One Patch Doesn’t Fit All: Adaptive Patching for Native-Resolution Multimodal Large Language Models
Authors: Wenzhuo Liu
Weijie Yin
Fei Zhu
Shijie Ma
Haiyang Guo
yi chen
Xiao-Hui Li
LiangXiao
ChaoFeng
Cheng-Lin Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Native Resolution
Multimodal Large Language Models
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Real-world visual signals are inherently variable in resolution, and it is natural to endow multimodal large language models (MLLMs) with such native-resolution perception capabilities. In principle, for general and straightforward multimodal understanding, low-resolution images are sufficient. While for images with nuanced details like documents and charts, it is crucial to preserve fine-grained details using high-resolution inputs, as naive resizing inevitably results in information loss. Recent advances employ sequence packing to process images of any resolution and aspect ratios. Despite these efforts, model performance degrades at both low and high resolutions, and high-resolution inputs incur substantial computational costs. We argue that the rigid use of a single patch size is the primary cause: when image resolution or information density varies, fixing patch size is intrinsically suboptimal.  To address this issue, we introduce Adaptive Patching (AdaPatch), a simple yet effective strategy that adjusts patch size according to image resolution and information density and could be seamlessly plugged into pre-trained fixed-patch MLLMs without any training efforts. Extensive evaluations demonstrate consistent improvements in native resolution performance without additional training. Besides, we provide a training-based method to further adapt MLLMs with dynamic patch sizes and enhance the performance.

Published as a conference paper at ICLR 2026
ONE PATCH DOESN'T FIT ALL: ADAPTIVE PATCH- ING FOR NATIVE-RESOLUTION MULTIMODAL LARGE LANGUAGE MODELS
Wenzhuo Liu Weijie Yin
Fei Zhu
Shijie Ma
Haiyang Guo Yi Chen
Xiaohui Li1.2 Xiao Liang
Chao Feng² Cheng-Linn Liu
School of Artificial Intelligence, University of Chinese Academy of Sciences "State Key Laboratory of Multimodal Artificial Intelligence Systems,
Institute of Automation, Chinese Academy of Sciences
'ByteDance Inc {1 uwenzhue2020, mashijiee0211 jiiaiaa.. ac..aaccoom en, zhfei2013@@mail com
'Centre for Artificial Intelligence and Robotics, HKISI-CAS
ABSTRACT
Real-world visual signals are inherently variable resolution, and it natural endow multimodal large language models (MLLMs) with such native-resolution perception capabilities. In principle, for general and straightforward multimodal understanding. low-resolution images are sufficient. While for images with anced details like documents and charts, it is crucial to preserve fine-grained de- tails using high-resolution inputs, naive resizing inevitably results in informa- tion loss. Recent advances employ sequence packing to process images of any resolution and aspect ratios. Despite these efforts, model performance degrades both low and high resolutions, and high-resolution inputs incur substantial com- putational costs. We argue that the rigid use of single patch size is the primary cause: when image resolution or information density varies, fixing patch size is intrinsically suboptimal. To address this issue, we introduce Adaptive Patching (AdaPatch), simple yet effective strategy that adjusts patch size according to im- age resolution and information density and could seamlessly plugged into pre- trained fixed-patch MLLMs without any training efforts. Extensive evaluations demonstrate consistent improvements in native resolution performance without additional training. Besides, provide training- method to further adapt MLLMs with dynamic patch sizes and enhance the performance.
INTRODUCTION
Multimodal large language models (MLLMs) (Liu al. 2024a Team al. 2023) have emerged as a central paradigm for joint visual and linguistic understanding. A common design couples pre- trained vision transformer with a large language model via a lightweight projector. real-world scenarios, visual inputs exhibit substantial variability in resolution and aspect ratio, ranging from low-resolution thumbnails to high-resolution documents, making robust native resolution capability key requirement for MLLMs (Guo et al.) 2024). Early MLLMs (e.g., the LLaVA (Liu et al. 2023a 2024b) and Intern VL (Chen et al. 2024b) series) typically rely on fixed-resolution vision encoders, resizing images to single canonical size partitioning them into tiles prior to encoding. Such preprocessing inevitably alters visual content and can degrade performance on inputs that demand preservation of fine detail global structure, e.g., charts and diagrams.
Following Vi (Dehghani et al. 2023), recent works retain images at native resolution and divide them into fixed-size, non-overlapping patches, producing token sequences whose length scales with image size. Multiple sequences are concatenated into single packed sequence and processed jointly by the encoder using per-sample image masks, a technique known as sequence packing. Several state-of-thhe-a models, including Qwen2.5- 5VL (Bai et al. (025), Ovis2.5 (Lu et al.) 2025), and Kimi-VL. (Team al. 2025), implement this paradigm and claim support for any input resolutions.
Corresponding author.

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Published as a conference paper at ICLR 2026
image
image
78%
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spiit
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(a) Fixed Resolution; Resize
(b) Fixed-Size Tillng
(eg, LLaVA)
Intern VL 2.5)
Image
image
fland purch preprocess
patch size
selection
Counersion
12.4
Sec.
Sec.
text
(c) Native Resolution; Fixed Patch size (eg. Qwea2.5-2 5-VL, Kimi-VL)
(d) Native Resolucion; Adaptive Patch size (Ours, AdaPatch)
Figure 1: Illustration of strategies for different image resolutions: (a) Fixed resolution: resize to a preset size: (b) Fixed-size tiling: split into uniform tiles: (c) Native resolution: fixed patch size with sequence packing; (d) Our method: adaptively adjust patch size.
In this work, we demonstrate that current designs for any-resolution processing still fail realize genuine native capability. Through systematic evaluation multiple benchmarks across broad spectrum of pixel range, we reveal that model performance fluctuates considerably between resolu- tion bands. It often degrades or becomes unstable at both low and high resolutions. These findings suggest that robust any-resolution understanding remains an open challenge.
