FLoC: Facility Location-Based Efficient Visual Token Compression for Long Video Understanding
Authors: Janghoon Cho
Jungsoo Lee
Munawar Hayat
Kyuwoong Hwang
Fatih Porikli
Sungha Choi
Venue: ICLR 2026 Poster (2026)
Keywords: large multimodal model
visual token compression
long video understanding
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent studies in long video understanding have harnessed the advanced visual-language reasoning capabilities of Large Multimodal Models (LMMs), driving the evolution of video-LMMs specialized for processing extended video sequences.
However, the scalability of these models is severely limited by the overwhelming volume of visual tokens generated from extended video sequences. To address this challenge, we propose FLoC, an efficient visual token compression framework based on the facility location function, a principled approach that swiftly selects a compact yet highly representative and diverse subset of visual tokens within a predefined budget on the number of visual tokens. By integrating the lazy greedy algorithm, our method achieves remarkable efficiency gains by swiftly selecting a compact subset of tokens, drastically reducing the number of visual tokens while guaranteeing near-optimal performance. Notably, our approach is training-free, model-agnostic, and query-agnostic, providing a versatile solution that seamlessly integrates with diverse video-LLMs and existing workflows. Extensive evaluations on large-scale benchmarks, such as Video-MME, MLVU, LongVideoBench, and EgoSchema, show that our framework consistently surpasses recent compression techniques, highlighting its effectiveness and robustness in addressing the challenges of long video understanding as well as its processing efficiency.

Published as a conference paper at ICLR 2026
FLOC: FACILITY LOCATION-BASED EFFICIENT VI- SUAL TOKEN COMPRESSION FOR LONG VIDEO UN- DERSTANDING
Janghoon Cho', Jungsoo Lee', Munawar Hayat Kyuwoong Hwang', Fatih Porikli', Sungha Choi 'Qualcomm Al Research" Kyung Hee University
ABSTRACT
Recent studies in long video understanding have harnessed the advanced visual- language reasoning capabilities of Large Multimodal Models (LMMs), driving the evolution of video-LMMs specialized for processing extended video sequences. However, the scalability of these models is severely limited by the overwhelming volume of visual tokens generated from extended video sequences. To address this challenge, we propose FLoC, an efficient visual token compression framework based on the facility location function, a principled approach that swiftly selects a compact yet highly representative and diverse subset of visual tokens within a predefined budget on the number of visual tokens. By integrating the lazy greedy algorithm, our method achieves remarkable efficiency gains by swiftly selecting a compact subset of tokens, drastically reducing the number of visual tokens while guaranteeing near-optimal performance. Notably, our approach is training-free -onel-ag, and query-agnostic providing versatile solution that seamlessly integrates with diverse video-LLMs and existing workflows. Extensive evalua- tions on large-scale benchmarks, such as Video-MME, MLVU, Long VideoBench, and EgoSchema, show that our framework consistently surpasses recent compres- sion techniques, highlighting its effectiveness and robustness in addressing the challenges of long video understanding well its processing efficiency.
INTRODUCTION
With the recent emergence of Large Language Models (LLMs) in natural language processing, there has been a surge of interest in extending their capabilities to the visual domain (Achiam al.) 2023). By utilizing the visual embed- dings as token inputs to the LLMs, referred to as visual tokens, these Large Multimodal Mod- els (LMMs) have already demonstrated their performances surpassing human-level accuracy on vision tasks, such as visual question answer- ing (Liu et al. 2024 Fang et al., 2024, Team et al. 2023). More recently, the research focus has shifted towards enabling these models to understand video sequences (Lin et al.
30'-
meergge Compresioon Time 0cl
clustering
Dia/vonz
PlaC (T=
sclsor
Mua [1=d]
LangVii
RruwAD
DyCoke
DO
50
52
54 db
Average Retative Performance 1h0
Figure I: Performance (Average relative accuracy compared to full token usage) versus compression time (log-scale) for number of compression algo- rithms. Details are described in Section
2023)
giving rise to video-L.MMs (Song et al. 2024
Xue al. 2024 Wang et al. [204aa] Balazevic
et (0024). Such models not only excel in tasks like captioning (Krishna et al. 2017 Xu et al. 2015
Vinyals et al. 2015), event detection (Xu et al. 22199] [hou al. 2021). }) and action recognition (Zhao
et al.) 2017 Simonyan & Zisserman 2014). but also show significant potential in various real-world
"Qualcomm AI Research is initiative of Qualcomm Technologies, Inc. Corresponning author. Work done while Qualcomm AI Research.

---

Published as a conference paper at ICLR 2026
Plug-and-play
User Query:
What is happoning
FLoC
this video?
MM 27,
Tokenizer
Temporal blocks
Encoder
tokens
Discarded tokens
(Off-the-sheie) Videa-LMMs
:Selected video tokens
V: Ground set, S: Selected subset, 6: Budget, T: Black Langth
LMM response: person playing golf
Figure 2: Overview of the proposed framework for selecting a visual token subset. Our method compresses the visual tokens extracted by a visual encoder from input video sequences into diverse and representative subset within a given budget. The selected visual tokens are then concatenated with text tokens and fed into the video-LMM. Since our method is training-free and model-agnostic, it can be seamlessly integrated into any video-LMM in plug-and-pla manner.
applications, including surveillance through CCTV systems, immersive experiences in smart glasses, and autonomous navigation for mobile robots.
Despite this progress, long video understanding remains particularly challenging due to the explosive growth in the number of visual tokens as the video sequence length increases (Xue et al., 2024] Fu et al. 2024). When dealing with high-resolution or long-duration videos (e.g., 4K content), it becomes computationally infeasible to process every token end-to-end, especially given that most LLM- architectures support input contexts of only 4K to 32K tokens. This limitation is exacerbated in real-worke scenarios: for instance, continuous CCTV footage span days or weeks, smart glasses may capture extended, first-person video streams, and mobile robots frequently operate in dynamic environments requiring real-time video analysis. Consequently, the gap between human-level performance and current model capabilities still exists, highlighting the complexity and significance of this research direction.
To tackle the issue of handling long video sequences, visual token compression is indispensable. In practice, when examining consecutive frames of video, many tokens share highly redundant information unless there is a substantial scene change (Potapov et al. 2014). Eliminating these redundancies often does not harm the downstream performance, while excessively pruning tokens could lead to the loss of critical information. It is therefore critical to strike a delicate balance in token compression to minimize information loss.
Previous approaches to selecting representative visual tokens often relied on filtering out temporally redundant tokens or frames (Shen et al., [024) Tao et al., (2224) or clustering techniques to extract representative information from each cluster (Wang et al. 2024b Shang et al. 2024 Zhang et al. 2024a). While these methods may work at reasonable level, they often fall short in capturing the full diversity needed to interpret complex visual scenes. Consider scenario where
wearing smart glasses searches for car keys a cluttered room. Visual tokens representing the small object of interest (the keys) occur infrequently and sparsely within the video sequence, whereas tokens depicting general scenery, such as furniture or background, appear repeatedly and redundantly. In this setting, clustering- based approaches are likely fail in capturing rare but important tokens-suce such as those corresponding to the since they primarily focus on densely populated regions in the feature space. Therefore, a visual token compression algorithm that simultaneously ensures representativeness and diversity is essential to effectively retain these critical but sparse visual cues.
In order overcome these limitations. we propose a novel visual token compression algorithm based on the facility location function (Lin & Bilmes] 201T Lin et al., 2009). Our approach interprets token selection through the lens of submodular optimization, ensuring that the selected set of tokens covers all original tokens under given budget constraint. Specifically, each subset considers the similarity between its subset and the entire tokens, enabling to include diverse information of the entire video sequence. While finding the optimal subsets in this manner is known to be Na-hard problem, we sidestep the computational overhead by utilizing the lazy greedy algorithm (Minoux 1978), enabling to select the visual tokens with minimal computational overheads. As result, the chosen tokens are both representative and diverse, effectively preserving essential information for video understanding tasks. Our experiments benchmarks such Video-MME, MLVU,
2

---

Published as a conference paper at ICLR 2026
Long Beng,,,eB,,nch, and EgoSchema (Fu et al., 2024| Mangalam et al. 2023 Zhou et al. 2024
Wu
et al. 2025) demonstrate the superionity of our method over existing approaches.
The remainder of this paper is organized as follows. In Section 2
provide comprehensive review of related work. Section dettails our proposed facility location-based algorithm for visual token compression. Experimental settings and results are presented in Section [ and we conclude in Section 5 by summarizing our key findings and discussing potential future directions.
2 RELATED WORK
Sampling/ Pooling A common and straightforward strategy to deal with the abundance of visual tokens in long video sequences is reduce the input size via pooling or sampling (Potapov et al.) 2014; Cai et al. 2024 Qu et al. 2024 Wu 2024). For instance, uniform sampling of frames or pooling across spatial/temprral dimensions can substantially cut down the computational overhead and memory usage. However, these methods often ignore the semantic importance of certain frames or regions. Such a one-size-fits--d approach may discard critical cues or overly compress redundant segments, leading to suboptimal performance when higher-level understanding of video content is required.
Clustering Another widely studied line of research involves clustering techniques to group similar frames or tokens and select representative exemplars (de Avila et al. 2011 Khosla et al., 2013 Wang et al. 2024b; Shang et al. 2024 Zhang et al. 2024a). By partitioning the visual space into clusters, these methods attempt to capture the overall distribution of the video content, retaining only the most "central" examples in each cluster. While clustering better preserve representativeness than naive sampling. it can still struggle to guarantee coverage of rare but potentially important events. Moreover, the offline clustering process may be computationally expensive, especially for long videos, and is typically not optimized in an end-to-end manner, which can result in mismatches between clustering objectives and downstream video understanding tasks.
Query-Aware Compression In query-aware or task-specific compression, the aim is to select those given query, user interest, or downstream task ZZang et al. 2016 [Shen et al. [024] Korbar et al. 2024] Wang et al.] 2024b). This category of methods can effectively reduce the search space by focusing on what deemed important. However, they require prior knowledge of the query task, making them less flexible for general-purpose or zero-shot scenarios. When the query space expands or changes, such approaches often need retraining or redesign, limiting their applicability in dynamic environments (e.g., surveillance systems, smart glasses, or robots) where the set of possible queries is not fixed.
frames tokens that are most relevant
Retraining Learnable compression algorithms employ neural networks to decide which tokens or frames to discard or keep (Zhang et al. 2025 Argaw et al. 2024 Lee et al. 2025). By training end-to- end, they can theoretically capture complex patterns and adapt different tasks. Nonetheless, these methods tend to require large labeled datasets and substantial training time. They are also dependent on model architecture and specific training objectives, which makes them less model-agnostic. Consequently, deploying such methods in rapidly evolving research fields or on resource-constrained platforms (e.g., embedded systems in mobile robots) can be challenging.
In contrast to the above approaches, our method operates in a training-free, plug-and-play fashion, allowing it to be easily integrated into existing pipelines with minimal overhead. Built the principle of facility location (Lin & Bilmes] 2011] Lin et al. 2009). it interprets token selection as a submodular and diversity under given budget constraint. Additionally, we adopt a lazy greedy algorithm that significantly reduces computation time while maintaining near-optimal performance (Minoux 1978). By decoupling the compression strategy from the underlying vision model, our approach remains model-agnostic, thus enabling seamless deployment various real-world scenarios, from large-scale video analytics to on-device processing for surveillance, smart eyewear, and mobile robots. Moreover, our proposed approach operates in a query-agnostic manner, independent of user input. Unlike query-aware methods that require recompression for each incoming query and must retain all uncompressed tokens in memory, method performs a one-time compression and stores only the compressed tokens. This leads to significant gains in both computational and memory efficiency.
optimization problem, ensuring both representativencee
3

