Visual Structures Help Visual Reasoning:  Addressing the Binding Problem in LVLMs
Authors: Amirmohammad Izadi
Mohammadali Banayeeanzade
Fatemeh Askari
Ali Rahimiakbar
Mohammad Mahdi Vahedi
Hosein Hasani
Mahdieh Soleymani Baghshah
Venue: NeurIPS 2025 poster ()
Keywords: Visual Reasoning
Large Vision-Language Models
Cognitive Science
Binding Problem
Primary area: applications
TL;DR: VISER adds simple visual scaffolds and spatial prompts to guide Vision-Language Models toward sequential processing, improving performance on visual reasoning tasks like counting, scene description, visual search, and spatial reasoning.

Abstract: Despite progress in Large Vision-Language Models (LVLMs), their capacity for visual reasoning is often limited by the binding problem: the failure to reliably associate perceptual features with their correct visual referents. This limitation underlies persistent errors in tasks such as counting, visual search, scene description, and spatial relationship understanding. A key factor is that current LVLMs process visual features largely in parallel, lacking mechanisms for spatially grounded, serial attention.
This paper introduces Visual Input Structure for Enhanced Reasoning (VISER), a simple, effective method that augments visual inputs with low-level spatial structures and pairs them with a textual prompt that encourages sequential, spatially-aware parsing. We empirically demonstrate substantial performance improvements across core visual reasoning tasks, using only a single-query inference. Specifically, VISER improves GPT-4o performance on visual search, counting, and spatial relationship tasks by 25.0%, 26.8%, and 9.5%, respectively, and reduces edit distance error in scene description by 0.32 on 2D datasets.
Furthermore, we find that the visual modification is essential for these gains; purely textual strategies, including Chain-of-Thought prompting, are insufficient and can even degrade performance. VISER underscores the importance of visual input design over purely linguistically based reasoning strategies and suggests that visual structuring is a powerful and general approach for enhancing compositional and spatial reasoning in LVLMs.

Visual Structures Help Visual Reasoning: Addressing the Binding Problem in LVLMs
Amirmohammad Izadi Mohammad Ali Banayeeanzade Fatemeh Askari, Ali Rahimiakbar, Mohammad Mahdi Vahedi, Hosein Hasani, and Mahdieh Soleymani Baghshah
Department of Computer Engineering Sharif University of Technology
Abstract
Despite progress in Large Vision-Language Models (LVLMs), their capacity for visual reasoning is often limited by the binding problem: the failure to reliably associate perceptual features with their correct visual referents. This limitation un- derlies persistent errors in tasks such as counting, visual search, scene description, and spatial relationship understanding. A key factor is that current LVLMs pro- cess visual features largely in parallel, lacking mechanisms for spatially grounded, serial attention. This paper introduces Visual Input Structure for Enhanced Rea- soning (VISER), simple. effective method that augments visual inputs with low- level spatial structures and pairs them with a textual prompt that encourages se- quential, spatially-awan aware parsing. We empirically demonstrate substantial perfor- mance improvements across core visual reasoning tasks, using only single-query inference. Specifically, VISER improves GPT-4o performance on visual search, counting, and spatial relationship tasks by 25.0%, 26.8%, and 9.5%, respectively, and reduces edit distance error in scene description by 0.32 on 2D datasets. Fur- thermore, we find that the visual modification essential for these gains; purely textual strategies, including Chain-of-Thousg prompting, are insufficient and can even degrade performance. VISER underscores the importance of visual input de- sign over purely linguistically based reasoning strategies and suggests that visual structuring is a powerful and general approach for enhancing compositional and spatial reasoning in LVLMs.
Introduction
Large Language Models (LLMs) have recently achieved remarkable progress, matching or even surpassing human performance across range of complex tasks [1-5]. Despite significant advance- ments, Large Dision-Languaa Models (LVLMs) still lag behind LLMs in core reasoning capa- bilities. While L.LMs show strong symbolic and abstract reasoning. LVLMs struggle with essen- tial aspects of visual understanding. They frequently miscount objects in cluttered or overlapping scenes [6, 7]. perform poorly in spatial reasoning (e.g., identifying left of above) [8], and often fail to accurately locate or recognize salient targets during visual search [9]. Moreover, composi- tional and relational reasoning, where understanding critically depends on structured relationships between objects, remains major challenge [6]. These limitations indicate that LVLMs, despite their power, lack key mechanisms for structured visual reasoning. Consequently, their performance in these areas does not yet match that of language-ong models in comparable text- bastd reasoning tasks.
Equua contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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mony there image?
Scan, the image aequentially based on herizontel lines exist in the image.
10
Getting Resporse
Raw Image
Applying row-wise
Special prompt for task
structure
Figure 1: The input image is augmented with low-level visual structure using three horizontal lines, optionally accompanied by row annotations the left side of the image. A corresponding textual prompt (Scan the image sequentially based on horizontal lines) is appended to encourage the model to adopt spatially guided, sequential parsing strategy.
The limitations in visual reasoning tasks can largely be attributed fundamental challenge known as the binding problem [9]. Originating from cognitive science and neuroscience, the binding prob- lem refers to the difficulty correctly associating visual features (like shape and color) and spatial properties (such as location and orientation) with the right objects in scene. It arises when a system must handle multiple entities simultaneously, leading to interference and misattribution of features [10-12]. In the context of LVLMs, the binding problem manifests errors in compositional under- standingmodels may recognize individual objects correctly but confuse or conflate their attributes. These issues are particularly prevalent in complex, cluttered scenes and persist across range of model architectures and benchmarks. As a result, reliable multi-object reasoning and generalization of visual concepts to novel settings remain difficult for current LVLMs.
Inspired by neuroscientific studies on human scene perception, we propose Visual Input Structure for Enhanced Reasoning (VISER), novel approach to improve LVLM binding performance. Our approach augments visual inputs with low-level spatial structures, such as horizontal or grid lines, complemented by appending textual instruction to the input prompt (see Figure 1 as example). The motivation behind our strategy is to encourage sequential processing in LVLMs guided by these structures in the inputs. Our method can be viewed as visual analogue of Chain-of- Thought (CoT) prompting[ 13] for LLMs, as it injects an inductive bias directly into the visual input to implicitly decompose the problem and guide the models reasoning.
Despite its simplicity, VISER yields significant performance improvements. The method is gen- eral and input-agnostic, requiring no additional processing for specialized image manipulation. We empirically show that both the visual structure and the textual part of our method are necessary and complementary. In particular, textual prompts for sequential scanning alone prove insufficient to resolve binding failures, and even attempts to elicit more structured reasoning through complex prompting strategies, such as CoT, can paradoxically degrade performance these visual tasks [14]. In contrast, the inclusion visual cues stimulates more structured internal processing in LVLMs and promotes spatial parsing mechanisms, enhancing the models ability to bind features accurately. These findings highlight the potential of targeted visual input modifications to improve graphical reasoning and compositional understanding in LVLMs. In summary, the main contributions of this paper are:
We propose VISER, novel method that combines explicit visual scaffolding (e.g., horizontal lines) with targeted textual prompts to improve feature-object binding in LVLMMs
Through extensive experiments, we demonstrate that our method significantly enhances LVLM performance across range of core visual reasoning tasks, including visual search, counting, scene description, and spatial relationship understanding. For example, on 2D benchmark, VISER improves GPT-4o performance on visual search, counting, and spatial reasoning by 25.0%, 26.8%, and 9.5% (absolute), respectively, and reduces the edit-distance error in scene description by 0.32.
Our results establish that purely textual interventions are insufficient to overcome binding limitations, and explicit visual manipulation is crucial for this purpose.
The proposed approach achieves these improvements efficiently, operating within a single query and incurring negligible computational overhead. Its effectiveness, simplicity, and gen- erality (both task- and model-agnostic) distinguish our method from multi-query or agentic approaches that rely on additional processing or tool use.

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2
Related Work
LLM and LVLM Reasoning. While early LLMS were regarded next-token predictors with little reasoning ability [15, 16], recent work has begun to extensively challenge this problem [17-20]. Todays state-of-the-at models deliver high performance across many tasks, including graduate-level question answering [21] and competitive programming. The improved reasoning abilities of LLMs have naturally sparked interest in reasoning for LVLMs. Initial approaches include Visual Chain-of- Thought [22], knowledge graph integration [23], and tree search [24]. Although these techniques show promise, LVLMs still struggle with tasks such as counting, visual search, scene description, and spatial reasoning, as demonstrated by benchmarks like EMMA [14] and SPACE [25]. Our work aims incorporate an explicit reasoning pathway into the visual component of the model to improve its performance on these challenging tasks.