Our analysis identifies the inflexibility of fixed patch size the principal source of degradation. In low-resolution or information-deee dense images, large patch size is too coarse to recover fine-grained cues. In contrast, in very high-resolution or information-spars. images, a small patch size becomes overly local and fails to capture global context. This mismatch constrains the model's receptive granularity and underlies most of the observed performance loss.
To address this limitation, we propose Adaptive Patching. simple drop-in method that computes lightweight information-densit estimate p and maps (p. r) + s to determine an appropriate patch size for image of native resolution r, as illustrated in Fig. [ We further present weight- preserving conversion that enables pretrained fixed-patch MILMS to operate with any patch sizes without additional training, effectively transforming them into adaptive patch models while remain- ing compatible with sequence packing. Extensive experiments show Adaptive Patching improves accuracy and stability across resolutions multiple benchmarks, while reducing encoder token counts at high resolutions to speed up inference. Our contributions are summarized as follows:
We conduct a comprehensive evaluation of AnyRes performance by rescaling benchmarks across wide resolution ranges, revealing significant degradation and instability in recent state-of-the-art MILMs with fixed patch size.
We uncover that the fixed patch size is the principal architectural cause of such degradation: it induces a representational mismatch across resolutions and information densitics.
We propose Adaptive Patching (AdaPatch) that adapts patch size according to the resolu- tion and information density. Our method provides both training- and training- based alternatives. Extensive experiments validate the improved accuracy and stability across resolutions and reduced computation for high-resolution images.
2 ANYRES EVALUATION AND ANALYSIS
2.1 BACKGROUND AND NOTATION
We consider causal MLLM that takes image and text sequence F1.7 as input and generates an autoregressive distribution p(y Det, x) over the next token, including the following steps:
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Qwen2.5-V/ BBVL335
SAIL-VL-2B
Ovis2.5-VL-2B
Kimi-VL-A3B
ChartQA (test)
DocVQA (val)
MMBench (EN)
80.0
Perforaace
60
Performance
77.3
75.0
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70.0
67.5
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1792.
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Figure 2: Anyres evaluation of SOTA MLLMs (Qwen2.5-VL, SAIL-VL, Ovis2.5, and Kimi-Va VL);
varying the preprocessing pixel range on a single benchmark causes marked changes in performance, contradicting claims of any-resolution robustness.
AnyRes preprocessing. Given an input image æ E hxxxxx with native resolution r = (h, w), the AnyRes preprocessor produces á TRxxwx target resolution F = (h, t²) The target dimensions h, ω are required be divisible by the patch size el and approximate the native size. Formally, is chosen to minimize:
min R 7, 720
(hw-wh)2 =[(h 2 (w--ww) rw) = 1)²
(1)
s.t. h E s8,, Pmin hao Pmaxx
where [Pminy Pmax] is the valid pixel range, 7 is an auxiliary scale parameter, and < < small tie-breaking weight. Resizing (e.g., bilinear interpolation) is applied independently to each channel and can regarded as a linear transformation:
= (m.) B. vec(a;) (i 1I.
where vec(æ,) E Rhw vectorizes channel and B phexkw is the interpolation matrix. The preprocessed image is क == [ Zz].
Patch embedding. Divide the image
Rxwxx into non-overlapping x patches (n [h/s] [ w/ss (oss] and embed into vision feature space Rd,, The patch-embedding layer go Rhxuxe
Raxd, is stride-s convolution with kernel wy Rexsxexd, and bias ba Rdd, i.e. go(x)
conve(æ; s), then flattened to an n x d, token sequence.
Vision encoder, projector, and LLM. From the preprocessed image, patch tokens Z go( are passed to a vision encoder & with an attention mask M, producing features V fm E Z; M) (when packing multiple images, M is block diagonal to avoid cross-image atten- tion). lightweight projector πΦ : Rdd >> Te maps tokens into the language space, V = {6 with éÚ = (wz). The causal LLM Le then reads the interleaved sequence (BOS), (IMG), V. (/IMG) T(yir)) and the next-token distribution is Pe Vee. V) Doltamax(Hea((ee ()) with causal masks enforcing left-to-right text generation.
'To simplify the analysis, ignore special-token merging. Under this assumption the factor is equal to the patch size; special-token merging applied, the factor is the patch size the merge size.
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1-14
5-42
DoCVQAua
ChartQAww
MNE
2300
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1400
1227
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J5847
Ingut resolution 6
Ingut resolution
Figure 3: Effect of patch size on AnyRes performance. Left: MME benchmark performance across patch sizes (s) and input resolutions (r)) Right: Relative optimal patch size at the same resolutions for MME, chart, and document images.
2.2 ANYRES EVALUATION OF SOTA MLLMS
To explore the ability of handling imputs with various resolutions, for the first time, we evaluate MLLMs from pixel-range perspective: we apply multiple pixel ranges P E [Puin Pmax] during AnyRes preprocessing and evaluate representative models (Qwen2.5-VI., Ovis2.5, Kimi-VI., and SAIL-VIL). Experimental Results in Fig. 2 show that different models (and tasks) exhibit distinet preferred resolution ranges, and the performance drops sharply at both low and high pixel ranges: (1) Low pixel budgets: the degradation is pronounced information-dense images (e.g.. charts, documents) because resampling at low resolutions range P destroys fine-grained signals, making it difficult to extract accurate from information-denss images. (2) High pixel budgets: most models decline markedly despite claims of supporting ~3K inputs. We argue that a fixed (rela- tively small) patch size struggles capture global context for information-sparse images with high resolution. In conclusion, a fixed patch size fails to cope with images at varying resolutions.
