AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models
Authors: Changwoo Baek
Jouwon Song
Sohyeon Kim
Kyeongbo Kong
Venue: ICLR 2026 Poster (2026)
Keywords: Large Vision-Language Models
Visual Token Pruning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive visual token sequences. While prior works primarily focus on either attention-based or diversity-based pruning methods,  in-depth analysis of these approaches' characteristics and limitations remains largely unexplored. In this work, we conduct thorough empirical analysis using effective rank (erank) as a measure of feature diversity and attention score entropy to investigate visual token processing mechanisms and analyze the strengths and weaknesses of each approach. Our analysis reveals two insights:  (1) Our erank-based quantitative analysis shows that many diversity-oriented pruning methods  preserve substantially less feature diversity than intended; moreover, analysis using the CHAIR  dataset reveals that the diversity they do retain is closely tied to increased hallucination 
frequency compared to attention-based pruning.  (2) We further observe that attention-based approaches are more effective on simple images where  visual evidence is concentrated, while diversity-based methods better handle complex images with  distributed features.  Building on these empirical insights, we show that incorporating image-aware adjustments into existing hybrid pruning strategies consistently improves their performance.  We also provide a minimal instantiation of our empirical findings through a simple adaptive 
pruning mechanism, which achieves strong and reliable performance across standard benchmarks as  well as hallucination-specific evaluations. Our project page available at https://cvsp-lab.github.io/AgilePruner.

Published as a conference paper at ICLR 2026
AGILEPRUNER: AN EMPIRICAL STUDY OF ATTEN- TION AND DIVERSITY FOR ADAPTIVE VISUAL TOKEN PRUNING IN LARGE VISION-LANGUAGE MODELS
Changwoo Back Jouwon Song"* Sohyeon Kim Kyeongbo Kong'!
Pusan National University, (higok18, 88, shkim0503, kbkong (bkonng@puuee @pussn.ac. ww..snn.c.k... 2LG Electronics, juwcn05.songe com
ABSTRACT
Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive visual token sequences. While prior works primarily focus on either attention- based or diversity- pruning methods, in-depth analysis of these approaches characteristics and limitations remains largely unexplored. In this work, we con- duct thorough empirical analysis using effective rank (erank) as measure of fea- ture diversity and attention score entropy to investigate visual token processing mechanisms and analyze the strengths and weaknesses of each approach. Our analysis reveals two insights: (I) Our erank-based quantitative analysis shows that many diversity-oriented pruning methods preserve substantially less fea- ture diversity than intended; moreover, analysis using the CHAIR dataset re- veals that the diversity they do retain is closely tied increased hallucina- tion frequency compared to attention-basel pruning. (2) We further observe that attention-based approaches are
effective simple images where visual evidence is concentrated, while diversity-based methods better handle complex images with distributed features. Building on these empirical insights, we show that incorporating image-aware adjustments into existing hybrid pruning strate- gies consistently improves their performance. We also provide a minimal in- stantiation of our empirical findings through simple adaptive pruning mech- anism, which achieves strong and reliable performance across standard bench- marks as well as hallucinationn-peecffe evaluations. Our project page available at actps://cves lab. github. 10/ Agilee
INTRODUCTION
Large Vision-Languag Models (LVLMs) (Liu et al., 2023 Wang et al. 2024 Liu et al. 2024a
have garnered significant attention by integrating various modalities such images, text, and video to achieve human-level vision-language reasoning capabilities. In particular, visual information is encoded into token embeddings that can be processed by language models, generating hundreds of visual tokens in this process. The increase in the number of these tokens causes the complexity of attention- based computations to scale quadratically, significantly impacting inference speed and efficiency.
To address these issues, numerous researchers have attempted to reduce computational costs by removing unnecessary
redundant visual tokens through token pruning methods (Xing et al. 2024 [Chnn et al.] 22244 Zhang et al., (0255). These existing methods typically employ two main methods. The first is attention-based methods (Zhang al. 2024 Yang al. 2025; Arif et al.) 2025), which consider tokens with high attention scores as important information and remove the methods (Alvar al.) (025). which reduce redundancy based on feature similarity between visual tokens. Each approach exhibits distinct tendencies. Attention-baa -based methods prioritize the preservation of highly weighted tokens, which can result in concentrated but sometimes repetitive selections. In contrast, diversity- based methods encourage broader coverage,
rest. The second is diversity-basse
"Equal contribution
1 "orresponning author

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Published as a conference paper at ICLR 2026
often at the cost of overlooking important tokens. There have also been attempts combine these two strategies through hybrid schemes (Zhang et al. 2025a Shang et al. 2025
Despite the emergence of actual behaviors remain insufficiently characterized. In particular, (i) how much feature-space diver- sity these methods truly preserve and (ii) how the retained-toker properties influence hallucination tendencies in LVLMs have not been systematically examined. Furthermore, (iii) it remains unclear whether different image types naturally favor attention-based or diversity-based pruning strategies.
siveesstt-bbssed and hybrid
strategies, their
To clarify these aspects, we conduct a two-part empirical analysis, First, we characterize the intrinsic their retained diversity using effective rank (erank) (Roy& & Vetterli| (2007) and examining how this relates to hallucination patterns across image types, Second, we analyze how pruning effectiveness shifts with image-level complexity, revealing when each paradigm is preferable.
behaviors of existing pruning paradigms-quantiin
Using erank and attention-eninn entropp,, our analysis shows that:
(Method-level behavior: diversity hallucination) Many diversity-awan pruning meth- ods preserve substantially less diversity than intended. More importantly, higher retained diversity is strongly associated with increased hallucination frequency (CHAIR (Rohrbach) et al.) 2018)). whereas attention based pruning- which retains lower-diversity token sets produces more conservative outputs with suppressed hallucinations.
(Image-level behavior: complexity-dependen preference) Attention-based pruning is more effective on simple images where essential cues are concentrated in a small number of tokens, while diversity- -ased pruning excels on complex images where semantic infor- mation is widely distributed.
Leveraging these empirical findings, we examine whether the observed behaviors can be trans- lated into practical improvements. By applying the identified tendencies to existing pruning strate- gies-including hybrid approaches as well as direct mixtures of attention- and diversity-based meth- ods ods-we incorporate a simple image-aware adjustment derived from our analysis. This adjustment consistently improves performance across benchmarks, suggesting that the empirical patterns un- covered in our study are robust and broadly applicable.
We further introduce a simple threshold-bant based pruning procedure that operationalizes the empirical behaviors identified above. The method explores tokens in descending attention order and removes redundant tokens based on similarity, using an adaptively set threshold informed by image-level complexity. Although intentionally minimal in design, this instantiation achieves strong performance across nine standard datasets often matching or surpassing existing pruning methods- and, con- sistent with empirical analysis, it further mitigates hallucination tendencies validated on the CHAIR benchmark. These results show that the empirical principles revealed in our study are not only explanatory but also practically effective. Moreover, we observe the same behavioral trends and improvements across larger and architecturally different LVLMs, including LLa LLa LLaVA-NeXT-7B, and Qwen2.5-VL-7B, indicating that the discovered principles robust and model-agnostic.
In summary, our contributions are three-fold:
We provide the first trank-based characterization of how existing pruning methods preserve feature diversity and how this retained diversity relates to hallucination behavior.
We reveal a consistent and diversity- pruning, explaining when each paradigm succeeds or fails.
preference between attention- based
We show that these empirical principles are actionable by improving existing pruning meth- ods and by presenting a minimal adaptive instantiation that achieves strong, consistent per- formance.
2
RELATED WORKS
Large Vision- Languag Models
The advances Large Language Models (LLMs) (Bai et al.
2023 Touvron et al. 2023 Yu et al. 2024 Cai et al. 2024 have extended to LVLMS (Liu al.
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Wang al. 2024 Liu et al. 2024a), which now widely applied across domains for rea- son over diverse modalities, Among them, LVLMs specialized for vision vision-language integration, have drawn particular attention. A typical LVLM architecture comprises vision encoder (Radford et al. 2021 T'channen et al., 2025). a modality projector, and an LLM. The vision encoder converts input images into visual tokens, and the projector aligns these tokens with the LLM's word-embedding space so that the LLM can effectively interpret and process visual information. However, visual tokens are not only numerous but also highly redundant-a tendency that becomes even more pro- nounced with high-resolution images or video inputs. Combined with the autoregressive nature of the LLM decoder, this leads substantial drop inference efficiency.
2023
Visual token reduction Reducing redundant and unnecessary visual tokens is an effective way to decrease computation and memory usage, thereby improving the inference efficiency of L.LMs. In particular, many studies have adopted token pruning methods that require additional training, and these methods can largely be categorized as follows. (i) Attention- based method: These methods prune visual tokens by leveraging the attention distribution in the penultimate layer of the vision encoder before the tokens are fed into the LLM (Zhag et al. 2024, Shang et al.) 2025 Yang et al.) 2023 Zhang al. 2025a Arif et al., 2025). Based on the observation that image information in the vision encoder tends to concentrate on a small set of key tokens, these methods utilize attention scores from the output layer to select limited number of tokens that aggregate global information.
However, they tend to retain similar tokens concentrated in specific regions, which results in insuf- ficient diversity fully represent the entire token set. (ii) Diversity- method: These methods leverage inter-token similarity to enhance the diversity of the selected token set (Alvar et al. (025), thereby encouraging the selection of more diverse tokens. However, they introduce additional com- putational overhead and risk discarding important tokens. There have been recent efforts to merge these two strategies through hybrid pruning schemes (Zhang et al., 2025a Shang et al. 2025). These approaches represent the dominant strategies for pre-pruning, and alongside them, there has also been increasing interest in dynamically determining the number of tokens to retain, as explored in recent work such ATP-LLaVA (Ye et al. 2025).
PRELIMINARIES
Visual token pruning. Since vision encoders often produce hundreds of tokens, passing all to- kens into the LLM incurs heavy computational cost. Visual token pruning addresses this issue by removing redundant tokens and retaining only K < N tokens. Formally, pruming defines function I such that V g(V, K), where V denotes the retained subset. Among existing methods, focus on two categories: (i) attention-based, which select tokens according to [CLS] token atten- tion scores, and (ii) liverrity-basedd which aim to reduce redundancy and preserve feature diversity. These strategies provide the foundation for our subsequent empirical analysis. Fig. | provides an overview of our analytical framework, which evaluates the vision encoder from two perspectives: attention concentration and token embedding diversity.