Binding Problem. The binding problem provides key explanation for the limitations of state- of-the-art LVLMs in reliably linking visual features to their corresponding objects [26, 27J. Prior work attributes performance issues in tasks such as counting, visual search, and scene description to failures in this featureobject binding in LVLMs [9]. Recent neuroscience research has shown that grid- grid-based frameworks enhance visual recogmition memory [28] and improve face recognition performance [29]. Additionally. humans reduce interference by detecting individual objects itera- tively [30, 10]. and grid structures facilitate movement- baseed object recognition [31], thus providing insight into how the human brain can deal with the binding problem. Inspired by these insights, we use horizontal lines in images to enhance model reasoning, both with synthetic images and real- world datasets, such as the Learning To Count Everything benchmark [32].
Agentic LVLMs. An emerging line of research treats LVLMs as autonomous tool users that can invoke external modules or produce executable artefacts to compensate for perceptual or reasoning gaps. LVLM-COUNT [33] decomposes enumerative queries into sub-counts solved by specialized counting utility. yielding gains on counting benchmarks. Visual Sketchpad [34] endows multimodal LMs with a drawable canvas, letting them sketch auxiliary lines and marks. Code execution agents such as ViperGPT [35] translate aatural-languaee questions into Python scripts that orchestrates off-the-shelf vision tools, achieving state-of-the results on compositional visual queries. Tool- former [36] shows that language models can self-supervise decisions about when and how to call external APIs Although such tool-using approaches improve task performance, they do not nec- essarily enrich models' intrinsic reasoning capabilities. In contrast, our work embeds an explicit reasoning pathway directly into the visual input to overcome these limitations.
Proposed Method
The binding problem, fundamental challenge in cognitive science and neuroscience, concerns how perceptual systems correctly associate features like color, shape, and location with the right objects in complex visual scenes [10, 12]. When multiple objects share representational resources, the system can produce illusory conjunctions, i.c., erroncous confusion of features from different objects (e.g., mistakenly perceiving a red square when shown red circle and a green square) [11]. These misbindings reveal the difficulty of forming coherent, object-specific representations from distributed visual inputs; their analysis provides useful framework for evaluating visual reasoning systems.
Neuroscientific studies indicate that the human visual system operates in two distinct modes: fast, imprecise parallel processing (often likened to System processing) and a more accurate sequential attention (System 2 reasoning) [37]. While parallel processing enables rapid scene understanding. it is prone to errors, particularly in cluttered environments where the demands for feature binding are high. Given sufficient time, humans transition to sequential attention, which allows for more metic- ulous feature binding and reduces errors. In contrast, current LVLMs predominantly process visual inputs largely in parallel. This approach, while beneficial for compositional representation learning and generalization, inherently risks interference and binding errors if feature-to-object linkages are not precisely managed [27].
To address the binding problem in LVLMs, we propose a lightweight, model-agnostic approach that enhances structured visual reasoning through minimal visual scaffolding and spatially grounded prompting. Inspired by cognitive science findings on serial attention and spatial structuring, our method guides LVLMs to process visual information region-by-region and reduces interference ef-
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Figure 2: A brief summary of tasks with one example of synthetic data along the specific prompt for each task. (a) Visual Search, (b) Scene Description, (c) Counting, and (d) Spatial Relationship.
fects from parallel processing, known contributor to binding errors in human and machine vision. This approach introduces negligible computational overhead, requires no task-specific fine-tuning multi-query visual processing, and comprises main components, which are illustrated with an example in Figure and explained below:
Visual Structuring with Horizontal Lines We augment each input image with n equidistant horizontal lines, creating
horizontal segments. Each segment is annotated with numerical label (1 through n + 1) placed sequentially from top to bottom. These lines serve as visual anchors, promoting localized attention within each region reduce cross-object interference and improve feature-object binding. Unlike dense grid-based methods that may obscure content, our minimalist design preserves clarity while providing sufficient spatial guidance. The value of n is set in our experiments.
Sequential Scanning Prompt To align the models attention with the visual scaffold, we prepend fixed instruction, specifically: "Sean the image sequentially based on horizontal lines exists the image." This prompt guides the model to adopt structured, row-wise processing strategy, encour- aging systematic evaluation of the image content. For task-specific adaptation (e.g., counting or spatial reasoning), we augment the base prompt with additional instructions (see Appendix C).
The use of manually added artifacts and the emphasis on sequential, region- processing may also align with the discussion of feature entropy in [9]. By partitioning the image into distinct seg- ments, our method encourages focused processing on smaller subsets of objects. This can simplify the distinction between objects within each segment by reducing the concurrent feature load, an ef- feet pertinent managing feature diversity (or entropy) as detailed therein. Such simplification is key factor contributing to improved visual reasoning, particularly in counting.
4 Experiments
4.1 Setup
Datasets To evaluate VISER, we use both synthetic and natural datasets. A Binding Problem Generator [9] produces synthetic data that can be controlled two and three dimensions and can incorporate a variable number of objects. Additionally, we benchmark on two real-world tasks: Learning To Count Everything [32] and the Spatial Reasoning [38] datasets.
Models We evaluated our proposed method across a carefully chosen mix of closed-source and open-source LVLMs. Specifically, we include two leading dlosed-source systemsOpenAlls GPT-4o [39] and Anthropics Claude3.5-sonnet [40]selected for their cutting-edge capabilities and solid performance on multimodal benchmarks. For GPT-4o, we evaluate both our method and Chain- of-Thought (CoT) prompting [13] to enable a direct comparison within the same architecture. Addi- tionally, we benchmark two state-of-the-art open-source models: Dwwen2.-VVL-7B-Insee I.LaMa--ccout 77 Tb-16e-Innstret [42]. Beyond these baselines, we assess the benefits of targeted fine-tuning by incorporating Mulberry [24], Qwen2.5VL-7B variant refined for enhanced multi-
[41] and

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Table 1: Performance comparison on the counting task using models GPT-4o, Sluuee.55soomeg LLaMa4, and Qwen2.5-VL, evaluated with accuracy (%) The comparison is conducted across 2D and 3D datasets with varying numbers objects the scene, as well as natural image dataset. Results are shown for base models and VISER.
GPT-4o
Claude-sonnet
LLaMa4
Qwen2.5-VV. VL
Dataset
Objects
Baseline
VISER
Baseline
VISER
Baseline
VISER
Baseline
VISER
2D
10
42.00
65.00
26.00
26.00
23,00
31.00
18.00
41.00
12
12.00
40.00
5.00
15.00
3.00
20.62
1.00
45.00
14
1.00
34.00
5.00
9.00
0.00
23.47
1.00
13.00
16
3.00
39.00
3.00
6.00
11.00
23.00
1.00
59.00
18
1.00
29.00
6.00
4.00
0.00
9.28
10.00
20
13.00
26.00
6.00
4.00
1.00
7.22
14.00
77.00
Avg
12.00
38.83
8.50
10.67
6.33
19.10
5.83
40.83
10
52.00
62.00
56.00
54.00
32.00
60.42
20.83
66.00
12
12.00
38.00
26.00
28.00
0.00
34.69
8.16
20.00
14
6.00
14.00
14.00
24.00
12.00
38.00
4.08
24.00
16
16.00
24.00
8.00
14.00
40.00
44.00
6.00
16.00
18
2.00
18.00
0.00
8.00
0.00
18.75
0.00
14.00
20
2.00
30.00
0.00
4.00
0.00
22.45
12.00
20.00
Avg
15.00
31.00
17.33
22.00
14.00
36.39
8.51
26.67
Natural
29.82
35.65
2.09
6.42
29.22
31.65
18.91
17.29
modal reasoning. This breadth of model selection allows to rigorously assess the generality and robustness of our approach across diverse architectures, training scales, and access constraints.
