ToProVAR: Efficient Visual Autoregressive Modeling via Tri-Dimensional Entropy-Aware Semantic Analysis and Sparsity Optimization
Authors: Jiayu Chen
Ruoyu Lin
Zihao Zheng
Jingxin Le
Maoliang Li
Guojie Luo
Xiang Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Image Generation
Autoregressive Models
Efficient Visual Generation;
Primary area: infrastructure, software libraries, hardware, systems, etc.

Abstract: Visual Autoregressive (VAR) models enhance generation speed but face a critical efficiency bottleneck in later stages. In this paper, we present a novel optimization framework for VAR models that fundamentally differs from prior approaches such as FastVAR and SkipVAR. Instead of relying on heuristic skipping strategies, our method leverages attention entropy to characterize the semantic projections across different dimensions of the model architecture. This enables precise identification of parameter dynamics under varying token granularity levels, semantic scopes, and generation scales. Building on this analysis, we further uncover sparsity patterns along three critical dimensions—token, layer, and scale—and propose a set of fine-grained optimization strategies tailored to these patterns. Extensive evaluation demonstrates that our approach achieves aggressive acceleration of the generation process while significantly preserving semantic fidelity and fine details, outperforming traditional methods in both efficiency and quality. Experiments on Infinity-2B and Infinity-8B models demonstrate that ToProVAR achieves up to 3.4× acceleration with minimal quality loss, effectively mitigating the issues found in prior work. Our code will be made publicly available.

Published as a conference paper at ICLR 2026
EFFICIENT VISUAL AUTOREGRESSIVE MODELING VIA TRI-DIMENSIONAL ENTROPY-AWARE SEMANTIC ANALYSIS AND SPARSITY OPTIMIZATION
TOPROVAR:
Jiayu Chen'
Ruoyu Lin Zihao Zheng* Jingxin Li²
Maoliang Lia
Guojie Luo Xiang Chen
School of Computer Science, Peking University
2 School of Electronics Engineering and Computer Science, Peking University
National Key Laboratory for Multimedia Information Processing, Peking University
ABSTRACT
Visual Autoregressive (VAR) models enhance generation quality but face critical efficiency bottleneck in later stages. In this paper, we present novel optimization framework for VAR models that fundamentally differs from prior approaches such as FastVAR and SARR Instead of relying on heuristic skipping strategies, our method leverages attention entropy to characterize the semantic projections across different dimensions of the model architecture. This enables precise identification parameter dynamics under varying token granularity levels, semantic scopes, and generation scales. Building this analysis, further uncover sparsity pat- terns along three critical dimensions token, layer, and scale- andee-and propose set of aie-ggainne optimization strategies tailored to these patterns. Extensive evalu- ation demonstrates that our approach achieves aggressive acceleration of the gen- eration process while significantly preserving semantic fidelity and fine details, outperforming traditional methods both efficiency and quality. Experiments Infinity-2B and Infinity-8B models demonstrate that ToProVAR achieves up to 3,4x acceleration with minimal quality loss, effectively mitigating the issues found prior work. Our code will be made publicly available.
Semantie
Structural Distortion
Detail Callapse
OriginaX (1.0)
Fus/VA((+2.53)
Original 1.0))
PastVAR(x2.533)
Originali 1.0)
FastVAR(>2.5
SkipVAR(=2.01)
ToPtoVAB
SkipVAR(>
ToProVAR(<3.00)
SkipVAR(-2.05)
ToProVAR(s3.15))
mintralie wonden oracclat batr with light yellow curtioned bastrest, bickgroudd realistic
realicti brawn hear ananding sprigir foiding
"On whine rableag, gigarr magflosers rinnds bokidd agalest soaliyy
wodem
depictad
beside
photorrattcii
Figure I: A comparison between our method and state-of-the-art compression methods. The SOTA methods often suffer from issues such semantic loss, structure distortion, and detail collapse.
INTRODUCTION
Traditional autoregressive (AR) models generate images via raster-scan next-token prediction Li et al. 2024b Liu et al. 2024 ai et al. 2025 Xie et al.) (024). which has long produced inferior results compared to diffusion models. Visual AutoRegressive modeling (VAR) (Tian et al. 2024
"Corresponding author.

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Published as a conference paper at ICLR 2026
Trequenicy Token hunng
Scale-Level Optimization
Layon-Leve
Tokern-Level Optimization
Fine-Grainet Tokoo Pruning
Optimization
Global and Detall-Scope Layer Sparsity Analysis
Object Finemass Specific Scale Depth Optimization
Lowe Frequency
Static Acceloration
FastV AR
Model losed sping
Hell Hells
Low Frequency Construction
Training Required Acceleration
(b) VAR
(c) ToProVAR
Figure 2: Different Optimization Dimensions FastVAR vs. VAR vs. ToPRoVAR
Han et al. 2024, Tang et al. 2024) reformulates generation as coarse-to-fine next-resolution pre- diction, enabling GPT-style AR models to, for the first time, surpass diffusion models in image quality. Despite these advancements, VAR-based methods still face a core problem: the number of tokens grows exponentially with image resolution and generation scales, resulting in inefficient computation later stages.
To improve the computational efficiency of VAR models, existing researchs, such as FastVAR (Guo et
et al., 2025) and SkipVAR et al. 2025a), have explored various token reduction strategies. As shown in Fig. 2 (b),, FastVAR retains a fixed ratio of high-frequency tokens in the token dimen- sion, while Skip skips certain scales or replaces unconditional branches in the scale dimension based a trained decision model. Although these methods have demonstrated significant value in accelerating generation, they mainly rely on single-dimensional sparsity analysis of intermediate image data, which introduces several limitations as illustrated in Fig. D (1)Semantic loss: specific tokens corresponding to key objects are pruned when their frequency in the image is too low; (2) Structural distortion: a single sparsity metric like frequency in FAstVAR cannot capture the complex relative relationships among objects, leading to noticeable deformations in complex regions; (3) De- tail collapse: fine-grained objects typically require deeper generation scales for adequate support, while methods like Skip VAR that skip entire scales result in severe loss of details.
Based on the above issues, we identify several critical challenges optimizing VAR models: (1) Fine-gne sine-granned sparsity analysis: Unlike prior work, need to design highly fine-grained approach to sparsity analysis that effectively prevents information loss caused by misalignment between spar- sity metries and image semantics. (2) Multi-diimensionl representation: It is essential to analyze the model across multiple dimensions, enabling not only the assessment of individual token im- portance but also the accurate characterization of their relative relationships in other dimensions. (3) Efficiency-preserving optimization: While pursuing fine-grained optimization, the analysis itself must remain efficient; otherwise, excessive overhead in modeling sparsity would compromise the overall benefits of the optimization.