2.3 How FIXED PATCH SIZE DRIVES ANYRES INSTABILITY
To verify assumptions and investigate the effect of patch size in Anyres, convert pretrained Qwen2.5-VL (patch size s = 14) to multi-sizes E {7, 14, 21, 28, 35, 42) (details in Sec. 3.2 and evaluate each configuration results in Fig. (Left) reveal a clear, resolution-depender preference: smaller patches improve performance low resolutions, while larger patches preferable high resolutions.
the MME benchmark across wide range of input sizes. The
However, the preferred patch size is not determined by resolution alone but also depends on infor- mation density. Fig. (Right) compares three image types: documents, charts, and general MME images. For given absolute resolution, the empirically optimal patch size is smaller for higher information-density images (e.g., Pdocument Pehant > PMME. where p denotes the information den- sity). Empirically, we observe the following approximate trend: s* x r/p where s* is the preferred patch size and denotes scalar measure of resolution (e.g., pixels on the shorter image side).
3 METHOD: ADAPTIVE PATCHING
Motivated by the observation in Sec. 2 that the optimal patch size is related to native resolution and information density, we propose Adaptive Patching (AdaPatch). Our method quantifies informa- tion density and maps it (together with native resolution) to patch-size estimate. We then extend fixed-patch models to any-patch, enabling per-image patch-size selection. The proposed method is illustrated in Fig. and described below.
3.1 ADAPTIVE PATCH SIZE ESTIMATION
Estimation of Information Density. In this work, we introduce a simple measure image in- formation density to guide patch size adaptation. Intuitively, downsampling high-density images leads to larger information loss than downsampling sparse images. To capture this effect, we quan- tify information density by comparing features extracted from the native images with those from
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Published as a conference paper at ICLR 2026
(a) Adaptive Patch Size Estimation
(b) The Adaptive Patching framework for Native-Resoluttin MLLM
Adaptive Patch Size s
Language Model
Patching Law
Patch & Posilion Embedding
Information Density Pinto (z)
Projector
Takenizer
Encoder
First Leyer Encoder
S/aclle Selection
What the status trailers ...?
Sec. 3.2
Adaptive Patching
Patching
Resize
Estimate
Information Density
Figure 4: Patching is drop-in method that estimates information density from feature similarity and maps (p,r) + to choose patch size. We also convert fixed-patch MILMS any-patch models for per-sample adaptation training or inference.
decreased resolution. Formally, the information density of an image x defined as
(E/ (convo s)); (conve (B vee vec(i)):/2))
Pinto( 1 Σ
( (convo (æ: x]][| (f..d) (couvo- (B veu(m); s22)
Here, e/h,il denotes the i-th token feature layer & of the vision encoder, and conve. is the patch embedding with patch size adjusted to s/2. In definition, p(x) E [0, where larger values signify increased downsampling loss and therefore higher information density.
Adaptive Patching Law. We adjust the patch size based the empirical observation in Sec. 2.3 that 8* x 5. For simplicity, we model this dependency as power-law relation and introduce hyperparameters a > 0 and / > 0 to control the relative sensitivity to resolution and information density, respectively. Specifically, the target patch size s* for image æ is given by
(x) = Quantize (cip ("(ra") (o(a)tt) Sminy Smax
(4)
where x(-) is a scalar measure resolution (e.g.. min(h, w)), and Fo- p. and ă denote the base resolution, information density, and patch size of the pretrained model, with default values set to 896, 14, and 0.2, The constant e is a small positive value for numerical stability. The operator clip(:, Smin: Smax) constrains specified bounds, while Quantize(-) maps the result the nearest value from a predefined discrete set in _t,
Eq. reflects trends: (1) higher resolution favors larger patches, and (2) higher information density favors smaller patches to preserve details. This rule enables per-image patch size selection that balances resolution and density, remaining compatible with existing soquence-packinng MLLMs.
3.2 CONVERTING LIXED-PATCH MLLMS TO ANY-PATCH
Given pretrained MLLM with patch embedding ge at patch size our objective is to extend it to any sizes [ while preserving model performance. We introduce two solutions: pseudo-inverse resize ttraining-free) and multi-scale patch embedding (training-based)
Pseudo-inverse resize (training-free) optimizes the patch-embedding layer conve, so that token embeddings consistent across patch sizes s; and corresponding resolutions r,. The optimization objective is:
(we, be.) } 0. arg min Em-x conve (x, s) conve, (B." vec(æ), s.) I,..,, A...,M.. M. (5)

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Published as a conference paper at ICLR 2026
Table Evaluation results under native resolution. Our method (Adaptive Patching) is compared with the baseline (fixed patch size = 14), using the same pretrained model. For each model, the larger score between vanilla and our marked in bold.
Qwen2.5.VV V-L..B.-VLL.3B
VL.2BB
Ovis2.5.2B
KimiiVL--ABB
Benchmark
vamilla
AdoPatch
vandlla
AdaPatck
vanilla
AdaPPach
wonilla
AdaPatch
General
MME
2135,90
2210.41
2026.27
2103.96
2156. 29
2208.60
2191.92
2230.94
MMMU
49.22
51.89
44.11
45.97
46.33
49.28
50.44
51.32
MMStar
54.27
55.42
59.87
60.68
59,47
59,22
50,93
52.86
MMBBnncheN
75.15
78,49
81.58
83,35
78.79
81.32
81.58
82.50
LLaVABench
66.30
65,34
52.50
53,53
42.00
44.81
70,00
68.70
RealWoridQA
65,36
66.35
69.80
70.76
67.84
68.15
69.80
70.30
SEEDBenchaxa
72.91
75.57
74.40
75.91
75.37
76.36
75.57
77.77
AI2Dae
80.31
81,16
82.32
82.67
84.55
84,26
80.47
80.87
HallusionBench
48.73
50.80
51.32
53.44
50.70
52.35
48.26
50.52
ChartQAuu
83.40
84.08
85.98
83.60
88.40
89.17
TextVQA.a
78.68
79.53
79.92
80.02
79.91
80.76
89.82
89.96
OCRBench
821
845
706
865
890
Doc
92.31
92.62
91.48
92.89
93.90
93.22
96.45
98.33
POPE
86.39
86.88
86.10
86.09
87.81
88.65
85.75
86.43
InfoVQA..)