Evaluation Metrics We use three metrics adopted in visual reasoning research: accuracy, har- monic mean, and edit distance. Accuracy, used for counting and spatial relationship tasks [14], measures the proportion of correct predictions. Harmonic mean evaluates visual search by balanc- ing performance across visible and invisible object detection [9], ensuring that high scores require consistent performance on both subtasks. Edit distance (Levenshtein distance) computes the mini- mum number of insertions, deletions, substitutions needed to transform model-generated scene description into the reference [9], offering a fine-grained measure of binding precision in generated text.
In addition to the main experiments, ablation studics are provided in Appendix A, where we isolate the effect of each component of VISER and evaluate various hyperparameters across different mod- els. Specifically, we assess variants that use only the visual scaffolding or only the sequential prompt (see Appendix A.1), as well different numbers of structural lines. Moreover, investigate the impact other design choices, such as the number and thickness lines (Appendix A.2). These analyses confirm that both components contribute the overall performance gains and that VISER remains effective across a reasonable range of hyperparameter settings. Finally, in Appendix B.3, show the significance of the proposed method by applying binomial sign test the results.
4.2
Counting
Counting involves determining the number of specific objects within scene. Although it may ap- pear simple, accurate enumerationespecially when multiple object types or attribute variations are involvedrelies heavily effective feature binding. Each object must be individuated, its defining features correctly associated, and then counted as a distinct instance. Failures binding can lead to omissions, double-counting, constraints, LVLMs show capacity limits counting. Performance often worsens as object num- bers grow, or when high object similarity (low feature heterogeneity) increases the interference and binding errors.
misclassification. Similar human rapid estimation under time
An instance of this task is demonstrated in Figure 2-(c); To evaluate our method, we generated 2D and 3D images, using the synthetic data set described above, each containing between 10 and 20 instances of the target object to increase complexity. We measure performance using counting ac- curacy; the results are summarized Table As presented, our countingspecifie prompting yields substantial gains synthetic benchmarks: in 2D scenes, GPT-4o jumps from 12.00% to 38.83%.

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Table 2: Harmonic mean comparison for the Visual Search task, evaluating VISER against base models across GPT-4o, Claude3.5 sonnee,, LLaMa4, and Qwen2.5-VLL on 2D and 3D datasets with varying numbers of objects.
GPT-4o
Claude-sonnet
LLaMa4
Qwen2.5-VL LL
Dataset
Objects
Baseline
VISER
Baseline
VISER
Baseline
VISER
Baseline
VISER
2D
20
0.71
0.91
0.55
0.82
0.00
0.06
0.31
0.43
30
0,50
0.80
0.54
0.70
0.00
0.00
0.28
0.37
40
0.25
0.64
0.18
0.59
0.00
0.00
0.14
0.23
50
0.46
0.55
0.10
0.55
0,00
0.00
0.47
0.56
Avg
0.48
0.73
0.34
0.66
0.00
0.02
0.30
0.40
3D
20
1.00
1.00
1.00
1.00
0.82
0.80
0.33
0.44
30
0.96
0.96
0.88
0.91
0,43
0.60
0.15
0.18
40
0.89
0.94
0.72
0.80
0.59
0.58
0.00
0.15
50
0.81
0.84
0.59
0.75
0.39
0.38
0.00
0.04
Avg
0.91
0.93
0.80
0.86
0.56
0.59
0.12
0.20
Claude-sonnet from 8.50% to 10.67%, LLaMa4 from 6.33% to 19.10%, and Qwen2.5-VVL surges from 5.83% to 40.83%; in 3D scenes, GPT-4o climbs from 15.00% to 31.00% Claude-sonnet from 17.33% to 22.00%, LLaMa4 from 14.00% to 36.39% and Qwen2.5-V. 5-V from 8.51% to 26.67%. On natural images, improvements from 2.09% to 6.42%, LLaMa4 from 29.22% to 31 6ndd Qwen2.5-VV sees a slight drop (18.91 % to 17.29%). Together, these results underscore the effectiveness of taskaware prompting in con- trolled settings while highlighting the remaining challenge of generalizing to realworld imagery.
more modestGPT-40 from 29. 92.8%% to 35.65%, Claude-sonner
4.3 Visual Search
Visual search task challenges models to locate target object among distractors, with task difficulty adjusted by the similarity between target and distractor features. In conjunctive search, where the target is defined by a unique combination of features (c.g., a green L-shape) and distractors par- tially share these features (c.g., red L-shapes, green T-shapes), successful identification depends on accurate feature binding. Models that struggle with binding may exhibit degraded performance as the number of distractors increases when features are highly confusable, mirroring human perfor- mance limitations when serial attentional scanning is prevented [10].
An instance of the visual search problem is presented in Figure 2-(a); we evaluate the proposed method using the synthetic dataset described earlier to generate 2D and 3D scenes containing be- tween 20 and 50 objects of varying complexities. We assessed performance by measuring accuracy in correctly identifying the targets presence or absence, distinguishing between visible searches (the object is explicitly in the scene) and invisible searches (the object is absent). This distinction is important because simpler LVLMs often default to predicting false, inflating their invisibleobject ac- curacy while failing to detect visible targets. After computing the accuracy for visible and invisible subtasks, we combined them via the harmonic mean to produce a final score.
2 Visible Invisible
Harmonic Mean
Visible + Invisible
Table 2 compares our approach against baselines, showing VISER delivers consistent and substantial gains over the bascline across 2D and 3D scenes and for all evaluated LVLMs. In 2D settings, the average harmonic mean for GPT-4o climbs from 0.48 0.73, for Claude3.5-somme 0.66, for LLaMa4 from 0.00 to 0.02, and for Qwen2.5- LL. from 0.30 to 0.40, with the largest relative improvements occurring as scene complexity increases (e.g.. at 40 objects GPT-4o rises from 0.25 to 0.64, and Sauude..5-sonee rises from 0.18 to 0.59). In 3D scenes. VISER still yields gains to 0.93, and weaker models benefit notably (Claude3.5-somen
from 0.34 to
0.80 0.86: LLaMa4: 0.56 0.59; Qwen2.5- VL: 0.12 0.20). These results confirm that our strategy not only corrects the always-false bias of simpler LVLMS invisible-ohject tasks but also markedly enhances their ability to detect visible targets, achieving robust performance regardless of object count, visibility, or scene dimensionality.

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Table 3: Comparison of scene description performance (lower better; measured as average dis- tance) for VISER versus baseline across GPT-4o and Claude3.5 on 2D and 3D datasets with varying numbers of objects.
GPT-4o
Claude-sonnet
LLaMa4
Qwen2.5-VL
Dataset
Objects
Baseline
VISER
Baseline
VISER
Baseline
VISER
Baseline
VISER
2D
10
0.70
0.67
4.68
3.12
2.03
2.29
4.38
4.24
15
1.79
1.48
2.89
2.24
3.19
3.99
7.71
7.35
20
3.32
2.73
1.45
1.24
5.74
6.89
12.29
10.59
Avg
1.94
1.62
3.01
2.20
3.65
4.39
8.12
7.39
3D
10
4.06
4.25
5.40
5,18
5.04
5.55
7.88
6.61
15
7.29
7.48
8.56
7.95
9,39
10.60
11,56
10.69
20
12.38
12.21
14.37
12.03
14.90
16.45
15.84
15.24
Avg
7.91
7.98
9.45
8.39
9.78
10.84
11.76
10.84
Table 4: Accuracy (%) comparison of spatial relationship predictions among GPT-4o, Claude 3.5-
Sonnet, LLaMA 4, and Qwen2.5-VL on 2D, 3D, and natural image datasets, using both baseline methods and VISER.
GPT-4o
Claude-sonnet
LLaMa4
Qwen2.5-VL
Baseline
VISER
Baseline
VISER
Baseline
VISER
Baseline
VISER
2D
43.00
52.50
34.18
36.26
29.50
27.50
48.50
50.00
3D
64.00
68.50
72.50
76.50
55,00
58.50
78.00
82.50
Natural
69.39
77.43
37.43
46.15
58.67
66.84
80.10
77.04
4.4 Scene Description
The scene description task requires models to generate accurate textual narratives that describe the objects, their attributes, and their relationships within an image. This task challenges L.LL''s ability to solve the binding problem. The model must not only detect features (e.g., red, cube, blue, sphere) but also correctly associate them with the corresponding objects in its textual output (e.g.. red cube and a blue sphere, not a blue cube and red sphere). Errors, such as attribute swapping or incorrectly pairing features with objects, often arise in complex scenes. This typically occurs when multiple objects share some features but differ in others, which inherently challenges precise feature-object binding.