To address the aforementioned challenges, we introduce novel optimization framework ToProVAR- for VAR modeling computing optimization.
First, unlike prior approaches such FastVAR or SkipVAR, which evaluate sparsity directly on intermediate image representations, we leverage attention entropy to analyze how semantics are projected within the model structure during generation. This enables precise tracking of dynamics under varying object salience, semantic scope, and fineness, thereby providing principled guidance for joint ceemantic-sparsiie analysis and optimization.
Second, we extend eatrop analysis beyond tokens to cover three complementary di- mensions: token-level, layer-level, and scale-level. This multi-dimensional perspective allows us to uncover semantic distributions and correlations that govern sparsity patterns along token, layer, and scale dimensions. As illustrated in Fig. c). this facilitates a series of fine-grained optimizations: token-level pruming of non-essential semanties. layer-level compression that distinguishes global from detail representation, and scale-level disentanglement and depth adjustment generation tai- lored to object fineness necessity.
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Finally, we design coordinated optimization algorithm that integrates these three dimensions, while further improving the efficiency attention attention-entropy entropy analysis itself. This ensures not only the effectiveness but also the practicality of our framework for real-world VAR acceleration.
We evaluated ToProVAR on mainstream VAR models, Infinity-2BB and Infinity- The experimental results show that, compared to FastVAR and SkipVAR, ToRRoVAR improves inference speed to nearly 3.5x with almost no loss in image quality. In Fig. | we demonstrate the high-quality visual results generated by PoProVAR based on the Infinity-8B model, effectively addressing issues such as semantic loss, structural distortion, and detail collapse.
2 PRELIMINARY
Visual Autoregressive Modeling. VAR redefines the traditional autoregressive paradigm, shifting the core from "next-token prediction" to "next-scale prediction." For given image, VAR first ob- tains a feature map through an encoder, which is then quantized into K multi-scale token maps R (ri, .2,...,fe TT J; The resolutions of these token maps increase progressively according scale schedule {(h,, w. (h2, w2)...., (hx, ]. Each token map rk E {1...., V.),,,)hee Vy tains hę wa discrete tokens, all from a codebook of size V.
x con-
The joint probability distribution over the multi-scale token maps is factorized autoregressively as:
II P2 -....
Specifically, at each autoregressive scale k. the previously generated token maps {ri,.... rk-1] are processed by I. layers of the VAR network to predict the current token map Tk- This multi- scale generation process supports parallel decoding of multiple tokens within single token map, significantly improving efficiency compared to traditional "token-by-token" autoregressive models.
Attention Entropy for Semantic Projection Analysis. Attention entropy quantifies how concen- trated the attention distribution for given query. A low entropy value indicates that a token focuses its attention only few targets, suggesting strong semantic selectivity; conversely, high entropy reflects a more uniform distribution over targets, implying weaker semantic focus. For- mally, given query qi and keys ky. the attention weights are defined scaled dot dot-products, and the entropy is computed as:
explg, Kog dd) Σ exp(a kt/ kv/vda)
H(q:) Σ aij log agi 0i
where qi and k; denote the query and key vectors, and du is their dimension. Previous studies (Cheng et al. 2022; Pardyl et al. 2023] Choi et al.) 2024) have exploited this property of attention entropy to distinguish between foreground and background regions. In this work, we build upon this intuition and employ attention entropy for a different purpose: performing fine mme-grained semantic projection evaluation of VAR models.
Unlike frequency-based fixed scopes, however, the range of j can be flexibly set across different model structures. Extending beyond local tokens encompass cross-layer and multi-scale rep- resentations enables entropy to jointly capture semantic evolution across layers and scales. This allows us not only analyze variations between adjacent tokens, but also to extend the activation range across layer and scale dimensions. In this way, we can simultaneously preserve fine- grained token-level analysis while examining sparsity from broader perspectives in the generation process.
TRI-DIMENSIONAL ATTENTION ENTROPY GENERALIZATION AND RELATED SEMANTIC AND SPARSITY ANALYSIS
We leverage attention entropy as a unified measure for semantic projection evaluation, enabling the analysis of data sparsity from three dimensions in VAR models.
Token-Level Attention Entropy for Semantic Salience Analysis. Previous approaches, such as frequency-based FastVAR, often overlook semantic information; as result, tiny and fine details
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Salvenee
Salignce
10
30.5
Inf 10.47
Infinity 2B. 30.47
10.0
Global
Layer
IPSV2 Score
50.0
29.5
29.0
Detail Tayer
13.0
Layer
High Salienes
Deviil Layer
Harld
10 30 10 40 50 MI 70 8o
20 30 40 50 60 10 MI 90
320x320
512x512
768x768
Token Proming (h)
Layer 10
Layer
Layer Comprestion Ratoo())
(a) The Semantic Salience in Token Dimension
(b) The Semamti Represcutation Layer Dimension
Scale
Scale
Scale 12
Senis 1%
Scale 16
Scaile
Scale
12
Scale 13
High Completenns
THC
116
Tat
0.8
SSIM
Myylich entieopommrpiiccc FoR with briihh FIIT
Grecn Grass lowa while sign
0.2
Campleteness
11
12
Cite with
Scale
(e) Completeness Scale Dimension.
Figure 3: Tri-dimensional attention entropy analysis VAR models: (a) Token-Level Semantic Salience: Pruning low-saliency tokens preserves quality, while pruning high-saliency ones causes severe degradation. (b) Layer-level Semantic Representation: Global Layers capture structure and gruning-sensiticg whereas detail layers refine local semantics and can be pruned. (c) Scale-level Semantic Depth: complex objects require deeper scales for fine details, while simple ones stabilize earlier, enabling adaptive depth pruning.
are easily over-optimized and consequently lost, as illustrated in Fig. 1 In contrast, by employing attention entropy. our analysis is performed at the model dimension, which enables to effectively capture generation semantics even for fine-grained content. This substantially improves both con- trollability and accuracy in generation optimization. Frequency-based approaches typically rely on local averaging within a region. When most of the region contains low-frequency content but only small portion carries high-frequency details, the averaging process suppresses the latter, leading to their removal during pruning (e.g., the cat's paw in the figure). By leveraging semantic cues, our semantic-based method overcomes this limitation and faithfully preserves both local details and global structures, even under high token pruning ratios.
