multimodal models

11 Published as a conference paper at ICLR 2026 Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models.arXiv preprint arXiv:2403.15388,

work page arXiv 2026
[9]

LLaMA: Open and Efficient Foundation Language Models

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth ´ee Lacroix, Baptiste Rozi `ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971,

work page internal anchor Pith review Pith/arXiv arXiv
[10]

Solving the many-electron schr ¨odinger equation with a transformer-based framework

Zhang et al. Solving the many-electron schr ¨odinger equation with a transformer-based framework. Nature Communications, 2025a. doi: 10.1038/s41467-025-63219-2. Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, and Ziwei Liu. Lmms-eval: Reality check on the evaluation of ...

work page doi:10.1038/s41467-025-63219-2 2026
[11]

The final result highlights the synergy between sparse representative tokens (yel- low) for stable content and dense event tokens (red) for dynamic actions

and event-driven tokens detected by DETS. The final result highlights the synergy between sparse representative tokens (yel- low) for stable content and dense event tokens (red) for dynamic actions. 13 Published as a conference paper at ICLR 2026 To provide a more intuitive understanding of the ST-SimDiff framework, we present some visualiza- tion samples...

work page 2026
[12]

Cluster 1

The figure illustrates the process on a sample video sequence featuring a dynamic object manipulation task. First, regard- ing the Similarity-based Representative Token Selection (SRTS), the rows labeled “Cluster 1” and “Cluster 2” demonstrate how our graph community detection algorithm functions. It successfully groups spatially and temporally redundant ...

work page 2026
[13]

For τsim, a value that is too low leads to imprecise community detection, while a value that is too high can disrupt the integrity of semantic clusters

The experiments show that the impact of both parameters on model performance follows a similar trend, first rising and then falling, while demonstrating good robustness within a certain range. For τsim, a value that is too low leads to imprecise community detection, while a value that is too high can disrupt the integrity of semantic clusters. Forτ dif f,...

work page 2026

[1] [1]

GPT-4 Technical Report

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774,

work page internal anchor Pith review Pith/arXiv arXiv

[2] [2]

Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding, local- ization, text reading, and beyond.arXiv preprint arXiv:2308.12966, 1(8),

work page internal anchor Pith review Pith/arXiv arXiv

[3] [3]

Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report.arXiv preprint arXiv:2502.13923,

work page internal anchor Pith review Pith/arXiv arXiv

[4] [4]

Fast unfolding of communities in large networks.Journal of statistical mechanics: theory and experiment, 2008 (10):P10008,

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks.Journal of statistical mechanics: theory and experiment, 2008 (10):P10008,

work page 2008

[5] [5]

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evalua- tion benchmark of multi-modal llms in video analysis.arXiv preprint arXiv:2405.21075, 2024a. Tianyu Fu, Tengxuan Liu, Qinghao Han, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefe...

work page internal anchor Pith review Pith/arXiv arXiv

[6] [6]

LLaVA-OneVision: Easy Visual Task Transfer

doi: 10.1038/s42256-025-01153-0. Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer.arXiv preprint arXiv:2408.03326,

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1038/s42256-025-01153-0

[7] [7]

NVILA: Efficient Frontier Visual Language Models

Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, pp. 26296–26306, 2024a. Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava-next: Improved reasoning, ocr, and world know...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[8] [8]

Llava-prumerge: Adaptive token reduction for efficient large multimodal models

11 Published as a conference paper at ICLR 2026 Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models.arXiv preprint arXiv:2403.15388,

work page arXiv 2026

[9] [9]

LLaMA: Open and Efficient Foundation Language Models

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth ´ee Lacroix, Baptiste Rozi `ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971,

work page internal anchor Pith review Pith/arXiv arXiv

[10] [10]

Solving the many-electron schr ¨odinger equation with a transformer-based framework

Zhang et al. Solving the many-electron schr ¨odinger equation with a transformer-based framework. Nature Communications, 2025a. doi: 10.1038/s41467-025-63219-2. Kaichen Zhang, Bo Li, Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, Kairui Hu, Shuai Liu, Yuanhan Zhang, Jingkang Yang, Chunyuan Li, and Ziwei Liu. Lmms-eval: Reality check on the evaluation of ...

work page doi:10.1038/s41467-025-63219-2 2026

[11] [11]

The final result highlights the synergy between sparse representative tokens (yel- low) for stable content and dense event tokens (red) for dynamic actions

and event-driven tokens detected by DETS. The final result highlights the synergy between sparse representative tokens (yel- low) for stable content and dense event tokens (red) for dynamic actions. 13 Published as a conference paper at ICLR 2026 To provide a more intuitive understanding of the ST-SimDiff framework, we present some visualiza- tion samples...

work page 2026

[12] [12]

Cluster 1

The figure illustrates the process on a sample video sequence featuring a dynamic object manipulation task. First, regard- ing the Similarity-based Representative Token Selection (SRTS), the rows labeled “Cluster 1” and “Cluster 2” demonstrate how our graph community detection algorithm functions. It successfully groups spatially and temporally redundant ...

work page 2026

[13] [13]

For τsim, a value that is too low leads to imprecise community detection, while a value that is too high can disrupt the integrity of semantic clusters

The experiments show that the impact of both parameters on model performance follows a similar trend, first rising and then falling, while demonstrating good robustness within a certain range. For τsim, a value that is too low leads to imprecise community detection, while a value that is too high can disrupt the integrity of semantic clusters. Forτ dif f,...

work page 2026