Figure 2-(b) shows an instance of the scene description task; we evaluate a synthetically generated dataset of 2D and 3D scenes, each containing between 10 and 20 objects to vary scene complexity. Model performance is quantified by the edit distance between the generated descriptions and the ground-truth annotations; we show more detail about this in Appendix B. This yields three outcome categories: (I) an exact match, (2) single-attribus double-attribute mismatch (both shape and color). The full results are presented Table 3. The greatest average gains (-0.81 in 2D and -1.06 in 3D) are driven precisely where the task's difficulty peaks, showing that the technique not only endures extra clutter but thrives on it. This confirms that VISER is the most reliable option for reasoning with high object count.
mismatch (either shape or color), and (3)
4.5 Spatial Relationship
In this task, the model expected to identify and verify the relative positions of objects in scene (e.g., Is the red cube to the left of the blue sphere?). This involves not only correctly binding intrinsic features (c.g., color, shape) to objects but also accurately associating each object with its spatial location, followed by a relational comparison. Failure to bind features properly can lead to misidentification of the objects being queried. Furthermore, if the LVLM cannot accurately associate each identified object with its precise location, its capacity to reason about their relative positions will be compromised. LVLMs often struggle with spatial reasoning due to their inability to maintain distinct, spatially grounded representations of multiple objects simultaneously [8].
An example of the spatial relationship task presented Figure 2-(d); To evaluate VISER, we generated 2D and 3D scenes using the synthetic dataset described earlier and posed four-option multiple-choice questions for each scene, asking models to select the answer that best describes the spatial relation between the target objects. The evaluation results are shown in Table 4. As you can
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Baseline
CoT
VISER
Visual Search
Counting
Spatial Relationship
Scene Descri
0.50
0.12
0.41
1.94
20
0.87
2D
0.25
0.43
20
1.09
39
0.53
0.92
0.15
0.64
7.91
0.94
0.31
0.69
7.98
0.6
0.8
1.0
0.1
0.2
0.3
0.4
0.4
0.5
0.6
0.7
6
Harmonic Mean
Accuracy
Accurscy
Average Distance (Lower is Better)
Figure 3: Comparison of VISER with Chain-of- Chain-of-Thought (CoT) prompting on the GPT-4o model across four tasks. Each subplot shows the performance of three methods (Baseline, CoT, and VISER) evaluated using a task-specific metric indicated on the x-axis. Bars are grouped into 2D and 3D datasets.
see, the new results show that VISER yields consistent gains for most models: GPT-4o rises from 43.00% to 52.50% (+9.50%) on 2D, 64.00% to 68.50% (+4.50%) on 3D, and 69.39% to 77.43% (+8.04%) 04%) on natural scenes; Claude-sonne jumps from 34.18% to 36.26% (+2.08%) on 2D, 72.50% to 76.50% (+4.00%) on 3D, and 37.43% to 46.15% (+8.72%) on natural images; LLaMa4 dips 2.00% on 2D but improves 3.50% on 3D and 8. on natural; and Qwen2.5-VL. gains 1.50% on 2D and 4.5% on 3D yet sees a 3.06% drop on natural scenes.
4.6 Chain of Thought
To examine whether purely textual reasoning cues can bridge the binding gap, we pit our visually structured prompt against a CoT baseline. The baseline employs GPT-4o with the canonical cue Lets think step by step [13] appended to each instruction, leaving the image untouched, mirroring standard practice for eliciting step-wise reasoning in language models. Across all four tasks, VISER consistently surpasses both the Baseline model (no reasoning prompt) and the CoT variant in 2D and 3D scenarios (Figure 3). Crucially, the gap is far more pronounced on the 2D datasets. Flat- tened scenes crowd objects into a single plane, intensifying cross-object interference and making accurate binding harder. In these cluttered 2D settings, the lightweight spatial scaffold provides the model with external, row-by-row traversal plan, producing markedly larger gains than CoT; in 3D scenes, the relative advantage narrows but persists. These results underline that verbal reasoning alone is not enough. A CoT prompt can encourage the model to explain its answer, but it cannot repair a noisy, globally pooled image embedding. Once early tokens are conditioned on entangled visual features, every subsequent step inherits the same misbindings. The scaffold, by contrast, pre- structures the visual input so that each attended region contains fewer competing objects, allowing the language model reason over cleaner, localized representations. Thus, reliable multimodal rea- soning emerges only when textual chains of thought are paired with minimal but crucial spatial guide.
4.7 Comparison with Fine-Tuned Models for Visual Reasoning
To evaluate whether our lightweight method can match the benefits of model-level adapta- tion, we compare it to Mulberry[24Ja VW variant fine-tuned for visual reasoningand OpenWLTTinnker(33, which enhances reasoning in a Qwen backbone via reinforcement learning using a GRPO implementation. As illustrated in Figure 4, we evaluate the base Qwen2.5-VL. model, VISER, the fine-tuned Mulberry, and Open Vh.Thieker. VISER consistently improves upon the base model and often matches or outperforms fine-tuned models, despite requiring neither fine-tuning nor access to model internals.
For example, on the 2D-Counting task, VISER achieves significant boost in performance4 1 % accuracy, compared to 15% by Mulberry and the same result from Open VLThinker. The only tasks where Open VLThinker shows advantage are 2D-Spatial reasoning and 3D-Counting, likely due to its reinforcement learning- based optimization for multimodal reasoning. This suggests that while training-intensive methods can excel in specific domains, they incur substantial computational costs.
In contrast, our training-free paradigm demonstrates broader effectiveness across diverse multimodal reasoning tasks, offering superior scalability and efficiency. These results show that addressing
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Owe Baselne
Mulberry
OpenVVihinker Spatial Relationship
OwenVISER
Counting
Description
2D
0.11
15.05
0.13
0.78
11.26
3D
0.05
08
30
0.35
0.36
0.79
0.82
30
31.36
15.22
0.21
0.27
0.82
10.84
0.0
0.1
0.2
0.3
0.4
0.1
0.2
0.3
0.4
0.4
0.5
0.6 Accuracy
0.7
0.8
10
12
14
16
Hamonic Mean
Acouracy
Average Distance (Lower is Better)
Figure 4: Comparison of VISER with visual reasoningfinetuned model on the Qwen2.5-VL base model across four tasks. Each subplot shows the performance of four methods: Qwen-Baseline (Qwen2.5-VL)) Qwen- VISER (VISER applied to Qwen2.5-VL.,, Mulberry (Qwen2.5-VI. finetuned for visual reasoning), and VpTnnknhe (RL-finetuned Qwen2.5-VL.), evaluated using task- specific metrics, and results are grouped into 2D and 3D datasets.
Table Performance comparison (Accuracy %) of VISER vs. baseline on the MMBench bench- mark. Results are reported across three key reasoning categories: Attribute, Logical, and Relation.
Model
Method
Attribute Reasoning
Logical Reasoning
Relation Reasoning
Baseline
88.67
78.25
75.17
GPT-4o
VISER
88.00
82.75
81.12
Baseline
80.67
80.00
82.99
QWen2.5-VI.
VISER
82.33
80.00
83.33
Table Comparison of VISER and the baseline on the PhysBench benchmark, covering physical reasoning tasks across four categories: Dynamics, Scene Understanding, Object Relationships, and
Object Property. Overall accuracy is also reported. All values are reported as Accuracy (%).
Model
Method
Dynamics
Scene Understanding
Object Relationships
Object Property
Overall
Baseline 35.00
33.33
75.00
55.00
35.68
GPT-4o
VISER
40.00
41.67
75.00
70,00
61.37
Bascline 40,00
50.00
61.11
60.00
54,55
Qwen2.5-VL
VISER
40.00
50.00
72.22
70.00
61.36
the binding problem through simple visual scaffolding and spatial prompting (without additional supervision or computational expense) can rival and even surpass state-of-the-art training-bassd techniques for multimodal reasoning.