Therefore, semantic salience refers to the salience distribution across tokens, where only subset of tokens carries critical semantic information. To further evaluate the impact of semantic salience on quality, we progressively pruned tokens from regions of different salience and measured image quality. The results, shown in the right of Fig. a],, demonstrate that on the Infinity-8B model, removing up to 90% of low-saliency tokens leads to only a slight decline in image quality (a drop of <2%). However, pruning just 10% of high-saliency tokens causes a significant loss in generation quality, resulting in noticeable artifacts and semantic gaps.
Based this exploration of semantic salience, we propose prioritizing the pruning of low-saliency token regions during the later stages of generation. This approach can accelerate the generation process while preserving the quality of the output.
Layer-level Attention Entropy for Semantic Scope Analysis. Attention entropy not only enhances granularity at the token level, but its flexible analysis scope also enables semantic patterns to be examined across broader dimensions. By extending the scope of the attention entropy (the range of index in Eq. [ to include all tokens within a layer, we can analyze token distributions in the layer dimension and characterize the generation scope of each layer. Specifically, as shown in Fig. Bi),, certain layers exhibit relatively uniform, grid-like attention distributions with prominent principal components, focusing capturing broad spatial relationships and establishing the overall image structure at a global scope. In contrast, the other layers display more varied, semantic-driven attention patterns with less distinct principal components, concentrating progressively refining local semantics and fine-grained textures within localized scope.
This observation motivates distinguishing layers according to their semantic scope, categorizing them as Global Layers and Detail Layers. Global layers, with their globally distributed attention, encode strong interconnections across the entire image, whereas detail layers, with more local-
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X+I
ModelisScllss
High Sulience Token
Prine Rine
Layer
UEVV
Non Prunsble
Prunable
Global
Datail
Entropy
104
Entruyy
Disuibution
(a) Scale-Aevel optimization
Layer-levl optimization
Token-level optimization
Figure 4: ri-Dimeesional Entropy-Aware VAR Sparsity Optimization: (a) Scale-level: compute the low-entropy ratio P, across scales and select the pruning start depth via threshold т. (b) Layer-level: for each scale, perform SVD on the entropy map to separate Global layers from Detail layers. (c) Token-level: within prunable layers/scales, increase pruning rate with scale and use entropy- entropy-based gating Pprune to remove low-salience tokens, preserving salient regions.
ized attention, concentrate on specific regions, leaving substantial sparsity in the unattended areas. Fig. (]b) further illustrates the impact of semantic scope on generation quality: compressing global layers by more than 50% significantly degrades output quality, whereas even aggressive compres- sion of detail layers- up to 90% on the Infinity-8B model-maine maintains high fidelity. This indicates that selectively pruning detail layers can accelerate generation with minimal quality loss.
Implementing this strategy, however, requires reliable method to distinguish global from detail layers. To this end, we propose to identify global and detail layers by the prominence of principal components in their attention entropy distributions and then prune only the detail layers.
Scale-level Attention Entropy for Semantic Fineness Analysis. VAR models generate images across multiple autoregressive (AR) scales. Prior approaches often applied coarse-grained scale reduction to reduce computation, but this came the cost of fine-grained quality. As shown in Fig. images containing fine details require optimization at the semantic granularity level rather than purely at the scale level.
Extending the scope of attention entropy (Eq. 2 beyond local tokens multiple scales enables us to capture the semantic evolution across scales. Specifically, images with high fineness, such as complex"cyber fox". exhibit predominantly low-salience distributions, requiring deeper scales to render fine details. In contrast, simpler objects like the letter "W" show predominantly high-salienes distributions, stabilizing shallower scales. As shown in Fig. c), this trend is further corroborated by their SSIM curves.
This scale-level evaluation of attention entropy thus provides a principled way distinguish the fineness requirements of different objects and adapt the depth accordingly. Based on this obser- vation, propose dynamically determining the starting scale for pruning according semantic fineness, enabling adaptive depth allocation for different generation tasks.
4 TRI-DIMENSIONAL VAR OPTIMIZATION
Based on our tri-dimensional generalization of attention entropy and the corresponding semantic analysis, we propose comprehensive framework with optimization techniques for each dimension. As illustrated in Fig. given the multi-scale token maps of VAR model. the framework optimizes the generation process across three dimensions. Specifically, it first esti- mates the semantie fineness of the scales R. dynamically determine the scale depth for pruning, T, Subsequently, within applicable scales, it analyzes each layer's semantic scope to distinguish between Global and Detail layers, pruning only the latter. Finally, fine-grained token-level sparsifi- cation is performed within these layers based on attention entropy, with unified gating function G integrating information from all dimensions to determine the pruning probability for each token.
{r1, r2, .e.......
Scale-level optimization via Semantic Fineness. Drawing from scale-level analysis, as the scale increases, the number of high-salience tokens (orange) gradually decreases, implying fewer tokens need to be processed while the semantic fineness of the generated image improves. To capture
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Published as a conference paper at ICLR 2026
this effect, quantify the distribution of high-salience tokens across scales by the proportion of low-entropy tokens: a higher proportion indicates finer semantics. As shown in Fig. a), we define the low-entropy ratio scale
i
H
< H
PS
(3)
N₂
where H denotes the attention entropy of token at scale H denotes the mean entropy at scale and N₂ denotes the total number of tokens.
Based on this measure, we determine the pruning start scale using a threshold т. Specifically, the scale depth discrimination function is defined as D = min 2 where D is the minimum scale index at which semantic stability is achieved. To calibrate т, we conduct multiple pre-sampling experiments. We observe that as generation converges, stabilizes, which provides a reliable criterion for dynamically selecting the pruning depth.
Layer-level optimization via Semantic Scope. Based our layer-level analysis, the next opti- mization step distinguish between Global Layers and Detail Layers according to their semantic scope. As shown in Fig. Ib), Global Layers exhibit pronounced dominant component, while De- tail Layers do not. To quantify this, we apply singular value decomposition (SVD) to the attention entropy map X of each layer, i..., X UEV In Global Layers, the gap between the largest and the second largest singular values in Σ is pronounced, whereas Detail Layers lack such dominance. We thus define the principal component ratio g!l..) o
[1.*) /oz (1.s) where oj (1.s) and (1.s) denote the two largest singular values the token representation matrix at layer and scale s.