74.65
75.29
70.61
71.62
76.48
77.21
84.92
85.86
MathVisiaanNi
60.30
62.37
67.40
69.99
63.10
66.90
65.00
67.04
= (B. The closed-form solution is w9. = uo the Swoore-Penrose pseudoinverse. For upsampling (s; > 8), inner products preserved exactly, ie., (B,x, (B, ) wa) = (æt, wn): for downsampling (si < s), the pseudoinverse yields the optimal approximation. We introduce Pl-resize weight-preserving mapping from sc:
B.)
wo. where =
= B and (.) denote
Pl-resize (w) = (B.") veo(w).
(6)
Multi-scale patch embedding bhining-basede allocate independent parameter {0:) for patch size 8; and jointly train MLLM with embedding layers end-to-end. During training, the patch size 8t is sampled adaptively, and we minimize the task loss
E(am))) of I (((BO)))))) (IMG), V., (/IMG), w.rr). (0.). V.e
min
where J the task loss and 0, initialized via Pl-resize (.). In summary, Pl-resize converts fixed- patch to any- any-patch models at inference, while MSPE trains embedding weights for multiple patch sizes, both supporting Adaptive Patching (Sec. 3.1 without altering the backbone.
EXPERIMENTS
Evaluation Setup. We evaluate on diverse public benchmarks, including comprehensive bench- marks (MME (Fu et al.] 2023). MMMU (Yue et al.) (022)). A12D (Kembhavi et al. 2016), MM- Bench (Liu et al. 2023b), LLaVABench (Liu et al.) 2023a). RealWorldQA Info (Mathew al. 2022). SEEDBench (Li al. (023a), MMStar (Chen et al.] 2024a)) and domain-specific benchmarks (ChartQA (Masry et al. 2022). TextVQA (Singh al. 2019), OCRBench (Liu et al. 2024d). DocVQA (Mathew et al. 2021), POPE (Li et al. (023b). HallusionBench (Guan et al, conducted on four representative native- resolution MI.MMs: Qwen2.5- VL, SAIL-VI, Ovis2.5, and Kimi-Vi VI. We exclude fixed-resolution or cropped-input models (e.g., LLaVA, InternVL) focus on native-resolution architectures.
2024), ScienceQA (Lu et al. 2022)). Experiments
Implementation Details. All experiments are conducted on 8xA100 GPUs with 80GB mem- ory. We evaluate the official checkpoints of Qwen2.5-VL, SAIL-VL. Ovis2.5, and Kimi-VL using VLMEvalKit (Duan et al. 2024]. Candidate patch sizes are set to integers within [6, 56). The infor- mation density is estimated from the 0 layer of the vision encoder with default hyperparameters (a, 3) = (0.5, 0.3). For MSPE, we train with AdamW (learning rate le 1e-5, weight decay 0.01).
Inference performed with maximum generation length of 512 tokens and temperature

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Published as a conference paper at ICLR 2026
QwenZ 5-VL-BBBBBBBBBB (vanilia)
Qwen2.5-VL-38 tour)
SAIL-VL-28 (vanilla)
SA-L-VL-2B (our)
ChariQA (test)
DocVQA [val)
MMBench (EN)
80
Perfrmence
Performance
Performance
60
20
1324
224
438
6990
1344
1791
1127
2247
440
136
17927
2608
3534
4224
2242
1366
2688
Pixel Range
Range
Pixl Range
MME
OCRBench
SEEDBench (IMG)
76
2200
800
Performance
2100
Perforance
Performance
700
600
1800
1220
224
4380
1785.
Jestr
350u
3127
400
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Pixel Range
Plxel Range
Pixel Range
Figure 5: Evaluation results across different pixel ranges of preprocessing pipeline. All MLLMs
exhibit notable performance degradation at low and high resolutions, whereas Adaptive Patching
achieves substantially more stable performance.
Table 2: Evaluation of Adaptive Patching under supervised finetuning. We evaluate the same pre- trained backbonn (SAIL-VL after incremental fine-tuning on LLaVA1.5-666 and LLaVA1.6 779K. Patch sizes are adaptively chosen in our method, while the baseline uses fixed size of 14.
Dataset
ChartQAses
Docl
MMBenchaN
MME
SEEDBenchaG
TextVQA OCRBench
L-vv1155666KK
47,96
69.65
70.12
1821.49
71.68
68.86
418
AdaPatch (Ours)
54.84
70.66
71.43
1854.14
72.24
70.34
535
(Lava1.6-7799
75.16
85,90
70.59
1790.26
71.44
72.60
466
AdaPatch (Ours)
77.53
87.00
71.89
1837.25
72.04
73.59
S79
4.1 MAIN RESULTS
Comparison with State-of-the-Art on Native Resolution. Table [ summarizes results across all public benchmarks. Adaptive Patching consistently improves performance over the patch setting across all evaluated MLLMs. The gains are particularly pronounced benchmarks that in- volve heterogeneous image resolutions and varying information densities. For instance, OCRBench, which contains images ranging from low resolution (56 x 56) to high resolution (1344 1344). Similarly, MMBench and MME, which cover diverse image types and complex information layouts, also benefit notably. On most general benchmarks, our method achieves stable and consistent im- provements without introducing additional training overhead. These results confirm that Adaptive Patching especially effective when input resolution differences are large or when the information density varies significantly, thereby enhancing the robustness and adaptability of MLLMs compared the fixed patch-size baseline.