4.8 Broader Evaluation Across Reasoning Benchmarks
To further validate the robustness and generality of method, we evaluate it additional reasoning-focused benchmarks, even in domains where visual structural cues might seem less rel- evant. These benchmarks include MMBench [44], PhysBench [45], RAVEN [46], and Visual Analogy task [9]. These evaluations span a broad range of visual reasoning challenges, covering attribute, logical, relational, physical, and analogical reasoning. Results consistently show that method improves performance across diverse tasks and modalities, demonstrating strong generaliza- tion both abstract and grounded visual reasoning scenarios.
MMBench MMbench is reasoning- focused benchmark composed of tasks in Attribute, Logical, and Relation Reasoning. We include all reasoning tasks in our evaluation. As shown in Table 5, our method surpasses the baseline across most categories using both GPT-4o and Qwen2.5-VI models.
PhysBench We also evaluate on PhysBench, a benchmark focused on physical commonsense rea- soning with categories including Dynamics, Scene Understanding, Object Relationships, and Object Properties. Table presents results on the validation split. Our approach consistently improves per- formance across most categories, showcasing its utility in physical reasoning tasks.

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We evaluate our method on the RAVEN benchmark, which already includes grid lines. Instead of altering the images, we added the prompt: Scan the image sequentially based on the grid lines present in the image to encourage the model to leverage existing structure. Using GPT-4o, our method correctly answered 26 out of 140 questions, compared to 19 for the baselinehighlighting that prompting alone, without image alteration, can yield tangible gains when leveraging structural cues.
RAVEN
Visual Analogy Task We evaluate the Visual Analogy task under a unified single-image setup. Using GPT-4o, both the baseline and our method achieve perfect accuracy (100%). For Qwen2.5- VI., our method improves performance from 72.0% to 77.0%, showing benefits in abstract analogical reasoning.
5 Discussion
This research investigated the binding problem in LVL.Ms, a key factor limiting their performance on structured visual reasoning tasks. We introduced VISER, method that combines simple vi- sual scaffolding, using horizontal lines, with a targeted textual prompt to encourage sequential and spatially aware processing. Our experiments demonstrate that this external intervention leads to sub- stantial and consistent performance improvements across visual search, counting, scene description, and spatial relationship understanding. Notably, VISER operates in fully input-agnostic manner, applying the same structure across visual inputs from diverse tasks without additional image process- ing. These results support the hypothesis that explicit low-level general visual structure improves feature-object binding.
A central finding is the critical role of visual structures. While textual prompts alone proved insuffi- cient and sometimes harmful, the addition of visual lines significantly improved reasoning accuracy. These findings suggest that current LVLMs, despite their advanced linguistic capabilities, can bene- fit greatly from external visual cues that facilitate a more systematic parsing of complex scenes. The visual structure appears help models approximate serial attentional process, mitigating the in- terference and illusory conjunctions when processing multiple objects and their features in parallel. This localized, sequential processing may also reduce representational interference by effectively managing feature entropy within sub-regions of the image, a factor highlighted in [9] as critical for robust visual reasoning. This supports the idea that addressing the binding problem may require in- terventions at the level of visual input processing, rather than relying solely on linguistic instruction.
VISER offers practical and efficient intervention, readily applicable due its simplicity, negligi- ble computational overhead, and single-query operation, without reliance on external tools, model fine-tuning, architectural changes (see Appendix G).. This highlights visual input structuring as key strategy for enhancing LVLM reasoning, beyond linguistic instruction. While effective, the cur- rent static nature of the visual scaffolding represents potential area for refinement (see Appendix E for failure cases). For instance, a fixed scaffolding structure might show reduced gains when the interference between lines and objects is too high, or when object clustering limits the spatial sepa- ration benefits of the scaffolds. Future research could investigate adaptive visual scaffolding, where cues are dynamically determined by image content or query specifics. Relatedly, mnsemble-based approaches that combine diverse scaffolds (c.g., varying line positions, density, or color) could en- hance robustness and reduce sensitivity to edge cases. Moreover, developing LVLM architectures that inherently support serial, spatially grounded attention, perhaps inspired by these findings, could offer more integrated and robust solutions to the binding problem. Extending VISER to other visual tasks, such as hallucination detection and mitigation, also represents a promising direction.
Last but not least, we emphasize that improved visual reasoning in Al systems can benefit wide range of applications, from assistive technologies and robotics to education and healthcare. However, as LVLMs become more capable, their potential misuse (for instance, in surveillance, automated decision-making or misinformation) also grows. Our approach does not require model retraining, making it widely accessible, but this also implies that such modifications could be adversarially deployed in black-box systems without transparency or oversight. To mitigate risks, we advocate for responsible use of these techniques in alignment with accountability and transparency guidelines. Future research should profoundly study the unintended biases that scaffolding might introduce in tasks other than spatial reasoning, especially when used in safety-critical applications.
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A Ablation study
A.1 Visual Structures
In our ablation study, we assess multiple visual-structuring strategies and their impact on task per- formance using the GPT-4o [39] model. All experiments are conducted on a consistent subset of our 2D synthetic dataset [9], with varying object counts per scene. The results in Table 7 show the average performance across these scenes.
Table 7: Comparison of VISER and other ablation methods on GPT-4o in 2D scenes across multiple
tasks.
Method
Scene Desc. (Edit Dist.)
Counting (Aecuracy)
Visual Search (Accuracy)
Spatial Rel. (Accuracy)
Baseline
1.81
16.85
0.48
39.00
Row
1.61
31.55
0.73
44.50
Column
2.00
0.76
43.50
Grid
2.43
17.40
0.72
$1.50
RowNoRowNNm
1.71
19.14
0.70
41.00
The baseline configuration prompts the model to describe the image without any additional structur- ing cues, As shown, this baseline yields the lowest or near the lowest accuracy counting and visual search, indicating that unstructured prompts are insufficient for supporting systematic reasoning.
Among the structured variants, the Grid-based layout consistently performs the worst, particularly in the scene description task, likely due to excessive spatial fragmentation hindering its interpretation of coherent groupings. The Column- based layout also underperforms in both scene description and counting tasks compared to horizontal-basee layouts, suggesting that vertical splitting is suboptimal for global scene comprehension. Interestingly, it achieves small gain in visual search, where object-level localization is more critical than spatial integration.
In contrast, our row-based horizontal structuring, which explicitly divides the image into horizontal bands, leads to improved performance across most tasks. This design encourages the model to scan the scene in consistent, line-by-lina fashion. Furthermore, removing numerical indices from rows (VISERNoRowNNn) results in substantial drop in accuracy, highlighting the value of explicit ordering cues in guiding sequential attention.
For the spatial relation task, we chose a grid because the answer space is symmetric across four directions (left, right, above, below), making the grid more natural fit. However, shown in Table 7, horizontal lines also perform well in this task. Specifically, for the synthetic spatial relation task with GPT-4o, accuracy improves from 39.00% (baseline) to 44.50% (row method) and 51.50% (grid method). Similarly, in the natural spatial relation task, accuracy increases from 69.39% to 76.92% and 77.43%, respectively. This demonstrates that our simple row-based method is also effective for spatial relations, but the grid method yields the best results.
These results collectively underscore the importance of structured, order-aware visual annotations in enabling LVLMs to reason more effectively over complex spatial arrangements.
A.2 Hyperparameters
In this section, we examined the effect of key scaffold parameters, focusing on the number and thickness of horizontal lines used to segment the scene. The initial configuration applied three horizontal lines of 1 thickness for all tasks, following the heuristic that too few lines (e.g.. 12) yield overly coarse partitions with limited benefit, whereas too many lines introduce visual clutter and potential interference artifacts. To systematically assess this, we varied the number of lines (112; Figure 5) and the line thickness (15 px; Figure 6) across three tasksCounting, Visual Search, and Scene Descriptionusing two representative models: GPT-4o (closed-source) and QWen2.5-VL (open-source). Each configuration was evaluated on 300 samples per task.
Across both models, performance dropped at the extremes but remained stable in the 26 line range. similar pattern was observed for line thickness, with consistently strong results up to 5 px. Task- specific effects also emerged: in Scene Description, increasing the number of lines often raised the edit distance (lower better), indicating that excessive segmentation can hinder coherent free-form output. Overall, these results validate our original heuristic and show that the method is robust to
14

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moderate changes in scaffold parameters. While the optimal configuration can vary slightly by task or model, substantial gains are achievable without extensive hyperparameter tuning.
Counting
Visual Search
Scene Desc.