If glls)) > 1, the layer is classified as Global; g(l.s) continuous score for pruning decisions, we further define the layer representation score:
considered Detail. To provide
R (1.s) exp (- (()( 1)), B > 0,
(4)
where ((,,)) + for Detail Layers and (1,,.) - 0 for Global Layers. This score offers quantita-
tive criterion for layer-level pruning.
Token-level optimization via Fine-grained Semantic Salience. After identifying the prunable scales and layers, the final stage to perform fine-grained token pruning based on semantic salience. The core idea is to eliminate tokens with low salience (i.e., high attention entropy). As illustrated in Fig. [c). to establish a consistent pruning basis across different layers and scales, we first normalize the token's attention entropy:
H!! (((.a)
Noa A Σ
A!!:)))
where
(5)
No. Σ/ ( H(l)))
We then integrate this normalized entropy with the layer score R(...) and monotonic scale factor Ø(s) s/Smax to define a unified pruning tendency: (a.1) o(s) R((,s). ((.)), This formulation smoothly incorporates all three dimensions, ensuring that tokens with higher entropy, in layers with broader semantic scope, and at deeper scales are more likely to be pruned.
Finally, the pruning tendency is mapped to a retention probability:
1,
if < D,
Preapli 8,
(6)
clip Amin (Amax amia) s...),, 0, 1).
otherwise.
This three-factor integration provides a coherent pruning policy across tokens, layers, and scales, effectively discarding redundant details while preserving semantically critical structures.
Computational Optimization for Attention Entropy. The attention entropy is formally defined in Equation 2 A straightforward implementation would require explicitly materializing the full N x N attention matrix in order to compute row-wise probability distributions and their entropy. However, such approach is computationally prohibitive in practice, since modern attention implementations such as FlashAttention never instantiate the dense matrix explicitly due to memory and runtime constraints.

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Published as a conference paper at ICLR 2026
Table Quantitative comparison on GenEval and DPG. Note, GenEval follows the official proto- col without rewritten prompts. Latency is measured on single GPU with batch size 1.
GenEval
DPG
Methods
Lateney(s)} Speedup
Two Obj.
Position Color Attri. Overall
Entity Relation Attribute Overallf
Infinity-2B
79.01
24.00
58.00
0.69
90.81
88.19
87.89
83.41
2.10
1.0
+FastVAR
78.79
27.75
59.50
0.68
91.57
87.46
83.39
0.80
2.6
+AKipVAR
76.77
26.50
57.50
0.67
89.30
87.07
87.01
82.94
1.10
2.0 x
+AoProVAR
78.80
29.50
62.00
0.69
87.39
88.92
90.01
83.07
0,61
3,4 x
Infinity-BB
96.97
61.00
75.00
0.83
90,92
93.57
88.83
86.68
4.86
1.0 x
+FastVAR 94.19
57.00
75.25
0.81
90.80
92.30
90.40
86.50
2.01
2.4
+AkipVAR 94.94
57.50
76.50
0.82
89.71
90.52
90.02
86.44
2.11
2.3
+ToProVAR
94.95
61.00
76.00
0.83
91.11
90.39
91.04
86.70
1.78
Table Quantitative comparison on HPSv2. and ImageReward, two human preference bench- marks. Latency is measured on a single GPU with batch size 1.
HPSv2.1
Methods
ImageReward"
Latency(s)
Speedup
Photo
Concept-Ar
Anime
Paintings
Overall
Infinity-2B
29.40
30.38
31.72
30.39
30.47
0.94
1.57
1.0
+FastVAR
28.86
29.90
31.12
29.92
29.95
0,92
0.62
2.5 x
+AKipVAR
29.25
30.25
31.50
30.45
30.39
0.93
0.87
1.8 x
VARProVAR
29.26
30.15
31.44
30.23
30.27
0.93
0.58
2.7 x
Infinity-8B 29.42
31.27
32.45
30.83
30.99
1.04
5.31
1.0 x
+FastVAR
29.87
30.42
31.80
29.89
30.24
1.02
1.97
2,6
+SkipVAR 29.09
30.86
32.04
30.55
30.64
1.03
2.65
2.0
+ToProVAR
29.19
30.89
32.09
30.24
30.58
1.04
1.75
To address this challenge, we extend the original FlashAttention algorithm with an online entropy computation mechanism, which we refer to as Flash Attention Entropy. The key idea is preserve the memory efficiency of FlashAttention while simultaneously maintaining sufficient statistics for entropy computation. Inspired by the online softmax strategy in FlashAttention, we design an in- cremental update scheme that avoids forming the full attention matrix. More concretely, recall that the entropy involves terms of the form x log x over normalized attention scores. By leveraging the algebraic identity kz log(kz) kz log x (log k:) zk We can decompose the entropy computation into two accumulative statistics: the standard normalization terms (rowmax m and expsum that already tracked in Fltentntention, and additional intermediate statistic that maintains log x. This decomposition ensures that the entropy can be computed in a streaming fashion with negligi- ble overhead relative the baseline FlashAttention kernel. The resulting algorithm, termed Flash Attention Entropy, thus inherits the linear-time and memory-efficient properties of FlashAttention while enabling exact entropy computation without approximation.
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Models and Evaluation. We conduct experiments on Infinity-2B and Infinity&8((aan al. 2024) We compare TAR with representative approaches such as FastVAR et al.) 2025) and VAip al.] 2025a), while keeping all hyperparameters consistent for fairness. For evaluation, adopt widely used benchmarks including Geneval(Ghosh
al. 2024). DPG-Bench(Hu et al. 2024). HPSv2 Wu et al. 2023). ImageReward (Xu et al. (023). and MJHQ30K et al.) (024a). We report Geneval Overall, DPG Overall, HPSv2 score, FID, and CLIP score quality metrics. Efficiency is assessed in terms of runtime, throughput, and speedup ratio, with latency measured on single NVIDIA L40 GPU (40GB).

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Published as a conference paper at ICLR 2026
Table Quantitative comparisons of FID and CLIP score with different generation categories on the MJHQ30K dataset. Latency is measured on a single GPU with batch size 1.
Landscape
People
Food
Method
Latency
FID.