---

Published as a conference paper at ICLR 2026
Budget K
Priority queue
Selected subset S
tolenn [V]
All tokens
max =2
maxungdu =
Makking 4
mnttuugple 2 =
Discarded tokens
max
Selected
D, index
26
24
Similarity [i u1
4,
coverage:
7 0
Coverage score:
Similarity matrix
Location
FLoC
index:
Figure 3: Illustration of the proposed algorithm for selecting a subset of visual tokens using the lazy greedy approach. The process iteratively selects tokens with the highest marginal gain while ensuring diversity and representativeness within a given budget K. This figure demonstrates the execution of Algorithm from line line 14 on one-dimensional toy example.
As demonstrated in Figure ! our proposed method, FLoC, empirically outperforms both previously proposed approaches and traditional clustering- methods in terms of accuracy and processing speed. This highlights its effectiveness in addressing the token compression challenge for long video understanding.
3 PROPOSED METHOD: FLOC
This section introduces our proposed method, FLoC, which employs the facility location function to select representative and diverse visual tokens. Section 3.1 outlines the overall framework, where visual tokens serve as inputs for video LMMs generate responses. Section 3.2 then describes the facility location function and its efficient implementation using the lazy greedy alogrithm.
3.1 FRAMEWORK FOR VISUAL TOKEN SUBSET SELECTION
Let V = {u1, u2,...; J be the ground set of all visual tokens extracted from an input video. Each token v2 corresponds to a feature vector that represents specific spatiotemporal segment (e.g., frame patch at given time). Our goal is to select a subset C V such that |S| < K, where K is budget on the number of tokens to keep. Formally, we want to find the subset S that maximizes utility (or coverage) function f:
S = arg max f(S).
where, S(S) quantifies how well the subset S collectively represents or covers the entire set V. Specifically, f should reward the chosen visual tokens (i.e., S) that preserve the essential information and diversity of all visual tokens (i.e. V), while respecting the budget constraint K. Therefore, the key is to design and optimize a suitable function that captures the core video content with minimal redundancy.
The input video is first parsed into a large set of tokens, from which our method selects representative and diverse subset. Although our method can be directly applied to the entire set of visual tokens, we divide the input video into smaller temporal blocks for computational efficiency, as shown Figure This design naturally allows future extension to streaming scenarios, where the algorithm could process accumulated tokens buffer. After selecting visual tokens within each block, the chosen subset is concatenated with a user-por providdd text prompt to form the final input for the video-LMMs This integration seamlessly combines crucial visual cues with linguistic context, enabling the LMM to perform downstream tasks such as captioning, question answering, event detection with improved efficiency and accuracy.
3.2 SUBMODULAR FACILITY LOCATION FUNCTION
We utilize the facility location function (Lin & Bilmes 2011 Lin et al. 2009). widely adopted submodular function, select representative and diverse subset of visual tokens. Formally, given
4

---

Published as a conference paper at ICLR 2026
ground set V of visual tokens, the facility location objective is defined as follows:
S(S)
Σ max sim u), WES
where sim(p, u) denotes the similarity between tokens and In this work, employ cosine similarity between token embeddings as our similarity measure:
v u
sim(v, u)
||||||||||
The motivation for adopting the facility lo-
Algorithm Lazy Greedy Algorithm for FLoC
cation function stems from its effectiveness
Require: Ground set V. budget
in balancing representativeness and diversity, making it one of the traditional and widely- used approaches for summarization tasks.
Ensure: Selected subset with |S| S K
1: S +-0
2: Initialize priority queue Q +- 0
By maximizing this function, the selected subset is encouraged to cover all tokens in the original set as comprehensively as possi- ble, while avoiding redundancy by penalizing highly overlapping selections. Due to this property, facility location has been success- fully applied across various summarization domains, including document summarization and video summarization tasks.
3: for V do
4: A, 4 f((u))
5: Insert v into Q with priority A.
6:
end for
7: while [S] K do
8:
u" +- arg maxxeq A. (pop from queue)
9:
+ f(S ( f(S)
10:
if 5 2 max A. then
11:
S SU {r²}
12:
elsc
Finding optimal subset that maximizes the facility location function is known to be NP-hard. To address this complexity, com- mon approximation method is the greedy al- gorithm, which iteratively selects tokens with the highest marginal gain until the budget constraint is satisfied, This greedy selection method guarantees a solution with a performance lower bound of (1-1/e) x 0.632 relative the optimal solution (Nemhauser al. 1978). Specifically, the greedy algorithm incrementally adds the token that provides the largest increase in coverage at each iteration.
Update priority of in Q to and re-insert
14: end if
15:
end while
return
To further enhance computational efficiency, we implement lazy greedy algorithm (Minoux 1978 which significantly reduces the computational overhead by avoiding unnecessary recomputation of marginal gains. Specifically, the algorithm exploits the submodularity (diminishing returns) property of the facility location function f. Formally, for any subsets A C B S V and a token v E V B, the marginal gain satisfies:
(AA
-
This inequality implies that the marginal benefit of adding visual token only decrease or remain constant as the selected subset grows. Consequently, the marginal gain computed a previous iteration serves valid upper bound for the current marginal gain. We leverage this by maintaining a priority queue of these upper bounds. In each step of the search process, we pop the candidate v" with the highest upper bound and recompute its exact marginal gain ó with respect to the current subset. If ó remains greater than or equal to the upper bounds of all other candidates in the queue, submodularity guarantees that 2* is the optimal choice for the current iteration without needing to re-evaluate the rest. Algorithm outlines the detailed procedure, and Figure 3 provides a visual illustration of this process.
The lazy greedy algorithm significantly reduces computational complexity compared to the naive greedy approach. While the naive greedy algorithm for maximizing submodular functions has time complexity of O(nK), the lazy greedy approach leverages the submodularity property to avoid unnecessary recomputation of marginal gains. By using a priority queue, updates marginal gains only when needed, achieving empirical speedups often approaching order of magnitude. Consequently, it becomes particularly efficient for handling numerous visual tokens and enabling real-time processing of long videos.
Compared to traditional clustering baased methods, our lazy greedy- based facility location method offers several advantages. First, eliminates iterative refinement and costly operations such as
5

---

Published as a conference paper at ICLR 2026
segen-dccompostionns Instead, approach directly selects tokens in single forward pass by maximizing global coverage, ensuring a diverse and representative subset is chosen efficiently. Thus, it provides highly efficient and scalable alternative, especially suitable for real-time on-device processing requirements. Next, the facility location function explicitly optimizes global coverage by selecting tokens that best represent the entire set of visual tokens. Unlike k-means, which tends to select tokens from dense regions and may overlook sparsely populated yet important regions (e.g., rare objects like keys, subtle actions, or fine-grained details such as small text or facial expressions), our method ensures that selected tokens span diverse feature regions by defining utility in terms of coverage, prioritizing selections that maximize representativeness. This prevents oversampling from dense clusters while preserving rare but meaningful patterns.
In our empirical evaluation, we observed that the proposed lazy greedy-based facility location algorithm significantly outperforms traditional clustering methods, such as k-means and spectral clustering, terms of computational efficiency. Specifically, our experiments demonstrate substantial runtime improvements, achieving speedups of several times more depending on the dataset size and scenario. We provide detailed experimental results and analysis comparing the runtime performance of our method against other clustering baselines in Section
4 EXPERIMENTS
4.1 MODELS
Qwen2.5-VL (Bai et al. 2025) is an advanced vision-language model capable of handling high- resolution images and long video sequences. It introduces dynamic resolution processing via Window Attention-based Vision Transformer and supports absolute temporal encoding.
Intern Zhu al., 2025) is multimodal model designed with native vision-language pretraining and Cascade Reinforcement Learning. For long video understanding, it incorporates a Visual Resolution Router to dynamically allocate visual token capacity across frames.
Others. We also conducted experiments on Qwen2-VI. (Wang et al.) 2024a) and VA-eNx- Video (Zhang al. 2024c) models further validate the generalizability of our approach. Due to space limitations, detailed results and analysis for these models are provided in the Appendix.
4.2 BENCHMARKS
Video-MME (Fu et al. 2024 is multi-modal evaluation benchmark designed assess visual and textual understanding in videos, covering diverse real-life footage across domains such sports, news, and user-generated content. It focuses on tasks like video captioning, event detection, and question answering.
Long VideoBench (Wu et al. 2025) is curated for long-form video understanding. featuring extended videos such as lectures, live events, and surveillance footage, emphasizing topic segmentation and global summarization.
MLVU (Multi-Level Video Understanding) (Zhou et al. 2024) evaluates hierarchical comprehension from frame-level recognition to storyline interpretation, using clips from movies, documentaries, and instructional videos
EgoSchema (Mangalam et al., 2023) evaluates egocentric video understanding through short, first- person perspective clips, emphasizing schema-level reasoning and activity prediction.
Among these, Video-MME, Bonn and MLVU include videos longer than one hour, making them suitable for long-form video understanding. In contrast, EgoSchema consists of relatively short, minute-level clips but remain widely adopted benchmarks for video understanding research.
4.2. IMPLEMENTATION
We effectively evaluated the performance of various visual token compression algorithms using the 1mms eval toolkit (Li et al. 2024 Zhang et al. 2024b) as our codebase, which supports multiple
"Qwen2.5 Qwen2-V1., and LLaVA-Video-77 7B-Qwen2 are under the Apache-2.0 license. InL is under the MIT license.
Widee-MMMF. Long Bench and MLVU all under the CC BY-SA 4.0 International I License.