Attention concentration via attention entropy. To assess the concentration of attention within the vision encoder, we compute the Shannon entropy of the class token's attention score. Given the head- averaged attention score E R obtained from the penultimate layer. we exclude the self-attention score of the class token and renormalize the remaining score into valid probability distribution:
LLM
Text tokena
Visual
Metrics
High Effective Rank :ligh Embedting divencity
DOOO
Effective Rank of inven emoessiggs
Tokenizer
T
Projector
The Diversty Token Enseddings
Low Effective Rank Low Embedding diverity
Text X
DOODoY
High Entropy Dispersed Attention
Vision Encoder Ey Attention
Entropy (C Amsition
Image
Amtettion
alion
Low Entropy Concentratec Attention
Pi ΣjtCss Σp=11 (1)
Figure I: Overview of attention entropy and erank.
The entropy is then computed as
H(p)
Σ ps log
The entropy value (p) quantifies how attention is distributed across tokens: a lower value indicates that the class token attends strongly to a few regions, whereas higher value suggests more uniform
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distribution over multiple visual tokens. We refer to this measure attention entropy in the rest of this paper.
Token embedding diversity via erank. To quantitatively assess the diversity of token embed- dings, we adopt the notion of erank (Roy & Vetterli] 2007). Unlike the conventional matrix rank, the erank an entropy- bbase measure that evaluates the number of dimensions effectively utilized by matrix.
Given a token embedding matrix E RNxd, we first obtain its singular values {a,} via singular value decomposition (SVD). Let
= min Wi),
di E RR..
(3)
Σj=1
j=
The erank is then defined as
erank(A) = exp Σ qi log qi
The value of erank( ranges between 1 and low erank indicates that the embedding rep- resentation is concentrated in few dominant dimensions, whereas a high erank suggests that the embedding space more evenly distributed across multiple dimensions.
EMPIRICAL STUDIES
This section presents empirical analyses of attention- and diversity-based token pruning meth- ods. We focus two aspects: the diversity preserved by existing methods and its relation to halluci- nations (Sec. 4. the impact of image complexity on token selection strategies (Sec. 4.2 Building on the insights from these two analyses, then introduce simple adaptive pruning framework (Sec.
4.1 EMPIRICAL ANALYSIS OF INTENTION-BASHE AND INVERSIT--AAER PRUNING
To better understand the intrinsic behaviors of existing pruning paradigms, we conduct an empirical study using erank, which captures the diversity of token-level visual features. By analyzing these signals on the token sets actually selected by different pruning methods, we aim characterize how much semantic diversity each approach preserves and how these differences in diversity ultimately influence the behavior as revealed through hallucination analysis.
4.1.1 ANALYZING DIVERSITY PRESERVATION IN EXISTING PRUNING PARADIGMS VIA FRANK
Quantitative Comparison of Diver- sity Preservation via erank We be- gin by assessing how well existing pruning methods preserve semantic di- versity by measuring erank (see Ta- ble. D). This comparison reveals a clear structure across approaches. DivPrune (Alvar et al. 2025) achieves the highest mean erank (21.84), reflecting its explicit objective of maximizing geometric dispersion among tokens. At the other end of the spectrum, PruMerge+ (Shang al. 2025) records the lowest erank (10.91), indicating lim- ited diversity under its ppatil--samplin strategy. VisPruner (Zhang et al., 2025a) (14.35) and Vi- sionZip (Yang et al.) (025)) (14.02) form mid-range group with similar diversity levels levels-higher than PruMerge+ but still lower than that of DivPrune. These results show that, even under the same token budget, different pruning strategies retain markedly different degrees of semantic diversity.
PruMerge+
VisionZip
VisPruner
DivPrune
Erank
10.91
14,02
14.35
21.84
Table
Mean erank of retained 64 tokens on POPE.
Relative Strengths of Diversity Mechanisms Across Methods A closer examination of these methods clarifies this pattern. Although VisPruner, VisionZip, and PruMerge+ all adopt strategy that prioritizes tokens with high attention scores, the strength of their auxiliary diversity mechanisms
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DivPrune
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Figure 2: Response patterns of DivPrune (diversity-basee) vs. FasterVLM (attention-basee), DivPrune's ' responses are more comprehensive but risk hallucination, whereas Faster VLM produces safer, more focused descriptions. In the annotations, GT Obj. and Hallucinated Obj. label object words; marks Prune----picifficc phrasing; red text indicates incorrect phrases.
differs substantially. VisPruner removes feature-redundant tokens before applying attention filtering, providing a more direct form of diversity preservation and resulting in slightly higher erank among the attention-drive driven approaches. In contrast, VisionZip's 's token merging and PruMerge+'s spatial sampling introduce only limited dispersion, leading to lower overall diversity. Nevertheless, all three remain fundamentally constrained by their reliance on attention-guice -uiiedd token selection, which in- herently limits the semantic diversity they can retain compared to a method like DivPrune whose primary objective is to maximize geometric dispersion.
4.1.2 THE RELATIONSHIP BETWEEN PRUNING METHODS AND HALLUCINATION
Object hallucination occurs frequently in LVLMs and critical issue that undermines their re- liabiliiy. In this section, we compare and analyze the characteristics of attention cooe-bbase and diversity-based methods from the perspective of hallucination, aiming to identify how the two prun- ing methods differ in inducing hallucinations. To this end, we evaluate object hallucination in the LLaVA-115-7B model using not only the datasets commonly employed in prior token reduction studies but also additional datasets, and we present the corresponding results.
Object hallucination. To assess the degree of object ballucination in the image captioning task, we employ the CHAIR dataset. CHAIR quantifies the proportion of objects mentioned in generated captions that are absent in the ground-truth annotations, providing two sub-metrics, Cj and Cs. as defined in Eq. [}
C₁ = [hallucinated objects]|
Cs = |ccaptions with hallucinated objects}|
(5)
[all mentioned objects}|'
Hall captions}|
Each metric evaluates hallucination at the instance level and the sentence level, respectively, and lower values indicate better performance. As auxiliary metrics, we also report recall and len, where recall denotes the proportion of ground-truth objects mentioned in the gen- erated captions, and len represents the average number of words in the generated captions.
Results on the CHAIR dataset. Table 2 presents the results of the attention- based and diversity-based methods on the CHAIR dataset. Despite small dif- ferences in Len, the diversity-based based methods exhibit higher values of the hallucination metrics Cs and C compared to the attention-based methods, sug- gesting that selecting diverse tokens with low fea- ture similarity may increase the likelihood of hal- methods
Method
C,
C, 1 Recallf
Len
LLaVA-I.5-7R
$1.0
13.9
78.7
101.4
Attention bused methods
FasterVI M (arXiv'24)
45.4
13.5
69.3
94.0
PruMerge+ (ICCV'25)
45.2
15.6
66.7
91.4
Vispruner (ICCV'25)
49.8
15.0
72.6
96.7
Diversity methods
DivPrune (CVPR'25) FPSPruner'
$7.4
18.0
76.4
101.1
lucination. In contrast, the diversity-base achieve higher recall, indicating that they are able capture a larger number of objects than the attention- based ones.
58.6
18.6
76.0
100.5
Table 2: Comparison on CHAIR. FPSPruner is based on farthest point sampling (FPS), which iteratively selects the farthest token to guarantee diversity.
Comparison of the two methods in terms of response patterns. The two methods also differ in their response patterns. Fig. illustrates this distinction, showing that the diversity-based method
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(4)
(b)
Simple Images
Complex Images
Mean Erank VS SQA
Moan Erank VS POPE
60.5
Scowe
00.0
00.1
08.0
70
0
HO
70
Mean Frank
Frank
Figure 3: Diversity vs, attention in pruning across datasets and image complexities. (a) High- erank methods perform better on complex datasets (POPE), while low-erank methods excel sim- ple datasets (ScienceQA). (b) Simple images show low entropy and erank, leading to concentrated attention suitable for sreettoo-bbsed pruning. Complex images show high entropy and erank, where diversity-based pruning becomes more effective.
DivPrune generates broader and more open-ended descriptions and often includes speculative ex- pressions, as highlighted in yellow. In addition, the diversity- response refers to larger set of ground-truth objects marked in green, but the same time also introduces hallucinated ob- jects highlighted in red and incorrect phrases emphasized in red text. In contrast, the attention- based method FasterVLM focuses on the main objects and provides more conservative and reliable expla- nations, thereby suppressing hallucinations that frequently appear in diversity-based outputs.
Effeet of attention-based selection on object hallucination. Based on the ob- servation that attention-based token selec- tion tends to reduce hallucination, we con- ducted experiments to quantitatively as- sess the effect of attention basedd selection on hallucination by varying the balance between diversity-based and attention- based selection.
Method
C,
C₁
Recall
Len
Mean crank
Mean
Retain 64 Tokens
R=0
57.4
18.0
76.4
101.1
21.14
0.0035
R=0.25
50.8
16.8
74.5
97.6
14.98
0.0065
R=0,50
46.2
14.5
95.5
0.0072
R=0.75
45.2
14.1
70.5
94.0
13.58
0.0076
Table 3: Effect of attention-based selection ratio R CHAIR metries. Higher
selection reduces hallucination (Cs, Cy) but lowers recall.
As shown in Table 3, we fixed the token budget at 64 and gradually reduced the number of tokens selected by the DivPrune, while replacing them with tokens having higher attention scores. The experimental results demonstrate that increasing the proportion of attention-based selection leads to gradual decrease in the hallucination metrics Css and Cj.
These findings suggest that selecting tokens solely based on diversity can induce relatively higher hallucination, whereas tokens with high attention scores, which concentrate critical information, play pivotal role in generating reliable captions and mitigating hallucination.
4.2 ANALYZING SAMPLE- DDEPENDETT EFFECTS OF PRUNING STRATEGIES
We previously examined how different pruning methods vary in the degree of semantic diversity they preserve, as measured by erank, and how these differences relate phenomena such as hal- lucination. Building on these observations, we now investigate how such characteristics influence model behavior in downstream reasoning tasks.
When diversity helps and when attention-based selection prevails. Our experiments show that neither seversity-based pruning nor
pruning is universally superior. As illustrated in 3 we observe clear dataset-dependent pattern: methods that retain more diverse tokens (i.e., higher erank) tend to perform better on datasets such as POPF, whereas attention- based approaches achieve higher accuracy on datasets like ScienceQA. Motivated by these contrasting trends, we further analyze which types of image samples favor diversity-basee methods and which benefit more from attnetion-based token selection.
Image complexity affects attention entropy and diversity. We first analyzed how image com- plexity affects LVLMs. To this end, we measured the concentration of attention using attention entropy and assessed the diversity of token features using erank. The analysis was conducted on LLaVA-v1.5.. using the MME Benchmark Yin et al. 2024), where tasks such OCR, Numer- ical calculation. and Text translation involve images with plain backgrounds and few key objects, whereas tasks such as Position, Scene, and Count involve images with mixed backgrounds and mul-

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MME
Method
MME
POPE
Method
Numerical Cal.