CLIPt
Latency
FID
CLIP
Latency
FID
CLIP
Infinity-2B
1.67
44.1
0.267
1.71
58.91
0.281
1.69
84.2
0.270
+Aa
0.60
45.1
0.264
0.61
71.8
0.274
0.61
84.7
0.273
+SkipVAN VAR
1.01
58.1
0.260
1.06
73.7
0.253
0.88
102.3
0.256
+AoProVAR
0.50
44.5
0.264
0.48
58.84
0.283
0.46
84.3
0.274
Infinity-28
Infinit-88
Ongins
Speodapp 2.5x
FaRVAR
Spoodap: 2.0x
Skip
TaPoVAR
Specdop:: 1.0s
Figure 5: Qualitative comparison of various methods on complex prompts. Our method effectively prevents semantic loss, structure distortion, and detail collapse while maintaining high visual fidelity.
5.2 MAIN RESULT
Quantitative Comparison GenEval and DPG. We evaluate ToP on GenEval and DPG
to assess the quality-efficiency trade-off (Table [ On Infinity-2B, ToProVAR achieves a 3.4 x speedup while maintaining the same GenEval score as the baseline, and even improves fine-grained
metrics such as Position (+5.5) and Color Attribute (+4.0). On Infinity-821 delivers 2.7 x speedup with no quality degradation, reaching the highest overall scores on both benchmarks. These results show that ToProVAR improves efficiency without sacrificing quality.
Quantitative Comparison HPSv2 and ImageReward. We conduct evaluations the HPSv2. and lewae benchmarks to comprehensively evaluate the human preference performance. As shown in Table 2 ToProVAR achieves significant acceleration while maintaining high-quality gen- eration. On the Infinity-2B model, ToProVAR reduces inference latency by 62.4% with a negligible drop in the overall HPSv2 score (<1%). The advantages of ToPro VAR are even more pronounced on the larger Infinity-88 8B model, where it reduces latency by 67% while preserving the same Im- aeeReward score and highly competitive HPSv2 score. These results powerfully demonstrate that ToR strikes an exceptional balance between efficiency and quality, effectively upholding sub- jective image quality and human preference alignment while improving inference speed.
Quantitative Comparison MJHQ30K. In Table MJHQ30K benchmark. It can be seen that our ToPro VAR achieves a reasonable performance while maintaining high speedup ratio. For instance, the challenging People category, our TAR even achieves a FID reduction with 3.5x acceleration, decreasing FID from 58.91 to 58.84. In other categories, our method also maintains good performance with significant acceleration.
validate the perceptual quality the
Qualitative Visualizations of Different Methods. Figure presents qualitative results of different methods on Infinity-2/88B.. Compared to FastVAR and Ski,VAR, ToPro VAR effectively mitigates
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Published as a conference paper at ICLR 2026
Table 4: Ablation study of the Three-Dimensional Table 5: Ablation Studies of Flash Attention Progressive Manipulation Framework on Infinity-2B, Entropy on Infinity-2B. "wo FAE" denotes where "+", "++", and "+++" denote progressive com- ponent addition over the previous stage.
naive attention entropy calculation, which creates computational overhead.
Method
Latency(s)] Speed f GenEvalt
Setup
Speedt Lstency(s))
Infinity-2B
2.10
0.690
Infinity-2B
2.10
+ Scale Depth Loc.
0.47
4.5 x
0.477
+FastVAR
2.6 x
0.80
++
Layer Repr. Ident.
0.57
3.7 x
0.679
+ToProVAR FAB)
1.9 x
1.10
++ Fine-grained Token Prun.
0.61
3.4 x
0.690
+ToProVAR(w/ FAF)
3.4 x
0.61
Infinity-28
Depth Loc.
I0
++ Layer
Repr. Ident.
++ Fine- grained Token Pruning
VIII
I
Figure 6: Qualitative Visualizations of the three components ablation Layer, Token) on Infinity-2B. The full tri-stage framework better preserves global layout and local details than par- tial variants that prune along only subset of dimensions.
semantic loss, structural distortion, and detail collapse. It maintains high visual fidelity while achiev- ing greater acceleration ratio.
5.3 ABLATION STUDY
Impact of Stages in the Tri-Dimensional Optimization Framework. To validate the effective- ness of design, analyze the contribution of each stage in the framework in Table and Figure A coarse-grained approach using only Scale Depth Localization and Layer Representation Identification is fast but suboptimal, with a GenEval score of 0.679. Incorporating Fine-grained Token Pruning completes the ToProVAR model, restoring the score to 0.690-nearly matching the baseline- while maintaining low 0.61s latency. This result highlights that our complete tri-stage framework is essential for optimally balancing acceleration and fidelity.
Ablation Studies on Flash Attention Entropy. We validate the efficiency of our integrated Flash Attention Entropy (FAE) module in Table 5 Naively attention entropy calculation without FAE creates a significant computational bottleneck, increasing latency 1.10s. In contrast, our fully tegrated module eliminates this overhead and reduces latency to 0.61s. This result confirms that FAE is critical for ToProVAR VAR, as it preserves and enhances the acceleration benefits of Flash Attention.
Visualization of pruned tokens with different methods. Fig. 7 visualizes the divergent pruning strategies of FastVAR and ToPro VAR. While FastVAR's frequency- based heuristic retains edges, erodes an image's 's semantic integrity by removing tokens from smooth yet vital areas like facial tours. This structural damage becomes severe as the pruning ratio increases. In contrast, ToProVAR leverages attention entropy to identify and preserve core semantic content. Even 90% prun-

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Published as a conference paper at ICLR 2026
Table 6: Time cost of frequency-, entropy,, and SVD-related operations on Infinity-8B
O(w/o)
50%
60%
70%
80%
FasVAR
Operation Time (ms) All Scales
Rep. Scale s
Priden
Frequency & Entropy (s 10)
Frequency-based scoring
1.30
0.16
Attention Entropy (naive)
125.73
12.06
Ours
FlashAttention
11.27
1.11
Flash Attention Entropy
12.97
1.28
Parople Tulhiie Cihiee
Layer-level SVD (s 6)
Figure 7: Visualization of pruned tokens by Fast- VAR and ToPRoVAR.
SVD per layer SVD over all layers
0.40
15.86
ing ratio, it protects critical features like the eyes. This visu al comparison confirms that attention entropy is a more robust heuristic than frequency for preserving semantic structure during pruning.
Computational Cost Analysis. We further quantify the overhead of our tri-dimensional sparsity framework, focusing on Flash Attention Entropy (FAE) and the layer-level SVD analysis. As shown in T naive attention entropy incurs severe bottleneck due to explicit attention- matrix mate- rialization, whereas FAE computes entropy on-the on-the-fh fly inside the FlashAttention kernel with only a lightweight æ: log reduction. This yields only 0.17 ms overhead over FlashAttention scale 10, yet reduces entropy cost by ~90%. For layer analysis, SVD is performed once per layer at a single representative scale (s=6). Table 6 shows 15.86 total over all layers, i.e., < 1% of the 1780 end-to-end latency, indicating minor overhead.
