Enhancing Multi-Image Understanding through Delimiter Token Scaling
Authors: Minyoung Lee
Yeji Park
Dongjun Hwang
Yejin Kim
Seong Joon Oh
Junsuk Choe
Venue: ICLR 2026 Poster (2026)
Keywords: LVLM
Multi-Image Understanding
Training-free
Primary area: foundation or frontier models, including LLMs

Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on single-image tasks, but their performance declines when multiple images are provided as input.
One major reason is the cross-image information leakage, where the model struggles to distinguish information across different images.
Existing LVLMs already employ delimiter tokens to mark the start and end of each image, yet our analysis reveals that these tokens fail to effectively block cross-image information leakage.
To enhance their effectiveness, we propose a method that scales the hidden states of delimiter tokens.
This enhances the model’s ability to preserve image-specific information by reinforcing intra-image interaction and limiting undesired cross-image interactions.
Consequently, the model is better able to distinguish between images and reason over them more accurately.
Experiments show performance gains on multi-image benchmarks such as Mantis, MuirBench, MIRB and QBench2. 
We further evaluate our method on text-only tasks that require clear distinction. 
The method improves performance on multi-document and multi-table understanding benchmarks, including TQABench, MultiNews and WCEP-10. 
Notably, our method requires no additional training or inference cost.

Published as a conference paper at ICLR 2026
ENHANCING MULTI-IMAGE UNDERSTANDING THROUGH DELIMITER TOKEN SCALING
Minyoung Lec', Yeji Park', Dongjun Hwang', Yejin K Scong Joon , Choc
Sogang University, *KAIST, University of Tübingen
ABSTRACT
Large Vision-Language Models (LVLMs) achieve strong performance on single- image tasks, but their performance declines when multiple images are provided input. One major reason is the cross-image information leakage, where the model struggles to distinguish information across different images. Existing LVLMs al- ready employ delimiter tokens to mark the start and end of each image, yet our analysis reveals that these tokens fail effectively block cross-image informa- tion leakage. To enhance their effectiveness, we propose method that scales the hidden states of delimiter tokens. This enhances the model's s ability to pre- serve image-specific information by reinforcing intra-image interaction and limit- ing undesired cross-image interactions. Consequently, the model is better able to distinguish between images and reason them more accurately. Experiments show performance gains on multi -ulti-image benchmarks such as Mantis, MuirBench, MIRB and QBench2. We further evaluate our method on text-only tasks that re- quire clear distinction. The method improves performance on multi-document and multi-table understanding benchmarks, including TQABench, MultiNews and WCEP-10. Notably, our method requires no additional training or infer- ence cost. Code is available at: https: /ttpttbttttttt///t////// com/ Deli DelimScaling
com/MYMY Bt////MMYYYY//// yyung//
INTRODUCTION
Large Vision-Language Models (LVLMs) demonstrate strong image understanding capabilities when a single image is provided (Li et al., 2023a; Liu et al., 2023). However, their performance significantly degrades when multiple images are given input (Zeng al., 2025; Jiang et al., 2024). recent study (Park et al., 2025) attributes this degradation to the model's inability to clearly dis- tinguish between individual images, phenomenon referred as cross-image information leakage. As result, the generated output often intermixes information across different images.
While existing models introduce special image delimiter tokens to separate images, the role and mechanism of these tokens remain largely unexplored in the literature. To address this gap, we analyze how delimiter tokens function within the model. Our analysis of attention scores shows that although these tokens help distinguish images to some extent, cross- imass-image interaction persists. This indicates that current models struggle fully isolate visual contexts across images, ultimately leading to information leakage.
To better understand this behavior, we examine how delimiter tokens contribute image separation and identify two key properties: their ability to absorb attention from other image tokens and their role reinforcing intra-image interaction. Based on these insights, we propose a simple yet effec- tive method that strengthens both properties by scaling the hidden states of delimiter tokens. This approach reduces cross tross-image interaction while preserving intra-image interaction, thereby helping the model distinguish between images more effectively.
To validate our findings, we apply the proposed method to a range of multi-image understand- ing tasks. Our approach significantly improves performance Mantis (Jiang et al., 2024), MuirBench (Wang et al., 2024a), MIRB (Zhao et al., 2024), and QBecch2 (Zhang al., 2024). Furthermore, we observe consistent gains in text-only scenarios
benchmark datasets such as
Corresponding author.

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Published as a conference paper at ICLR 2026
where clear separation is also essential, such multi-table and multi-document tasks. The method improves accuracy on TQABench (Qiu et al., 2024), MultiNews (Fabbri et al., 2019), and WCEP- 10 (Ghalandari et al., 2020). Notably, these improvements are achieved without any additional training or inference overhead, highlighting the practicality and efficiency of our approach.
RELATED WORK
2.1 MULTI-IMAGE UNDERSTANDING
Recently, there has been active research multi-image understanding in Large Vision-Language Models (LVLMss. One line work focuses on training-based approaches improve performance on multi-image tasks. For example, Jiang et al. (2024) constructed a multi-image instruction dataset to address the performance gap between single-image and multi-image understanding, and achieved performance gains through supervised fine-tuning However, such training-based approaches face two major limitations: the high cost of curating high-quality multi-image instruction data and the need for substantial computational resources.
To overcome these limitations, training- free approaches have also been proposed. AVAM (Zeng al., 2025) points out that visual redundancy becomes more severe in multi-image settings. mitigates this issue by using text-image alignment to select only the most relevant visual regions. However, its reliance on an external text encoder and separate preprocessing module introduces structural complexity and limits flexibility. FOCUS (Park et al., 2025), another training-fii free method, tackles confusion between images by introducing contrastive decoding strategy that separates out- puts based on image-specific contexts. While effective, this approach requires n + forward passes for n images, leading to high inference costs.
In contrast, our method aims to enhance multi-ime image understanding without requiring additional training, inference-time overhead, or architectural modifications.
2.2 SINK TOKENS IN LARGE LANGUAGE MODELS
Recent studies on large language models have drawn attention to the phenomenon where certain tokens exhibit unusually high activations (Gu et al., 2024; Guo et al., 2024; Sun al., 2024; Barbero et al., 2025). These sink tokens, often placed at the beginning of input scquences, tend to receive strong activations that act as implicit bias terms uniformlyy influencing attention patterns across the sequence (Sun et al., 2024).
Although research token-level irregular behaviors has been active, most prior work has focused on the <BOS> sink token in text-based LLMs. In contrast, our paper presents new observations and detailed characteristics of Image Delimiter Tokens, which have not been analyzed previously, offering a perspective that differs from existing sink-token studies.
We examine how LVLMs behave differently from LLMs when processing multi-image inputs. Sim- ilar to LLMs, the first token in LVLMs is also a sink token and receives high attention. (Kang et al., 2025) However, due the structural properties of multi-image inputs, the sink patterns observed in LLMs do not fully generalize to LVLMs. The delimiter tokens inserted to separate multiple images each receive substantial attention, and as result, the relative amount of attention allocated to the conventional sink token decreases compared to the case without delimiter tokens.
Furthermore, although delimiter tokens resemble sink tokens in that they receive high attention, their operational behavior is distinct. Unlike conventional sink tokens that influence the entire input globally, delimiter tokens attend primarily to tokens within their corresponding image, functioning as localized bias terms. This sink-like but localized behavior is unique to LVLMs under multi-image settings and has not been explored in prior work, largely because earlier studies focused on text- only or single-imag imputs. Building these observations, paper provides in-depth analysis of how delimiter tokens shape and regulate the attention structure for each image in -imtii prompts.
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Figure I: Impact of image delimiter tokens on attention maps. (a) With delimiter tokens, clear triangular patterns mark image boundaries. (b) Without them, these patterns disappear. (c) Replacing them with other special tokens (< im.start >) yields the same effect.