Text Translation
ScienceQA
Position
Scene
Count
OCR
Metric
Metrie
Att. entroyy
4.61
4.47
4.39
4.45
Att. entropy
4.90
4.86
4.82
4.87
Brank
78
58
49
74
Erank
109
103
102
106
Scores after pruning 576 - 64 tokens
Scores after pruning 576
64 tokens
Att. based
140
55
100
69,51
Att. based
105
157
120
77.4
Div. based
130
40
67.53
Div. based
111
168
140
86.0
(a) Results datasets with simple images.
(b) Results on datasets with complex images.
Table 4: Attention entropy and erank on simple and complex image datasets. Simple images exhibit lower entropy and erank, while complex images show higher values, and the two pruning methods show contrasting performance between simple and complex images.
Method
GQA
SQ4 TM
POPE
MME
Rel.
6Q4
SQ4
POPE
Tokeas
575 Tolluu
LLAVA 15 7B
6L.9
69.5
$5.9
1862
100.0%
1aVA-115-78
61.9
68.5
85.9
1962
100.0%
Brinin 128 Toiess
(CVPR'2)
Retule 128 Tokeus
Insers
68,
1734
96.46%.
Disprane (UVV98225)
$9.4
68.6
87.0
1907
M0A
BAT Ours
58.8
69.4
96.8
1782
97.91%
Fase VLM (Adovv 24)
$7.9
65.5
83.2
1757
95.80%
Vigranzz (ICCV225)
96,
Vigranzr+ + Inverse
$7.9
85, 3
1244-23
96.356.
Dispran PasterV1.M
58.4
84.9
Visprnne- + Ours
88.6
69.1
85.5 os
1787
90.32%
Disprune Vas (adaptive)
86.0
Retain 64 Tokous
Rokone
(CVPR
81.5
93.01%
Disprane (CVPR'25)
$7.5
85.5
94.25%
BAT Inverse
66.7 68.7-11
79.7-1a
1602
91.98%
RAT Ours
56.9
69.1
R35
1692
94.85%
FoserVLM (Arkev
77.4
91.91%
Visgraner (CCVV225)
92.78%
Dispeane FasterVLM
56.8
68.6
82.2
1681
94.00%
Vingaane + faverve
55.2
66.9
78.0
1643.
91,8316
Divprune EasterVIM (adaptive)
57.2
68,9
85.5
1690
94.87%
Vispruner + Ours
S5.M
69.3
$1.5
1071
93.76%
Table 6: Adaptive combination of attention- Table 5: Performance results of combining and diversity-based pruning outperforms fixed diversity- and attention-based pruning methods. schemes.
tiple objects, making them relatively complex. In addition, we included ScienceQA (simple) and POPE (complex) from external benchmarks for a more comprehensive analysis. Indeed, as shown in Table simple images exhibited lower attention entropy, as in the case of OCR (4.61), where the vision encoder could readily concentrate information into a few dominant regions. In contrast, com- plex images such as POPE reached higher values (4.87), indicating more dispersed attention across multiple regions. Consistently, simple images also showed lower erank, such as OCR (78), while complex images reached higher values, such as POPE (106), reflecting redundant versus diverse token representations.
Performance divergence by image complexity. Our analysis reveals that the performance of these two approaches diverges depending dataset characteristics, as shown in Table 4 As result, diversity-based methods outperformed in high erank tasks, while attention-based methods were superior in low erank tasks. This performance reversal appears to be driven by differences in image complexity. As illustrated in Fig. 3 (b), simple images allow the vision encoder to eas- ily concentrate attention on specific regions, leading concentrated information. In such cases, attention-based methods can effectively select these concentrated tokens. In contrast, complex im- ages contain multiple objects and mixed backgrounds, causing information to be dispersed across the entire image. In this scenario, diversity-baser methods that capture a broader range of features become more effective.
4.3 FROM EMPIRICAL INSIGHTS TO ADAPTIVE TOKEN PRUNING
Analysis-driven enhancement of pruning methods. Sections 4.1 and 4.2 revealed two consis- tent empirical patterns: (i) different pruning paradigms preserve distinct levels of feature diversity and exhibit characteristic hallucination behaviors, and (ii) image complexity systematically deter- mines whether attention- or diversity-oriented selection is preferable. To examine whether these findings are practically useful, we first apply them to existing pruning strategies. Whereas hybrid methods such as VisPruner (Zhang et al. 2025a) and BAT (Long et al.) 2023) ratios, our approach maps erank to a linear weighting function, assigning a larger proportion of attention-base based tokens for low-erank (simple) images and larger proportion of diversity- to-
fixed mixing
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Attention-Base Based
Token Selection under Distance Thresholds
Diversity-Baser -Based
Selection
Prune tokens with d <
Selection
Attention Score Hank)
Attentoo Score Ranit
Attenion Score Rank)
Attenton Score Kank)
Tal
1st
3nc
3nd
Ind
2nd
Add
2nd
1th
N/th Nh
Nth
Nin
Strict Threshold
Mah
Loose Threshold
High-AAtention Tokans
Diverse Tokens
threshold
#5
Figure 4: Effect of similarity threshold 7 on token selection. A low (strict) prioritizes high- attention tokens, while a high (loose) T increases the diversity of the selected tokens.
kens for high-erank (complex) images. Applying this adaptive rule to VisPruner and BAT leads to consistent accuracy improvements across both 128 and 64 token settings, as shown in Table Con- versely, an inverse adaptation- which deliberately assigns more importance tokens to high-erank images-result in a clear performance drop across benchmarks. Furthermore, as shown in Table we also applied the same adaptive rule to pairwise combination of a purely attention- based method and purely diversity-basee method, FasterVLM + DivPrune. Even this setting, the adaptive rule achieves higher accuracy than fixed, non-adaptive combination.
Empirically guided adaptive similarity thresholding. We further introduce simple threshold- based pruning procedure that operationalizes the empirical behaviors identified above. Our method iteratively selects high-attention tokens and prunes similar neighbors, thereby modulating the diver- sity of the final token set based on the chosen threshold. The process as follows:
1. All tokens are sorted in descending order based on their attention score.
2. Starting with the highest ranked token, we select it and then prune all other candidate tokens whose cosine distance d to the selected token is smaller than an adaptive threshold 7;-
3. The process moves to the next highest-ranked token that has not been pruned and repeats the pruning step until the desired number of tokens is selected.
In this framework, the threshold T is the parameter that directly governs the diversity of the final token set. illustrated Fig. 4 applying low (strict) threshold results in the pruning of only few, highly similar tokens. Conversely, a high (loose) threshold removes wider range of similar tokens, constructing a final token set with greater diversity. Detailed quantitative experiments verifying this correlation between and token diversity (measured via erank) and performance under different image complexity levels are provided in Appendix B.4
However, the optimal level of diversity is dictated by the image's 's internal characteristics, leading to contrasting outcomes. For images with low erank & attention entropy(i.e., concentrated infor- mation), where critical information is focused in high-attention tokens, even highly similar tokens can contain vital fine-grained details. Consequently, aggressive pruning with a high threshold (r) degraded performance, whereas conservative low threshold proved more effective. In contrast, for images with high erank & attention entropy (i.e., dispersed information), where information is more distributed and redundant, higher threshold improved performance by effectively eliminating this redundancy and facilitating the selection of more diverse token set.
Building on these empirical insights, we formulate statistics driven adaptive strategy that intrin- sically adjusts to image complexity. To directly translate our findings into robust mechanism, we define the dynamic threshold Ti based on the normalized image complexity:
crankinput erankavg
order, x
x 0.01
(6)
where order, represents the rank of the token (1-based), and erank is the average effective rank of the LLa VA training set. To ensure stability, the final threshold is capped by a statistical upper bound Tmaz- This formulation naturally embodies our findings through the following mechanism:
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Method
VQA'
GQA
VizWiz
SQA
SQAIMc
TextVQA
POPE
MME
MMB
MMB
Average
Vanila 576 Tokens
LLaVA-1.5-7B
78.5
1862
64.7
100.00%
Retain 128 Tokens
FastV (ECCV'24)
71.0
54.0
$1.9
69.
56.4
68.2
1490
63.0
55.9
92.31%
PDrop (CVPR'25)
74.3
57.1
70.1
56.7
77.5
1696
62.3
95.17%
SparsVLL (ICML/25)
75.1
57.3
49.7
69.0
56.3
83.1
1761
62.6
56.9
96.61%
PruMerge+ (ICCV'25)
75.0
VisionZip (CVPR'25)
75.6
57.6
51.6
68.7
56.9
83.3
1763
62.1
57.0
97.19%
VisPruner (ICCV225)
75.8
58.2
57.0
84.6
1768
62.7
98.01%
DivPrune (CVPR'25)
76.0
59.4
52.8
68.6
54.5
85.5
1707
60.1
52.3
97.25%
Ours
76.4
59.4
53.0
68.6
57.0
87.4
1748
61.8
55.5
98.04%
Retain
FastV (ECCV'24)
55.9
46.0
49.1
70.1
51,6
35.5
1256
50.1
42.1
76.86%
PDrop (CVPR'25)
56.3
46.1
46.3
49.2
40.8
1505
48.0
36.6
76.41%
VLM (ICML/25)
66.9
52.0
49.4
69.2
52.1
69.7
1561
58.3
49.6
88. 68.60%%
PruMerge+ (CCVV22))
71.3
55.4
53.7
69.5
52.0
75.7
1640
59.6
52.1
92. 76%
VisionZip (C))R'25)
72.4
55.1
68.7
55.5
77.0
1690
60.1
94.46%
VisPruner (IOCV(25)
72.7
55.4
53.3
69.1
53.8
80.4
1650
61.3
55.1
95, 95.07%
DivPrune (CVPR'25)
74.1
02%
Ours
75.5
57.4
54.0
68.6
56.0
84.1
1703
60.7
55.8
96.76
Retoin 32 Tokens
PruMerg+ (ICCV'25)
65.6
49.2
55.1
87.01%
VisionZip (CVPR'25)
67.1
51.8
52.4
69.1
53.1
69.4
1579
57.0
50.3
89.41%
Vispruner (5CCV225)
67.7
53.9
72.7
DivPrune (CVPR'25)
71.2
54.9
53.3
68.6
52.9
81.5
1594
57.6
49.1
92.16%
Ours
74.0
54.1
53.4
69.0
54.5
80.1
1603
60.4
53.6
94.02%
Table Results of different token pruning methods on multimodal benchmarks. Average is normalized to the full-token LLaVA-155-7 (set to 100%). MME is reported in its original score units.