Comparison with State-of-the-Art on Pixel-range. To assess robustness with respect to input resolution, we evaluate models across pixel ranges from 112x112 to 3584x3584. shown in Fig. 5) recent MILMS exhibit strong resolution preferences: performance deteriorates at both low and high pixel budgets, with different models peaking at distinct ranges. In contrast, Adaptive Patching markedly mitigates such instability. By dynamically adjusting patch sizes, method preserves fine-grained detail under limited budgets while maintaining global context at larger scales. Consequently, performance remains substantially more stable across the entire spectrum. These results indicate that resolution sensitivity in currett MLLMs largely stems from fixed patching, and Adaptive Patching is effective solution.
Comparison with SAIL-VL-2B on Supervised Finetuning. realized by modifying the patch embedding layer (Eq. (7) and initializing multi-scale patch sizes with Pl-resize (Eq. 6). where the default set {s, ) {8, 12, 14, 16, 24, 28}. Under limited computational resources, incrementally finetuned the SAIL-VLL-BB model the L.aVA1.5-
shining-based Adaptive Patching
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Published as a conference paper at ICLR 2026
Table 3:
Performance comparison across different model scales. The table reports evaluation results of Adaptive Patching on Qwen2.5-VL. (3B, 7B, 34B) and Ovis2.5 (2B, 9B).
Model Size
SFEDBench DaeVQA MMBench MME OCRBench MMMU TextVQA Hallusion aABBBchc ChariQA
Qwen2.5-VL
3B
72.91
92.31
75.15
2135,90
821 .00
54.27
78.68
48.73
66.30
82.92
our method
75.57
92.62
78.49
2210.41
845.00
55.42
79.53
50.80
.3
83.40
76.15
2313.17
62.13
54.73
74.70
method
76.50
94,80
82.04
2345.96
897.00
63,55
85,38
55.91
76.79
86,42
32B
76.47
92.47
85.84
2433.57 57
839.00
66.07
77.76
57.21
77.50
72.16
method
76.92
92.68
86.56
2460.71
859.00
66.18
79,61
58.22
77.36
74.78
Ovix2.5
2B
75.37
93,90
78.79
2156.29
706.00
59.47
79.91
50.70
42.00
83.60
method
76.36
93.22
81.32
2208.60
814,00
59.22
80.76
52.35
44.81
84.11
9B
77.12
95.08
84.67
2353,73
722.00
67. 17,00
81.85
55.96
78.30
85,92
our method
77.19
95.19
85.51
2390.41
843.00
67.31
82.09
56.81
77.45
86.73
Pi-resize
Area
Bilinear
MMBenchaw
OCRBench
SEEDBenchuc
73.5
77.5
Performance
77.0
Performance
750
Performance
73.0
78.5
820
72.0
71.5
1122
224'
4480
17927
112
224
1.44
17927
2009
2245'
1344r
1792
Range
Range
Range
Figure 6: Comparison of different resizing methods on Qwen2.5-V V.-33. We evaluate Bilinear, PI-
resize, and Area for adjusting the embedding layer weights, where Pi-resize performs the best.
665K and LLaVAI LLaVAI.6-7799 .6-779K datasets, which inevitably leads to some degradation compared to the original pretrained model. During supervised finetuning, the patch size is adaptively selected per image, while the remainder of the training pipeline is kept unchanged. As shown in Table Adaptive Patching yields consistently higher performance across datasets at their native resolutions compared to the fixed patch size baseline (s = 14). These results indicate that dynamically adjusting patch sizes during training can provide more benefits.
4.2 ABLATION STUDY AND ANALYSIS
Impact of Model Scale. We examine whether the advantages of Adaptive Patching scale with model size by evaluating Qwen2.5-VL. (3B, 7B, 34B) and Ovis2.5 (2B, 9B). As shown in Table 3 consistent gains are observed across all scales, indicating that the benefits stem from mitigating the rigidity of fixed patch sizes rather than from model capacity. The improvements pronounced on OCRBench and MME, consistent with the findings in Sec. 4.1 that tasks involving diverse image resolutions and information densities benefit most.
particularly
Impact of Resizing Methods. We investigate the effect of alternative interpolation strategies (bi- linear, Pl-resize, and area) for adapting pretrained models with fixed patch sizes to different target patch resolutions. Using Qwen2.5-VL-3B as a case study, we compare these resizing methods in conjunction with Adaptive Putching. As shown in Fig. 6 Pi-resize consistently outperforms other approaches, demonstrating its ability to effectively realign the patch embedding layer without addi- tional training. In contrast, bilinear and area interpolation lead to notable performance degradation.
Impact of Hyperparameters and B. As shown in Fig. 7 two hyperparameters, which control sensitivity to resolution and information density when adap- tively scaling the base patch size = 14). The results indicate that a has stronger influence, with larger values leading to notable performance degradation. By default, we set = 0.5 and B = 0.3.
evaluate different values of the
Comparison with Resizing Images. As shown in Table Adaptive Patching consistently outper- forms direct image resizing. Although resizing brings gains benchmarks such OCRBench, it substantially degrades performance on others (e.g., DocVQA and MME). This highlights the limited robustness of resolution adjustment and underscores the importance of preserving native resolution, which enables the model to directly process heterogeneous visual regions without distortion.
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Table Comparison of image resizing and Adaptive Patching. Images are resized fixed pixel range [896, 1344] and compared with Adaptive Patching on Swen2.5-VI. and SAIL-VL.