---

Published as a conference paper at ICLR 2026
Table Comparison of visual token compression methods. The ratio indicates the compression ratio relative to the original number of visual tokens.
Model
Qwen2,5-VL.. VL.7B
Intern VL.3-BB
Comp. Ratio
Video MME
Ego Schema
Video MME
Egg Schema
Method
MLVU
LVB
Avg.
MLVU
LVB
Avg.
66.33
70.31
60.51
61.40
64.64
66.63
72.68
59.39
70.00
67.18
TS-LLaVA
61.15
67.57
55.20
59.60
60.88
62.78
67.30
56.02
68.20
Lo
62.19
66.61
55.42
59.40
60.91
64.70
69.50
55.35
69.20
64.69
DivPrune
61.63
67.57
56.17
58.40
60.94
64.07
70.06
56.92
65.00
64.01
Random
60.30
66.24
55.72
58.60
60.22
60.59
65.69
56.02
65.20
61,88
DyCoke
62.11
67.53
55.12
59.60
61.09
63.96
68,45
55,72
69.00
64.28
Prunc Vid
58.19
64.54
54.15
54,20
$7.77
57.41
62.05
53.48
62.80
58.94
STTM
59.52
63.57
54.60
55.80
58.37
63.52
64.26
54,90
66.20
62.22
Scissor
58.59
65.04
54.08
56.40
58.53
61.15
67.76
55.12
65.80
62.46
FastVID
60.89
67.31
57.14
58.60
60.99
FLoC (Ours)
63.33
68.81
58.12
60.00
62.57
64.93
71.57
56.69
69.40
65.65
TS-LLaVA
58.78
64.67
$2.51
$7.20
58.29
$9.63
53.85
62.80
60.31
Lo
58.07
62.97
52.73
55,40
$7.29
$6.48
60.12
51.31
60.40
57.08
DivPrune
58.85
64.67
54.00
55.80
58.33
61.93
68.08
54.82
61.80
61.66
Random
57.44
63.80
53.63
58.20
58.27
$9.74
64.77
54.23
66.60
61.34
2-4
DyCoke
57.00
63.02
53.78
54.00
56.95
61.37
65.13
53.10
67.40
61.75
Prune Vid
54.11
61.59
51.83
52.00
54.88
53.81
59.48
52.28
58.40
55.99
STTM
57.15
61.73
51.68
50.80
55.34
60.15
62.93
52.28
63.40
59.69
Scissor
55.26
60.95
$3.55
54.00
64.44
53.77
FIstVID
58.67
65.52
54.23
57.20
58.91
FL.C (Ours)
60.89
66.19
55.27
58.00
60.09
63.41
69.09
56.47
66.20
63.79
TS-LLaVA
55.07
62.37
50.49
54.60
55.63
$8.89
63.89
53.33
61.00
59.28
VU
53.41
58.42
50.34
53.20
53.84
55.96
59.52
51.01
58.80
56.32
DivPrune
55.78
61.91
52.28
53.60
55.89
60.85
65.46
52.88
59.40
59.65
Random
55.56
61.41
49.89
$3.60
$5.12
$7.30
63.57
$2.13
60.40
58.35
2-5
DyCoke
54.37
59.98
51.38
$4.60
55.08
$9.22
62.60
51.98
63.00
$9.20
PruneVit
51.11
58.51
49.66
49.00
52.07
51.41
56.39
49.96
53.00
52.69
STTM
55.26
59.25
50.11
49.40
53.51
57.52
52.43
58.12
Scissor
51.89
58.74
51.46
50.00
56.33
61.68
51.31
62.60
57.98
FastVID
57.19
62.94
52.95
55.00
$7.02
FLoC(Ours (Ours) 58.63
64.08
53.10
54.00
57.45
60.81
66,93
54.23
63,80
61.44
Table 2: Evaluation of token compression with extended temporal input (1 FPS, up 7200 Frames)
Model
Qwen2.5-VL-77
Qwen2.5-VL-3228
Max Frames
Video MME
Video MME
Method
MLVU
LVB
Avg.
MLVU
LVB
Avg.
768
66.33
70.31
60.51
65.82
70.41
71.57
62.60
68.19
TS-LLaVA
65.07
72.40
62.08
66.52
70.22
73.09
65.00
69.44
LongVU
65.04
71.02
62.75
66.27
70.37
72.22
64.62
69.07
DivPrune
64.93
70.19
62.30
65.81
70.26
73.37
64.32
69.32
7200
Random
64.56
70.52
61.63
65.57
69.70
72.49
64.62
68.94
DyCoke
65.78
71.30
62.98
66.69
71.00
72.26
63.87
69.04
Prune Vid
62.96
68.63
62.45
64.68
68.00
70.19
63.50
67.23
Fl.oC (Ours)
65.85
72.63
62.60
67.03
71.56
73.83
66.49
70.63
video LMM models and diverse benchmarks. All experiments conducted leveraging NVIDIA H100 GPUs and multiprocessing for efficient computation.
4.3 BASELINES
Recent Algorithms We compared the performance of recently proposed algorithms, Long VU Shen
al. 2024). DyCoke (Tao et al. 2024). TS-LLaVA (Qu al. 2024 PruneVID (Huang et al. 2024
DivPrune (Alvrr et al. (025). ), STTM (Hyun et al. (202). LLaVA Scissor (Sun et al. 22225). ) and
FID (hen et al. 2025). Implementation details are described in Section of Appendix.
Clustering Algorithms We used K-means, K-medoids, and Spectral clustering algorithms as our baselines.
7

---

Published as a conference paper at ICLR 2026
4.4 RESULTS
To simulate realistic deployment scenarios where memory resources are constrained such as on- device execution of LMMs-W compress visual tokens reduced lengths (1/8, 1/16, 1/32 of the optimal visual token number) and evaluate the models' robustness through long video understanding. This setup allows us to assess how well LMMs retain performance under severe token budget limitations. We additionally measured the compression time of each algorithm to analyze the trade-off between performance and efficiency, providing insights into their practical applicability.
Table I presents comparative analysis of video understanding performance using above-mentioned benchmarks with 9 different baseline methods as described in We evaluate these methods under various visual token compression ratios of 2 $.2 2- and 2 33 Figure illustrates the performance of each compression algorithm in terms of accuracy retention (x-axis), measured as a percentage relative the full-token baseline, and compression time (y-axis). The results are based on a 1/8 compression
ratio using the Qwen2.5-VLL-7B model.
As shown in the results, method consistently outperforms existing visual token compression techniques across different datasets, compression ratios, and backbone models. We attribute this superiority FLoC's ability to overcome the structural limitations of prior approaches. Specifically, graph-based merging methods (e.g., STTM, LLaVA Scissor) often suffer from the "weak connection" problem, where distinct tokens-such as small objects and their background are irreversibly merged based on local similarity thresholds, leading to significant detail loss especially at low compression ratios. Similarly, while diversity-band -based methods (e.g., DivPrune) effectively capture outliers, they often fail to retain representative tokens that describe the core context of the video. In contrast, our facility location-basee approach mathematically guarantees a balance between representativeness and diversity, successfully retaining both the central narrative and fine-grained visual cues that other methods overlook.
As shown in Figure clustering based methods such as K-Means and Spectral Clustering occa- sionally achieve performance comparable to our proposed approach. However, these methods incur approximately 10x higher compression time, indicating significant disadvantage in terms of ef- ficiency. A detailed comparison of the efficiency of clustering- based methods is provided in the following subsection.
In the final experiment, we aimed to fully leverage the optimal token length of the LMM by extracting all visual tokens from many frames as possible in long video sequence, and compressing them to the model's optimal token length. Specifically, we modified the default Qwen2.5-Vi vision processing tcriptwhhiee originally supports up to 768 frames-to handle up 7,200 frames. The resulting visual tokens were then compressed to 24.576 tokens, corresponding to the optimal token length of the model. The performance under this setting is presented Table 2
As shown in Table 2 FLoC can significantly improve the performance of LMMs that are convention- ally measured using limited number of frames. For the 7B model, the accuracy increased by an average of 1.21 points, and for the 32B model, it rose by an average of 2.44 points. These results indicate that while existing LMMs are forced to process fewer frames due to their limited context length, our proposed algorithm enables them to handle a larger number of frames through efficient compression. We believe this approach substantially enhances their overall video understanding capabilities.
These findings demonstrate that our proposed algorithm enables LMMs to generate high-quality responses under resource-constrainee conditions, with significantly reduced processing time.
4.5 ANALYSIS
4.5.1
REPRESENTATIVE AND DIVERSE VISUAL TOKENS
We demonstrate the effectiveness of our method in selecting representative and diverse visual tokens through t-SNE visualization. For the visualization, we use Qwen2-VL VL 7B as the model and a randomly selected video in VideoMME as the dataset. We compare the projected embedding spaces obtained using K-means, K-Medoids, spectral clustering, and ours. In Fig. [ red-colored stars and black-colored dots represent the selected and discarded visual tokens for each algorithm, respectively.
As shown, K-means and K- K-Medoids clustering predominantly select representative visual tokens from dense regions while failing capture diverse tokens. In contrast, facility location selects
8