Complex images (erankmpurt > crankagg): A larger scaling factor increases T, enabling stronger pruning and promoting token diversity.
Simple images (erankinput erankwvg): A smaller factor keeps low, preserving fine-
grained, high-attention tokens.
This formulation ensures consistent adaptation across varying image complexities by grounding the threshold in dataset statistics.
EXPERIMENTS
Baselines and Models We apply our method to the widely adopted open-source model LLaVA- 1.5-7B (Liu et al. (0244)) which is a fine-tuned variant of the LLaMA family. Based on this architec- ture, we compare our approach with several vision token pruning techniques. The baselines include methods leveraging attention scores within the LLM (FastV (Chen et al., (024),, Sparse VL.M (Zhang) itt al., 2025b), PyramidDrop (Xing et al. (0244), approaches utilizing attention scores from the vi- sion encoder (VisionZip (Yang al. 2025), VisPruner (Zhang et al., 2025a)), and Diversity-based strategies such as DivPrune (Alvar et al. 2025). To ensure consistent, fair, and reproducible eval- uation, fixed the pretrained weights of LLaVA-1155777 and set the temperature to 0 across all experiments to produce deterministic outputs.
Datasets We conduct evaluations on a total of nine multimodal benchmarks. VQAv2 (Goyal et al., 2017) and GQA (Hudson & Manning 2019) are large-scale visual question answering datasets that assess general vision-language understanding. VizWiz (Gurari et al., 20188) and TextVQA (Singh al. 2019) introduce more challenging scenarios involving accessibility-relteed queries and text recognition in images. ScienceQA (Lu et al.) (022) requires scientific knowledge for complex reasoning tasks. MME (Yin et al. (2024) provides comprehensive metric for fine ine-grained multi- modal understanding, Finally, MMBench and MMBench- CN (Liu et al., (024c) serve as multilin- gual benchmarks that evaluate overall performance across diverse tasks and languages. In addition, as introduced in Section 4.2, we further analyze the hallucination problem by using the CHAIR dataset (Rohrbach et al. 2018 to quantitatively evaluate the occurrence of object hallucination in the model.
Main results We evaluate our adaptive thresholding approach on LLaVA-1.5, focusing on the effect of dynamic threshold adjustment token diversity and downstream task performance. As

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Published as a conference paper at ICLR 2026
shown in Table 5, our method consistently preserves accuracy under aggressive pruning. With 128 tokens, our method achieves competitive performance, showing modest gains of 0.85% over Vi- sionZip and 0.79% over DivPrune. When reduced to 64 tokens, attention- based pruning methods suffer more than 25% degradation, whereas our method incurs only a 3.24% drop and achieves a slight performance edge over VisionZip and DivPrune by 2.2% and 1.74%, respectively. While cent hybrids such as VisPruner (Zhang et al. 2025a), are primarily attention-based approaches that modestly complement their selection with distant tokens for diversity, they remain non-adaptive and thus lack robustness, particularly on datasets where attention-basee pruning is weak, such as POPE and MME datasets. The efficiency analysis provided in Appendix [A] and additional results on a broader set of LVLMs- including Qwen2.5-VL-7B. -LaVA-1.5-1381 and LLaVA-NeXT-7B- reported in Appendix B.I
In summary, our empirical analysis reveals the distinct tendencies of existing pruning approaches and demonstrates that an adaptive approach is essential to balance information preservation and diversity. Building on these findings, we establish that adaptive thresholding provides principled and effective alternative to fixed or non-adaptive methods.
Hallucination analysis. As shown in Table and consistent with the ob- servations in Section 4.2 our empir- ical analysis on the CHAIR dataset reveals clear contrasts between prun- meth-
Retain 64 Tokens
Retain Tokens
Method
C.
C.4
Recall
Len
C.
C.4
Recall
Len
LLaVA-L5-7B
$1.0 13.9
78.7
101.4
51.0
13.9
78.7
Attemton-based methods
ing strategies. Diversity- ods generally achieve higher halluci- nation scores (Cs. Cr) with higher recall, whereas attention-based meth- ods show the opposite trend. This trade-off is likely because tokens with high attention scores tend to contain more reliable visual informa- tion, which is essential for mitigating hallucination.
FasterVLM (arXiv'24)
45.4
13.5
69.3
94.0
45.8
13.3
75.4
97.0
PruMerge- (ICCV'25)
45,2
15.6
66.7
91.4
46.8
14.4
71.5
Vispruner (ICCV'25)
49.8
15.0
72.6
96.7
528
15.4
77.1
98.7
Diversiry based methods
DivPrune (CVPR'25)
57.4
18.0
76.4
101.1
58.6
18.1
78.4
103.1
FPSPruner
58.6
18.6
76.0
100.5
59.4
18.8
81.1
104.1
Ours
52.2
15.9
75.7
99.1
54.4
16.5
78.1
101.1
Table 8: CHAIR evaluation (64 /128 tokens).
Further analysis indicates that methods primarily relying on token diversity tend to show weaker performance on overall benchmarks, as they do not incorporate attention scores and are less effective in capturing concentrated and reliable information. Conversely, attention-based methods preserve such concentrated tokens but lack diversity, which restricts their ability to handle questions involving multiple objects.
Building on these empirical findings. we propose an adaptive method that balances attention and diversity. This approach achieves 52.2 on Cs. 15.9 on C1, and a recall of 75.7, which are close to the values obtained with the full set of visual tokens. These results emphasize findings from empirical analysis in clarifying the tendencies of different pruning strategies and provide evidence that adaptive approaches are needed to hetter align with varying image characteristics.
CONCLUSION
In this work, we presented a systematic empirical study of visual token pruning in LVLMs. Our analysis using effective rank and attention entropy revealed two consistent behavioral patterns that have not been previously characterized: (i) pruning methods differ substantially in the diversity they retain, and this retained diversity is closely linked to hallucination tendencies; and (ii) the effectiveness of sreentionbbaedd versus diversity-based pruning shifts predictably with image com- plexity, with simple images favoring intention-based selection and complex images benefiting from diversity- based retention. These findings provide a unified perspective that explains when and why different pruning strategies succeed or fail. Building on this empirical understanding, we showed that incorporating image-aware adjustments into existing hybrid and mixed pruning methods yields consistent improvements across benchmarks, demonstrating that the empirical principles we iden- tify are broadly applicable and model-agnostic. We further provided a minimal instantiation of these principles through an adaptive thresholding procedure, which achieves strong performance and re- duces hallucination while significantly lowering computational cost. Overall, our study highlights the importance of understanding the underlying behaviors of pruning paradigms in LVLMs and offers empirical principles that can guide the design of future adaptive pruning strategies.
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ACKNOWLEDGMENTS
This research was supported by grants from the National Research Foundation of Ko- rea(NRF) funded by the Ministry of Education (No. RS-2025-25433078) and the Korea govemn MSST( (No. RS-2022-00056155)) This work was also supported by LG Electronics, and the Perforerform Computing Support NPU Program of the National IT Industry Promotion Agency(NIPA) (No. N2025-01 58). Computational resources were provided by the Cluster Server for Computational Science at Pusan National University.
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APPENDIX TABLE OF CONTENTS
Efficiency Analysis
B Additional Results
B.
Evaluation on other models
B. 2
Entropy-Based Adaptive Thresholding
B.
Supplementary Examples of Image Cumplexity-Dependdee Pruning Differences
4
Effect of Similarity Threshold on Token Diversity
B.5
Extending Our Analysis to Adaptive Token Count
Farthest Point Sampling
Attention entropy and erank seale
Robustness Analysis of erank under Input Corruptions
F
Qualitative Analysis of Token Selection in Fine-Graineds Reasoning Tasks
F.1
Token-Selection Behaviors and Their Impact on Fine-Grained Reasoning
Failure Modes of the Adaptive Method in Fine-Grained Reasoning Across Image
Complexity
More Examples on CHAIR
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Retain Tokens
FLOPs (T)
Latency (ms/sample)
GPU Memory (GB)
Method
Accuracy
Vanilla (LL-VA-I.5-7BB
576
13.60
58.2
PDrop (CVPR'25)
64
0.51
128
13.30
55.0
SparseVL.M (ICML'225)
64
0.52
16.26
55.2
DiyPrune (CVPR (25)
64
0.48
110
13.30
55.8
VisPruner (ICCV'25)
64
0,48
115
13.30
55,4
Ours
64
0.48
113
13.30
56.0
Table 9: Efficiency and accuracy comparison single RTX 4090 at TextVQA dataset. All models are evaluated under identical settings.
Batch Size
3
erank overhead (ms)
3.65
10.21
16.84
33.40
Table 10: erank computation time across different batch sizes on single RTX 4090.
A EFFICIENCY ANALYSIS
Computation Overhead of Attention entropy and erank. The erank quantifies the representa- tional complexity of a feature matrix X E NXD based on the distribution of its singular spectrum. To improve computational efficiency, compute the covariance matrix across tokens using a fast N x N formulation. The definition is given as follows:
S C=XX,, S= V=CC,,, P: Σ, Sj
erank(X) = exp
Σ Ps logps
(7)
Here, C denotes the N x N covariance matrix, A(C') its eigenvalue spectrum, S the square roots of these cigenvalues (i.c., singular values), and p; the normalized spectral ratio. Thus, the crank corre- sponds the exponential of the Shannon entropy of the normalized spectrum. Eq. (7) is equivalent to the SVD-based definition, since the singular values of X match the square roots of the eigenvalues of XXX..
In typical LVLM settings, the number of tokens much smaller than the feature dimension (N D). For example, I.LaVA-B-1.55 uses N=576 visual tokens with D=4096 embedding. This allows computing erank efficiently using the smaller covariance matrix C N
To assess the practical cost of these metries, compare their theoretical complexity with measured runtime. Naively applying SVD to X E N has ONN D²) complexity, since decomposition is performed tall N D matrix. In contrast, the fast formulation computes the spectrum of the smaller covariance matrix C = XX reducing the overall cost to O(N D + -N3) when N & D.