Dataset
ChartQAges
DocVQAual
MMBeneheN
MME
SEEDBenchawo
TextVQA.I
OCRBench
Qwen2.5-VL-J8 5-VL-BB
IMG-resize
83.16
91.53
76.32
2165.01
72.89
78.48
803.00
Adaptive Patching
83.40
92.62
78.49
2210.41
75.57
79.53
845.00
SAII-VIL-2R
IMG-resize
84.56
90.24
81.73
2007 54
74.59
79.43
822.00
Adaptive Patching
85.98
92.89
83.35
2103.96
75.91
80.02
855.00
Fixed Patching (s- 14)
Adeptive Paching is 563)
ChartQAust
DocVQA.al
100
2200
Time (mins)
80
scare
60
40
2000
20
1900
0.0 0.1
02
0.3
0.4
0.5 0.6 value
0.7 0.8 0.9 1.0
11.8
224
448
896
1344
17227
3584
22
A48
1344
1792'
2638
3534
(a) Impact hyperparameters a and B.
Comparison inference time.
Figure 7: Overall impact of hyperparameters and inference efficiency.
Comparison on Inference Time. Adaptive Patching introduces additional computational over- head from two sources: (1) lightweight information-densityy estimation, and (2) variation in the sequence length of image tokens, which is the dominant factor. As shown in Fig. 7b] we compare
inference cost against fixed patch sizes. While our approach introduces higher cost low resolu- tions, it achieves substantial speedups at high resolutions.
5 RELATED WORK
Early works on any-resolution aimed to improve classification accuracy or reduce computation. For example, Learn-to-Resize (Talebi & Milanfar 2021) replaced fixed interpolation with learnable resizing layer; Resolution Adaptive Networks (Yang et al. (020) and Dynamic Resolution Net- works (Zhu et al., 2021) routed inputs to different sub-networks or resolutions depending on task difficulty. However, these methods are closely tied to CNN architectures and are difficult to transfer languaeee systems.
For ViT-based model, FiexiViT (Beyer et al. 2023) resizes patch-embedding weights to trade off accuracy and computational cost. Pix2struct (Lee et al., 2023) and NaViT (Dehghani et al., (023) preserve native resolution while using fixed patch size, producing variable-length token sequences and using sequence packing to handle different input sizes. Liu et al. (2024c) extends FlexiViT by learning resized embeddings jointly with the model, which improves cross-resolution performance. Recent MLLMs (e.g., Qwen2.5-VL (Bai et al., 20255) follow NaViT to process images at native resolution, but fixed patch size degrades performance resolution extremes. We propose Adaptive Patching, which selects a per-image patch size from the native resolution and an information-density estimate improve accuracy and efficiency.
6 CONCLUSION
We revisit the widely claimed "any-resolutton" capability of recent MILMS and show that their per- formance is fact highly sensitive to input resolution, largely due to the rigidity fixed patch size. To address this, we introduce Adaptive Patching, lightweight and training-free method that adjusts patch size according to image resolution and information density, and converts fixed- sred-patch MLLMs into any-patch models via Pl-resize MSPE. Extensive experiments marks demonstrate that adaptive patching improves both accuracy and stability across resolutions while reducing computation high resolutions. Our study highlights patch size a key bottleneck
broad suite of bench-

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for vision language langone modeling and provides simple, general, and effective solution toward genuine
native resolution robustness.
Limitations and Future Work. In this work, We address the native resolution issue from the per- spective of visual processing before LLMs. Future work will incorporate more high-quality multi- resolution data and explore settings with evolving knowledge and continual updates (Guo et al.,
20256 a). We also plan to combine our approach with knowledge-oriente augmentations (Jiang al. 2025b a) and efficient vision token processing such recoverable compression (Chen al, models like (OpenAl 2025).
2025
Finally, our method may benefit
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A APPENDIX
A.1 VALIDATION OF THE INFORMATION-DENSTI FORMULATION
We provide additional analyses to validate the proposed information-density formulation. Repre- sentative visualizations are shown in Figure g and Figure 9
Layer-wise consistency. Information-ded density maps computed from different VIT layers exhibit highly similar spatial patterns. Maps from layer 0 closely match those from deeper layers, indicating that the formulation is stable across the feature hierarchy.
Comparison with statistical measures. We also compare information-density formulation with two statistical pixel-level measures: gradient entropy, computed from the histogram of Sobel gra- dient magnitudes, and Laplacian variance, computed from the variance of the Laplacian response. As shown in Figure / these traditional measures primarily capture edges and fine- nee-graieed textures, whereas the information density defined in Eq. better identifies regions that semantically mean- ingful in the feature space.
A.2 ETHICS STATEMENT
This work adheres the ICLR Code of Ethics. No human subjects or animal experimentation were involved. All datasets used, including MME, MMMU, AI2D, MMBench, LLa VABnnhh,, Re- alWorldQA, Info SEEDBench, MMStar, ChartQA, TextVQA, OCRBench, DocVQA, POPE, HallusionBench, and ScienceQA, are publicly available and widely adopted benchmarks in the re- search community, ensuring no violation of privacy. We have taken care avoid biases or discrim- inatory outcomes in our research process. No personally identifiable information was used, and no experiments were conducted that could raise privacy or security concerns. We are committed to maintaining transparency and integrity throughout the research process.
A.3 REPRODUCIBILITY STATEMENT
We have taken extensive measures to ensure reproducibility of our results. The experimental setup, including evaluation procedures, model configurations, and hardware details, is described in detail
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Input
Layer-0
Layer=2
Layer-3
KRESS
BRESS
KRESS
KRESS
Figure 8: Information-density maps computed across different VIT layers.
Input
Our
Gradient
laplacian
KRESS
KRESS
KRESS
KRESS
Figure 9: Comparison between the information-density formulation and statistical measures.
in the paper. We evaluate the official checkpoints of Qwen2.5-VL. SAIL-VL, Ovis2.5, and Kimi- LL using open-source code (VL.Evalliit, ensuring consistent and verifiable results. These measures enable other researchers to reproduce our work and build upon it to further advance the field.