2.3 CROSS-IMACE INFORMATION LEAKAGE
Cross-image information leakage refers to the phenomenon where a model fails to clearly separate multiple input images, resulting in unintended mixing of information across them. This issue was first reported by Park al. (2025), which confirmed existence but did not analyze the underlying cause. In this work, we examine the attention patterns of image delimiter tokens and offer de- tailed analysis of how cross-ine cross-image information leakage arises inside the model. Our findings reveal how delimiter tokens behave during multi-image processing and motivate the design of an effective strategy to mitigate cross-image information leakage.
Do IMAGE DELIMITER TOKENS REALLY WORK?
Cross-image information leakage exists despite the use of special tokens designed distinguish individual images (e.g.. < <lvision_stars vestigate the cause of this phenomenon, we analyze the behavior of these image delimiter lokens. Our goal is to determine whether these tokens enable the model to discriminate between images and identify their limitations.
and < ilviiion_ent
in Qwen2.5-VL). To in-
Do Image Delimiter Tokens Function as Intended to Distinguish Images? To assess whether image delimiter tokens fulfill their intended role, remove them and observe the resulting changes in the attention score map. This allows us to assess how the presence or absence of these tokens affects attention patterns across multiple images.
As shown in Figure 1a, when delimiter tokens are present, the attention map exhibits distinct tri- angular block patterns that clearly delineate image boundaries. contrast, removing the delimiter tokens (Figure 1b) eliminates these triangular patterns, making it difficult to distinguish which to- kens belong to which image. Similar results are observed when the image delimiter tokens replaced with other special tokens commonly used in LVLMs. For example, in Figure 1c, replacing <lvision start > and < <lvision end with iim start >, token typically used to indicate the start of message, results in the disappearance of triangular patterns and image-wiss wise confusion. Additional experiments using other special tokens exhibit the same pattern, with full details provided in Appendix A.I.
We also find that the presence triangular attention patterns correlates with model performance: removing or replacing the image delimiter tokens leads to performance drop of approximately 10 percentage points, as shown in Appendix A. These findings suggest that image delimiter tokens play critical role in enabling the model to distinguish images in multi-image LVLMs. Without them, the model fails to form clear attention boundaries and exhibits notable performance degrada- tion, underscoring their importance.
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Limitations of Image Delimiter Tokens. While the preceding analysis confirms that image delim- iter tokens support distinguishing between images both in attention maps and performance -hea do not fully prevent interactions across different images. Specifically, cross-image interaction in the attention score map (see the red box in Figure la). This suggests that although the tokens help distinguish between images, their distinguishing effect remains incomplete. To address this limitation, we conduct a more detailed analysis of their behavior and identify two key properties that guide the design of our method.
observe some degree of
IMAGE- WISE TAGGING VIA DELIMITER TOKENS
We analyze the attention patterns of image delimiter tokens in multi-image LVLM inputs and un- cover two key properties that contribute distinguishing images.
The i-th image delimiter token receives strong attention from the tokens of the i-th image, forming correspondence between the delimiter token and the image.
Property 1:
As shown in Figure la, tokens in the i-th image consistently attend to the i-th delimiter token, form- ing distinct vertical stripe in the attention map. This localized attention pattern contrasts with the global behavior of sink tokens, which attract attention from all tokens. Instead, each delimiter token predominantly attended to by tokens from single image, establishing clear one-to-one mapping between images and delimiter tokens. Figure 2a further confirms this: the i-th image delimiter token receives strong attention from its corresponding image, while receiving little attention from others.
The strong attention image delimiter tokens serves as an image tag, thereby reinforcing intra- intra-image interaction.
Property 2:
Based on Property we interpret each delimiter token as an spege-s tag associated with particular image block. This tagging effect strengthens intra-image interaction. As shown in Figure la, the triangular patterns the attention map reflect intensified mutual attention among tokens within the same image. In contrast, Figures 1b and 1c-where the delimiter tokens are removed or replaced-an show weaker, more diagonal-domine structures, indicating reduced intra- image interaction.
The mechanism behind this illustrated Equation 1, where the attention output is expressed as the weighted sum of value vectors:
Attention(Qq. Kg Vss))=
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Pq.d Ud EPss Uj de D. D (1)
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In this formulation, and i denote token indices. The term Pq.i represents the attention score signed by query to token i. The set = {di, d2, ... contains the indices delimiter tokens. For notational simplicity, omit the query index q in the following explanation.
Due to the attention pattern established in Prop- erty each token in image assigns a dominant weight to its associated delimiter d; As a re- sult, all tokens within an image share a common additive term Pd, 0a, in the attention output, ef- fectively functioning as a localized bias that en- hances intra-image interaction. The rightmost plot of Figure 2b shows that for Image 3, Pa, Ud, about 15 times and 30 times larger than Pa, Udz and pa, Vd, respectively, confirming its dominant effect on the output.
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We refer to this mechanism as image-wise tag- ging: each delimiter token contributes a localized,
Figure 2: (a) Attention to the second-image delimiter. (b) Image tagging values.
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Transformer Block
Legend
Delimiter Tokens
Other Tokens (mages, Text)
Scaled Attention Weights
(c) Attention Output
Before Scaling Delimiter Tokens
(b) MatMuul
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After Scalling Delimiter Tokens
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Figure 3: Effect of scaling image delimiter tokens attention. Left: attention computation flow trans- former block. Right: before scaling (top), delimiter tokens receive limited attention, leading to cross- image leakage. After scaling (bottom). delimiter tokens act as strong attractors (like sink tokens), distinguishing be- tween images while preserving intra-image interactions (Property 2).
shared bias to the attention outputs of its associated image. This common additive term is shared across all tokens within the same image, thereby reinforcing intra-image interaction. As shown in the before-scaling part of Figure 3c, this shared term can be clearly observed being added to the attention outputs.
METHOD
Based on the two key properties image delimiter tokens, we introduce simple yet effective method that strengthens the model's ability discriminate between images without compromising intra-image interactions. Specifically, we explore a simple strategy to reinforce the importance of delimiter tokens during attention computation. Among various possible implementations, adopt a particularly simple approach: scaling the hidden states of delimiter tokens.
Let h2 IRd denote the hidden state of token at layer 1, and let D denote the set of image delimiter token indices. modify the hidden states as follows (A is a scaling factor):
AA 1
U if D. otherwise.
(+) 1
Our hidden state scaling method amplifies both properties of delimiter tokens discussed in Sec- tion 4. We adopt this approach due to its simplicity, strong empirical impact, and compatibility with standard attention implementations such as FlashAttention (Dao, 2023). Other variants such as layer-specific scaling, scaling query value vectors directly, and modifying attention scores are also feasible. However, directly modifying the attention scores requires substantial resources, as discussed in Section 5.3. In comparison, we find that hidden state scaling strikes favorable bal- ance between effectiveness and efficiency. We consider these alternatives as promising directions for future research. Below, analyze how this mechanism enhances the role of delimiter tokens in multi-image attention.
5.1
How OUR METHOB ENHIANCES DELIMITER TOKEN PROPERTIES
Scaling the hidden states of image delimiter tokens reinforces Property 1 by increasing the attention they receive. This follows the general principle behind sink tokens (Gu et al., 2024), where higher activation leads stronger attention.
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Before Scaling
After Scaling
Before Scaling
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Attention maps w/ and w/o our method
(b) Zoomed-in views of the red boxes
Figure 4; Qualitative comparison of (a) attention maps hefore and after applying our method, and (b) zoomed- in views of the red boxes. Afler applying our method, cross-image interaction is reduced.
We effectively reduce cross-image interaction by amplifying this behavior. Due to the normalization effect of the softmax function, emphasizing delimiter tokens reduces the attention allocated to tokens from other images. However, intra-image interaction were also suppressed, delimiter tokens would no longer fulfill Property 2 timage-tagging effect).