---

Published as a conference paper at ICLR 2026
K-means Clustering
K-medoids Clustering
Spectral Clustering
Ours
Figure 4: TSNE visualization of visual tokens. The red-colored stars and Mlack-colored dots indicate the selected and discarded visual tokens, respectively. As shown, our method selects both representa- tive and diverse visual tokens.
DivPrune
TS-LLaVA
FLOC (Ours)
woman wearing
during the summer sunsert
(A] Winter coor
[8] dress and heels
soimsult
Prediction: [B) dress and heels
Prediction: (B) dress and heols
Prediction: (D) hat and sunglatses
Figure 5: FLoC captures diverse visual tokens (e.g., hat, sunglasses) missed by DivPrune and TS- LLaVA, enabling accurate answers about what the woman is wearing.
visual tokens those are evenly distributed from both major and minor clusters, ensuring more diverse representation. This visualization clearly highlights that our proposed method effectively preserves both representative and diverse visual tokens, which are crucial for comprehensive video understanding.
Additionally, shown in Fig. 5 our proposed FLoC selects diverse tokens, successfully capturing visual cues like hats and sunglasses, unlike DivPrune and TS-LLaVA, which often miss them. This enables more accurate answers to questions about what the woman is wearing. Additional results with more examples are provided in the Appendix, specifically illustrated in Figure g and
We further validate that visual tokens compressed by FLoC compared to those produced by alternative compression algorithms, supported by both quantitative metrics and empirical evidence. These comparisons are visualized Figure of the Appendix, where representativeness and diversity are explicitly quantified. Moreover, as shown in Table of the Appendix, our framework achieves outstanding performance on the MLVU dataset, particularly in tasks requiring fine- ene-grained video understanding such as Needle QA and Ego Reasoning, further substantiating the superiority of our approach.
representative and diverse
4.5.2 MINIMAL COMPUTATIONAL OVERHEADS
We also compare the computational overhead of our proposed method with other visual token compression techniques. We use Qwen2-VL VL 7B as the model and VideoMME as the dataset for the experiment. We measure the time taken by each method to perform visual token compression.
As shown in Table 3 our method consistently achieves the lowest computational cost across different numbers of the block length, denoted T. Notably, the performance gap in computational efficiency between our method and clustering- -based approaches widens as T increases, further highlighting the scalability of our approach. Clustering- methods, such K-Means, K-Medoids, and spectral clustering, often incur substantial computational overhead when applied to visual token compression. For instance, K-Means requires multiple iterations to update cluster centroids until convergence, involving computations proportional to O (nnKdd di). where d denotes the dimensionality of features, and indicates the number of iterations. Although K-Medoids selects actual data points as cluster centers and may converge faster in practice, it still typically scales O(K(n K)*), becoming computationally intensive as grows. Similarly, spectral clustering involves expensive dggenddccomposition of similarity matrices, incurring computational complexity of approximately Onn)) in general. These inherent limitations significantly reduce the practicality of clustcring- methods for compressing visual tokens, especially in long video sequences with extremely large token sets.
In contrast, method circumvents these computational bottlenecks by leveraging the lazy greedy algorithm, which exploits submodularity to efficiently select near-optimal subset of tokens. Instead of exhaustively evaluating all possible token selections, the lazy greedy approach prioritizes promising

---

Published as a conference paper at ICLR 2026
Table 3: Comparisons of average computation times (sec).
Methods
Complesit
T=
7=
2-5
32
7 2
T=
2-4
T
y= 2
y=
2-1
7=
Average Accuracy
K-Mcans
O K d)
0.551
4.600
59.00
0.790
8.860
113.0
1.390
16,80
218.0
58.66
K-Medoids
O k)*)
0,022
0.113
0.716
0.018
0.119
0.747
0.021
0.135
0.877
56.22
Speciral Clustering
(n²)
0.232
0.569
5.160
0.794
2.260
9.650
0.270
1,180
21.10
58.97
FLaC (Ouns)
O R)
0.010
0.056
0.413
0.012
0.065
0.475
0.014
0.075
0.527
59.74
candidates while skipping redundant computations, significantly reducing the runtime. These results demonstrate that our method not only provides superior video understanding performance but also achieves minimal computational overhead, making it highly practical for real-world applications.
4.5.3 ROBUSTNESS ON BLOCK LENGTHS
To examine the impact of the sole hyperparame- ter in our proposed algorithm-the block length T-we evaluated performance across various datasets and compression ratios while varying T. In this experiment, the Qwen2VL-7B model was used.
VideMMN
compresion
As illustrated in Figure 6, we observe distinct behaviors depending on the block length T. In the region where 7 < 4, performance tends to degrade because the narrow temporal window prevents the algorithm from identifying redun- dancy across adjacent blocks (inter-block redun- dancy). Conversely, T increases, the facility location objective optimizes representativeness and diversity
102 comprssionn
Rerfrmance
broader temporal context, leading to performance saturation. Crucially, un- like traditional clustering methods where com- putational cost scales quadratically with input size, our lazy greedy implementation ensures that increasing T incurs negligible latency over- head. This suggests that a sufficiently large fixed block length (e.g., T = 32) serves as robust and efficient default, minimizing the need for per-video hyperparameter tuning. However, we acknowledge that relying on fixed uniform seg- mentation is a heuristic simplification and may not be strictly optimal for every video content. We anticipate that developing an adaptive mech- anism to automatically determine T' based on temporal dynamics could yield further perfor- mance improvements. A more detailed discussion on this limitation and potential future directions is provided in Appendix!! !
108 compresion
5/2 compaciion
Blok Eog)
LangridesBench
Perfomaacce
LG0 compression compresion
Blok length Fog2 scale)
Figure 6: Performance versus block length T (log2-
scale) for a number of benchmark datasets.
5 CONCLUSION
As long video understanding advances, handling the overwhelming number of visual tokens remains a key bottleneck. While prior methods such as uniform sampling and clustering have addressed this issue, they often fail to capture sufficient visual diversity and add computational overhead. We tackle these limitations by proposing a visual token compression framework based on the facility location function. Our method selects tokens that are both representative and diverse, preserving essential scene information while significantly reducing computation via lazy greedy algorithm. Extensive experiments on large-scale benchmarks, including Video-MME, Long VideoBench, MLVU, and Egoschema show that our method consistently outperforms existing compression techniques. Its efficiency and strong performance without added overhead make it well-suited for real-world applica- tions such as surveillance, augmented reality, and autonomous navigation. As video-LMM scale, improving efficiency and information retention will be key to advancing long video understanding.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et Gpt-4 technical report. arXiv preprint 2rXhv:2333.088744 2023.
Saeed Ranjbar Alvar, Gursimran Singh, Mohammad Akbari, and Yong Zhang. Divprune: Diversity- based visual token pruning for large multimodal models, arXiv preprint arXiv:2503.02/75. 2025.
Dawit Mureja Argaw, Seunghyun Yoon, Fabian Caba Heilbron, Hanieh Deilamsalehy, Trung Bui, Zhaowen Wang, Franck Dernoncourt, and Joon Son Chung. Scaling up video summarization pretraining with large language models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 1332-8341, 2024.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang. Peng Wang, Shijie Wang. Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang. Zhaohai Li, Jianqiang Wan, Pengfei Wang. Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report, 2025. URL
https://arxiie abs 2502 13923
Ivana Balazevic, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, and Olivier J Hénaff. Memory consolidation enables long-context video understanding. In Forty-first Interna- tional Conference on Machine Learning, 2024.
Mu Cai, Jianwei Yang. Jianfeng Gao, and Yong Jae Lee. Matryoshka multimodal models. arXiv preprint arXiv:2405.17430 2024.
Sandra E. F. de Avila, Antonio Lopes, Cesar A. A. da Luz Jr, and Arnaldo de Albuquerque Araujo, Vsumm: A mechanism designed to produce static video summaries and novel evaluation method. In Pattern Recognition Letters, 2011.
Yunhao Fang, Ligeng Zhu, Yao Lu, Yan Wang, Pavlo Molchanov, Jan Kautz, Jang Hyun Cho. Marco Pavone, Song Han, and Hongxu Yin. Vila?: Vila augmented vila. arXiv preprint 2rXiv22477.7455. 2024.
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 evaluation benchmark of multi-modal Ilms video analysis. arXiv preprint arXiv:2405. 2007....5..... 2024.
Xiaohu Huang, Hao Zhou, and Kai Han. Prunevid: Visual token pruning for efficient video large language models. arXiv preprint arXiv:2412. 2614111611111 17, 2024.
Jeongseok Hyun, Sukjun Hwang, Su Ho Han, Taeoh Kim, Inwoong Lee, Dongyoon Wee, Joon- Young Lee, Seon Joo Kim, and Minho Shim. Multi-granular spatio-temporal token merging for training-free acceleration of video 1lms. arXiv preprint arXiv.5507.0999.. 2025.
Aditya Khosla, Jie Chen, Serena Yeung, and Li Fei-Fei. Large-scale video summarization using web-image priors. In Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
Bruno Korbar, Yongqin Xian, Alessio Tonioni, Andrew Zisserman, and Federico Tombari. Text- conditioned resampler for long form video understanding. In European Conference on Computer Vision, pp. 271 278-288. Springer, 2024.
Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles. Dense-captioning events in videos. International Conference on Computer Vision (ICCV), 2017.
Seon-Ho Lee, Jue Wang, Zhikang Zhang, David Fan, and Xinyu Li. Video token merging for long video understanding. Advances in Neural Information Processing Systems, 37:13851-13871. 2025.
Bo Li, Peiyuan Zhang, Kaichen Zhang, Fanyi Pu, Xinrun Du, Yuhao Dong, Haotian Liu, Yuan- han Zhang, Ge Zhang, Chunyuan Li, et al. Lmms-eval: Accelerating the development of large multimoal models, March 2024. http//tttp:///thubbbbbbb com/ /wt///vooownnnniseeeeeese Labb 1mms eval
11