A.4 LARGE LANGUAGE MODELS USAGE
LLMs were used only to assist with writing and polishing the manuscript. They supported tasks such as sentence rephrasing, grammar checking, and improving readability, but were not involved in the idcation, methodology, experimental design, or data analysis. All research concepts, analy- ses, and conclusions were developed by the authors. The use of LLMs was limited to enhancing linguistic quality, without affecting the scientific content. The authors take full responsibility for
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the manuscript and have ensured that the use of LLMs complies with ethical standards and avoids plagiarism or scientific misconduct.
15


Reviews:
[{"review_id": "LWpMNz0S6d", "reviewer": "Reviewer_2qzG", "summary": "This paper identifies a critical weakness in current \"native-resolution\" Multimodal Large Language Models (MLLMs): their performance is unstable and often degrades at very low or very high resolutions. The authors convincingly argue that the root cause is the rigid use of a fixed patch size, which creates a mismatch between the receptive field granularity and the image's resolution or information density.\nTo address this, the paper proposes Adaptive Patching (AdaPatch), a simple yet effective method that dynamically selects an appropriate patch size for each input image based on its resolution and a novel information density metric. The authors also introduce PI-resize, a training-free technique to adapt existing fixed-patch MLLMs to handle variable patch sizes, making AdaPatch a practical drop-in solution. Through extensive experiments on several state-of-the-art MLLMs and a wide range of benchmarks, the paper demonstrates that AdaPatch not only consistently improves performance but also significantly enhances performance stability across resolutions, while reducing computational costs for high-resolution inputs.", "questions": "1.\tGiven that AdaPatch is specifically designed to optimize detail preservation, which is crucial for OCR and document tasks, could the authors provide a deeper analysis into why the absolute performance gains on benchmarks like OCRBench and DocVQA are marginal, particularly for the largest models in Table 3?\n2.\tThe results in Figure 5 indicate that stability is dramatically improved, but the performance ceiling does not rise substantially as resolution increases. Do the authors view this plateauing effect as an inherent limitation of the current ViT-MLLM paradigm, or are there planned extensions to the Adaptive Patching Law (Eq. 4) that could further unlock performance gains at ultra-high resolutions?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe proposed AdaPatch method is intuitive, simple, and addresses the identified problem directly. The core idea of adapting patch size to resolution and information density is logical, and the proposed implementation is straightforward.\n2.\tThe introduction of PI-resize, a training-free method to convert existing models, is a significant strength. It allows AdaPatch to be seamlessly integrated into powerful, pre-trained MLLMs without requiring costly retraining, which dramatically increases the practical utility and potential impact of this work.\n3.\tThe evaluation is comprehensive, covering multiple base models (Qwen2.5-VL, SAIL-VL, Ovis2.5, Kimi-VL), a diverse suite of benchmarks, and various model scales. The head-to-head comparisons showing improved stability across pixel ranges (Figure 5) are particularly compelling and strongly support the paper's claims.", "weaknesses": "1.\tLimited Improvements on Information-Dense Tasks and Scaling: While AdaPatch shows consistent overall gains (Table 1), the improvements on VQA and OCR tasks are often surprisingly marginal. This is counter-intuitive, as OCR  tasks should benefit most from the adaptive preservation of fine-grained details. Furthermore, upon scaling the models (Table 3), the performance uplift provided by AdaPatch becomes extremely limited for larger backbones (e.g., Qwen2.5-VL 7B and 32B), suggesting that the technique's benefits might not scale effectively with model capacity.\n2.\tPerformance Plateau and Ceiling Limitations: As demonstrated in the pixel-range evaluations (Figure 5), although AdaPatch successfully maintains superior stability compared to fixed-patch baselines, it appears to primarily mitigate performance degradation rather than significantly elevating the overall performance ceiling. Specifically, performance still seems to plateau or slightly decline at the highest tested resolutions. This raises questions about the long-term potential of the method to achieve true native resolution robustness.", "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": "enH3wBQ2YI", "reviewer": "Reviewer_kjPY", "summary": "This paper addresses the limitation of fixed patch sizes in MLLMs that claim “any-resolution” capability. The authors propose AdaPatch, a training-free and training-based framework that adjusts patch size according to both image resolution and information density. The method estimates information density using feature similarity between original and downsampled representations, then determines the patch size via a simple power-law mapping. A “pseudo-inverse resize” operation enables pretrained fixed-patch MLLMs to operate under any patch size without retraining. Experiments on multiple benchmarks (MME, MMBench, OCRBench, DocVQA, etc.) across four representative MLLMs (Qwen2.5-VL, Ovis2.5, SAIL-VL, Kimi-VL) demonstrate consistent improvements in native-resolution stability and accuracy.", "questions": "(1) Information density estimation (Eq.3): Have you compared this definition with more intuitive statistical measures (e.g., gradient entropy or Laplacian variance), and how do the results differ? What is the rationale for choosing the 0-th layer features of the visual encoder for this computation? How do you verify the effectiveness of Eq.3 as a valid information-density estimator?\n\n(2) Candidate patch sizes ([6, 56]): How was this specific range determined? For other image domains such as medical or remote sensing, would this range need to be extended or adjusted?\n\n(3) Notation inconsistency: The symbol r̃ is referred to as “target resolution” in Section 2.1 but as “base resolution” in Eq.4, which is inconsistent. The notation throughout the paper should be unified, and it is recommended to include a notation table in the appendix for clarity.\n(4) Figures:\n(a) In Figure 1, several labels use “patchsize” instead of “patch size.”\n(b) In Figure 3, the proportional relation s ∝ r / ρ is not visually evident.\n(c) Figure 7 lacks a descriptive title.\n\n(5) Performance on LLaVA-Bench: According to Tables 1 and 3, the proposed method performs relatively modestly on LLaVA-Bench. Could the authors analyze the potential reasons for this difference?