We further measure the per-image runtime under the LLaVA-1.5577 configuration. Computing erank takes 3,4 ms on average, while the full model inference takes 1 115 ms. Thus, erank accounts for only 33.2% of the total inference time, remaining lightweight in practice. During batched inference, As shown in Table TO, the cost scales approximately linearly with the number of images, as both covariance construction and eigenvalue computation operate independently across samples.
shown in Table 9 the proposed method reduces FLOPs by 89% under the 64-token setting, while still preserving 96.2 % of the original performance compared to the vanilla LLaVA-1.5-4 1.5-7B model. Notably, our method outperforms in-LLM pruning approaches such SLa (Zhang et al. (2225)aand PyramidDrop (Xing et al. 2024 in terms of accuracy, achieving a better pruning approaches such as VisPruner (Zhang al.) 2025a) and DivPrune (Alvar al. 2025). the computational indicators (FLOPs, latency, GPU memory) remain nearly identical, while our method still attains the highest accuracy among them.
Efficiency Comparison.
performance trade-off. Meanwhile, when compared with recent pre-
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Method
GQA
SQA
POPE
MME
TextVQA
Rel.
All 576 Vokens
LLaVA-1.5-BB
Retain 125 Tokens
Erunk-basee
99.4
68.6
$7A
1748
$7.0
98.13%
Att. baase
49.4
68.6
87.0
1721
56.9
98.06
Retain 64 Tokons
Erank-hased
Att. entropy-based
Table 11: erank- vs. entropy- based thresholding.
These three methods- VisPruner, DivPrume, and ours share the property of performing pre- pruning before the LLM imput, which substantially reduces the internal computation of the LLM. Compared to pruning inside intermediate layers of the LLM, pre-pruning provides a much stronger efficiency gain since the token reduction applies all subsequent layers, yielding significant savings in FLOPs, memory, and latency with minimal overhead. In contrast, pruning at intermediate layers inside the LLM, as exemplified by methods such as Sparse and PyramodDrop. allows richer contextualization before tokens are removed and thus carries a lower risk of discarding important information, but its efficiency benefit limited because the early layers still process the full set of tokens. Therefore, pre- prenprunnn is preferable in terms of efficiency.
In addition, our method is fully compatible with FlashAttention (Dao et al. 2022), enabling further efficiency gains when combined with state-of-the-art acceleration techniques. Overall, these results demonstrate that our method strikes an effective balance between computational efficiency and ac- curacy.
B ADDITIONAL RESULTS
B. EVALUATION OTHERS MODEL
In addition to I.L. 1.5-7B, we also conducted experiments on larger and architecturally diverse LVLMs, including I.aVA-1.5-13B (576 tokens), I.LAVA-NeXT-B (2880 tokens), and Qwen2.5- VL-7B. Across all these settings, method consistently demonstrated stable and strong perfor- mance, further validating the effectiveness and generality our approach. Detailed results are pre-
sented in Table 14 Table [5] and Table 16
B.2 ENTROPY-BASED ADAPTIVE THRESHOLLING
We first examined the relationship between atten- tion entropy and effective rank (erank). As shown in Figure [5] the two metrics exhibit a moderate Pearson correlation of 0.63 across the full MME dataset, indicating that both capture a related no- tion of visual information dispersion. While en- tropy reflects the spread of attention weights, er- ank additionally incorporates feature-space ge- ometry through its singular-value spectrum.
Attention Entropy vs Erank
54
core = 200634
5.2
Motivated by this correlation, we investigated whether entropy directly replace erank in adaptive thresholding rule. Specifically, we sub- stituted attention entropy for erank in the formu- lation of Eq. 6 and computed the reference aver- age entropy once using the LLaVA training set.
4.2
4.0
40
100
120
140
Attencion Entrogy
Figure 5: Attention entropy vs. erank on MME dataset.
Table II presents results across multiple bench- marks token budgets of 64 and 128. Entropy-
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(a) Simple Images
(b) Complex Images
Altention bused method
Diveraity besed metrrd
Attention based muthed
Divuraity based muthed
=+a=
Entropy: 4.58
Entropy: 4.81
Erank: 55
Erani: 111
Attention based method
Diversity based method
Atteention based method
Diversity based method
BEATLÉS SIDRY
Entropy: 4.53
Entropy: 5.05
Erani: 74
Erank: 129
Attention based based method
Oiversity based mathod
Attention based mathod
Civeratty based mathod
3
Entropy:: 4.52
Entropy: 5.08
Erank: 58
Erank: 111
Attention based mathod
Diveraity besed mathod
Altention based methed
Divaraily based muthed
SSOP RITE
Entropy: 4.54
Entropy: 5.13
Erank: 81
Erank: 119
Artention based method
Diversity based method
Attention based method
Diversity based method
SCARKET
18's
SUNDRIES
Entropy:: 4.54
EEnrooy:: 4.70
Erank:
Erank: 118
Figure 6: Extended examples for simple vs, complex images. The same trend as in Fig. 3 is ob- served: iitentioo-bbsed methods work well simple images, while diversity-based methods cover complex images more broadly.
based adaptation yields performance trends closely aligned with those of erank-based adaptation, with only minor variations about 13.
B.3 SUPPLEMENTARY EXAMPLES IMAGE COMPLEXITY SEPREXIITYDDPENDENY PRUNING DIFFERENCES
As shown in Fig. 6 the qualitative patterns observed consistently reproduced across additional samples. For simple images (with low entropy and erank), iitentioo-based pruning effectively cap- tures the concentrated regions, while the additional benefits of diversity-basee pruning are limited. Conversely, for complex images (with higher entropy and erank), diversity- -based pruning ensures broader coverage, highlighting its strength in dispersed scenarios. These supplementary examples reinforce that image complexity is a key determinant of pruning effectiveness and motivate the need for adaptive strategy that integrates both approaches.
B.4 EFFECT OF SIMILARITY THRESHOLD ON TOKEN DIVERSITY
We varied from to 0.25 and observed its impact on token diversity. As shown in Table 12 the results demonstrate direct positive correlation between the similarity threshold and the diversity
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Published as a conference paper at ICLR 2026
Metric
Similarity Threshold (r)
0.01
0.05
0.15
0.20
Erank Performance
Hligh erank (complex images) datasers
MME (High)
13.85 / 1362
16.72 / 1358
17.44 / 1368
18.58 / 1379
19.55 1363
20.13 1381
19.60 / 1380
POPE
14.40/77.5
17.13 /7.7
17.81/79.1
18.94/ 81.5
19.83 83.4
20.43 85.3
20.49/ 86.1
Low erank (simple images) datasets
MME (Low)
/ 309
14.46 311
15.38 / 307
16.39 292
17.21/ 304
17.00 / 284
15.50 290
ScienceQA
11.91 69.1
15.29 68.9
16.22
17.33/
18.09 /
18.45
68.5
Table 12: Comparison of performance across different similarity thresholds. For each metric, the boldface indicates the best performance.
Method
Avg. tokens
GQA
SQA
POPE
MME
MMBench
Rel.
LLaVA-1.5-7B
576
61.9
69.5
85.9
1510
64.3
100.0%
ATP-LLaVA (CVPR'25)
88
56.8
67.2
82.8
1401
64.7
95.1%
Our (fixed count)
88
58.1
68.0
84.2
1405
62.3
95.4
Our (Adaptive count)
85.5
58.4
68.2
85.2
1408
63.1
96.0%
Table 13: Results of adaptive-count vs. fixed-count pruning
of the selected token set. As increases, the erank of selected tokens consistently rises across all datasets. This indicates that higher threshold causes more tokens to be treated as redundant and pruned, which turn enhances the diversity the final set. Notably, token diversity was at its lowest when the threshold was close to zero, as very little similarity- baased pruning occurs under this condition.
While increasing T consistently improves token diversity, its impact on performance varies depend- ing on image complexity. For more complex images with higher erank, larger 7 helps eliminate redundant tokens and improves performance by encouraging more diverse token representation. In contrast, for images with lower complexity, where informative content concentrated in a small number of high-attention tokens, overly aggressive pruming with large remove fine- mne-grained but important details, leading to degraded performance.
B.5 EXTENDING OUR ANALYSIS TO ADAPTIVE TOKEN COUNT
ATP-LLaVA Ye et al. 2025) have actively explored adaptive to- ken count strategies that adjust the number of retained visual tokens per input. Our empirical analysis naturally extends to this direction. High-erank (complex) images contain more dispersed visual in- formation and therefore benefit from retaining more diverse set of tokens, whereas low-erank (sim- ple) images have more concentrated information and are better represented by selecting a smaller focused tokens. Based on this analysis, we design an adaptive-count strategy that preserves more tokens for complex images and prunes more aggressively for simple ones. For comparability with ATP-LLaVA, we adopt the same reference budget of 88 tokens, reducing the count by up to 20% for low-erank images and increasing it proportionally for high-erank images. Since the number of retained tokens varies across inputs, we report dataset-level averages. As shown Table [3] the adaptive-count variant- formance than both ATP-LLaVA and our fixed-count baseline on GQA, SQA, POPE, MME, and MMBench, while also providing slight efficiency gains. These results confirm that adjusting the to- ken count aligns naturally with our crank- based analysis and enables more effective allocation of computational budget according to image complexity.
Recent approaches-inclueee
average of approximately 85.5 tokens tokens-achieves achieves higher per-
18

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Published as a conference paper at ICLR 2026
Method
"QA"
GQA
VizWiz
SQA
TextVQA
POPE
MME
MMB
MMB
Average
Vanilla 576 Tokens
LLaVA-1.5-13B
80.0
63.3
53.6
72.8
61.2
86.0
1531
68.5
63.5
100%
Retain 128 Tokens
FastV (ECCV"24)
75.3
58.3
54.6
74.2
58.6
75.5
1460
66.1
62.3
96.0%
PDro (CVPR'25)
78.2
61.0
53.8
73.3
60.2
83.6
1489
67.5
62.8
98.4%
Vp. (5CM)/25)
77.6
59.6
51.4
74.3
59.3
85.0
1488
68.4
62.6
97.8%
PruMerge+ (2CCV
76.2
73.3
56.1
95.8%
VisionZip (CVPR'25)
76.8
62.5
(CVPR'25)
77.1
72.8
60.7
Ours
77.5
59.1
72.8
1481
67.6
61.9
97.6%
Retain 6d Tokens
FastV (FCCV"24)
65.3
51.9
53.8
73.1
53.4
56.9
1246
59.2
55.1
85.8%
PDrop (CVPR'25)
70.8
54.1
50.5
73.1
55.3
66.1
1247
63.1
56.6
88.7%
VLM (ICML'25)
73.2
55.9
52.1
73.0
57.1
77.9
1374
65.2
60.3
93.5%
PruMerge+ ((CCV'25)
72.6
56.3
52.4
73.5
54.4
75.7
1338
65.0
59.3
92.3%
VisionZip (CVPR'25)
73.7
56.2
53.2
74.2
57.4
1380
93.9%
DivPrune (CVPR'25)
75.2
71.7
57.4
95.6%
75.7
Table Results different token pruning methods on 9 multimodal benchmarks. Average is normalized to the full-token LLaVA-1.5-139 (set to 100%). MME is reported in its original score units, and it is included only in the Perception section to enable broader comparison with existing methods.