---

Published as a conference paper at ICLR 2026
Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arX1v:2311.10122, 2023.
Hui Lin and Jeff Bilmes. A class of submodular functions for document summarization. In Human Language Technologies: The 2011 Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), 2011.
Hui Lin, Jeff Bilmes, and Shasha Xie. Graph-based submodular selection for extractive summarization. In 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, pp. 381-388 IEEE, 2009.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024.
Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding, 2023. URL hnttps://arst
axg/aba/2308. 09126
Michel Minoux. Accelerated greedy algorithms for maximizing submodular set functions. Optimiza- lion Techniques, pp. 334-243, 1978.
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher. An analysis of approximations for maximizing submodular set functions-is Mathematical progrumming, 14:265-294 1978.
Kirill Potapov, Matthijs Douze, Zaid Harchaoui, and Cordelia Schmid. Category-specific video summarization. In European Conference Computer Vision (ECCV), 2014.
Tingyu Qu, Mingxiao Li, Tinne Tuytelaars, and Marie-Francine Moens. Ts-llava: Constructing visual tokens through thumbnail-ande -an-mliln for training-free video large language models. arXiv preprint arXiv:2411. 11066 2024.
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, 2024.
Leqi Shen, Guoqiang Gong, Tao He, Yifeng Zhang, Pengzhang Liu, Sicheng Zhao, and Guiguang Ding. Fastvid: Dynamic density pruming for fast vidco large language models, 2025. URL
httpp::/ //tr:v//rxvv.../ wttp//ggg//////ooo//o//.ee abs/ 2503. 11187
Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, et al. Longyu: Spatiotemporal adaptive compression for long video-language understanding, arXiv preprint arXiv:2410.17434 2024.
Mike Zheng Shou, Stan Weixian Lei, Weiyao Wang, Deepti Ghadiyaram, and Matt Feiszli. Generic event boundary detection: A benchmark for event segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8075-8084, October 2021.
Karen Simonyan and Andrew Zisserman. Two-stream convolutional networks for action recognition in videos. In NeurlPS, 2014.
Enxin Song. Wenhao Chai, Guanhong Wang. Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, et al. Moviechat: From dense token to sparse memory for long video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 18221-18232, 2024.
Boyuan Sun, Jiaxing Zhao, Xihan Wei, and Qibin Hou. Llava-scissor: Token compression with semantic connected components for video llms. arXiv preprint arXiv:2506.2 2025.
Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang. Dycoke: Dynamic compression of tokens for fast video large language models. arXiv preprint arXiv:2411.15002 2024.
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew Dai, Anja Hauth, Katic Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312. 1805, 2023.
12

---

Published as a conference paper at ICLR 2026
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In CVPR, 2015.
Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprini arXiv:2409.12/99 2024a.
Ziyang Wang. Shoubin Yu, Elias Stengel-Eskin, Jaehong Yoon, Feng Cheng, Gedas Bertasius, and Mohit Bansal. Videotree: Adaptive rree-based video representation for Ilm reasoning on long videos. arXiv preprint arXiv:2405.11920 2024b.
Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-context interleaved languaa understanding. Advances in Neural Information Processing Systems, 01228888-285577 2025.
Wenhao Wu. Freeva: Offline milm as training-free video assistant. arXiv preprint arXiv:2405.0779) 2024.
Huijuan Xu, Boyang Li, Vasili Ramanishka, Leonid Sigal, and Kate Saenko. Joint event detection and description in continuous video streams. WACV, 2019.
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. In ICMI., Proceedings of Machine Learning Research, 2015.
Fuzhao Xue, Yukang Chen, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Haotian Tang, Shang Yang, Zhijian Liu, et al. Longvila: Scaling long-context visual language models for long videos. arXiv preprint arXiv:2408. 2024.
Haoji Zhang, Yiqin Wang, Yansong Tang, Yong Liu, Jiashi Feng, Jifeng Dai, and Xiaojie Jin. Flash-vstream: Memory-based real-time understanding for long video streams. arXiv preprint arXiv:2406.00m 08085. 2024a.
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 large multimodal models, 2024b. URL https::/arrxi..or///bb...
12772
Ke Zhang, Wei-Lun Chao, Fei Sha, and Kristen Grauman. Video summarization with long short-term memory. European Conference on Computer Vision (ECCV), 2016.
Shaolei Zhang, Qingkai Fang, Zhe Yang, and Yang Feng. Llava-mini: Efficient image and video large multimodal models with vision token. arXiv preprint arXiv:2501.0388 2025.
Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02773,, 2024c.
Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xiaoou Tang, and Dahua Lin. Temporal action detection with structured segment networks. In ICCV, 2017.
Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Shitao Xiao, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: A comprehensive benchmark for multi-task long video understanding. Xrxiv preprint arXiv:2406.0422 2024.
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, Zhangwei Gao, Erfei Cui, Xuchui Wang, Yue Cao, Yangzhou Liu, Xingguang Wei, Hongjie Zhang, Haomin Wang, Weiye Xu, Hao Li, Jiahao Wang, Nianchen Deng, Songze Li, Yinan He, Tan Jiang, Jiapeng Luo, Yi Wang, Conghui He, Botian Shi, Xingcheng Zhang, Wenqi Shao, Junjun He, Yingtong Xiong, Wenwen Qu, Peng Sun, Penglong Jiao, Han Lv, Lijun Wu, Kaipeng Zhang, Huipeng Deng, Jiaye Ge, Kai Chen, Limin Wang, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models, 2025. URL https: //arxiv org/ prg/abs/////ooooooooo/g/////o abs/ 2504 10479
13

---

Published as a conference paper at ICLR 2026
APPENDIX
A REPRESENTATIVENISS AND DIVERSITY
To quantitatively verify the representativeness and diversity of our proposed facility location-based visual token selection algorithm, we conducted an analysis using two complementary metrics: (1) averaged sum coverage, measuring how comprehensively the selected tokens cover the entire set of visual tokens, defined as
Averaged Sum Coverage(S)
ΣΣ sim(u, u),
[VISS|
VEV WES
where V the entire set visual tokens, is the selected subset, and sim u) the cosine similarity between tokens v and and (2) averaged distance, computed as the average pairwise distance (using - sim(u, u, w)) among the selected tokens:
Averaged Distance(S)
Σ
Σ
(1- simsim(u, w)).
|S|(||] 1)
WES WES,wg
We compared our method against three clustering- baselines: K-means, K-medoids, and spectral clustering.
utilized 50 randomly selected videos from the Video MME dataset and employed the Qwen2-vl 7B model. Due to the significant variability in the range of measures across different data points, normalized the six measures obtained from six algorithms for each video to have a zero mean and a standard deviation of one. The normalized results were then visualized using scatter plot.
kmeans
kmedoids
0.75
FLoC
TS-LLaVA DivPrune
0.50
0,25
Diversity
0.00
-0.75
-1.00
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Representativeness
Figure 7: Scatter plot of each algorithm's representativeness and diversity. As shown in Figure 7 facility location approach consistently outperformed the basclines in both representativeness and diversity measures. Specifically, our method achieved higher averaged sum coverage scores, indicating superior representativeness, and greater averaged distance, demonstrating its effectiveness in selecting both representative and diverse tokens.
In the scatter plot, the values obtained using the proposed FLoC algorithm are predominantly located in the first quadrant. This indicates that, after normalization, the values are on average more representative and diverse compared to other algorithms. When compared k-medoids, the FLoC
14

---

Published as a conference paper at ICLR 2026
algorithm shows lower representativeness but superior diversity. When compared to DivPrune, proposed algorithm shows slightly lower diversity but superior representativeness. Additionally, when compared to TS-LLa VA, k-means and spectral clustering, the FLoC algorithm demonstrates superiority in both representativeness and diversity.
These results suggest that when selecting representative samples from entire ground set, two crucial factors to consider are representativeness and diversity, which inherently exist in a trade-off relationship. If samples are densely distributed in a specific region, selecting a disproportionnatly large number of samples from that area can reduce overall diversity. Conversely, focusing excessively on diversity might lead to neglecting important samples from these densely populated, and potentially critical, regions. Our proposed FLoC effectively addresses this trade-off by selecting tokens that are both representative and diverse. Consequently, Fl.oC achieves superior performance in long video understanding tasks.
B COMPREHENSIVE PERFORMANCE EVALUATION
We present the performance of all evaluated visual token compression algorithms across the three benchmark datasets and three backbone LLM models in Table 4 and Table 5 In the previously submitted manuscript, results for several clustering-basee methods, namely k-means, k-medoids, and spectral clustering, were omitted from the main performance tables due to space constraints. These are now included for a comprehensive comparison. Table 4: Full comparison of visual token compression methods. Backbone LLM is --VV-io-V----- Qwen2.
Ratio
Tokens
Frames
Methods
Short
Medium
Valeo-MME
Long
Ovenal
15
64)
Long Vidco
600
Bench
3600
Overal
MIVU
Avg.
100SE
21632
128
75.78
63.33
$4.67
64.59
66.67
68.61
58.98
51.77
58.27
70.39
664.2
16
Prame Liniform
68.78
54.78
57.63
54.37
50.00
$4.08
53.66
50.33
Poeling
65.33
53.89
18.67
55.96
56.61
68.61
$6.31
48.05
$1.45
61.24
57.22
LoneVU
68.89
58.44
51.67
59.67
56.61
65.12
55.83
52,31
55.65
62.57
59.30
2-3
2704
128
TS. LL-L
DivPrune
69.11
59.22
59.56
$2.56
60.30
58.20
65.12
56.55
51.42
58.72
65.00
60.34
K-means
71,78
59.22
50.11
60.37
60.38
69.93
60:41
50.89
57.19
66.59
61.38
K-medoids
50.71
SC
72.11
61.78
51.56
61.81
60.32
68.02
59,22
52.13
57.52
66.07
61.80
FLaC (Ours)
71.68
60.56
50.89
61.04
61.91
69.19
60.19
51.60
57.97
67.43
62.15
8
Frame Uniform
60.78
51.89
48.56
$3.74
43.92
56.40
53.16
49.82
50.86
57.20
53.93
Pooling
60.00
50.11
45.33
$1.81
54.55 50
59.30
50.73
44.86
.99
57.56
LongVU
62.56
53.78
47.11
51.48
51.85
62.21
$1.21
49.65
$2.06
56.74
54.43
TS-LLaVA
67.22
56.78
50.56
58.19
56.09
68.02
58.25
47.52
$4.68
61.52
58.13
DivPrune
57.67
128
K-means
69.22
55.67
50.33
$9.41
59.26
66.23 2X
58.33
49.11
55.72
63.08
59.07
K-medoids
65.56
53.67
47,89
55.70
53.44
66.25 28
54.85
47,34
52.95
59.17
55.94
SC
56.07
FLeC (Ours)
69.33
57.44
$1.00
59.26
60.32
68.02
58.74
48.76
55.95
64.54
59.92
4
Prame Uniiform
52.56
49.45 .44
44.44
48.81
43.92
$1.16 16
51.
46.99
48.47
53.66
50.331
Pooling
$7.00
49.11
45.00
50.37
50.79
56.40
49. 27
43.97
48.17
54.94
$1.16
LongVU
57,33
49.78
45.78
50.96
49.21
48.
.9.44
53.61
51.34
2-5
676
128
TS LLaVA
DivPrune
64,00
64.22
56.22
55.00
47.78
49.00
56.41
55.67
53.97
52.38
65.12
55.81
53.64
$1.94
46.10
46.45
50.26
$1.91
59.43
57.52
55.37
55.03
K-means
65.22
49.56
47.22
54.00
56.67
67.02
57.50
46.87
54.12
58,499
55
K-meskids
63.67
50.67
47.44
53.93
$1.52
63.57
55.00
46. 26.28
51.91
55.82
53.89
SC
65.00
54.56
47.89
55.81
49.74
63.95
53.88
48.05
52.13
59.40
55.78
FLoC (Ours)
54.00
67.44
54.07
61.22
57.17
As evidenced by these tables, our proposed model achieves the highest average performance across all three benchmark datasets for all considered backbone LLM models and at all compression ratios. This consistent superiority indicates that our algorithm effectively selects representative visual tokens crucial for long video understanding, irrespective of the specific backbone model architecture or the nature of the question query.
C DETAILED SSS-SPECIFIC PERFORMANCE ANALYSIS ON MLVU
To thoroughly investigate the factors contributing to the performance improvements of our proposed algorithm, we conducted comparative analysis of its performance seven distinct sub-tasks within the MLVU dataset. The MLVU dataset is broadly categorized into three main types of tasks: Holistic Long Video Understanding (LVU),, Single Detail IVU, and Multi Detail LVU. These further
15