Interestingly, we find that hidden state scaling not only enhances Property but also strengthens image-tagging effect, thereby preserving intra-image interaction. We assume that when scaling is applied, the attention from tokens within an image increases most significantly toward their corre- sponding delimiter token (see Appendix A.2). According assumption, this results in tokens from the i-th image assigning a higher attention score Pd, to their corresponding delimiter de than to other delimiters. In parallel, scaling also increases the magnitude of the value vectors vs all delimiter tokens (Figure 3b), which amplifies the contribution of the term Pa, Ud, in the attention output (Fig- ure 3c, red box). As a result, the suppressive effect of the softmax mitigated, and intra-iman mtra-image attentions are more effectively preserved.
5.2 EMPIRICAL EVIDENCE
We empirically validate the effectiveness our method using Qwen2.5-VL-3B, focusing on both cross-image leakage suppression and intra-image interaction preservation.
Reduced Cross-Image Information Leakage. We analyze attention maps to determine whether
undesirable cross-image interactions are reduced. To quantify this, we compute the average attention score between all token pairs from different images. For example, the interaction from Image 3 to Image 1 is calculated as the average attention that tokens Image 3 allocate to tokens Image
Figures 4a and 4b illustrate clear reduc- tion in cross-image interaction. In partieu- lar, as in Figure 5a, the attention from Im- age to Image and from Image to Image 2 drops by approximately 50%. These sults indicate that our method substantially suppresses undesired interactions across im- ages.
Interaction Changes
Tagging Changes
Before Scaling
After Scalling
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Preserved Intra-Image Interaction. To evaluate whether our method preserves intra-image interaction, we compute the at- tention scores between all token pairs within Image 3. As seen in the rightmost plot of Figure 5a, the interactions within Image remain largely unaffected, indicating that intra-image interaction is preserved.
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Figure S: (a) Inter-image interaction changes before and
after scaling (normalized 1.00 before scaling). (b) After scaling, Image tokens receive the strongest attention from the third delimiter token.

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Published as a conference paper at ICLR 2026
Table 1: Performance across four multi-imag benchmarks (Mantis, MuirBench, MIRB, QBench2). Applying
our method the Qwen2.5-Va. IL3 and LLaVA-One Vision model families generally improves results.
Qwen2.5-2
Intern
LLaVA-OV
Dataset
Model
14B
0.5B
7B
Baseline
59.91
68.66
68.20
47.00
$2.07
67.28
71.89
40.09
62.21
Mantis
Ours
63.13
69.12
70.05
49.77
54.38
69.12
72.81
41.01
64.06
Baseline
37.31
45.23
53.12
28.62
27.69
36.88
42.42
24.58
35.04
MuirBench
Ours
42.42
48.15
53.82
29.38
27.65
36.92
42.58
24.85
35.35
Baseline
56.45
63.57
54.90
38.49
44.38
52.32
56.45
31.79
47.88
MIRB
Ours
57.38
63,05
55.21
40.25
46.96
52.63
57.59
32.30
48.19
Bascline
62.70
75.80
81.40
50.80
65.20
76.50
79.60
51.70
73.90
QBench2
Ours
63.30
76.50
81.70
50.20
65.60
76.60
80.10
51.90
74.20
Table Results WCEPIO. R-1, R-2, and R-L denote ROUGE-I, ROUGE-2, and ROUGE-L.
Table Results on MultiNews. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, 2, and ROUGE-L.
Model
R-1
R-2
R-L
Model
R-1
R-2
R-L
Qwen2.5-3B
27.30
9.75
18.42
Qwen2.5-3B
37.16
10.85
18.81
Ours
27.52
9.99
18.47
Ours
37.24
10.90
18.84
Qwen2.5-7B
29.74
11.59
20.30
Qwen2.5-7B
37.18
11.26
19.15
Ours
29.77
11.70
20.35
Ours
37.19
11.29
19.17
Phi-1.5
1.45
7.94
Phi-1.5
26.30
5.73
14.55
Ours
9.80
1.49
8.09
Ours
26.36
5.76
14.61
This preservation can be attributed to the reinforced image-tagging behavior of delimiter tokens. To support this claim, we compute the average attention output received by all tokens in Image from each delimiter token. Figure 5b shows that Image 3 tokens receive the strongest attention output from the third delimiter token, confirming that image tagging is enhanced and helps maintain intra-image interaction.
5.3 DISCUSSIONS
Computational Benefits. Our method modifies hidden states without altering the attention mech- anism, allowing compatibility with optimized attention kernels such as FlashAttention (Dao, 2023). In contrast, modifying attention weights directly would disrupt these optimizations and significantly increase memory usage, especially in multi-image inputs. For example the MIRB dataset, infer- ence with Qwen2.5-VIL 3B model fails due to memory constraints even with 140GB VRAM when attention is modified. In contrast, our method using FtasnAttention successfully with the 32B model and highlights its efficiency.
Preservation of Text- Image Interaction. Enhancing image tagging behavior may raise concerns about interfering with text-vision interactions. However, our experiments show that such effeets minimal. Text tokens are already known to receive strong mutual attention (Chen et al., 2024) and largely unaffected by the strengthened delimiter tokens. In practice, text-to-image interaction scores drop by only 10%, and the overall interaction between modalities remains robust, indicating that text-vision alignment well preserved.
EXPERIMENTS
6.1 BENCHMARKS AND SETTINGS
We applied our method to four multi-image benchmarks to evaluate its effectiveness. Mantis-Eval (Jiang et al., 2024) a benchmark suite designed to evaluate multi- image capabilities, consisting of 8 multi-image benchmarks and single-image benchmarks that test
Multi- Imate Benchmarks.
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Table 4: Accuracy the TQABench dataset for Qwen2.5 models.
Table 5: Ablation results delimiter tokens, M- RoPE, and our method.
Model
Accuracy
Delim
M RoPE
Ours
Accuracy
Qwen2.5-3B
37.38
x
X
59.91
Ours
37.84
V
x
53.92
Qwen2.5-7B
37.50
V
x
62.21
Ours
38.14
X
V
63.13
various skills such as co-reference, comparison, and temporal reasoning. MuirBench (Wang et al., 2024a) evaluates 12 types of multi-image understanding with 2,600 questions over 11,264 images, covering spatial, diagram, and retrieval tasks. MIRB (Zhao et al., 2024) is a benchmark designed to evaluate I.V.Ms' ability to compare, analyze, and reason across multiple images, covering per- ception, world knowledge, reasoning, and multi-hop reasoning. Q-Bench2 (Zhang al., 2024) is a benchmark designed to evaluate the low-level visual perception abilities MLLMs, extending beyond single image to include image pairs. It specifically assesses cross-image reasoning and human-like comparative judgment by testing models on pairwise visual inputs.
Hulti-Document and Multi-Table Benchmarks. Motivated by the idea that our approach could generalize to other multi-instance settings, we further evaluate it on multi-document and multi-table benchmarks. Specifically, we applied our method MultiNews and WCEP-10 for multi-document benchmarks, and to TQABench for the multi-table benchmark. MultiNews (Fabbri et al., 2019) is large-scale dataset for nulti-document summarization, consisting of clusters of news articles and corresponding human-writte written summaries. WCEP10 (Ghalandari et al., 2020) is a multi-document summarization dataset pairing news article clusters with short human-writted summaries from the Wikipedia Current Events Portal. TQABench (Qiu et al., 2024) is multi-table QA benchmark designed to assess LLMs' ability to handle complex question answering over relational data. We applied our method the 8k split of TQABench.
Implementation Details. For the multi-image tasks, we used Qwen2.5-VL. (3B, 7B, 32B) (Bai al., 2025), VnternVI.3 (1B, 2B, 8B, 14B) (Zhu et al., 2025), and LLaVA-One Vision (0.5B, 7B) (Li et al., 2024) as the vision-language models. For the multi-table task, we employed Qwen2.5 (3B, 7B) (Team, 2024), and for the multi-document task, we used Qwen2.5 (3B, 7B) and Phi-1.5 (Li et al., 2023b). All other details are in Appendix A.3.