Method
VQA"
GQA
VizWiz
SQA DMG
TexVVQA
POPE
MME
MMB
MMB
Average
Vanilla 2880 Tokens (Upper Rownd)
LIVVA-NeXT-78
81.3
62.5
55.2
67.5
60.3
86.8
1512
65.8
57.3
100%
Retain 640 Tokens
FasV (ECCV'24)
77.0
58.9
53.9
67.4
58.1
79.5
1412
63.1
53.5
95.2%
PDrop (CVPR'25)
79.1
66.7
57.8
1475
55.2
97.0%
SparseVLM (ICML'25)
79.2
67.6
59.7
98.8%
PruMerge+ (ICCV'25)
78.2
VisionZip (CVPR'25)
79.1
DisPrune (CVPR'25)
79.3
61.9
55.7
67.8
57.0
86.9
1469
65.8
57.3
98.9%
Ours
79.3
62.0
56.0
67.8
59.0
86.1
1502
65.9
58.3
99.64%
Retuin 320 Tokens
FastV (ECCV"24)
61.5
49.8
51.3
66.6
52.2
49.5
1099
53.4
42.5
79.9%
PDrop (CVPR'25)
66.8
50.4
49.7
66.7
49.0
1171
55.5
44.7
82.5%
SparseVLM (ICML/25)
74.6
57.9
54,2
67.2
56.5
1386
63.1
56.7
94.6%
PruMergo= (IC)V'25)
75.3
54.0
63.0
55.6
95.7%
VisionZip (CVPR'25)
76.2
DivPrune (CVPR'25)
77.2
Ours
77.8
60.1
55.8
67.3
58.4
1475
64.5
57.1
97.94%
Table 15: Results different token pruning methods on 9 multimodal benchmarks. Average is normalized to the full-token LLaVA-NeXT-7B (set 100%). MME is reported in its original score units, and it is included only in the Perception section to enable broader comparison with existing methods.
C FARTHEST POINT SAMPLING
Farthest Point Sampling (FPS) is one of the simplest methods that guarantees diversity. Starting from initial point, it iteratively selects the point that is farthest from the already chosen set by measuring the distance to the nearest selected point. For each point p,. the minimum distance to the selected set S is defined
d(p;) = min ||p 8||2, #ES
and the next point is chosen
Pi. = arg max d(p,). P
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Published as a conference paper at ICLR 2026
Method
TextVQ4
ChartQa
A12D
OCRBBecc
MMR (Pere.)
MME (Cogn.)
MMB-EN
MMB-CN
Rel.
Vanilla
(100%))
Q-en225-VL-78
82.1
77.5
83,0
84.1
1691
642
83.2
83.2
100,0%%
Retain 312 Tokens (60.5% Reduction)
CCVPR2)
79.7
Ours
79,8
71.5
82.1
78.5
1700
630
81.3
80.8
96.9%
Retain 256 Tokons (79.1% Reduction)
CVPP22)
75.2
79.7
66.4
79.8
Ours
74,2
62.4
80.5
65.8
1670
630
80.9
80.3
92.1%
Table 16: Performance comparison on Qwen2.5-VL-7B at different token retention ratios. Rel. the relative performance normalizedd the full-token Owen2.5-VL--7B (set to 100%). MME scores are reported as separate Perception (Perc.) and Cognition (Cogn.).
erank histogram
attention entropy histogram
Stadkan: 23
Median: 4.
Lowdbrk $1.55
20w23% 4.b/
count
60
75
40
50
20
25
40
60
80
100
120
140
3.0
4.0
4.2
4.4
4.6
4.0
5.0
5.2
3.4
erank
attention entropy
Figure 7: Histograms of erank and attention entropy measured on the MME dataset.
Repeating this process until the desired number k is reached ensures that the selected points are evenly distributed across the data space, providing a more balanced representation than simple ran- dom sampling.
D ATTENTION ENTROPY AND ERANK SCALE
Entropy and erank exhibit fundamentally different scaling behaviors. As illustrated in the Figure entropy computed from CLIP-L (using 576 visual tokens) is concentrated within a very narrow range. Specifically, the mean entropy over all samples is 4.80, the median is 4.78, the upper quartile (Q3) is 4.96, and the lower quartile (QI) is 4.63. In other words, entropy values predominantly vary within the interval of approximately 4.0-5.4. Therefore, even seemingly small differences (c.g.. 0.3-0.5) represent meaningful relative changes within this restricted scale.
In contrast, erank spans much broader scale. The mean erank is 94.87, the median is 95.40, the upper quartile 108.80, and the lower quartile 81.59. This wide spread demonstrates that erank captures coarser-grained variations in token correlation structure and spectral dispersion, comple- menting the fine-grained sensitivity of entropy.
E ROBUSTNESS ANALYSIS OF ERANK UNDER INPUT CORRUPTIONS
Robustness erank to Noisy or Degraded Inputs To evaluate the stability of the erank met- ric under degraded visual conditions, conducted sensitivity analysis following the corruption protocol COCO-C (Hendrycks & Dictterich) 2019). Fifteen corruption types were applied to all 2,374 images in the MME dataset, and the resulting erank values were compared against those com- puted on clean images. Two corruption severities (1 and 3) were used (see Fig corresponding to mild and moderate perturbation strengths commonly examined in robustness studies.
Table 17 summarizes the mean absolute deviation in erank and the corresponding relative change with respect the clean-image mean. Overall, erank exhibited high degree of stability across all
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Published as a conference paper at ICLR 2026
Severity =1
Severity =
Corruption Type
Mean erank diff.
Relative change
Mean erank diff.
Relative change
Zoom blur
5.85
6,14%
7.62
8.12%
Brightness
1.48
1.56%
2.52
2.70%
Contrast
2.38
2.54%
4.38
4.62%
Defocus blur
2.06
2.28%
3.74
4.16%
Elastic transform
2.18
2.54%
3.38
4.05%
Fog
3.24
3.73%
4.05
4.71% %
Frost
3,96
4,56%
6.10
7.24%
Gaussian noise
2,83
3.23%
4.17
4.73%
Impulse noise
3.34
4.04%
4.37
5.10%
Jpeg compression
2.31
2.48%
2.77
2.98%
Motion blur
2.11
2.33%
3.38
3.91%
Pixelate
1.22
1.32%
1.61
1.76%
Shot noise
2.74
3.14%
4.06
4.62%
Snow
3.84
4.17%
5.37
5.76%
Glasses blur
2.15
2.46%
4.07
4.79%
Average
2.78
3.10%
4.11
4.62%
Table 17: Sensitivity of erank under 15 corruption types from COCO-C, evaluated on the MME
dataset.
100
F
(a) Severity
(b) Severity
Figure 8: Visualization of the 15 COCO-C corruption types applied to MME images at severity
levels and 3.
corruption types. The average deviation was 2.78 at severity 1 and 4.11 at severity 3, representing only small fraction of the natural variation in erank. For reference, the clean MME dataset spans wide range of erank values (mean 94.86, min 44.08, max 147.49, standard deviation 19.46).
Corruptions that alter the global spatial structure of the image-such as zoom blur, frost, snow, moderately larger deviations (typically 4-7 points), reflecting their
and elastic transform-prode broader impact on the singular-vale value spectrum. In contrast, distortions that modify only local pixel- level appearance, including brightness changes, pixelation, and JPEG compression, had minimal effect (1-2.5 points). Increasing corruption severity from to resulted in only marginal increases in deviation, indicating that erank maintains consistent behavior under stronger degradation.
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Published as a conference paper at ICLR 2026
F QUALITATIVE ANALYSIS OF TOKEN SELECTION IN FINE-GRAINED REASONING TASKS
F. TOKEN-SELECTION BEHAVIORS AND THEIR IMPACT ON FINE-GRAINED REASONING
The characteristics of the tokens selected by attention-based and diversity bassed pruning influence only hallucination generation but also the model's s overall reasoning behavior. As shown in the examples in Fig. 2 the tendencies identified in Section 4.1 also appear in more fine-grained rea- soning tasks such as counting and spatial relation inference. Accordingly, we conduct qualitative analysis comparing how diversity-basee attention-based, and adaptive pruning methods select to- kens and how these selections affect the open-ended responses they generate.
Examples I and 4 demonstrate that attention-based pruning, due to its narrow focus on specific regions, may overlook certain objects and produce incorrect answers. In contrast, diversity- pruning preserves broader spatial information, captures more objects, and often provides more de- tailed descriptions, resulting in more accurate predictions in such cases. However, in Examples and 5, the opposite pattern emerges: attention-based pruning correctly predicts the object count by concentrating on the primary instances, whereas diversity-based pruning-owing to its dispersed token selection-iicorreedd) infers the presence additional objects, leading to hallucination. The adaptive method exhibits intermediate behavior between the two. Although it fails in Examples and 4 by focusing on a narrower area than the diversity-bait basedd method, all three methods struggle with these particularly challenging cases. Conversely, in Examples 2 and 5, the adaptive method predicts the correct count alongside the attention- method, while the diversity -based method hallucinates additional objects.
similar pattern arises in spatial reasoning. As seen in Examples and 6, the diversity- -bsed method leverages a wider range of spatial cues and produces richer relational descriptions, but this increases the likelihood of inferring relations that do not actually exist. In contrast, the attention-based method provides stable, object-centered relational deseriptions, though with more limited coverage com- pared to the diversity- based approach. The adaptive method again lies between the two, capturing the key objects while reducing the hallucinations introduced by the diversity- based method, ulti- mately yielding more stable spatial reasoning.
To further examine whether the adaptive method overlooks small or rare objects, evaluated all three pruning strategies on an Existence task, which asks whether a specific object is present in the image (Fig. TOP. In Example 1, the adaptive method successfully retained the necessary tokens to detect a small object that the attention-based method missed. Similarly, in Example 2, where both the attention- based and diversity-bar baseed methods failed to identify a very small object, the adaptive method preserved the relevant cues and produced the correct answer. These results indicate that the adaptive strategy reliably maintains important tokens and remains effective even when small objects serve as critical evidence for reasoning.
Overall, across diverse reasoning tasks, the adaptive method demonstrates robust and reliable be- havior, as supported by both quantitative results and qualitative examples.
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Counting
Spattairassoing
Hnww imager
Thers severa pople the
Phere
Theik
bewigggeee fargrooun
There are ban
whrre the bepooeeti postee reve
many mater sutties
te dee moriee The dunt then demed with commattte meuun peleee came anication
casther
Theraae lav
There ere ben
dakk The desk has en shorr manuse on creating
peogle inch
ani..