Upp4l Uvend SA1
33.00
(005
1524
50
27.85
15
412
Figure 5: Performance of GP and Qwen2.5-VL LL across different tasks (Counting, Visual Search, and Scene Description) with varying numbers of horizontal lines in the input. Accuracy is reported for Counting and Visual Search, while edit distance is used for Scene Description. Baseline repre- sents performance with no horizontal lines.
Counting
Visual Seurch
Scene Desc.
73
5523
1005
5300
0355
Awand
Th6kem0
Thidkeon
Figure 6: Performance GPT-4o and Qwen2.5-VL. across different tasks with varying line thick- nesses (in pixels) introduced by VISER. Each column represents results for a specific line width, ranging from px to px. Accuracy reported for the Counting and Visual Search tasks, while lower edit distance indicates better performance for the Scene Description task. The Bascline corre- sponds to performance without added lines (i.c., line thickness of zero).
A.3 Imaginary Lines
To evaluate the impact of prompting the model to imagine auxiliary structures (e.g.. horizontal lines) without rendering them, introduced to scan the image row by row using imaginary linessimilar to the setup illustrated Figure 27but without explicitly drawing them. As shown in Table 8, VISER outperforms the maaiinrrybbsellin across all tasks: achieving an 8.5% gain in spatial relation accuracy, 13.31 % improvement in counting, 0.12 reduction in edit distance for scene description, and 5.00% increase in harmonic mean accuracy for visual search.
variant. This baseline prompted
Table Performance of the baseline when prompted to imagine horizontal lines (as in Figure 27), evaluated across tasks using the GPT-4o model.
Method Baseline
Visual Search (Acc.)
Counting (Acc.)
Scene Desc. (Edit Dist.)
Spatial Rel. (Acc.)
48.00
16.85
1.84
43.00
Imaginary-Baseline Baseline
68.00
18.24
1.75
44,00
VISER
73.00
31.55
1.63
52.50
A.4 Reasoning trace evaluation
To evaluate the impact of visual structure on step-by-step reasoning, we compare two prompting strategies for the 2D scene description task. In the first setting, provide GPT-4o with the original
15

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Ours
Base
jaccard Siniaarty
jaccadd Similarity
joccand Simiarity
1.83
Edit Distance
Edit Distance
Edit Distance
0.76
F1-
F2
F1
2
10 Objects
15 Objects
20 Objects
Figure 7: Comparison between VISER and GPT-4o in the 2D scene description task, where models sequentially scan the image row by row to identify objects. Results are reported for scenes with 10, 15, and 20 objects using three evaluation metries: score, Jaccard similarity, and Edit distance.
image without any row markings and use a prompt that instructs: Divide the image into equal horizontal sections from top to bottom, and list the shape and color of each object in each row In the second setting, we explicitly modify the image to include four horizontal lines that visually segment the scene into rows. As shown in Figure 7, this structural addition significantly improves performance across all three evaluation metrics (F1 score, Jaccard similarity, and Edit distance) on scenes with 10, 15, and 20 objects. These results suggest that textual instructions alone are insufficient: incorporating structural cues directly into the visual input helps the model follow the intended sequential scanning process more accurately. For consistency, all objects in the scenes were rendered as colored squares, and in cases where a square overlapped multiple rows, it was assigned to the row containing the majority of its area. This assignment policy was also reflected explicitly in the prompt.
B Benchmark and score details
B.1 Benchmarks
In this study, we introduce series of benchmarks designed evaluate the performance of LVLMs on various visual reasoning tasks, including visual search, counting, scene description, and spatial reasoning. Each benchmark specifically developed to provide rigorous and systematic assess- ments of different VLM capabilities, utilizing both synthetic and real-world data. These benchmarks structured to facilitate controlled experiments while maintaining diversity in task difficulty, scene configurations, and object arrangements. Below, we provide detailed descriptions of each bench- mark used in our evaluation.
B.1.1 Visual Search Benchmark
Building on the same synthetic generation framework from [9]. we adapted the pipeline with task- specific configurations for visual search evaluation.
2D Scenes: We created scenes containing 20, 30, 40, and 50 objects. Each configuration comprised 100 images (50 with targets present and 50 absent), totaling 400 2D scenes.
3D Scenes:
Using the same object count progression, we generated 50 images per count (25 with targets present and 25 absent), resulting in 200 3D scenes. The reduced quantity accounts for the higher rendering complexity.
Natural image datasets are not suitable for this evaluation, as they lack two critical properties: (1) the ability to systematically adjust distractor characteristies (shape, color, and spatial arrangement) and (2) precise control over target-distractor similarity levels. Our synthetic paradigm provides this essential controllability, allowing a rigorous assessment of binding capabilities through progressive difficulty scaling while maintaining balanced presence/absence of the target (50% distribution).
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B.1.2 Counting Benchmark
To assess the counting ability of LVLMs, we developed a comprchensive benchmark based on the generation methodology outlined in [9].
2D Scenes: We generated synthetic 2D scenes with object counts ranging from t0 20, incremented in steps of 2 (10, 12. 14, 16, 18, 20). For each object count, we produced 100 distinct images, resulting in total of 600 2D images.
Similarly, we created 3D scenes with object counts following the same progression (10 to 20, step size = 2). We generated 50 images per object count, yielding total of 300 3D images.
3D Scenes:
To assess real-world generalizability, we incorporated the "Learning to Count Everything" bench- mark [32]. We used approximately 300 images containing up to 20 objects to maintain reasonable task difficulty.
This structured approach enables a rigorous assessment of counting performance in controlled syn- thetic and real-world scenes.
B.1.3 Scene Description Benchmark
Following [9], we define a feature triplet as any set of three objects where one pair shares a feature (e.g., color) and another pair shares different feature (e.g., shape). For example: green X, green triangle, yellow triangle forms a triplet through shared green and triangle features.
To evaluate scene description capabilities in LVLMs, we developed a comprehensive benchmark using the feature triplet methodology from [9] with controlled object counts and systematically varied triplet configurations.
We generated synthetic 2D scenes with 10, 15, and 20 objects. For 10 objects, created configurations with 5 to 20 triplets in increments of 5 (5, 10, 15, 20 triplets). For objects, extended the triplet counts up to 50 (in steps of 5), and for 20 objects up 70 triplets (also in steps of 5), with 50 images generated for each configuration.
2D Scenes:
3D Scenes:
Following the same progression, we created 3D scenes with identical objects and triplet counts, generating 30 images per triplet count account for rendering complexity.
The increasing number of feature triplets introduces progressively greater interference among object features, making the binding problem more challenging and testing the model's reasoning capabili- ties under increasingly difficult conditions.
This approach enables systematic assessment of scene description performance across: (1) con- trolled object counts, (2) graduated levels of feature interference through triplet counts, and (3) both 2D and 3D representations. The detailed results for 10, 15, and 20 objects and triplet configurations are presented Figures 8, 9, and 10, respectively.
B.1.4 Spatial Relationship Benchmark
To evaluate spatial reasoning in LVLMs, we developed a comprehensive benchmark using the gen- eration methodology from [9] complemented by natural images.
2D Scenes:
We generated 200 synthetic 2D scenes with controlled object configurations. For each scene, we created multiple-choice questions testing spatial relationships (top-left, top-right, bottom- left, bottom-right).
Similarly, we produced 200 synthetic 3D scenes with three-dimensional arrangements, following the same question generation protocol as the 2D scenes.
3D Scenes:
For real-world evaluation, we used 200 natural images from the benchmark [38], which provides diverse object arrangements in unconstrained environments.
This approach enables systematic assessment spatial reasoning across: (1) fundamental 2D rela- tionships, (2) complex 3D configurations, and (3) real-world scenarios.
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Gph-b
Figure 8: Results of the objects Scene Description task across varying numbers of triplets for and 3D scenes, using different VLM models (GPT-40, Claude-Sonnet, Qwen2.5 and LLaMa4).
Gpt-4a
Chrade
VISE 2:20
21.
Bextloe-1
V152 11:30
309
30
Figure 9: Results of the 15 objects Scene Description task across varying numbers of triplets for 2D and 3D scenes, using different VL.M models (GPT-40, Claude-Sonnet Qwen2.5-VL, and ILaMa4).