\n\n(6) Hyperparameter tuning (α, β): Were α and β tuned jointly or separately? Based on Figure 7(a), the final selected values appear to be determined independently.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) This paper thoroughly evaluates the performance of AnyRes by systematically rescaling benchmark datasets across a broad spectrum of input resolutions, uncovering substantial degradation and instability in recent state-of-the-art multimodal large language models (MLLMs) that employ fixed patch sizes.\n\n(2) This paper introduces AdaPatch, a simple yet highly effective approach that dynamically adjusts patch size based on both input resolution and estimated information density, enabling more adaptive and content-aware visual tokenization.\n\n(3) This paper provides two complementary implementations: a training-free variant that allows immediate deployment without any model modification, and a training-based version that supports further fine-tuning for enhanced performance.\n\n(4)  This paper demonstrates consistent improvements in accuracy, stability, and computational efficiency across multiple benchmarks and model architectures, with especially notable gains at high input resolutions.\n\n(5) This paper leverages publicly available models and open-source evaluation toolkits, ensuring that all empirical results are transparent, reproducible, and easily verifiable by the research community.", "weaknesses": "(1) The information-density formulation (Eq.3) lacks theoretical justification and comparison with simpler statistical metrics.\n(2) The patch-size range [6,56] is empirically set without analysis of generalization to other domains.\n(3) Notation and figures show minor inconsistencies and labeling issues that reduce clarity.\n(4) The method’s performance on LLaVA-Bench is relatively weak and not discussed.\n(5) The tuning process of α and β is unclear and may not reflect joint optimization.\n(6) The authors can address these points by responding to the Questions section.", "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": "1XBpu22mKk", "reviewer": "Reviewer_jyqy", "summary": "This paper addresses the problem that multimodal large language models (MLLMs) with a fixed image patch size perform suboptimally across varying image resolutions. The authors propose a plug-and-play method that dynamically adjusts the patch size based on an image's resolution and information density. Additionally,  a trainable version is also offered for further performance gains. Experimental results demonstrate the effectiveness of the proposed method.", "questions": "See the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed AdaPatch can be seamlessly plugged into pre-trained fixed-patch MLLMs without any training efforts.\n2. The proposed AdaPatch achieves promising results on several benchmarks.", "weaknesses": "1. The most significant issue with this paper is that the baseline metrics do not align with the results from the original paper or the vlmevalkit. For instance, on MME, the reported Qwen2.5-VL-3B metric in this paper is 2135.90, whereas the vlmevalkit reports 2199.9. On MMMU, the paper reports 49.22 for Qwen2.5-VL-3B, compared to vlmevalkit's 51.2. On MMStar, the paper reports 54.27, while vlmevalkit reports 56.3. The discrepancies are quite substantial. Since the authors used vlmevalkit as the evaluation framework, it would be better to align the baseline with the official vlmevalkit evaluation results. This would enhance the credibility of the experiment results.\n\n2. Lack of metrics regarding the additional computational load and the impact on inference speed brought by AdaPatch.", "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": "dOEgcMIqcW", "reviewer": "Reviewer_JDNT", "summary": "This paper focuses on the native resolution issues of MLLMs, and propose an adaptive patching (AdaPatch) method to achieve the optimal patching and visual embedding of input images. The experiments on a set of experiments show the performance gains obtained by AdaPatch for a set of MLLMs.", "questions": "1. In Sec.3.2, what are the purpose and motivations of Pseudo-inverse and Multi-scale patch embedding for AdaPatch? In particular, why MS patch embedding should be proposed considering the training-free target claimed in Abs and Intro. \n\n2. Following last question, what are the settings of AdaPatch for the results of Tab.1? Similarly, the settings in other tables should be also specified. \n\n3. The comparison of Tab.2 is unfair. Most MLLMs train SFT data only for once, and the additional fine-tune is likely to get performance gains. In other words, Tab.2 should also provide the SFT performance of MLLMs being trained for twice. \n\n4. What about the results of using the optimal patch sizes for different tasks, i..e., task-specific setting rather than example-level adaptive settings. \n\n5. In terms of Fig.1, does AdaPatch has other differences to dynamic-res operations of QWen and InternVL in addition to adaptive patching?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The motivation is clear and plausible. This paper shows that the existing any-res. solution are not optimal due the use of fixed patching. Also, the authors also provide experimental analyses to support their intuitions. \n\n2. The effectiveness of the proposed methods are validated on a set of recent MLLMs. As shown in Tab.1, the proposed AdaPatch can bring performance gains to the compared MLLMs.", "weaknesses": "1. The method description is vague. The method section introduces various concepts,  but the most important method procedure lacks a clear and intuitive description. For example, how AdaPatch chooses the optimal patch sizes based on the resolution and information density? Subjectively speaking, this paper over-packages theoretical descriptions but lacks explanation of specific details and procedures. \n\n2. More ablation are required. Although this papers provides a lot of comparisons and charts, it still lacks some critical ablations to show the superiority of the proposed methods. For instance, what about the alternatives to AdaPatch that can also adaptively set patch size? What about the results of using task-specific patch size?  \n\n3. Insufficient qualitative analyses. The authors take more efforts to show the resolution inference to MLLMs on different tasks. However, this seems not the key to AdaPatch, since AdaPatch focuses more on the size of patching, as shown in Fig.1. Besides, the authors are required to give more information about the results of AdaPatch. For instance, what are the patch size set to different tasks? the avg. path size of different benchmarks?", "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)