Figure 9: Qualitative comparison of three pruning aatigiiissaateetioo-aabeeee diversity- based, and our adaptive method-on counting and spatial reasoning tasks. Diversity- pruning retains broader range of spatial cues, often producing detailed descriptions but occasion- ally introducing hallucinated objects or relationships. Attention-basee pruning focuses on salient regions, offering stable but sometimes overly narrow predictions. The adaptive method balances both mechanisms, mitigating hallucinations while preserving essential visual cues for reliable rea- soning.
Existence
No
Yee
No
No
No
X
X
Figure 10: Qualitative comparison on the Existence task. Adaptive pruning correctly preserves tokens needed to identify small objects, outperforming attention- and diversity-basee methods in challenging cases.
23

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Published as a conference paper at ICLR 2026
Simple Images
How many there, and where they positioned of the scene?
There people in the and positioned top of hill rocky outcrop, standing next their bicycles.
Frank:
How many birds are there, and where they positioned relative to the rest
There are five birds in the image, and they are perched branches of
Complex Images
(c)
many computer and where are they positioned relative to the rest of the scene?
computer
they are positioned en the desk front computer monitor.
Erank: 101
How many baskets are there, and where are they positioned relative to soene?
There are two baskets in the scene, and they
positioned on top of bookshelf.
Figure 11: Examples illustrating how image complexity governs the adaptive pruning method's success and failure in fine-grained reasoning. For simple, low-erank images, the method performs well when key objects are spatially concentrated (a) but struggles when many similar objects are scattered across the scene (b). For complex, high-erank images, it succeeds when semantic cues are broadly distributed (c) but fails when crucial evidence for fine-grained reasoning is highly localized within cluttered scene (d).
F.2 FAILURE MODES OF THE ADAPTIVE METHOD IN FINE-GRAINED REASONING ACROSS IMAGE COMPLEXITY
We analyze cases in which the adaptive warthod-whore token selection is guided by image com- plexity-fails on fine- -ne-irained reasoning tasks. To do so, we use questions that jointly require object counting and spatial relation reasoning across images of varying complexity, and summarize the observed failure patterns below. Representative examples are shown in Fig.
(1) Low-erank images with many objects. In simple images with low erank, the adaptive method allocates greater weight high-attention tokens and compensates with limited diversity. This results in selection pattern similar to attention-based pruning. When objects are few and localized (e.g., Fig. IT),, this focused selection enables accurate counting and spatial reasoning. However, when many objects spread throughout the image (Fig. [Tp), the concentrated selection fails to capture the broader spatial layout, leading to counting errors and incomplete reasoning.
(2) High-erank images with locally concentrated key evidence. For complex images with high erank, the adaptive method behaves more like diversity-based pruning and tends to select widely dispersed tokens. When objects semantic selection is advantageous and supports accurate counting and relational reasoning. However, when the crucial evidence is highly localized within the scene (Fig. TTd). the wide-spread token selection dilutes attention around the important region, causing the method to overlook key cues and fail in both counting and spatial inference.
broadly distributed (Fig. 1Tp), this dispersed
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Published as a conference paper at ICLR 2026
Please describe this image in detail.
DivPrune
FasterVIM
The image features large brown dog lying on touch comfortably resting pillow. The dog appears to relaxed and enjoying time the
image
large brown (og)
lying a couch) appearing to relaxed and comfortable. The dog positionad in the middle the couch, occupying The living
room, there Ehair positionad couch, and a dining table can seen the background. personjis also present in the scené, standing near the cauch and the table. Additionally, thare person
significant portion of the itself providing a cozy environment for dog to rest.
placed
table, further away.
GT: dog / teddy bear / dining table person / cell phone / couch
Please describe this image in detail.
beautifu real (umbrlllaj placed top
image
The image features beautifully decorated gravesite with umbreila, Iikeyy tracitional placed top and covers the entire gravesite, creating a unique and visuelly striking scene. addition to the umbrella, there are several flowers gravesite, adding
The umbrella is open, previding shade and adding touch of color to the scene. the gardan, there are several potted plants, with scene and Additionally, there are two vases placed the garden, one near the canter and the othar towards the right side. A person can sean the backgreund, possibly enjoying the peaceful atmosphere the garden. the umbrella, potted plants, creates a serene and inviting environment.
umbrello,
umbrella opan
located plants
around the elegance to the overall presentation. combination of the red umbrella and pink flowers creates serene and peaceful atmosphere, reflecting the beauty and into
of
CT: potted plant umbrella
Please describe this image in detail.
DivPrune
FasterVLM
The image features delicious (azza with variety of toppings, including olives, sitting cutting board kitchen counter. The baked. pan,
large, pizzaliitting on metal tray. pizza topped with varicty of tappings, including olives and herbs. There are several olives scattered across the with
image
is freshly appears background, there microwavs and an lavnn which are likely used for cooking and heating food the kitchen. Additionally. there are bottles, located the left right counter,
and
the odges.
are also distributed evenly across the pizza, adding burst of flavor to the dish. The pizza appears be ready to be baked, looks dalicious appetizing.
A lknitolcan likely used for cutting the pizza.
oven / microwave pizza/ spoon
Figure 12: CHAIR qualitative comparisons: FasterVLM DivPrune (Set 1). In the annotations,
GT Obj. and Hallucinated Obj. label object words; red text indicates incorrect phrases.
25

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Published as a conference paper at ICLR 2026
G MORE EXAMPLES ON CHAIR
To further illustrate the differences between attention-based and diversity-based pruning in the im- age captioning task, we provide additional qualitative samples from the CHAIR dataset comparing FasterVLM as an attention-based method and DivPrune as a diversity-based method [2 and
Fig. 13p. These cases illustrate how the two approaches differ in response style and hallucination tendency: DivPrune often yields broader and more descriptive captions but introduces hallucinated objects, whereas Faster produces more conservative and focused descriptions
Moreover, Fig 14 presents controlled experiment where the token budget is fixed at 64 and the ratio between DivPrune- and iivention-selecter tokens (DivPrune-to-Attentio ratio, R) is varied in steps of 25%. We observe that the hallucinated objects frequently generated under pure DivPrune (R = 0) gradually diminish as the share attention-selecreed tokens increases, disappearing entirely when R 2 50%. In parallel, the response style evolves from speculative and exploratory to more factual and reliable as increases, showing the stabilizing effect of attention-bra basedd token selection.
26

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Published as a conference paper at ICLR 2026
Please describe this image in detail.
DivPrune
image
traffic
The image features traffic light with two red lights illuminttdd, display cherry blossoms, traffic light located scano, with the blossoms surrounding on all sides. The blossoms are scattered throughout the
The traffic light currently displaying a red light, signaling vehicles to stop. The traffic light is positioned top left corner of the car located
light hanging above
by
middle
street, image,
center another car further the right, and car closer to the boltom left corner. The cars are waiting for the traffic light change before they praceed.
with closer further away, creating picturesque and serene atmosphere.
traffic
oT: traffic light
DivPrune
Please describe this image in detail.
FasterVLM
The image depicts a lively city street at night, with neon sign Iluminating the nama 'Hoil Dogs' above a restaurant. The restaurant located on the neon sign several ppoopla along There are several street, with some closer to the camera and others further away. Alarr i parked the side of the street, and a Itaffic light the background. The bustling atmosphere city night.
The image depicts lively night scens hot dogistand stand, likely
"Essie's illmmintted lights, creating vibrant stmosphere.
Dogs.
of street, and the
Bants
There are several paople in the scene, with one person standing close to the left side the another
middle, right on stand, with one located near the center and
third sen
dogs
captures
another towards the right side. A (person
also holding hannbarg which is placed
of the image. bustling scene captures essence
traffic light / hot dog person caa
night at popular hot dog stand.
Please describe this image in detail.
DivPrune
FasterVLM
image features young gir standing ternis court, holding Jennis racketin her hand. She appears to be focused and ready play. There seiveral sports balls scattered around indicating
The image features young standing court, holding her hand. She appears be focused and ready play. There are several sports balls) scattered around the court, indicating that she has been practicing har skilis. addition other people in the scene, ane standing near the left side the court and the other the right side, Alchair also visible the background, likely for players between games.
rackel
practicing tennis
addition to the main girlthere are few other people in the background, possibly watching her play or waiting for their turn to play. The the excitement energy of session.
girl,
two
GT: tennis racket person/ / sports ball
Figure 13: CHAIR qualitative comparisons: Faster vs. DiyPrune (Set 2). In the annotations,
GT Obj. and Hallucinated Obj. label object words; marks DivPrune's phrasing; marks FasterVLM's phrasing; red text indicates incorrect phrases.
27

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Published as a conference paper at ICLR 2026
Attention-based selection ratio:R
depicts bathroom
and and The tollstis positioned
DivPrune
right appeapeer procass being remodeled, evidenced by the exposed beams and the overall unfinished appearance: The room is also quite spacious, with large open area that extends from the left side of the tolei to the right side of the gmnk)
while bathroom
small, padestai Bmmg
Please describe this image:
R=0%
in detail.
The image festures a bathroom with totel situated the corner of the room. lolel is; positioned next to wall, and appears to be small, compact space. The bethroom also has alinng which is located near theffoles
Re25%
addition (oilel and Ean, there toilet roll placed wall, providing suggests
appearance compact and functional space.
R=50%
The image features white Gold sitting the middle a room, surrounded by while tiles. The Kollet is positioned on raised platform, giving unique appearance. The room appears to be under construction, evidenced by the unfinished walls and the overall unfinished look
The image features white tollet sitting the middle of room, surrounded by white tiles. The loilll pesitioned raised platform, by unfinished appears fixtures.
R=75%
The
GT: toilet
FasterVLM
room is positioned the corner of the room, and appears be part of bathroom. The floor is covered with white tiles, which gives the room clean and modern look.
image small,
R=100%
Figure 14: Effeet of varying the Attention-based selection ratio R under a 64-token budget. As R increases, hallucinated objects produced by DivPrune progressively suppressed, and the responses shift from exploratory to fact-oriented descriptions. the annotations, GT obj. and Hallucinated obj. label object words; denotes DivPrume-specife phrasing.
28

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Published as a conference paper at ICLR 2026
STATEMENT ON THE USE OF LARGE LANGUAGE MODELS
In the interest of transparency and in compliance with the ICLR 2026 guidelines, we report that large language model (LLM) was used assist the refinement of this paper's text.