6 RELATED WORK
Autoregressive Visual Generation. AR models (Li et al. 2024b Liu et al. 2024 ai et al. 2025
Xie et al., )) originally successful in language, have been extended to image generation through next-token prediction (Van Den Oord et al., (217 et al.] al.] (202).. Recent scaling has narrowed the gap with diffusion models (Xie et al. 2024 Wu al. 2024b Wang al.
2024 Wu et al. 2024a).
Yet efficiency remains a bottleneck. To address this, Visual Autoregressive (VAR) modeling Tian al. 2024 Han et al., 2024 Tang al. 2024) introduces next-scale paradigm, where images are predicted in hierarchical token maps from coarse to fine resolution. This strategy reduces the
number of autoregressive steps and improves both speed and quality.
Efficient Visual Generation. The acceleration of diffusion models has been extensively studied,
with mature methods including distillation (Kim et al., 2025 /hhai et al., 2024 Yin et al. 2025). quantization (Zhao et al.] [2024a] Xi et al.] [025 Wu et al.] 2025), pruning (Zou et al. 2024
Fang
al. 2025| Heo et al. |l.]| (025). ) and feature caching (Liu et al.) 2025b Zhao et al.] 2004bb Ma al.)
2024
Lv et al. 2024:1 Liu et al. 2025a). Efficient though, they are tailored to diffusion architectures
and cannot be directly applied to the hierarchical prediction in VAR.
Efficiency optimization for VAR is still nascent. Early works such as FastVAR (Guo et al. 2025 and SkipVAR (L et al., al.l|2222555 (225a) exploit fixed pruning or frequencc- skipping. while recent methods explore semantic- and structure-awarc acceleration, e.g., Sparse VAR (Chen et al., 2025a) for token sparsity and CoDe (Chen et al. 20256) for collaborative decoding. Complementary insights are provided by methods such HACK (Qin al. 2025) and S (Lietal. 2025b), which focus on KV cache compression.
7 CONCLUSION
In this work, we present ToProVAR, novel acceleration framework that addresses tri-dimensional redundancies of Visual Autoregressive models. ToProVAR leverages attention entropy to uncover sparsity patterns across tokens, layers, and scales. With fine- -reingrrindd semantic modeling and pruning strategy, critical contents preserved even with aggressive acceleration, achieving 3.4x speedup with minimal quality loss, surpassing existing methods. Our study highlights the value of semantic- driven optimization for AR generation and future extensions in video and multimodal modeling.
10

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Published as a conference paper at ICLR 2026
REFERENCES
Sand. ai, Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zhang, W. Zhang. Weifeng Luo, Xiaoyang Kang, Yuchen Sun, Yue Cao, Yunpeng Huang, Yutong Lin, Yuxin Fang. Zewei Tao, Zheng Zhang, Zhongshu Wang, Zixun Liu, Dai Shi, Guoli Su, Hanwen Sun, Hong Pan, Jie Wang, Jiexin Sheng, Min Cui, Min Hu, Ming Yan, Shucheng Yin, Siran Zhang, Tingting Liu, Xianping Yin, Xiaoyu Yang, Xin Song, Xuan Hu, Yankai Zhang. and Yuqiao Li. Magi-1: Autoregressive video generation at scale. arXiv preprint abs 200..o00000/aaa/35511111111
1rXi::2505.13211. May 2025. URL https::ttasssst
Zhuokun Chen, Jugang Fan, Zhuowei Yu, Bohan Zhuang, and Mingkui Tan. Frequency-aware autoregressive modeling for efficient high- image synthesis, 2025a. URL https:
//aaxiv.ogg/aat http:///rrgaaaa//557...// 2207 20454
Zigeng Chen, Xinyin Ma, Gongfan Fang, and Xinchao Wang. Collaborative decoding makes visual auto-regressive modeling efficient. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.
June 2025b.
Yi Cheng, Ying Sun, Hehe Fan, Tao Zhuo, Joo-Hwee Lim, and Mohan Kankanhalli. Entropy guided attention network for weakly-supervised action localization. Pattern Recognition, 129:108718, 2022.
Joonmyung Choi, Sanghyeok Lee, Jaewon Chu, Minhyuk Choi, and Hyunwoo J. Kim. vid-tldr: Training free token merging for wigght vidco transformer. In CVPR, 2024.
Ruiting Dai, Chenxi Li, Yandong Yan, Lisi Mo, Ke Qin, and Tao He. Unbiased missing-modality multimodal learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 24507-24517, October 2025.
Haipeng Fang, Sheng Tang, Juan Cao, Enshuo Zhang, Fan Tang, and Tong- Yee Lee. Attend to not attended: Structure-then-detia token merging for post-training dit acceleration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18083- 18092, June 2025.
Dhruba Ghosh, Hannanch Hajishirzi, and Ludwig Schmidt. GenEval: An object-focused framework for evaluating text-to-image alignment. NeurIPS, 36, 2024.
Hang Guo, Yawei Li, Taolin Zhang, Jiangshan Wang, Tao Dai, Shu-Tao Xia, and Luca Benini. Fastvar: Linear visual autoregressive modeling via cached token pruning. arXiv preprint arXiv:2503.23367. 2025.
Jian Han, Jinlai Liu, Yi Jiang, Bin Yan. Yuqi Zhang, Zehuan Yuan. Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. arXiv preprint arXiv:2412.044 04431, 2024.
Jachoon Heo, Adiwena Putra, Jicon Yoon, Sungwoong Yune, Hangyeol Lee, Ji-Hoon Kim, and Joo- Young Kim. Exion: Exploiting inter-and intra-iteration output sparsity for diffusion models. In 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 324 337, 2025. doi: 10.
Xingjian Hu, Renrui Wang, Yizhou Fang, Bo Fu, Peng Cheng, and Gang Yu. Ella: Equip diffusion models with Ilm for enhanced semantic alignment. arXiv preprint arXiv:2403.05135, Mar 2024.