---

Published as a conference paper at ICLR 2026
Table 5: Full comparison of visual token compression methods. Backbone LLMs are Qwen2-VL-2B and Qwen2-VL-7HB
Model
Ratio
Tokens
Frames
Methods
Short
Medium
Video-MME
Long
Overall
15
6O
Long Video
600
Bench
3600
Overall
MLVU
Avg.
100%
34.56
256
61.39
50,56
45.89
53.78
|5556
58.14
50.19
42.20
48.69
62.25
51.91
32
Frame Uniform
65.11
45.67
52.20
53.97
63.00 03.95
47.57
43.79
59.54
53.74
Pooling
47.37
47.57
41.67
50.59
LongVU
65.00
$2.44
47.11
54.85
56.09
50
48.
41.67
48.09
60.46
54. 47.47
2-a
4320
236
TS-LLaVA
DivErune
K-means
65.44
63.67
66.39
50,78
52.33
19.67
46.11
45.33
45.64
54.85
$3,89
52.48
55.03
56,09
56.61
61 63
02 21
62.21
46.55
50.49
47.57
44.68
42.73
42.38
49.29
48.62
49.14
60.69
61.10
56.76
53.1
S4.86
51.15
mededdss
63.44
51.56
53.30
62
46.85
41.14
47.64
53.71
55.41
48.05
43.62
55.33
FLoC (Ours)
52.44
47.000
55.19
53.44
04147
47.57
48.77
62.33
55.42
16
Frame Uniform
62.41
47.22
42.44
50,70
53.97
60:47
46.85
43.26
48.09
56.32
51.70
Pooling
47.56
39.67
39.22
42.15
49:21
51.16
46, 36
41.14
45.18
$2.69
46
LongVü
61.56
47.56
45.78
50.96
56.09
50 59.88
46.
42,91
47.94
55.77
$1.56
2160
256
TS-LJaVA
DivPrune
61.44
50.56
44,44
43.56
52.85
56.61
61
47.09
40166
41.67
47.94
60.23
53.67
K-menns
47.11
42.11
50.26
50.73
42.012
$2.93
K-medoids
62.67
49.00
41.56
51,07
52.38
60:47
47.33
41.51
47.20
59.23
52.50
SC:
65:22
$1.67
44.78
53.80 89
55.56
50 88
47.57
45.
49.30
59.45
54.23
FLoC (Ours)
64.67
52.78
45.67
$4.37
55.36
61.63
09.03
43.97
49.44
60.74
54.85
8
Frame Uniform
58.11
44.67
41,56
48.11
53.44
02.79
46, 36
41.84
47.57
52.74
492.47
Pooling
44.44
38.89
38.56
40.63
47.09
50.00
45.39 $5.39
39.72
43.85
50.34
44.93
L
59
43.79
49.44
2-
1080
256
TS-LLaVA
DivPrune
K Kmeans
56.33
61.78
44.33
47.33
47.00
43.89
40.64
50.89
47.04
51.26
55.56
53.44
58.14
58.72
48.30
46.
40.96
40.60
40.43
47.42
47.35
46.60
57.38
54.19
52. 30
50.56
50.59
K-medoids
58,89
45.56
41.11
48.52
52.18
55.81
45.15
41.67
46,077
56,00
50.20
SC
63.00
52.52
56.40
47.33
42.58
58.62
53.00
(Ours)
53.36
100%
34560
236
72.10
63.20
53.90
63.07
64.35
71.51
54.85
48.05
55.50
64:60
661.09
32
Frame Unilorm
71.00
56.00
48.89
58.63
67.73
70.93
53.
46.99
55.05
64:51
59.40
Pooling
63.33
50.89
46.00
53.41
60:32
62: 62.79
51.70
48.25
52.88
63.40
56.56
L
71.11
52.67
47.89
58.89
68.
73.26
53.15 36.16
49.47
$6.40
65.01
60.10
2-5
4320
256
DivPrune
72:40
59.00
19.60
50.80
60.67
60.95
69.25
72.67
73.76
56. 31
57.52
49.11
49.11
57.14
66.53
61.55 01.53
61.36
K-means
55.00
56.93
72.67
57:97
46.45
59:29
K-medkids
59.78
50.89
60.33
63.49
52.67
53.25
50 56
SC:
71.22
61.00
51.00
61.07
67.73
72.67
58.01
47.87
56.99
67.36
61.81
FLoC (Ours)
72.00
60:22
50.45
60.899
09:84
72.09
57.04
50.00
$7.82
67.77
62.16
16
Prame Uniform
67.22
53.00
47.22
55.81
64.55
70.93
54.37
46.81
54.75
61.30
57.22
Pooling
57.78
18.33
43.78
49.96
51,50
60.47
48.55 54
45.39
19.59
60.92
53. 53.19
7B
LongVU
899
2-
2100
256
DivPrune
K-mmnns
65.78
70.67
70.00
$2.89
57.00
47.22
50.33
59,30
55,30
65.61
66.14
72.67
70.93
56. 55
56. 36.80
46.81
47.16
02.82
55.57
56.10
63.62
62.76
59:67
59.68
88
K. medodds
69.67
56.89
50.78
59.11
61.38
65. 70
51.90
45.92
52.43
61.43
$7.66
SC
68.78
57.89
51.00
59.22
63.08
70.35
56.55
46.81
55.42
65.79
60.14
FLoC (Ours)
58.00
59.41
72.67
55.83
60.97
Frame Uniform
53.19
61.91
65.12
51.21
43.97
57.56
51.07
Pooling
47.23
47.81
43.01
46.67
LongVi
63.70
50.20
48.70
54.19
62.96
68. 05.12
50.97
43.50
51.76
57.61
54.52
2-
1080
256
TS-LLaVA
DivPrane
68.22
67.40
54.30
53.67
48.30
49.33
57.07
56.70
66.13
68.25
68.61
30.35
53.40
$3.64
47.54
46.81
54,90
54.67
61.66
61.22
57.75
57.65
K-menns
52.74
61.91
$3.40
45.04
K-meskidk
54.35
43.97
SC
$3.56
48.33
56.90 59
64.35
69 69.19
52.11
46.54
$3.70
63.26
57.85
FLoC (Ours)
69.33
35.56
48.22
57.70
61.35
69.77
54.37
47.34
51.82
64.23
58.92
divided into total of seven sub-categories: Temporal Recognition (TR), Action Recognition (AR), Needle Question Answering (NQA), Ego Reasoning (ER), Plot Question Answering (PQA), Action Order (AO), and Action Count (AC).
As demonstrated in the Table our proposed algorithm consistently achieved the best performance across all compression ratios for two specific tasks: Needle Question Answering (NQA) and Ego Reasoning (ER). The NQA task involves inserting a relatively very short video segment, with content entirely diffferent from the original video, into long video sequence and then posing questions about this inserted segment. The Ego Reasoning (ER) task predominantly features questions about the location or state of objects that appear fleetingly in videos recorded from first-person perspective (e.g., a user wearing a smart device while navigating daily life or performing tasks).
When conventional token compression methods applied to such tasks, critical information pertaining to these fine details can be easily lost during the compression process. However, the empirical results robustly demonstrate that our proposed algorithm maintains its effectiveness in these
16

---

Published as a conference paper at ICLR 2026
Table 6: Performance comparison on MLVU sub-tasks across different compression ratios. Our proposed method is highlighted.
Holistic
Single Detail
Multi Detail
Ratio
Methods
Overall
TR
AR
NQA
ER
PQA
AO
AC
Frame Uniform
80.68
65
54.37
47.16
56.03
41.7
26.7
53.66%
Pooling
81.44
52
59.15
47.16
57.7
47.1
32.52
54 94%
K-means
84.85
62.5
62.82
52.27
66.79
46.33
28.16
58.49%
K-medoids
85.98
58.87
50.28
56.59
43.63
27.67
55.82%
2-5
SC
86.74
65.5
61.13
52.84
64.75
45.17
30.58
59.40%
LongVU
80.68
61.5
52.96
46.59
57.51
42.86
27.67
53.61%
TV-L.LaVA
85.61
65
59.44
$0.85
61.78
44.4
27.67
57.52%
DivPrune
85.17
69
68.45
54.55
60.85
44.79
24.76
59.43%
Ours
85.17
65.5
71.27
56.25
66.79
45.17
23.3
61.22%
Frame Uniform
81.44
68.5
57.18 18
50.57
61.22
44.4
32.04
57, 20%
Pooling
82.95
64.79
48.58
61.41
48.26
30.1
57.56%
K-means
85,98
69.01
$4.26
34.47
K-medoids
87.12
63.5
62.82
52.27
44.79
59.17%
2-4
SC
88.64
70
67.04
54.55
72.17
50.97
33.01
64.05%
VU
84,47
66.5
57.18
50.57
59.37
44,79
29.61
56.74%
TS--LavA
85.61
73
65.35
52.56
66.98
50.19
28.16
61.52%
DivPrume
85.17
72.11
58.24
63.64
47.1
28.64
62.24%%
Ours
84.79
74.93
Frame Uniform
84.85
67.32
54.55
66.6
41.7
31.55
60.83%
Pooling
83.71
70.42
51.74
33.01
61.24%
K-means
84.85
73.52
60.51
73.65
44.66
66.59%
K-medoids
86.74
68
67.61
52.27
69.39
48.26
30.58
62. 11%
2-3
SC
86.74
73.5
70.99
56,82
72.91
54,05
36.89
66.07%
LongVU
86.74
74.5
67.89
54.55
64.94
49.03
35.44
62.57%
TS-LLaVA
85.61
72
72.39
55.11
72.17
49.81
37.86
65. 15%
DivPrune
85.17
71.5
74.08
60.8
68.27
50.58
33.98
65.00%
86.31
76.06
62.22
67.43%
challenging scenarios. Furthermore, it is evident that our algorithm's performance on the other tasks does not lag behind that of competing algorithms. This suggests that our approach not only preserves global contextual information but also minimizes the loss of crucial details.
subsequent subsection dedicated qualitative result analysis, we will delve into a more specific examination of the visual tokens selected by proposed algorithm.
17