Scope of Use. include:
The model's 's role was strictly limited to that of a writing assistant. Its contributions
Correcting grammatical emrrr,, spelling, and punctuation.
Improving sentence structure and flow for enhanced clarity.
Refining word choices for greater precision and conciseness.
29


Reviews:
[{"review_id": "gvdUVrvK22", "reviewer": "Reviewer_zvYn", "summary": "This paper conducts an empirical analysis of attention-based and diversity-based visual token pruning strategies in vision-language models (VLMs). The study reveals that attention-based pruning strategy shows good performance on simple images (low erank), while diversity-based pruning strategy shows better performance on complex images. It proposes a method that adaptively determines the visual tokens preserved based on the erank of the images.", "questions": "Please address questions in the weaknesses section.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Provides diverse experiments to validate its approach \n2. The paper is well-written and easy to follow.", "weaknesses": "1. I am not sure the findings of the paper is novel enough. [1] shows that \"a satisfied pruning method should jointly take the token importance and diversity into account.\" to preserve both local (important) and global (diverse) information, which is what the paper proposes to do.\n2. I think an important related work is missed [2]. It also determines pruning threshold based on input instance adaptively.\n3. From my understanding, the number of visual tokens is fixed in the experiments. Why didn’t the authors extend their method to adaptively determine the number of visual tokens retained? It seems that dynamically adjusting the number of preserved tokens could further improve the efficiency and effectiveness of visual token pruning.\n\n[1] Long, Sifan, et al. \"Beyond attentive tokens: Incorporating token importance and diversity for efficient vision transformers.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.\n\n[2] Ye, Xubing, et al. \"Atp-llava: Adaptive token pruning for large vision language models.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.", "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": "eMleOG7biP", "reviewer": "Reviewer_17pH", "summary": "This paper presents a comprehensive empirical study on visual token pruning in Large Vision-Language Models (LVLMs), which are computationally expensive due to the large number of visual tokens produced by image encoders. Existing pruning strategies generally fall into two categories:\n\n- Attention-based methods, which retain tokens with high attention scores.\n\n- Diversity-based methods, which keep tokens that are dissimilar to others to preserve feature diversity.\n\nKey Findings:\n\n- Impact of Image Complexity. The authors quantify image complexity using two metrics: Attention entropy for measures how concentrated the model’s attention is. Effective rank (erank) for measures the diversity of token embeddings.\n\n- Hallucination Analysis: Using the CHAIR dataset, the study shows that Attention-based methods produce more conservative outputs with lower hallucination rates. Diversity-based methods generate richer but more hallucinated descriptions due to speculative inclusion of objects.\n\n- Adaptive Pruning Framework: Based on these insights, the authors propose AdaVTP (Adaptive Visual Token Pruning), which dynamically balances attention concentration and token diversity. The method adjusts a similarity threshold (τ) for pruning redundant tokens based on image complexity (measured by erank). A low τ is used for simple images (preserving fine details), while a higher τ enhances diversity for complex images.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Comprehensive empirical analysis and validation:\n- It goes beyond performance reporting and explores why each method behaves differently, grounded in measurable concepts like attention entropy and effective rank (erank).\n- Extensive experiments on nine multimodal benchmarks (VQAv2, GQA, TextVQA, ScienceQA, MMBench, etc.) and the CHAIR hallucination dataset demonstrate the robustness of the approach.\n\nInsightful findings with practical relevance：\n- The study reveals clear patterns: attention-based pruning excels in simple images (low erank) while diversity-based pruning performs better in complex ones (high erank).\n\n- These findings can directly guide pruning strategy selection in real-world LVLM deployments where image complexity varies widely.", "weaknesses": "Limited novelty in algorithmic design:\n- The proposed adaptive pruning framework (AdaVTP) mainly combines two existing ideas — attention-based and diversity-based pruning — using an adaptive threshold determined by image complexity.\n- While insightful, this combination strategy is heuristic rather than fundamentally new in algorithmic form.\n\nLimited scope of model diversity:\n- Most experiments are based on a single LVLM backbone (LLaVA-1.5-7B).\n- The generalizability of the findings to other architectures (e.g., Qwen-VL) is not demonstrated.\n\nComputational trade-off not deeply analyzed:\n- The paper claims efficiency improvements but provides limited quantitative analysis on actual latency or memory usage.\n\nPotential instability in adaptive thresholding:\n- Since the adaptive rule relies on erank estimated from token embeddings, noisy or atypical embeddings (e.g., from cluttered or low-quality images) might lead to inconsistent threshold adjustments.\n- The paper lacks ablation or sensitivity studies showing the stability of AdaVTP under such variations.", "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": "VLIw7W22IK", "reviewer": "Reviewer_2wEG", "summary": "This paper presents an empirical study on visual token pruning in large vision-language models (LVLMs). The authors analyze two main pruning paradigms: attention-based and diversity-based, and characterize their behavior using attention entropy and effective rank (erank) metrics, respectively. They show that attention-based pruning works better on simple images with concentrated features, while diversity-based pruning performs better on complex images with distributed features. They further study hallucination behavior and propose an adaptive pruning framework that adjusts token similarity thresholds according to image complexity, claiming improvements over several baselines across multiple benchmarks.", "questions": "See weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "1.\tThe paper provides a thorough and systematic empirical comparison between attention- and diversity-based pruning strategies, which is less explored in depth before.\n2.\tThe adoption of effective rank and attention entropy as quantitative measures for image complexity is conceptually reasonable.", "weaknesses": "1.\tThe proposed adaptive thresholding strategy is relatively simple and heuristic (a logarithmic mapping between erank and threshold). It does not provide strong methodological or theoretical innovation beyond straightforward empirical observations. \n2.\tThe proposed adaptive thresholding strategy introduces several hyperparameters, notably the scaling coefficients and other implementation choices. These parameters may influence pruning behavior, yet the paper does not provide a clear justification or sensitivity analysis for them.\n3.\tPrior token pruning works (e.g., DivPrune, VisionZip, PruMerge+) already explore balancing attention and diversity. This paper’s “adaptive” version appears incremental and lacks a compelling distinction.", "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": "uqS1nTGV4T", "reviewer": "Reviewer_g8H8", "summary": "This paper analyzes the limitations of existing token pruning methods designed to improve the efficiency of LVLMs and proposes a novel token pruning framework to address them. The authors employ attention score entropy and effective rank to examine the characteristics of each image token, and their proposed method is designed to adaptively apply pruning based on the specific properties of the image.", "questions": "1. The values in Table 1 differ from the descriptions in Section 4.1 (OCR: 4.39, 49, POPE: 4.90, 109).\n2. After token pruning in the Table 1 experiments, how does the attention entropy change?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1. The properties and limitations of attention-based and diversity-based token pruning methods have not been clearly studied before. This paper provides both quantitative and qualitative analyses of these issues, making the argument convincing.\n2. Based on the observed trends, the authors propose an intuitive token pruning method.\n3. The proposed approach demonstrates high reliability by balancing the performance–stability trade-off between attention-based and diversity-based methods, which is further supported by extensive experimental results on various benchmarks.", "weaknesses": "1. In Table 1, the difference in entropy scores seems small. Additional explanation regarding the scale of these scores would be helpful.\n2. The performance seems sensitive to the similarity threshold. Although the authors attempt to make the design adaptive using the effective rank (erank), parameter tuning (alpha, beta) may still limit the generalizability of the proposed method.", "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": "2Fwuws1zP2", "reviewer": "Reviewer_DUt4", "summary": "Large vision language models (LVLMs) generate hundreds of visual tokens that must be concatenated with textual tokens for processing, incurring quadratic time and memory costs. Many works have proposed attention‑based or diversity‑based pruning strategies to drop redundant visual tokens. Attention‑based methods keep tokens with high [CLS] attention scores but may select similar tokens, while diversity‑based methods maximize feature diversity but can discard important high‑attention tokens. The authors provide a systematic empirical study comparing these strategies across image types, using effective rank (erank) to quantify feature diversity and attention entropy to measure how concentrated the attention distribution is. They show that simple images (low erank / low entropy) benefit from attention‑based pruning, whereas complex images (high erank / high entropy) favor diversity‑based pruning. Furthermore, using the CHAIR hallucination benchmark, they find that attention‑based methods produce more conservative captions with lower hallucination scores, while diversity‑based methods capture more objects but hallucinate more. Motivated by these insights, the authors propose an adaptive token‑pruning framework. They explore tokens in descending order of attention scores and remove redundant tokens based on a similarity threshold that is adaptively set via a logarithmic mapping of erank. Simple images (low erank) use stricter thresholds; complex images use looser thresholds. Experiments show that the adaptive strategy maintains competitive performance.", "questions": "Q1. How is erank computed in practice? Does it require an SVD for each image? What is the runtime overhead relative to token pruning, and how does it scale with image resolution and batch size?\n\nQ2. Have you experimented with different mapping functions (e.g., linear or learned mappings) for tau_adaptive? How sensitive are the results to the chosen alpha and beta values across different datasets?\n\nQ3. You use both erank and attention entropy to analyze image complexity, but only erank is used for threshold adaptation. Did you test using attention entropy or other metrics for adaptation?\n\nQ4. For tasks requiring fine‑grained spatial reasoning or counting, does the adaptive pruning ever remove tokens critical to the answer? Can you provide qualitative examples where the method fails or underperforms?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. The paper systematically evaluates attention‑ and diversity‑based pruning across datasets, using effective rank and attention entropy to quantify image complexity. This analysis clarifies when each strategy excels.\n\nS2. By measuring CHAIR metrics and recall, the authors show that diversity‑based pruning has higher recall but induces more hallucinations, while attention‑based pruning yields safer outputs.\n\nS3. Experiments span nine multimodal benchmarks and the CHAIR hallucination dataset, showing the adaptive method maintains accuracy under aggressive pruning and reduces hallucination.", "weaknesses": "W1. The adaptive threshold function is heuristic and requires tuning coefficients alpha and beta. There is no principled justification or learning of this mapping.\n\nW2. Computing effective rank involves singular‑value decomposition of token features, which may be expensive for large sequences. The paper does not quantify this overhead or compare it with more lightweight measures.\n\nW3. Experiments are conducted exclusively on LLaVA‑1.5‑7B. Results may not generalize to other LVLMs or larger models, where token distributions or attention patterns differ.\n\nW4. Improvements over baselines are relatively small (1–3% absolute), which may not justify the added complexity of computing erank and adaptive thresholds. Statistical significance is not reported.\n\nW5. The paper notes that diversity‑based methods hallucinate more, but does not examine whether the adaptive method sometimes discards critical tokens (e.g., rare or small objects) or affects reasoning tasks beyond object captioning.", "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)