6.2 RESULTS
Results on Multi-Image Understanding. As shown in Table 1, our method consistently improves performance across all model families, including Qwen2.5-Vi VI. VL.3, and L.LVAA-ms Vision. The improvements are observed across wide range of benchmarks such as Mantis, Muirbench, MIRB, and Qbench2, demonstrating the robustness of our approach. For example, on the Muir- bench benchmark, the (wwe2255-LL-33 model improves from 37.31 to 42.42, and the Intern VL3-2B model improves from 52.07 to 54.38 on Mantis. Notably, performance gains appear across models of various sizes, from small-scale (e.g., 0.5B) to large-scale (e.g., 32B), indicating that the proposed delimiter token scaling method is effective regardless of model capacity. These consistent improve- ments across diverse models and benchmarks for multi-image understanding highlight the generality and practicality of our method.
Results on Multi-Document and Multi-Table Understanding. Tables and 3 present the ROUGE scores multi-document summarization tasks. On both the WCEP10 and MultiNews datasets, the proposed delimiter token scaling method consistently improves ROUGE-I I, ROUGE-2, and ROUGE-L scores across all models. Similar improvements 7B and Phi-1.5 models. Table 4 further shows consistent gains on the multi-table reasoning bench- mark, TQABench. Notably, the Qwen2.5-3B model with our method even outperforms the 7B baseline, which is compelling result. This indicates that our delimiter token scaling method can yield meaningful performance gains beyond what be achieved by increasing model size. These
observed in both the Qwen2.5-
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Image
Image 2
Image
Image
Question: is there man riding a bike both Image1 and Image2?
What animals could possibly live in the environments depicted Image1 Image2 respectivaly?
Question:
Qwen2.55VV L- There riding bike In both images.
Qwen2.5 Seenn..-VLL Camels and Polar bears
X
Ours: There riding bike image but image
Ours: Polar bears and Camels
aa)
(b)
Figure 6: Qualitative results the Mantis benchmark. Although the tasks are multi-choice, answers shown sentence form show that our method reduces cross-image leakage while the baseline Qwen2.5-VI. fails.
results demonstrate that our method is broadly applicable across different input modalities, not just limited to multi-image settings.
Qualitative Results. We qualitatively analyze the model outputs in Figure 6. In Figure 6a, the baseline model incorrectly states that both images contain only the second image contains this. This shows a case of cross-image information leakage, where the information from the second image contaminates the understanding of the first. In contrast, our method enables the model to correctly identify that only the second image contains the man on bicycle. In Figure 6b, the correct answer is "polar bears and camels", with each animal appearing in different image. However, the baseline model returns "camels and polar bears", reversing the correspondence. With our method, the model preserves the distinction between the two images and produces the correct answer. These examples show that our method effectively reduces cross-image information leakage, leading to more accurate and disentangled reasoning across multiple images.
riding bicycle, although in reality,
Comparison with M-RoPE. In Qwen2- VL (Wang et al., 2024b), temporal positional embeddings are applied to video frames to distinguish them along the temporal axis. This conceptually similar our image-specific tagging method. Motivated by this, we conducted comparative experiment with the M-RoPE-based temporal embedding method, where temporal positional embeddings were injected into each image.
As shown in Table 5, applying M-RoPE alone results in lower performance than the baseline. When combined with image delimiter tokens, M-RoPE improves performance beyond the baseline but still lags behind our method. These findings suggest that introducing mechanisms to help the model bet- ter distinguish between images- such as M-RoPE delimiter tokens can mitigate performance degradation caused by cross-image information leakage. Notably, although M-RoPE was origi- nally designed for temporal distinction video tasks, also improves performance multi-image settings. This further supports our hypothesis that insufficient image distinction is a key cause of performance drops. Overall, these results indicate our simple hidden state scaling approach can be more effeetive at resolving such confusion than more complex temporal embedding strategies.
Few-Shot Evaluation with Interleaved Examples. We additionally conducted few-shot evaluations. We reorganized the single-image dataset into few-shot setting by constructing 4-shot interleaved inputs, where each image followed in order by its corresponding question and answer. Evaluating this setup on the validation-lite (Imms-lab/LLMM Eval- Lite) splits of TextVQA (Singh et al., 2019) and OKVQA (Marino et al., 2019), we observed con- sistent performance improvements across Wwen2.5-VL-3B Qwen2.5-VL-7B and InterVVL--BB, as shown in the Table Since this task requires understanding both the example images and the accompanying text, image-text interaction crucial. The improved performance, demonstrating
Table 6: Few-shot Performance on OKVQA and VizWiz.
Qwen2.5-VL
Intern
Dataset
Model
3B
7B
8B
Baseline
18.04
27.56
46.84
OKVQA
Ours
20.00
28.24
48.68
Baseline
42.38
53.70
47.04
VizWiz
Ours
42.88
54.36
50.92

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Table 7: Performance the Mantis Benchmark for Large Models.
Table 8: Our method introduces additional cost in memory (GB) or inference time (s).
Method Qwen2.5-VL
Intern VL3
Avg
Peak Memory
Inference
72B
78B
Memory
Time
Baseline
74.19
74.65
Baseline
8.3 1
10.2 =
100
Ours
75.58
76.50
Ours
8
10.2 =
99.7
that our method can also be effectively applied to downstream tasks where the relationship between images and plays an important role. These findings further indicate that our approach is appli- cable to interleaved data and remains effective in few-shot settings, showing that it can generalize beyond the originally tested specialized input format and extend to a broader range of scenarios.
Performance on Larger-Scale Models. We conducted additional experiments even larger models. We evaluated our method on the Mantis benchmark using Qwen2.5-VI.-22B and Intern VL3-78B, which represent the largest models in the Qwen2.5-VL and InternVL3 families, respectively. As shown in the Table 7, the results show that both models exhibit performance im- provements when applying method. This indicates that approach remains effective model scale increases and can be reliably applied to extremely large-scale models.
Inference Cost. We empirically verified that our method introduces no additional inference cost. Using four GPUs, we repeated the experiment three times and averaged the results. As shown in the Table 8, both the average and peak VRAM usage were identical to the Baseline, and the inference time also remained unchanged. These results demonstrate that, in practice, our method does not incur any additional inference overhead. Our approach fully compatible with FlashAttention, as it operates the hidden-state level without requiring any direct modification the attention computation.
Hyperparameter Sensitivity. As shown in Figure we experimented with range of scaling values for the A and analyzed their impact. The results demonstrate con- sistent performance improvements across most settings compared to the baseline, indicated by the red dashed line, showing that our method is robust to variations in this hyperparameter. These findings support the idea that appropriately amplifying the hidden states of image de- limiter tokens effectively mitigates cross-image informa- tion leakage.
Model Performance
0.64
Accuracy
0.63
Baseline
7
10
Scaling Parameter
Figure Sensitivity on hyperparameter A.
LIMITATION
Our method currently not applicable to videos that lack explicit frame-separating tokens. In future work, plan extend our approach to the video domain by incorporating mechanisms model temporal transitions between frames, which are crucial for understanding dynamic visual content.
8 CONCLUSION
In this work, we address the issue of cross-image information leakage multi-image input settings by analyzing the role and limitations of image delimiter tokens, which are responsible for separating visual inputs. Based on this analysis, we propose simple method that enhances the functionality of these tokens, effectively suppressing interactions across different images while preserving in- teractions within the same image. Our method consistently improves performance across various multi-image benchmarks and also demonstrates its generalizability text-only settings, such as docti-do and multi-table understanding. It is easy to integrate and introduces no additional training inference cost.
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Ethics Statement. We use only publicly available datasets and do not involve human subjects or sensitive data. The method is training-fris free and efficient, with no foreseeable ethical concerns beyond responsible use.