B.2 Score Metrics
To evaluate the performance of different models across visual reasoning tasks, we employ five met- rics: Edit Distance, Accuracy, Fl score, Jaccard Similarity, and Mean Squared Error (MSE). Each metric applied according the nature of the task, and formal definitions provided below.
Edit Distance (Scene Description)
For the scene description task (both 2D and 3D), we define a custom edit distance that penalizes discrepancies between the predicted and ground truth object lists. The metric is computed in the following steps:
1. Exact Matches: All objects with matching shape and color are removed from both sets.
2. Partial Matches:
Objects with the same shape but different colors incur penalty of 1.
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Gpb-4
YIGER
Basdne
Figure 10: Results of the 20 objects Scene Description task across varying numbers of triplets for 2D and 3D scenes, using different VL.M models (GPT-4o, Claude-Sonnet, Qwen2.5-VL. and LLaMM4)..
Objects with the same color but different shapes incur penalty 1.
3. Missed Ground Truth Objects: Any remaining unmatched ground truth objects pe- nalized with points.
The total edit distance is calculated as:
EditDistance = x Npartial + 2 x Nuissed
(2)
where Npattial is the number of shape-only color-only matches, and Nmissed the count of com- pletely missed objects. This asymmetric formulation reflects the observation that models more often miss objects than hallucinate them.
Accuracy (Visual Search, Counting, Spatial Relationship)
Accuracy is used for classification-bass tasks and is defined as the ratio of correct predictions to total predictions:
Accuracy Noonect
Niotal
This metric is used in visual search, counting and spatial relationship evaluations.
Score (Reasoning Trace Evaluation)
F1 score measures the harmonic mean between precision and recall and is particularly useful for object-level evaluation:
Precision Recall
Fl = 2-
(4)
Precision + Recall
with:
TP
TP
Recall TP + FN where TP (true positives) are objects correctly predicted with matching shape and color, FP (false positives) are incorrect predictions, and FN (false negatives) are missed objects.
Precision
(5)
TP + FP'
Jaccard Similarity (Reasoning Trace Evaluation)
Jaccard similarity measures the set-level overlap between prediction and ground truth:
PP G
Jaccard
(6)
PUG
where and G are the sets of predicted and ground truth objects respectively, and equality requires both shape and color to match.
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Table 9: Mean squared error (MSE) of counting task across GPT-40, (laude3.5-somme LLaMa4, and Qwen2.5-VL SL on 2D, 3D and natural image datasets using base models and VISER.
GPT-4o
Claude-sonnet
LLaMa4
Qwen2.5-V7 VL
Baseline
VISER
Baseline
VISER
Baseline
VISER
Baseline
VISER
2D
7.50
1.33
5.32
5.25
5,515
5.575
15.03
3.89
3D
6.13
2.44
2.28
3.01
2.74
1.53
22.04
6.21
Natural
37.33
6.33
1048.63
42.20
6.30
5.89
35.10
24.32
Mean Squared Error (Counting)
For the counting task, we compute the mean squared eтror (MSE) between the predicted and true object counts:
MSE = N 1=1
_ (0 y.)
where ii is the predicted count and ys is the ground truth count for scene i, and N is the total number of scenes. MSE penalizes large deviations in count predictions more heavily.
MSE Results: Table 9 presents the Mean Squared Error (MSE) for object counting across the evaluated models. While our primary analysis focused on accuracy metrics, the MSE results provide complementary insights, particularly regarding the magnitude of counting errors.
VISER demonstrates consistent error reduetion compared baseline performance. The most table improvements occur in natural image scenarios, where exact count prediction remains challeng- ing but approach achieves substantial MSE reduction Claude3.5-sonnee 42.20 versus 1048.63). Similar improvements are evident in synthetic environments, with GPT-4o (1.33 versus 7.50 2D) and Qwen2.5-VI. (3.89 versus 15.03 in 2D; 6.21 versus 22.04 in 3D) showing significant decreases in squared error.
These MSE results complement accuracy findings by demonstrating that VISER not only in- creases correct predictions but also reduces the severity of counting errors when they occur. The notable MSE improvement natural images, despite the inherent difficulty of exact counting in real-world scenes, highlights VISER's s ability to prevent large counting deviations.
B.3 Performance Variance and Statistical Significance
Each reported score in our experiments is averaged over substantial number of samples (typi- cally 50100 per configuration), which provides a stable estimate performance. To minimize non-determinism, all evaluations use greedy decoding (temperature 0), ensuring deterministic outputs for each input. This reduces variance introduced by stochastic generation and makes the results reproducible. While do not repeat each setup multiple times, emphasize robustness by evaluating across a broad set of conditions, including four tasks, both 2D and 3D synthetic data, real-world datasets, and multiple open- and closed-source models.
Across 120 pairwise comparisons between VISER and the baseline (excluding supplementary ma- terial), VISER outperforms in 103 cases, shows decreases in 17 cases, and yields ties in 7 cases. Most drops occur with LLaMA4, a model that generally performs poorly on these tasks. Consider- ing only non-tied cases (96 wins vs. 17 losses), one-sided binomial sign test yields p-value of 7.4 x 10-15, 15 strongly rejecting the null hypothesis that VISER is no better than the baseline. The tied cases typically correspond to trivial scenarios with saturated performance.
C Implementation Details
In this section, we describe the prompts used across our four evaluation tasks. While we maintain a consistent prompt template for all models, each task requires tailored instantiation to address its specific objectives. In Figures 11, 12, 13, and 14, we detail the exact prompt formulations employed for each task, which produce the results reported the Experiments section. Additionally, to assess the models Chain-of- Thought (CoT) reasoning ability, prepend Lets think step by step
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to each prompt, encouraging the model to articulate intermediate reasoning before arriving at its final prediction.
Baseline
VISER
Task:
Task:
You presented with image containing letters, specifically the letters 'L' and 'T'. These letters will appear in either red or green. Your task is to determine there are any green 'L's in the image. Follow these steps carefully:
You presented with image containing of letters, specifically the letters 'L' and 'T'. These letters will appear in either red or green. Your task is to determine if there are any green 'L's the image. Follow these steps carefully:
1. Describe each shape in the image, noting their color.
Describe each shape the image, noting their color.
Conclude your response by stating [True] if the letter 'L' appears in green, or [False] if there are green 'L's.
2. Conclude your response by stating [True] if the letter 'L' appears in green, or [False] if there are no green 'L's.
Enclose your final onswer in square brackets (Final Answer: (]), shown.
Enclose your final onswer in square brackets (Final Answer: (]), as shown. Scan sequentially based on harizontal lines exist in the image.
Figure 11: Displaying the prompt used for the 2D scenes in the visual search task. The only differ- ence between the two prompts is the additional instruction: Scan sequentially based on horizontal lines exist in the image.
Baseline
VISER
Task:
Task:
How many Circles are there in this image?
How many Circles there in this image?
Always respond with just the final numeric
Scon the image sequentially based on
answer.
harizontal lines exists in the image.
Always respond with just the final numeric
answer.
Figure 12: Displaying the prompt used for the 2D scenes in the counting task. The phrase Scan sequentially based horizontal lines exist in the image is added to our prompts, in contrast the baseline input.
D Input and Output examples
In this section, we provide series of examples to demonstrate the outputs of our row-wise structure across different tasks, including visual search, counting, scene description, and spatial relationship analysis. Each task is illustrated with both synthetic (2D and 3D) and, where applicable, worldw scenes. For each task, we compare the outputs applying VISER with those of a baseline model, highlighting the impact of our approach on VLM.
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Baseline
VISER
Task:
Task:
You presented with image contains multiple colored objects, each defined by shape and a color.
presented with image contains multiple colored objects, each defined by shape and color.
Your task is to identify all objects the image and return list of dictionaries. Each dictionary must contain exactly two
Your task is to identify all objects in the image and return a list of dictionaries. Each dictionary must contain exactly two
keys: "shape' and 'color'. Only use shapes from this list:
keys: 'shape' and 'color'. Only use shapes from this list: airplane, triangle, cloud, cross, umbrella, scissors, heart, star, circle, square, infinity, up- up-arrow, pentagon, left-arrow, flag.
airplane, triangle, cloud, cross, umbrella, scissors, heart, star, circle, square, infinity, up-arrow, pentagon, left-arrow, flag. Only use colors from this list: red, magenta, solmon, green, lime, olive, blue, teal, yellow, purple, brown, gray, black, cyan, orange.