---

Published as a conference paper at ICLR 2026
Dataset: MLVU, Task: Needle QA, Filename: needle_ 92.mp4
Q: What the woman waaring during the summer sunset?
winter coat
(B) dress and hools
swimsuit
(D) A and sunglasses
FLeC
Prediction: (D)
DivPrune
Prediction:
X
TSLLaVA
Prediction: (B)
Spectral Clustering
Prediction: (B)
X
X
K-means
Prediction: (D)
K-medoids
Pradiction: (B)
X
Dataset: MLVU, Task: Ego, Filename: ego. 16.mp4
W:Wee was the water bottle?
dining (B) In the red drawer in the kitchen
Under black
(C) On the white table the kibbd
(D) On the blue table the living room
Prediction: (C)
DivPrune
Prediction: (C)
TSLLavA
Prediction: (D)
Spectral Clustering
Prediction: (C)
X
K-means
Prediction: (D)
K-medoids
Prediction: (D)
X
X
Figure 8: The first and second examples of qualitative analysis.
18

---

Published as a conference paper at ICLR 2026
Dataset: MIVU, Taak: Ego, Filenere:: ego 38.mp4
O: Where was the yellow bag?
the drawer
(8) on the black cabinet top
under the white chair
(D) next to the blue vaser
FLOC
Prediction:
DivPrune
Prediction:
X
TS LLaVA
Prediction: (C)
Spectral Clustering
Prediction: (B)
X
K-maans
Prediction: (D)
k-madoids
Prediction: (C)
X
X
Dataset: MLVU, Task: Eggo, Filenames egoo _41 41.mp4
Q: Did leave the desktop on?
(A) Maybe
(B) Yes
(C) don't know
(D) No
FLoC
Pradiction:
DivPrune
Prediction:
TS LLaV
Prediction: (D)
Spectral Clustering
Prediction: (D)
X
X
K-means
Prediction: (D)
K-madolds
Prediction: (D)
X
X
Figure 9: The third and fourth examples of qualitative analysis.
19

---

Published as a conference paper at ICLR 2026
D QUALITATIVE RESULT ANALYSIS
To further substantiate the efficacy of our proposed visual token compression algorithm, we conducted qualitative analysis. This analysis specifically focuses on examples from the MLVU dataset, particularly the needle QA and ego reasoning tasks, where our method demonstrated pronounced performance gains. We meticulously examined four video-question pairs, comparing the token selection and prediction outcomes of our algorithm against baseline methods. For all experiments, compression ratio was uniformly set to 1/32. The first three examples were processed using the Qwen2-vl 7B model, while the final example utilized the Llava Next Video Qwen 7B model.
As illustrated Fig. and Fig. 9 these tasks present a significant challenge: they require the identification of minute details within long video sequences, often spanning hundreds of frames, where the crucial information for answering the question embedded only a few key frames. The visual tokens selected by each compression algorithm are highlighted with green bounding boxes overlaid on their corresponding patches in the video frames.
The results compellingly demonstrate algorithm's superior ability to pinpoint the decisive visual tokens essential for inferring the correct answer in all evaluated scenarios.
In the first example, our method successfully identified patches corresponding to the woman's sunglasses and hat, leading to the correct answer.
For the second example, the crucial visual tokens representing the water bottle on the white table were accurately selected.
In the third example, our algorithm focused on the yellow bag placed on the black cabinet.
The fourth example saw our method select patches depicting the powered-on monitor.
Consequently, our algorithm correctly answered all four questions.
In stark contrast, the baseline algorithms rarely selected the visual tokens corresponding these critical objects. While they occasionally managed to infer the correct answer by selecting nearby or contextually related tokens, they failed in the majority of these challenging instances. This observation underscores the baselines" limitations in preserving fine-graines details under high compression.
These qualitative findings strongly suggest that our proposed algorithm can effectively retain detailed visual information, even at extreme compression ratio such as 1/32. This capability is paramount for tasks that demand a granular understanding of visual content within extensive video data. The ability to isolate and preserve these "needle-in-a-haystan visual cues is a key differentiator of our approach.
E BASELINE IMPLEMENTATION DETAILS
This section outlines the implementation specifics and hyperparameter settings for the baseline algorithms used in our experiments.
K-means, K-medoids, Spectral Clustering: For these clustering- based approaches, we utilized the scikit-learn library, employing its default parameters. For k-means and spectral clustering, after determining the clusters, the representative token for each cluster was selected as the token closest to the mean of all tokens within that cluster. Due to significant increase in computation time with larger block sizes, the block size was set to for these methods.
LongVU: We implemented and utilized only the spatial token compression component of VUn excluding the query-based cross-attention mechanism. To ensure precise control over the compression ratio, which is not achievable with fixed similarity threshold, implemented an adaptive thresholding mechanism. This approach dynamically determines the appropriate threshold value to merge token pairs based their similarity, thereby achieving the target compression ratio.
PruneVID: We utilized only the first stage of the algorithm, which performs query-agnostic spatial-temporal token merging. To ensure fair comparison, the subsequent query-aware
20

---

Published as a conference paper at ICLR 2026
stage excluded. All experiments were conducted based on the official GitHub repository provided by the authors.
DyCoke: we adopted the query-agnostn compression component corresponding to Stage 1, specifically the visual token temporal merging module. The implementation was based the official GitHub repository provided by the authors.
TS-LLa VA: TS-LLaVA originally combines two strategies: creating thumbnails from raw frames and uniformly sampling visual tokens. However, in experiments with the selected benchmark datasets and backbone LLMs, incorporating the thumbnail generation aspect led degradation performance. Consequently, we only included the uniform token sampling component of TS-LLaVA in our baseline comparisons.
DivPrune:
Due to code compatibility issues with the officially provided GitHub repository, we re-implemented DivPrune based the pseudo-code presented its original publication. The algorithm was straightforward to implement from the provided pseudo-code. For our experiments, the block size for DivPrune was set to 32.
STTM and LLaVA Scissor: We used the official implementations provided by the authors kept their default settings, while threshold
for all benchmarks. Hyperparameters values were adjusted to achieve the desired compression ratio.
FastVID: We conducted experiments based on the official GitHub repository provided by the authors. Among the models tested, implementation was available only for the Qwen2.5-Vi VL model; therefore, experiments were limited to this model. We varied only the retention ratio while keeping all other hyperparameters at their default values.
T-SNE VISUALIZATION OF TOKEN DISTRIBUTIONS
While t-SNE visualization of the selected token distributions was included originally submitted manuscript, space constraints necessitated the use of smaller images. For enhanced clarity and easier inspection, we have attached larger versions of these visualizations in Fig 10
These visualizations demonstrate that the tokens selected by proposed method more uniformly cover the entire t-SNE distribution compared to those selected by other baseline approaches. Notably, while the DivPrune method also aims to select diverse tokens based on min-max distance criterion, its chosen tokens do not achieve the same level of even coverage across the entire distribution as observed with our algorithm. This suggests method is effective at capturing comprehensive and representative set of visual features.
G PROFILING OF COMPUTATIONAL AND MEMORY FOOTPRINT
Method
Inference (s)
Compression (s)
Total (s)
GFLOPS
VRAM (GB)
Full
3.22
2.69
27.33
FLoC
0.99
3.00
318
17.96
DyCoke
0.13
1.83
0.46
17.96
PruneVID
0.19
1.89
1.69
17.96
STTM
0,07
1.77
0.17
17.96
Scissor
1.24
2.94
783
17.96
FastVID
1.70
0.44
2.14
17.96
DivPrune
2,37
4.07
317
17.96
VU
0.38
2.08
317
17.96
kmeans
12.31
14.01
82
17.96
kmedoids
2.41
4.11
17.96
spectral
8.12
9.82
82
17.96
Table 7: Performance comparison of different methods
To compare the resource consumption and speed of our proposed algorithm against baseline methods, we measured LLM inference time, compression time, FLOPs, and peak VRAM usage. All experi- ments were conducted using the Qwen2.5-VI. 7B model NVIDIA H100 GPU, with compres-
21

---

Published as a conference paper at ICLR 2026
FLoC
DivPrune
TSLLaVA
Spectral Clustering
K-means
K-medoids
Figure 10: T-SNE plots for proposed and other visual token compression algorithms.
sion ratio of 12.5%. The reported times correspond to end-to-end processing of 784 frames, each con- taining 60 visual tokens. FLOPs were estimated by accumulating the actual operation counts during ex- ecution, with approximations for modularized components (which may introduce minor inaccuracies). Peak VRAM usage was recorded using PyTorch's 's torch. cuda wwx_mmaaxy......m located () function.
Overall, graph-based methods tend to achieve faster compression and require fewer operations, but-as shown in previous experiments-they exhibit inferior performance. In contrast, clustering- based methods incur significantly higher computational costs, resulting in slower processing speeds. Our proposed algorithm performs compression in less time than the LLM inference step, demonstrat- ing practical efficiency. However, exhibits relatively high FLOPs, most of which are attributed to pairwise similarity computations.
22