Reproducibility Statement. We will release full code and scripts. All experiments with fixed seeds, and datasets and hyperparameters are documented to ensure reproducibility.
ACKNOWLEDGEMENTS
We thankful to Beomyun Kwon, Jimin Hong, Elena Kuular and Arnas Uselis for their thoughtful discussions and constructive comments. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF- 1A441155330004000 Contribution: 50%) and by the Institute Information & Communi- cations Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) RKS-2025-25461990 Elite Research-driven Technology Development for Advanced Large-Scale LLM/VLMs and ASEAN Language Expansion, Contribution: 50%).
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A
APPENDIX
A.1
ROLE OF IMAGE-DELIMITER TOKENS
1011
2000
2000
28mg
durry gdbll
Avenp gihul
10
1400
199
1214
1mg1
Img4
1mg1
Img2
Img3
mmg4
Img1
Img2
Img3
mmg4
Key
Key
Key
(a) Replace w/ < immendd
(b) Replace w/ < <lbooosttrt
(c) Replace w/ </endoftext:
Figure Replacing < <lvision.star >> with other tokens (e.g.. < iimend >, </endoftext (>) fails to separate images effectively.
<tbox.start
We additionally replaced the image delimiter token with various other special tokens beyond < ii..starti// |>, including <I <mimendd | >, < <lbox_atart >>, and endoftext ?>, and analyzed the resulting attention maps. As shown in the Figure A1, these tokens exhibited trends similar to im.start >, suggesting that spe- effectively serve
Table Al: Effect of removing or replacing image delimiter tokens with other special to- kens.
Delim
Special Token
Accuracy
cial tokens like <lvision [lvisioo_start/ as image delimiters that separate visual content. This observation was also reflected in the performance results. Removing the image delimiter tokens or replacing them with other special tokens led performance drop. As shown in Table A1, clearly observe this degradation in performance. The sample used in this analysis (i.c., the four images and the corresponding text) is provided in A6, while additional example attention maps included in Section A.8.
59.91
x
V
53.92
53.46
A.2 EMPIRICAL EVIDENCE OF ASSUMPTION
Interaction Changes
We previously assumed that tokens within each image would show the greatest increase in attention toward their correspond- ing image delimiter token. In this section, we empirically val- idate this assumption by analyzing the attention score increases for each delimiter token when the query token belongs to the third image. The third delimiter token, which corresponds to the third
0.225
Before rc Scaliing
0.03
0.025
Interaction
0.02
0.01
0.005
image, exhibits the most significant increase, approximately 52 times greater than the first delimiter token and 9 times greater than the second. These findings confirm that attention concen- trates most strongly on the aligned delimiter token.
0.001
0.002
0.00
mmg3
lmg3
Img3
Delim1
Delim2
Delima
Figure A2: Tokens in Image 3 show
the largest attention increase to its delimiter.
A.3 EXPERIMENTAL SETTINGS
For the multi-image tasks, we use 10% the test set as validation set to determine hyperparameters such as the scaling layer and scaling factor. In contrast, for multi-document and multi-table tasks, we search hyperparameters directly on the test set without a separate validation split. For all reported results, tuned scaling hyperparameter for each model to identify and apply the optimal value. To avoid excessive tuning, we fix the selected scaling layer for each model and use it consistently across all benchmarks. Details provided in the code.
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Table A3: First Token Scaling Ablation
Table A4: Delimiter Replacement Ablation
Method
Accuracy
Method
Accuracy
Baseline
59.91
Baseline
59.91
First token scaling
60.37
< <limsttart Scaling
53.00
Ours
63.13
Ours
63.13
When applying delimiter token scaling, we use task-specific special tokens to distinguish input units such as images, documents, and tables.
In multi-image benchmarks, we apply delimiter token scaling the model-specific spe- cial tokens that separate each image. For Qwen2.5-VL, we use <I visioon caclioo_sttaae start > and |vision_endd for InternVL3, <img> and <\ing>, and for LLaVA-OneVisson line breaks (An).
In multi-document and multi-table settings, we designate appropriate separator tokens as special delimiters to split individual documents or tables within the input sequence. For example, in the MultiNews dataset, documents are separated using the token I, and we designate this token as special delimiter to apply our method.
All experiments are conducted on NVIDIA GPUs, including A5000, A6000, and H200, depending on resource requirements.
A.4 ABLATION STUDY
Comparing scaling applied individually to Q, K, and V. When the hidden state is scaled, it subsequently transformed through the projection layers into Q, K, and V, meaning that the scaling effect directly influences all three components. Based ablation study ana-
Table Ablation on Query, Key, Value Scaling.
Method
Accuracy
on this observation, we conducted lyze how the scaled hidden state affects each component when applied independently. For each of Q, K, and we applied scaling after the corresponding projection. As shown in the Ta- ble A2 results show that applying scaling to any single compo- nent Qunty Key. or Value-leade to performance improve- ments over the Baseline. Notably, scaling K yields a larger performance gain than the Q-only or V-only settings. This is because increasing the Key of the delimiter token makes the Queries match more strongly with it, which then produces a clearer block-wise attention pattern across the im- age chunks. However, this effect alone is insufficient to induce meaningful image tagging. Our method, which applies scaling to Q, K, and V simultaneously, achieves the highest performance. This demonstrates that Q K, and V each contribute strengthening attention and establishing the image-tagging structure, and that jointly scaling all three components is most effective for multi- image understanding. These results based on experiments conducted on the Qwen2.5-VL-3B model using the Mantis Benchmark.
Baseline
59,91
Q scaling
61.75
K scaling
62.67
scaling
61.29
Ours
63.13
Scaling an Alternative Special Token. We observed that delimiter tokens inherently distinguish images to some extent, and we proposed delimiter token scaling to further enhance this effect. To verify this, we conducted two ablation studies: 1) keeping the delimiter tokens unchanged while scaling the first token, which is known to receive strong attention, and 2) replacing the delimiter tokens with other special tokens and applying the scaling method those alternatives. All experi- ments were conducted on the Mantis Benchmark using the Qwen2.5-VL-3B model.
When we scaled the first token, < imm.startt >>, as shown in the Table A3, the performance im- proved slightly compared to the Baseline but remained substantially lower than Ours. We consider that this small improvement arises because the first token a sink token; scaling it further amplifies its already dominant attention, allowing it to absorb some cross-image interactions However, be- cause this approach cannot provide any image-level tagging effect, its performance significantly inferior to method.
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Baseline
Scaling factor
Baseline
Scaling factor
2014 2e
2014
10-1
10-2
10-1
0.000
0.001
0.002
0.003
Atten maps of baseline and scaling factor<;1
(b) Zoomed-in views of the red boxes
Figure A4: Qualitative comparison of (a) attention maps before and after applying scaling factor smaller than I, and (b) zoomed-in views of the red boxes. With scaling factor below 1, cross-image interaction is still present.
Next, when we replaced the delimiter token with the < im start >> token and applied scaling in the same manner, as shown in the Table A4, the performance dropped below the Baseline. This indicates that scaling a token that merely appears between images does not yield any performance gain; only scaling the actual delimiter token leads to improvements.
Together, these experiments demonstrate that performance does not improve by simply scaling an arbitrary token. Instead, the effectiveness of our method arises specifically from the structural and functional role that the delimiter token plays in multimodal models.
Scaling with value smaller than 1. Our method applies a scaling factor greater than to the delimiter token. To perform ablation
Model Performance
Baseling
0.60
study on the scaling behavior, we additionally conducted experiments in which the scaling factor was set to a value smaller than 1. When the scaling factor less than 1, the delimiter token is expected to receive insufficient atten- tion, making it difficult to effectively suppress cross-image information leakage. This experi- ment was conducted on the Mantis Benchmark using the Qwen2.5-VL-31 model. As shown the Figure A3, performance drops below the Baseline. The attention map analysis in the Figure A4 also reveals that cross-image information leakage persists and, some regions, becomes even more pronounced. We attribute this behavior to the reduced scaling factor, which prevents the delimiter token from receiving sufficiently strong attention.