URL httttttt///ttiiiit org/ abs 05135
Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schönfeld, Jonas Kohler, Markos Geor- gopoulos, Albert Pumarola, Ali Thabet, and Artsiom Sanakoyeu. Autoregressive distillation of diffusion transformers. In Proceedings of the IEEE/CVF Conference Computer Vision and Patlern Recognition (CVPR), pp. 15745-15756, June 2025.
Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation. arXiv preprint arXiv:2402. 402.:2400..124455 2024a.
11

---

Published as a conference paper at ICLR 2026
Jiajun Li, Yue Ma, Xinyu Zhang, Qingyan Wei, Songhua Liu, and Linfeng Zhang. Skipvar: Ac- celerating visual autoregressive modeling via adaptive frequency-awart skipping. arXiv preprint arXiv:2506.00m 20X,::2506.08908, 08908, 2025a.
Kunjun Li, Zigeng Chen, Cheng- Yen Yang, and Jenq-Neng Hwang. Memory-efficieed visual au- toregressive modeling with scale-aware kv cache compression. In Thirty-Ninth Conference Neural Information Processing Systems (NeurIPS 2025), Mexico City, November 30-December 2025b.
Tianhong Li, Yonglong Tian, He Li, Mingyang Deng. and Kaiming He. Autoregressive image generation without vector quantization. arXiv preprint arXiv:2406. 1838, 2024b.
Dongyang Liu, Shitian Zhao, Le Zhuo, Weifeng Lin, Yu Qiao, Hongsheng Li, and Peng Gao. Sumina-mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal gener- ative pretraining. arXiv preprint 0rXiv:2408.02657, 2024.
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu, Junjie Chen, and Linfeng Zhang. From reusing to fore- casting: Accelerating diffusion models with taylorseers. In Proceedings of the IEEE/CVF In- ternational Conference Computer Vision (ICCV), 2025a. htthhhtsssssttteee com/
Shenyi-2/Taylorsee
Joseph Liu, Joshua Geddes, Ziyu Guo, Haomiao Jiang, and Mahesh Kumar Nandwana. Smooth- cache: A universal inference acceleration technique for diffusion transformers. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops, pp. 3229-3238, June 2025b.
Zhengyao Lv, Chenyang Si, Junhao Song, Zhenyu Yang, Yu Qiao, Ziwei Liu, and Kwan-Yer K. Wong. Fastercache: Training-free video diffusion model acceleration with high quality. In arxiv, 2024.
Xinyin Ma, Gongfan Fang, and Xinchao Wang. Deepcache: Accelerating diffusion models for free. In The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.
Adam Pardyl, Grzegorz Rypeść, Grzegorz Kurzejamski, Bartosz Zieliński, and Tomasz Trzciński. Active visual exploration based attention-map entropy. In IJCAI, 2023. doi: 10.24963/ijcai. 2023/145.
Ziran Qin, Youru Lv, Mingbao Lin, Zeren Zhang, Danping Zou, and Weiyao Lin. Head-aware kv cache compression for efficient visual autoregressive modeling, 2025. URL bttps://///:
arg//ba/200 09261
Haotian Tang, Yecheng Wu, Shang Yang, Enze Xie, Junsong Chen, Junyu Chen, Zhuoyang Zhang, Han Cai, Yao Lu, and Song Han. HART: Efficient visual generation with hybrid autoregressive transformer. arXiv preprini arXiv:2410. 10812, 2024.
Keyu Tian, Yi Jiang. Zehuan Yuan, Bingyue Peng. and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. NeurIPS, 2024.
Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 30, 2017.
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. EMU3: Next-token prediction is all you need. arXiv preprini 2409 18869, 2024.
Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, al. Janus: Decoupling visual encoding for unified multimodal understanding and generation. arXiv preprint arXiv:2410. 13848, 2024a.
Junyi Wu, Zhiteng Li, Zheng Hui, Yulun Zhang, Linghe Kong, and Xiaokang Yang. Quantcache: Adaptive importance-guiide guided quantization with hierarchical latent and layer caching for video generation. arXiv preprint arXiv:2503.06545, 2025.
12

---

Published as a conference paper at ICLR 2026
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to- image synthesis. arXiv preprint arXiv:2306. 09341, 2023.
Yecheng Wu, Zhuoyang Zhang, Junyu Chen, Haotian Tang, Dacheng Li, Yunhao Fang, Ligeng Zhu, Enze Xie, Hongxu Yin, Li Yi, et al. VILA-U: A unified foundation model integrating visual understanding and generation. arXiv preprint arXiv:2409.0444 04429, 2024b.
Haocheng Xi, Shuo Yang. Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, et al. Sparse videogen: Accelerating video diffusion transformers with spatial-temporal sparsity, arXiv preprint arXiv:2502.0177. 2025.
Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang. Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408, 12528, 2024.
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36: 15903- 15903-15935, 2023.
Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. In CVPR, 2025.
Yuanhao Zhai, Kevin Lin, Zhengyuan Yang. Linjie Li, Jianfeng Wang, Chung-Ching Lin, David Doermann, Junsong Yuan, and Lijuan Wang. Motion consistency model: Accelerating video diffusion with disentangled motion-appearance distillation. arXiv preprint urXiv:2406.06890 2024.
Tianchen Zhao, Tongcheng Fang, Enshu Liu, Wan Rui, Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefei Ning, and Yu Wang. Vidit-q: Efficient and accurate quantization of diffusion transformers for image and video generation, 2024a.
Xuanlei Zhao, Xiaolong Jin, Kai Wang, and Yang You. Real-time video generation with pyramid attention broadcast. arXiv preprint arXiv:2408. 12588, 2024b.
Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, and Linfeng Zhang. Accelerating diffusion transformers with feature caching. arXiv preprint arXiv:2410.05317, 2024.