---

Published as a conference paper at ICLR 2026
H ABLATION STUDY ON SIMILARITY METRICS IN FACILITY LOCATION
To investigate the impact of the similarity measure on the facility location function, we conducted an ablation study comparing our default Cosine Similarity with Euclidean Distance. Since the facility location function requires similarity matrix, we converted the Euclidean distance into similarity measure using Gaussian kernel:
||x y/12 2x
Senc (I, exp
where a is set to the median of all pairwise Euclidean distances within the set, following the standard median heuristic. We performed experiments using the InternVI33 model across different compression ratios (1/8, 1/16, and 1/32).
Table 8: Performance with different distance metrics.
T
Metric
VideoMME
MLVU
LVB
EgoSchema
Average
Difference
Cosine 64.93
71.57
56.69
69.40
65.65
2-3
+0.55
Euclidean 64.26
72.26
58.49
69.80
66.20
Cosine 63.41
69.09
56.47
66.20
63.79
2
-0.27
Fuclidean 62.74
69.50
55.42
66.40
63.52
Cosine
60.81
66.93
54.23
63.80
61.44
2-5
-0.97
Euclidean 59.59
65,59
53.70
63.00
60.47
The results are summarized in Table g We observed that while the Euclidean-basct metric showed slight advantage (+0.55 accuracy) at low compression ratio (1/8). Cosine Similarity consistently outperformed Euclidean similarity as the compression ratio increased. Specifically, Cosine similarity achieved higher accuracy at 1/16 (+0.27) and 1/32 (+0.97) ratios.
This suggests that while Euclidean distance captures fine-grained magnitude differences useful when retaining many tokens, Cosine similarity is more robust for abstract feature space coverage, particularly in high- compresomeesio regimes where capturing the dominant semantic directions is crucial. Based on these findings, we adopted Cosine similarity the default metric to ensure consistent performance across varying degrees of compression.
I LIMITATIONS AND FUTURE DIRECTIONS
A key limitation of the proposed FLoC algorithm lies in the empirical determination of its sole hyperparameter: the block length (T). The choice T involves a critical trade-off that can impact both performance and computational efficiency.
Longer block lengths allow the algorithm to consider representativeness: and diversity a more extended temporal context. This can be advantageous for capturing the nuances of slowly evolving scenes. However, it also leads to proportional increase in computational overhead during the token selection process.
Shorter block lengths reduce the computational cost. However, they can introduce a risk of inter-block redundancy. For example, if long, statie scene is segmented into multiple short blocks, the algorithm might select very similar (or even identical) tokens from each block. This diminishes the diversity of the final selected set, redundancy is only minimized within each block, not across them.
This trade-off implies that the optimal setting for the block length is content-dependent. For instance, a static video (c.g., a lecture) might benefit from a longer block length, whereas a highly dynamic video with frequent cuts may be better served by shorter one.
promising direction for future work is to develop a method for automatically determining the block length. One could, for example, employ pre-processing step using scene detection algorithm. By aligning block boundaries with detected scene changes, the algorithm could dynamically adapt the block length to the video's temporal structure. This would not only make the framework more robust
23

---

Published as a conference paper at ICLR 2026
but could also further enhance performance by ensuring that each block represents semantically coherent segment, thereby mitigating inter-block redundancy and improving the quality of the selected tokens.
24


Reviews:
[{"review_id": "MhgLR4yrnd", "reviewer": "Reviewer_nZDL", "summary": "This paper proposes FLoC, which is a training-free, model-agnostic, and query-agnostic algorithm, for efficient visual token compression. Specfically, it aims to find the optimal token subset which maximizes the submodular objective function. To this end, it uses lazy greedy algorithm. Experimental results show that the proposed algorithm achieves good performance on Video-MME, MLVU, and LVBench.", "questions": "Please see the weakness section for my concerns. My main concern is about the novelty of the proposed method. If this concern is well addressed through the rebuttal, I would be willing to increase my score.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The motivation is solid and the paper is easy to follow.\n- The proposed algorithm achieves the good performance on various benchmarks such as Video-MME, MLVU, and LVBench.", "weaknesses": "- The citation is mis-formatted across the entire paper. For example, in L124-125, \\citep should be used in the LaTeX.  \n- In addition to Algorithm 1, a more detailed explanation of the optimal subset search should also be provided in the main text. \n- I think the novelty of the proposed method is somewhat limited. Sampling a token subset is not a new idea, and simply applying the well-known lazy greedy algorithm for this sampling seems to offer only a modest contribution. If there are additional aspects of novelty that I might have missed, I would appreciate clarification.\n- It would also be helpful to include a comparison of experimental results with the following papers.\n[1] Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs, ICCV2025\n[2] LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs (optional)\n- It would be good to include experimental results on datasets commonly used in this field, such as Next-QA or EgoSchema.\n- typo\n\t- L193: arg   max -> argmax", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZitFAe1RUj", "reviewer": "Reviewer_9yVb", "summary": "This paper proposes FLoC (Facility Location-Based Efficient Visual Token Compression), a novel framework designed to tackle the scalability limits of video-Large Multimodal Models (LMMs) when processing long video sequences. The core challenge addressed is the overwhelming volume of visual tokens generated from extended videos, which makes end-to-end processing computationally infeasible given the limited context lengths of most LLM-based architectures.", "questions": "See weakness above, please.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Submodular Optimization via Facility Location: FLoC is the first visual token compression algorithm based on the facility location function and submodular optimization for long video understanding. This interprets token selection as maximizing a utility (or coverage) function that rewards tokens for preserving the essential information and diversity of the entire visual token set within a strict budget constraint (K).\n2. Targeting Rare Information Loss: The facility location framework is specifically motivated to overcome a key limitation of clustering methods: their tendency to fail in capturing rare but important tokens (e.g., small objects like car keys) because they focus on densely populated regions in the feature space. FLoC explicitly optimizes for both representativeness and diversity simultaneously.\n3. Lazy Greedy Efficiency: The implementation utilizes the lazy greedy algorithm to efficiently approximate the optimal NP-hard solution. This approach significantly reduces computational overhead by postponing the update of marginal gains until necessary, offering a novel and practical way to handle massive token sets.", "weaknesses": "1. The primary operational weakness of FLoC is the reliance on the empirical determination of a single hyperparameter: the block length ($T$). The paper explicitly states that the choice of T involves a critical trade-off that impacts both performance and computational efficiency. The optimal setting for the block length is acknowledged to be content-dependent; for instance, a static video (e.g., a lecture) benefits from a longer block length, while a highly dynamic video requires a shorter one.\n2.  FLoC is designed as a query-agnostic and model-agnostic solution. While this offers advantages in memory efficiency (performing compression once) and flexibility, it fundamentally limits the model's performance in real-time, query-specific tasks or dynamic interactive environments. Query-aware compression methods, which FLoC contrasts itself against, \"can effectively reduce the search space by focusing on what is deemed important\" to the user.\n3. FLoC utilizes the lazy greedy algorithm to approximate the maximization of the facility location function, which is NP-hard. While this is significantly faster than naive greedy and traditional clustering methods (e.g., 10× faster than K-Means in compression time), the complexity remains dependent on both the initial number of tokens ($n$) and the budget ($K$), scaled as $O(n⋅K)$.\n4. The facility location function relies on the cosine similarity between token embeddings ($sim(v,u)$) as its similarity measure. Cosine similarity effectively measures angular distance, focusing on the direction of feature vectors, which is suitable for abstract feature space coverage. However, it may not perfectly capture complex perceptual differences or contextual relationships that could be better represented by other distance metrics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QUy3S7AymY", "reviewer": "Reviewer_nG1P", "summary": "This paper proposes a facility-location-based efficient visual-token compression framework named FLoC for long video understanding. Specifically, FLoC includes two core modules: a Visual Token Selection Module and a Compressed Embedding Module, optimized through a lazy-greedy algorithm. The Visual Token Selection Module tokenizes videos into visual tokens, leverages the submodular facility-location function to measure token representativeness and diversity, and compresses them into compact embeddings. The Compressed Embedding Module concatenates the selected token embeddings with text prompts and feeds them into video-LMMs for downstream tasks. The entire process is guided by the greedy selection strategy, ensuring semantic fidelity and efficient compression without fully decoding the video.\n\nThe experimental evaluation in this paper assessed the performance of the proposed FLoC across three compression ratios and three video understanding benchmarks, comparing it with state-of-the-art baselines. The results indicate that the proposed FLoC achieves superior accuracy while reducing sequence length, thereby providing computational and memory efficiency gains.", "questions": "Please refer to Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The compression process does not rely on specific model architectures, nor is it tailored to particular queries or tasks; a single compression can support multiple downstream applications.\n- A visualization video of the lazy-greedy algorithm is provided in the supplementary material, making it more persuasive.\n- The experiments are sufficient, with the method’s performance verified on multiple models and benchmarks.", "weaknesses": "- The method performs diversity selection within individual blocks, with no information interaction between blocks. Therefore, the choice of hyper-parameter T is crucial: a T that is too large may cause redundant computation, while a T that is too small may lead to redundant similar tokens across blocks. In Tab. 4, the optimal T differs under different compression rates, making it difficult to select an appropriate T across settings with different models, compression rates, and benchmarks.\n- FLoC aims for “global coverage,” which may cause extremely sparse but important tokens (e.g., a key object appearing in only one frame) to contribute very little to the total sum and risk being discarded, thereby affecting performance on needle-in-a-haystack tasks.\n- There is no comparison with graph-based video LLM compression methods such as FastVID or LLaVA-Scissor.\n- Only computation times of FLoC and traditional algorithms are compared; FLOPs and actual benchmark latency versus other compression methods are not reported.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BfOZ0w4OFq", "reviewer": "Reviewer_kqKm", "summary": "This paper introduces FLoC, a training-free and model-agnostic visual token compression framework for long video understanding. By using a facility location function and a lazy greedy algorithm, FLoC efficiently selects a compact and representative subset of visual tokens, significantly reducing token volume while maintaining near-optimal performance. Extensive experiments show that FLoC outperforms recent compression methods on large-scale benchmarks in both effectiveness and processing speed.", "questions": "1. As shown in Table 3, basic clustering methods already achieve comparable performance. Does this suggest that sophisticated or complex designs may not be necessary?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The objective of the paper is well-motivated and clearly stated.\n\n2. The proposed method achieves state-of-the-art performance and efficiency in nearly all cases.\n\n3. The authors conduct comprehensive experiments across multiple benchmarks and baseline MLLMs.", "weaknesses": "1. The authors should evaluate throughput, inference speed, and GPU memory usage to further highlight the efficiency of the proposed method compared to existing approaches.\n\n2. In the comparison experiments, it is surprising that PruneVid performs worse than random selection, given that PruneVid employs both clustering and a query-aware selection algorithm. Some discussion or analysis of this result would be helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)