Accuracy
0.55
0.50
0.45
0.3
0.5
0.8
1.0
Scaling Parameter
Figure A3: Scaling smaller value
Layer Selection. Layer selection determined through hyperparameter tuning, and for the same model, we kept the selected layers consistent even when the benchmark changed. In some models, only a sin- gle layer was chosen, while in others, multiple lay- ers were selected. Guided by prior study (Yu & Lec, 2025) reporting that early layers play an important role in grounding and other vision-related processing, we primarily selected consecutive early layers. Moreover, 32,33.34.35
Table AS: Layer Selection Ablation
Select Layer
Accuracy
Baseline
0.5991
0,1,2,3
0.6313
10,11,12,13
0.5991
22,23,24.25
0,5484
0.5945
since the scaling applied in early layers propagates through subsequent layers, considered early- layer selection to more effective. We validated the performance differences arising from layer selection through experiments. Using the Mantis benchmark and the Qwen2.5-VL- model, which consists of 36 layers (0-35), we selected layers O, 1, 2, and 3. As shown the Table A5, selecting early layers yields the best performance.
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Table A6: Comparison with FOCUS.
Table A7: Memory and runtime comparison.
Method
Qwen2.5-VL.
Intern VL3
Method
Avg VRAM
Peak VRAM
Inference Time
3B
7B
1B
8B
Baseline
59.91
68.66
47.00
67.28
Baseline
8.27 GB
10.18 GB
Im
FOCUS
58.53
47.93
FOCUS
10.76 GB
21.86 GB
5m 21s
Ours
63.13
69.12
49.77
69.12
Ours
8.27 GB
10.18 GB
Im 41s
Attention entropy
4-
3-
2-
1-
Baseline
Ours
0
14
Image
289
Image 2 577
Image
963 Image 1238
Text
Token Index
Figure A5: Delimiter tokens exhibit lower entropy and our method further reduces entropy in the image region by suppressing cross-image attention.
A.5 COMPARISON WITH FOCUS.
We compared our method with FOCUS (Park et al., 2025), a previous method designed to mitigate cross-image information leakage. For faimess, performed grid search over the hyperparame- ters, which resulted in a total of 81 configurations, and we used the best-performing one for compari- son. As shown in the Table A6, method consistently outperforms FOCUS the Mantis Bench- mark. Table A7 further reveals that FOCUS incurs substantially higher memory consumption, to the extent that certain configurations could be executed on Qwen2.5-VL-7B and Intern VL338B due to memory limitations. In contrast, our approach is markedly more memorr-efficient,, requiring roughly half the peak VRAM usage of FOCUS, while also achieving better runtime efficiency.
A.6 ANALYSIS OF ATTENTION ENTROPY CHANGES
We also conducted attention entropy analysis to further examine how our method affects cross- image attention. We first measured token-level attention entropy in the baseline setting with multi- image inputs and then compared how the entropy changes when method is applied. As shown in Figure A5, the baseline exhibits a sharp drop in entropy at the delimiter position between images. This behavior matches the attention patterns in Figure la. For example, immediately before the delimiter of Image 3 (i.e., at the final Query position of Image 2), strong intra-image interaction leads higher entropy. Once the delimiter token of Image 3 becomes the Query token, however, attention to Images and 2 largely disappears and concentrates on the sink token and the delimiter itself, resulting lower entropy. short, the delimiter token absorbs much of the attention and suppresses backward cross-image attention, reducing entropy. At the same time, as highlighted by the red box in Figure la, delimiter tokens in the baseline still allow a non-negligible amount of cross-image attention. In contrast, Figure 4 shows that our method substantially reduces these cross- image interactions. Accordingly, we expected the entropy in the image region to decrease when applying our method. This is confirmed by the orange curve in Figure A5: entropy in the image region is consistently lower than in the baseline, whereas entropy the text region remains nearly unchanged, indicating that text-image interactions are preserved. These findings consistent with our earlier analysis: our method suppresses cross-image information leakage while maintaining text-image interaction, and this effect is clearly reflected in the entropy patterns.
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Table A8: Performance with the entropy- based
Table A9: The entropy based extension adds extra cost in both memory usage (GB) and inference time (s/input).
extension.
Method
Intern VL3
Llava-OV
Method
Inference Time
Peak
Avg
14B
0.5B
Memory
Memory
Baseline
71.89
40.09
Bascline
0.77
60.7
60.0
Ours
72.81
41.01
Ours
0.77
60.7
60.0
Entropy
73.27
41.47
Entropy
1.78
114.8
95.7
A.7 FURTHER EXTEND OUR METHOD THROUGH ENTROPY
We extended our original method by incorporating an additional mechanism that leverages attention entropy. Specifically, we dynamically determined the scaling parameter by computing the entropy of each delimiter token's 's attention- weight distribution. When the entropy was higher-i.e., when the attention was more dispersed we applied stronger scaling to the corresponding delimiter's hidden state. As shown in the Table A8, this extended mechanism provides additional performance gains on the Mantis Benchmark when using the Intern VL33-4BB model. Following same evaluation protocol as the Baseline and Ours settings, all experiments were conducted the test set, with 10% of the test set used as a validation split. However, a critical drawback this method is that requires direct access the full attention map for entropy computation, which prevents the of FlashAttention. As shown in the Table A9, the inference speed becomes more than twice as slow compared
original method, and the memory overhead is also substantial. The average GPU memory consumption increases by approximately 1.6x, requiring an additional 36GB 36GB-already exceeding the capacity of single 24GB GPU. Moreover, the peak memory usage increases by about 54GB, effectively requiring more than two additional 24GB GPUs, making the method highly impractical in real- world scenarios.
A.8 ADDITIONAL QUALITATIVE RESULTS
In addition to the original sample, we analyzed several additional cases, and the results are presented in Figures A7, A8, A9, A10 and All In particular, these two samples illustrate situations where the input images are visually very similar to each other. Even in such challenging settings, the results remain consistent. When the delimiter token is present, the images are clearly separated, forming triangular boundary pattern. In contrast, when the delimiter token is removed replaced with another special token such as < llm.start >, the attention maps no longer show clear separation between images. These findings indicate that the delimiter token plays crucial role in distinguish- ing images, even when they are visually similar.
A.9 ADDITIONAL LIMITATION
Our method requires modifying hidden states, and its current form, this imposes a limitation in that is applicable only open-source models. Although not directly applicable to proprietary mod- els, our simple and lightweight reweighting mechanism can be casily integrated into such systems. Although external users of commercial models cannot access hidden states, for model developers, incorporating our method internally poses little technical difficulty. Thus, rather than being a fea- ture limited to open-source models, approach be seen intuitive improvement that is reasonably applicable to commercial systems as well.
A.10 ADDITIONAL DISCUSSION
Recent studies the LLM literature have examined the behavior of models under parallel context encoding, where multiple interleaved contexts are presented within a single input sequence. These works highlight that sink tokens often exhibit abnormal hidden-state activations under such settings, which in turn lead to increased and irregular attention entropy. Prior analyses further argue that the model encounters a multi-sink pattern at inference that has never observed during training, and that this distributional shift directly contributes to performance degradation.