13


Reviews:
[{"review_id": "8ZjZOFL8co", "reviewer": "Reviewer_myfg", "summary": "The authors show that attention entropy can be generalized across three dimensions (token, layer, and scale) to guide semantic-aware acceleration in visual autoregressive (VAR) models based on next-scale prediction. They first quantify semantic fineness across scales via the low-entropy ratio, determining the depth at which generation can safely begin pruning. Next, the authors classify layers into Global vs. Detail using the principal-component dominance (SVD-based ratio) of attention entropy, pruning only the Detail layers. Finally, the authors apply token-level salience pruning within prunable layers via a unified gating function that combines normalized entropy, layer scope, and scale depth. Empirically, the paper shows that compressing Global layers harms structure, whereas up to 90% compression on Detail layers preserves fidelity. Images with complex local detail show lower entropy and thus require deeper scales.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Integrated semantic perspective. The tri‑dimensional entropy viewpoint connects token salience, layer scope, and multi‑scale semantics, which motivates where to prune.\n\n* Layer taxonomy with a quantitative analysis. SVD‑based gives a reproducible criterion to distinguish Global/Detail layers before pruning.\n\n* Scale‑aware depth selection. The low‑entropy ratio offers a principled way to adapt depth to content complexity rather than using a fixed scale budget.\n\n* Empirical validation. The layer-specific compression curves (Fig. 3b) and ablations (Table 4) quantitatively support the semantic analysis, showing that pruning Detail layers up to 90% yields minimal quality degradation.", "weaknesses": "I'm not an actual expert in this area, but I have some concerns about the paper (including some appendix) based on my understanding.\n\n* In terms of overhead and practicality, computing entropy per token, SVD per layer × scale, and tri‑dimensional gating online can be expensive. What is the actual (e.g., net) wall‑clock speedup vs. simpler frequency‑based methods once analysis overhead is included?\n\n* Calibration burden: Depth threshold $\\tau$ is selected by pre‑sampling (in Appendix). How robust is $\\tau$ across prompts, resolutions, and models? Is there an online estimator to avoid pre‑runs?\n\n* I think that the ablation studies should be done across dimensions. Could the authors isolate the incremental contributions of scale‑level, layer‑level, and token‑level pruning (three‑way ablation)? Current evidence is mostly qualitative.\n\n* Generalization beyond Infinity‑series: Do the layer‑scope statistics and ρs behavior hold for other VAR families and codebooks? Please include failure cases where Global/Detail separation is ambiguous.", "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": "hWyDZDEizW", "reviewer": "Reviewer_TaTd", "summary": "This paper introduces ToProVAR, which is a training free acceleration framework for VAR that uses attention-entropy to drive sparsity decisions jointly across tokens, layers, and scales. The method authors proposed can generalize entropy beyond local tokens to cross-layer and cross-scale scopes. ToProVAR has shown good performance, where on infinity-2b/8b, it reports 3.4x and 2.7-3.0x speedups respectively with little quality loss on GenEval/DPG/HPSv2/ImageReward and MJHQ30K.", "questions": "$\\bullet$ Can authors clarify the intended scope of attention-entropy as a salience proxy? How is it calibrated across heads/layers?\n\n$\\bullet$ Can authors discuss interactions across the scale -> layer -> token stages, provide the fallbacks, stop conditions, and outline a safe operating range where compounding errors are unlikely?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "$\\bullet$ New sparsity signal: moves from frequency heuristics to entropy based semantic salience, and the results with illustrations are convincing. \n\n$\\bullet$ three dimension design is well structured: starting with low entropy ratio, layer classification via principal component ratio from SVD, then unified token retention probability. \n\n$\\bullet$ Nontrivial novelty in the engineering design: FAE integrates entropy into flash attention to avoid $N \\times N$ instantiation and keep the linear-time behavior at the same time. \n\n$\\bullet$ Solid Empirical performance", "weaknesses": "$\\bullet$ The paper takes low attention-entropy as salient, but there is no proof it correlates with task loss or semantics, nor normalization across heads and layers with different logit temperatures. \n\n$\\bullet$ Deriving the error bounds or optimality for the tri-stage greedy pruning: Scale -> layer -> tokens decisions are locally heuristic with no suboptimality gap, stability, or compounding-error control. \n\nI understand this work focuses on empirical contributions, but it is often necessary to justify the empirical findings with explanations. I am not asking the authors to develop a full theoretical framework to address these weaknesses; I will be satisfied if the authors can answer these questions in text.", "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": "qyF9UcZxF7", "reviewer": "Reviewer_2xuz", "summary": "The paper proposes ToProVAR, a novel acceleration framework for Visual Autoregressive (VAR) models that leverages attention entropy to perform fine-grained sparsity analysis across three dimensions—token, layer, and scale. By dynamically identifying and pruning semantically redundant computations, ToProVAR achieves up to 3.5× inference speedup on Infinity-2B and Infinity-8B models with minimal degradation in generation quality, effectively addressing issues like semantic loss, structural distortion, and detail collapse observed in prior methods such as FastVAR and SkipVAR.", "questions": "See weakness.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of employing attention entropy is interesting and the motivation is strong.  \n2. Solid experiments and promising performance.", "weaknesses": "1. It would be beneficial to include results on additional backbones beyond Infinity, such as HART [1] and STAR [2], to demonstrate the generalizability of ToProVAR across different VAR architectures.\n2. The paper does not provide a detailed analysis of the computational overhead of SVD. Is there an analysis of the layer representation score? What are the patterns in the emergence of Global Layers and Detail Layers? Figure 10 shows an alternating pattern, is this behavior general? \n3. According to Table 4, Layer Representation Identification contributes most significantly to preserving generation quality (GenEval ↑ from 0.477 to 0.679), suggesting its critical role. Thus, more analysis such as qualitative cases would strengthen the paper’s claims.", "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": "AcqGOuHDPN", "reviewer": "Reviewer_FVpx", "summary": "This paper proposes ToProVAR for visual autoregressive models' acceleration. ToProVAR can identify parameter dynamics and redundancy at different granularities, semantic scopes, and generation scales, enabling fine-grained acceleration and pruning. In the experiments, the authors conduct comprehensive quantitative and qualitative evaluations on mainstream VAR models (Infinity-2B and Infinity-8B). Results show that ToProVAR achieves an average 3.5× inference speedup with almost no loss in generation quality, significantly outperforming existing SOTA methods.", "questions": "- It seems that the proposed method requires performing online SVD for different samples. What is the time cost of this step?\n \n- What is the computational overhead of the proposed FlashAttn-Entropy?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- To obtain the entropy of the attention map while remaining compatible with FlashAttention, the authors propose FlashAttn-Entropy, which is noteworthy.\n\n- The proposed method achieves faster speed against previous methods like FastVAR.\n\n- The paper is well-written and easy to follow.", "weaknesses": "- The proposed method is only evaluated on the Infinity model families. How does it perform on other model families such as HART?\n\n- The proposed method requires searching for the best hyperparameter combinations on different datasets. What is the computational cost of this process?\n\n- In Figure 3, the meaning of the lines with different colors is unclear. It is recommended to add a legend in this figure. Moreover, in Table 1, the bolded performance on the DPG dataset seems to be incorrect (FastVAR’s 83.39 should be better).", "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)