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um
Which the following figure described with the caption: HLA-Cw6-GFF transfers from cells to NK cells at the inhibitory light image
immune synapse Top Tields view different times, showing a vesicie containing HLA-CWG-GFF moving away from the Immune synspse depict identical The white arrow indicates the position of the vesicle in the first frame in all images, white scale bars represent jm Cells shown are jepresentative of numerous cells least three independent experiments FigTcVideo1 and Fig3bVideo2 are movies of time-lapse respectively Using time-lapse imaging, vesicles containing cytoplasm
fluorescence
the cell
confocal microscopy data, individuel frames clearly seen migrating away from Immune synapse and blood NK cell line, KO07, which expresses KIR2DL1 and FigieVidea1 Therefore, even though live-call time -lase imaging of transfer and uptake of CLLA-C-FFP by NK cells showed movement of vesicles reminiscent of travel along the cytoskelston and FigteVideo11 ) appear necessary
HLA-CWS--OF
cell from another perlphoral
active polymerization actin myosin operation, cells (Fig.1 target call HLA-OW6-GFP transferred
process Like transfer the plasma membrane and cytoplasm KIR2DL1 Fig. 3 This directly domonstrated transfor of HLA-C from target to pariphoral blood NK calls (Fig. 1 and FigteVideo1 and variable supramolecular organization of H-A-C at immune synapses ( (iig. 6, Fig6bVideo3, and Fig6cVideo4 )?
Into
paripheral
C.
D.
E. None the choices provided option's
given directll..
Figure A6: Input sample consisting of the images and the query used for the attention map in Section A.
Our work shares part of this motivation, as we also investigate how a model processes and regulates multiple interleaved regments-sure as multi-image witthi one sequence. However, the setting we study differs from the parallel context encoding scenario in several fundamental ways.
First, prior work focuses on LLMs, whereas our study centers on LVLMs, whose input structure and training signals differ substantially. Second, the types of patterns the models observe during training. well as the phenomena that arise at inference, diverge meaningfully between the two settings. In the parallel context encoding literature, the abnormal hidden states of sink tokens are considered a root cause of unstable attention behavior, and performance deterioration is attributed to the model's 's first exposure to a previously unseen multi-sink pattern during inference.
In contrast, analysis (see Figure la) shows that LVLMs naturally experience multi-sink--o like patterns during training due to their reliance on delimiter tokens. Because these delimiter tokens introduce repeated structural cues throughout the training corpus, I.LMSs are routinely exposed to patterns that closely resemble the multi-sink configuration. As result, LVLMs do not face a novel or unexpected pattern at inference time; rather, they encounter familiar structural arrangement that they have already internalized during training.
For this reason, even if attention entropy increases some extent in LVLMs, such changes unlikely to directly cause performance degradation. This distinction highlights that the mechanisms underlying multi-con multi-context processing in LVLMs differ substantially from those observed in LLM- based parallel context encoding, owing largely to LVL.Ms* structural use of delimiter tokens and the training distributions they induce.
A.11 BROADER IMPACT
Our method improves efficiency and sustainability by enhancing performance without additional training or inference costs, reducing reliance on large datasets and retraining. This lowers energy and research expenses, contributing smaller carbon footprint.
A.12 THE USE OF LARGE LANGUAGE MODELS (LLMs)
We used large language model (ChatGPT) to improve the clarity and fluency of the manuscript. All technical ideas, analyses, and results were solely developed by the authors.
18

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Published as a conference paper at ICLR 2026
Image
Image
Image
Image 4
Which image best depicts the caption 'A woman kissing in front of a
kitchen counter'?
(A) Image 1
(B) Image
(C) Image 3
(D) Image 4
Answer with the option's letter from the given choices directly.
(a) Input sample consisting of the images and the query used for the attention map.
mgg
1mgl
img?
Img2
10-2
imga
mm
8
mmg4
10-
1
[
lmg1
lmg2
lmg3
Img4
T
lmg1
lmg2
lmg3
Img4
T
(b) Baseline
(c) Ours
EBua
lmg1
lmg2
Jmg3
Img4
T
Img1
Img2
lmg3
Img4
1mg1
Img2
lmg3
Imq4
w/ delimiter tokens
(e) w/o delimiter tokens
(f) Replace w/ <| <lim. wtm.statttii |>
[Bai
Lewil
gBul
8Bui
[
lmg1
Img2
Img3
Img4
Img1
Img2
Img3
Img4
Img1
Img2
lmg3
Img4
Replace w/ < <lím end |>d
(h) Replace w/ < box start >>.
(i) Replace w/ < endo |>>.
Visualization attention patterns bottled wine example. compare baseline model, our delimiter-token scaling method, and ablations where the image delimiter token is removed or replaced with other special tokens.
Figure A7:
19

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Published as a conference paper at ICLR 2026
Image
Image 2
Which image shows more bottled wine?
(A) Image
(B) Image 2
(C) They are the same
Answer with the option's letter from the given choices directly.
(a) Input sample consisting of the images and the query used for the attention map.
10
Ouery
OlenO
2000
10
1mg1
Img2
lmg1
Img2
T
Key
Key
(b) Baseline
(c) Ours
Assm
Asmo
Img1
Img?
Img1
Img1
Img?
Key
Key
Key
(d) w/ delimiter tokens
(e) w/o delimiter tokens
( Replace w/ <| <mimstarrt >>>
Asono
11
Key
Key
Key
(g) Replace w/ < imd >
(h) Replace w/ < libox_starrt |>,
(i) Replace w/ < endoftext |>.
Figure A8: Visualization of attention patterns for the bottled wine example. We compare the baseline model, our delimiter-token scaling method, and ablations where the image delimiter token removed or replaced with other special tokens.
20

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Published as a conference paper at ICLR 2026
The temperature increases. What is the correct order of images according to the given context?
(A) the first image, the second image, the third image
(B) the third image, the second image, the first image
(C) None of the choices provided
(D) the first image, the third image, the second image
Answer with the option's letter from the given choices directly.
(a) Input sample consisting of the images and the query used for the attention map.
lmgi
Ouery IسP2
100
10
lmg1
lmg2
lmg3
Img1
Img2
Img3
Key
Key
(b) Baseline
(c) Ours
Ateno
Alano 20mu
Ateno 20mm
gBug
5dug
[
Img1
Imgg
Jmg3
Imgl
Imp2
Img3
Img1
Img2
Img3
Key
Key
Key
(d) w/ delimiter tokens
(e) w/o delimiter tokens
(1) Replace w/ <mi.starrt
KJano
Alnn
Kiang
gbuu
gdus
gduu
[1]
Img1
Img2 Key
Img3
Img1
2mg2 Key
Img3
Img1
Img22
Img9
Key
(g) Replace w/ <| immendd] >,
(h) Replace w/ < lbbx.starr |>
(i) Replace w/ <
endoftext >>
Figure A9: Visualization attention patterns bottled wine example. compare baseline model, our delimiter-token scaling method, and ablations where the image delimiter token is removed or replaced with other special tokens.
21

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Published as a conference paper at ICLR 2026
Image
Image
Image C
Image
Image
Could you locate structure from the identical university as depicted in
Image A?
(A) Image
(B) None of the choices provided
(C) Image C
(D) Image D
(E) Image
Answer with the option's letter from the given choices directly.
(a) Input sample consisting of the images and the query used for the attention map.
[mggl
tmpp
OienO
Imgg
gmgs
lmg1
lmg2
lmg3
Img4
lmg5
1
lmg1
Img2
Img3
Img4
Img5
Key
Key
(b) Baseline
(c) Ours
2a0g Hunl
gBba 2001
280g
Assmo
Asemo pous gliu
Asomo
plus (Ba))
pous
gbug
It gamg
ghug
Imgd
Img5
Img1
Img2
Img3
Imga
Img1
Img2
1mg5
Key
Key
Key
(d) w/ delimiter tokens
(c) w/o delimiter tokens
(f) Replace w/ < [mm_staar|
phu! Ubug
10mg
glmn
Ateno
Alaoo
gaug
sBa
mmp5
Imp5
Key
Key
Key
(g) Replace w/ < <limmedd (h) Replace w/ < lbbo.sstart 1>.
(i) Replace w/ < endoffext >>
Figure A10: Visualization of attention patterns for the bottled wine example. We compare the baseline model, our delimiter-token scaling method, and ablations where the image delimiter token removed or replaced with other special tokens.
22

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Published as a conference paper at ICLR 2026
Image D Are you able to identify a building from the same university as the one
Image A
Image
Image C
shown in Image A?
(A) Image
(B) Image
(C) Image
(D) None the choices provided
Answer with the option's letter from the given choices directly.
(a) Input sample consisting of the images and the query used for the attention map.
10
1mp
Ojany
10-2
10
11
lmg1
Img2
lmg3
Img4
Img1
Img2
lmg3
Img4
T
Key
Key
(b) Baseline
(c) Ours
Assm 2hus
AsenO
gluu
goun
gluu
you
M
Img1
Img?
3mg3
2mg4
Img1
Img?
2mg3
Img1
Img2
Img3
Img4
Key
Key
Key
(d) w/ delimiter tokens
(e) w/o delimiter tokens
(1 Replace w/ imimstarrt
pbu
Aleno
Xieno
Aiao
gdug
phug
[ pang
phun
Key
Key
Key
(g) Replace w/< < emm_edd>>
(h) Replace w/ < (/box_startt >>.
(i) Replace w/ < endoffeett >>
Figure A11: Visualization of attention patterns for the bottled wine example. We compare the baseline model, our delimiter-toker scaling method, and ablations where the image delimiter token removed or replaced with other special tokens.
23


Reviews:
[{"review_id": "Pful1hgqxz", "reviewer": "Reviewer_exND", "summary": "This work focuses on interleaved inputs for multi-modal large language models. \n\nIt is motivated by the hypothesis that **adjacent frames carry stronger contextual relevance.** \n\nUnlike previous interleaved approaches that rely on special textual tokens, this work introduces a delimiter token reweighting mechanism.", "questions": "1. In Appendix A.1, several details require clarification. For instance, what are the four images and the corresponding text shown? How many samples were used to construct this figure? If the images share visual similarity, would the corresponding results change?\n\n2. Can the proposed method be extended to interleaved inputs (e.g., alternating image–text sequences)?\n\n3. For multi-image inputs, could there be potential attention sink issues similar to those discussed in “When Attention Sink Emerges in Language Models: An Empirical View”?\n\nI may reconsider my evaluation after reading the authors’ rebuttal.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The interleaved image–text processing still lacks clarity in terms of how information flows across modalities. The explanation within MLLMs remains underexplored, though this work makes a valuable attempt to highlight the importance of key regions.\n\nThe motivation is clear.\n\nThe core idea is **very simple yet interesting**, and the method section is clearly presented. The method do not bring additional training cost and just reweight the hidden states.", "weaknesses": "1. The main concern is the **limited scope of evaluation**. The paper focuses primarily on math and multi-view benchmarks, whereas multi-image input represents a special case of __interleaved data__ that can be applied to a broader range of scenarios. The performance under few-shot settings, where multiple instances are concatenated together, remains unclear and differs from the explored benchmarks.\n\n2. The performance improvements are sometimes marginal, suggesting limited generalization.\n\n3. The proposed reweighting operation introduces inconvenience during inference, as it requires modification of hidden states and is therefore applicable only to open-source models.", "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": "bnWUnwhdkZ", "reviewer": "Reviewer_pZZk", "summary": "This paper presents a training-free method for limiting cross-image interactions in LVLMs by scaling the hidden states of delimiter tokens across multiple images. Comprehensive evaluation results on multi-image, multi-document, and multi-table benchmarks are provided to demonstrate the effectiveness of the proposed approach. Both qualitative and quantitative analyses are included to illustrate the role and impact of image delimiter tokens.", "questions": "1. Could the authors propose a more technically substantial method or extend the current approach with additional mechanisms? Further ablation studies building on the discovered delimiter token properties may help strengthen the contribution and highlight the novelty of the work.\n2. The writing could be further polished for clarity, and several figures and tables could be improved for readability. Enhancing the presentation quality would significantly improve the paper.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The problem of cross-image information leakage in multi-image LVLM settings is important and worth investigating.\n2. The prior analysis on delimiter tokens and the characterization of their key properties is clear and insightful, helping readers better understand the mechanism.\n3. The experiments are extensive, covering multiple LVLM families and sizes, four multi-image benchmarks, two multi-document benchmarks, and one multi-table benchmark.", "weaknesses": "1. The concept of “sink tokens” has been studied in prior works, and there are also existing methods addressing cross-image leakage. Thus, the novelty and significance of the current findings appear limited.\n2. The technical contribution of the proposed method is relatively weak. Scaling the hidden states of delimiter tokens provides only marginal performance gains. For instance, when applied to larger LVLMs such as InternVL3-14B or Qwen2.5-VL-32B, the improvements are minimal (e.g., 42.42 → 42.58).", "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": "BRyU5lBsiH", "reviewer": "Reviewer_4BeG", "summary": "This paper addresses a critical limitation of LVLMs, i.e., cross-image information leakage in multi-image tasks, by scaling delimiter token hidden states, a simple yet effective training/inference-cost-free method. Experiments across multi-image (Mantis, MuirBench) and text-only (MultiNews, TQABench) benchmarks show consistent gains, validating its robustness and generality. The analysis of delimiter tokens’ two key properties also provides valuable insights into LVLM attention mechanisms.", "questions": "- Your work shares similarities with parallel context encoding [1] and attention sink, both focusing on context separation/attention regulation. How does your delimiter scaling method relate to attention entropy (a core factor in Zhang et al.’s work)? Does scaling delimiter states reduce cross-image attention entropy, and if so, how? \n- Is there a run-time analysis to show the exact additional inference cost? \n\n\n[1] Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models\n, Z Zhang et al, ACL 2025.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Solves cross-image leakage in LVLMs via delimiter scaling, with gains in multi-image/text tasks, no extra cost.\n- Analyzes delimiter tokens’ key properties, offering clear theoretical basis for the method.\n- Generalizes to text multi-instance tasks, works across models (0.5B–32B), and fits optimized kernels.", "weaknesses": "- Though claiming minimal impact on text-image interaction, it only mentions a 10% drop in text-to-image attention scores without detailing how this drop affects downstream cross-modal tasks (e.g., image-text retrieval), leaving uncertainty about real-world cross-modal performance", "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": "O48VZAgF5D", "reviewer": "Reviewer_Fgst", "summary": "The method is evaluated on a wide range of models and benchmarks, demonstrating consistent performance improvements on multi-image understanding tasks (Mantis, MuirBench, etc.). Crucially, the authors show the method's generality by applying it to text-only multi-document and multi-table tasks, again achieving performance gains. The proposed approach requires no additional training or inference costs.", "questions": "please see the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper's primary strength is its clear and insightful analysis of how delimiter tokens function and where they fall short. The \"image-wise tagging\" concept provides a strong theoretical motivation for the proposed solution.\n\nThe method is remarkably simple (a single scaling operation) and highly efficient (no training, no inference overhead, compatible with optimizations like FlashAttention). This makes it a very appealing and practical technique.\n\nThe effectiveness of the method is demonstrated across a wide variety of models (Qwen2.5-VL, InternVL3, LLaVA-OV), model sizes (0.5B to 32B), and task domains (multi-image, multi-document, multi-table). This shows it is a general principle for improving multi-instance understanding, not a model-specific trick.", "weaknesses": "The paper states that the scaling layer and factor λ are tuned for each model. The appendix mentions the scaling layer is fixed for each model, but the process for choosing this layer is not detailed. A more principled explanation or heuristic for selecting the optimal layer(s) to apply scaling would make the method even more robust and easier to adopt. The current approach feels slightly post-hoc.\n\nThe method involves scaling a single chosen layer's hidden states. A deeper analysis on why a particular layer is optimal or an exploration of scaling multiple layers could provide further insights into the model's internal workings.", "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)