Only use colors from this list: red, magenta, salmon, green, lime, olive, blue, teal, yellow, purple, brown, gray, black, cyan, orange.
A Always respond only with JSON list of the detected objects.
Always respond only with JSON list of the detected objects.
Scon sequentially based on horizontal lines exist the image.
Showing the prompt used in our 2D scene description task. The phrase Scan sequentially based on horizontal lines exist in the image is the only addition to our prompt.
Figure 13:
Baseline
VISER
Task:
Task:
You are a helpful assistant. Just provide the complete text of appropriate choice without any additional text.
You are helpful assistant. Just provide the complete text of appropriate choice without any additional text.
The grid lines are added help compare the ob objects better.
Figure 14:
Showing the prompt that we use the 2D scenes for the spatial relationship task. The only difference from the baseline is the added instruction: The grid lines are added to help you compare the objects better.
D.1 Visual Search
For the visual search task, present input and output examples from both 2D and 3D datasets (shown in Figures 15 and 16, respectively). In the first example (Figure 15), the objective was to locate the green 'L' among set of 'L' and 'T' letters within the scene. The baseline model failed to detect the target, returning [False] output. In contrast, applying row-wise structure successfully identified the green and returned [True] output, demonstrating its effectiveness. This pattern was similarly observed in the 3D scene, where the baseline model again failed to locate the target object, while applying VISER accurately detected the presence of the described object within the scene.
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D.2 Counting
For the counting task, in addition to the 2D and 3D scene examples, incorporate sample from the natural-image dataset utilized in the evaluation of Learning to Count Everything [32]. The corresponding examples
presented in Figures 17, 18, and 19, respectively, In all three cases, the VLM model misses some objects or hallucinates extra objects and inaccurately counts the number of objects. In contrast, when proposed method is applied, the VLM model provides the correct object count.
D.3 Scene Description
In Figures 20 and 21, we present two synthetic examples (one from 2D scene and the other from 3D scene) both relevant to the scene description task. The VLM initially fails to accurately identify all objects and their respective attributes within the scenes. However, by applying our proposed method, we improve the model's performance, enabling it to more accurately and precisely describe the objects in the scenes.
D.4 Spatial Relationship
For the spatial relationship task, present three examples: from 2D synthetic scene (Fig- ure 22), one from a 3D synthetic scene (Figure 23), and one from the natural Spatial Reasoning dataset [38] (Figure 24). In all of these examples, the VLM initially selects the wrong choice. How- ever, adding grid lines helps the model better compare the spatial relationships between the objects, leading to more accurate answers.
Standard prompt
The the follwwng Rei L'S soatfered thrsughou the image.
Baseline
Green 'I' scathined througheut the image No green L are present the inage.
T
[Foise]
X
besonbe each
Line Red "L" -Breen Grrce "T" Red "L Red
Lane zi Red Greee "I' -Green "T"' -Gren
VISER
Green -Gneer Green
Geeen
Green Red
Green 'I' Red
Determine there
Ves, there
Standard prompt "scan sequentially based on rows"
Figure 15: An example of a 2D visual search task illustrating the impact of our proposed method on the output of the VLM model. The baseline fails detect the green 'L', returning [False], while our row-wise structure successfully identifies it.
E
Failure case
VISER adds lines in a fixed configuration, independent of image content. While this design ensures consistency and simplicity, it can introduce limitations. For instance, added lines may intersect with important objects, potentially introducing ambiguity about those objects. Alternatively, objects may lie in regions where the added lines provide little to no benefit in decomposing the task. To address this, we conduct two additional controlled experiments on the 2D counting task, targeting conditions where our intervention may help hurt.
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Standard prompt
The follow
Baseline
-reen sphores.
2. There red ophores visble the
Find Antwur (Faise]
X
Image
Desor
Live 1: objects present
Line 2: Containa red caber and grees sphenes No red spheres are siable.
VISER
Line Castairs red cubes green ophores, and ore spnere Cestara nrom spheres, spheres
ophern Line Fina Andwer [True]
Standard prompt sequentially based on rows"
Our methed
Figure 16: An example of a 3D visual search task illustrating the impact of our row-wise structure on the VLM model's output. The baseline fails to detect the red sphere, returning [False], while applying VISER successfully detects it and returns [True].
Standard prompt
To count the circles the image
Baseline
Green circles: circlesi
Number of Circles:
Raw Image
count the circles the image
Row 1: circles
VISER
Row circles
Total Number of Circles: 10
Standerd prompt "scan sequentially based on rows"
Our method
Figure 17: An example of 2D counting task and the impact of proposed method the output of the VLM model. The baseline approach incorrectly counts the number of circles in the 2D scene, whereas our row-wise structure method enables the VLM accurately count the number of circles.
E.1 Object-Line Interaction Analysis
We synthetically vary the level of object-line interference in scenes with circular objects and group data into bins (0.00.2: low interference; 0.81.0: high interference). As illustrated in Table 10, Our method improves performance significantly in low-interference cases. However, in ingerfenereeeece scenarios (e.g., dense line-object overlaps), the benefit diminishes. This highlights key failure mode: when scaffolding introduce ambiguity in object detection. For example, overlapping lines can cause objects to be either double-counted (across both lines) or missed entirely (not clearly belonging to either region), leading to reduced model accuracy. Interestingly, GPT--4os baseline

---

Standard prompt
spheres
image:
Baseline
spheres
the imoge. of Spheres
Image
Let's count the spheres row by row:
No spheres
visible this row. Count:
VISER
Row 2: There are 9 spheres
Row There is sphere
visiale this row. Count:
No spheres
visible this row. Count: O Total Number of Spheres: 10
Standard prompt sequentially based rows"
method
Figure 18: An example of a 3D counting task and the impact of our proposed method on the VLM model's output. The baseline method inaccurately counts the number of spheres the 3D scene, whereas applying VISER enables the VLM to accurately count the number of spheres.
Standard prompt
Baseline
There are strowberries in the image.
x
Row Image
Let's count the strowberries honzontal lines previded:
VISER
Row 1: strowberries.
Row strawberries.
strawbernies
strawberries
Adding these together, there strowbernied total.
Standard prompt "scan sequentially based rows"
Our method
Figure 19: example of a natural dataset counting task and the impact of our proposed method on the output of the VLM model. The baseline method fails to accurately count the number of strawherries in the dataset, whereas row-wise structure enhances the VLM''s ability to determine the strawberry count correctly.
accuracy drops with increased density, while Qwen2.5-VL. shows the opposite trend, example of this synthetic dataset is illustrated in Figure 25a.
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('dopet: Ap-arce" "cele" "venage'.
Standard prompt
Chaax arrow "oder" "slee). (thopet: "athammw"" coler: 'gra?' Pehapee
Baseline
('shope" letharren" "ealor "haaf). ('shopah unbreld "oaer "snoy), (atopel "ccaseef" "odar") "lme") I'hhpe". Thaart" "esket. (grear)). ("shapeh start, kolae" "ulee)
X
Rew Image
("'vase "ryisagl"" Toolar" (saock)) I'thppe:: 'star "oder"""pppe""" "paple") ("ahape' "ator" "eoler biue")
(["avape" "hear", "odte") "ynoor"). ('thopa":
VISER
"bhue")
Row (T'shge" feef crow" "cdae" "rad
('thope" "attiarrow icelant 'gray".
l'shape" "uparren" "colort "crogee")
Standard prompt "scan sequentially based on rows"
(Pthopot: "entrale"" ('sgger sosseee "cabet
"yrny":
Figure 20: An example of 2D scene description task and the impact of VISER the VLM's output. The VLM fails to describe the purple star shape in the scene, whereas VISER correctly describes all the objects.
blue")
PPshppa: tabe"" "gnen''
Standard prompt
"tora" grean')
cole" gream'
Baseline
("Shope" "cone" "oalort
"olle" "reen''
('shape" "caler"
('shopel' "oibe" "celor" "eronge").
("shape" "cylindor" "colon" "cyor").
("shape": "sphene" "colon" "broan"). ( ('shape') "sylinder,, "caler") Tbrown)
Image
[.]]
Row "oube" "calor" "grwen") ('shape" "cylidde "color: forown"). "cube" "color "crange"). i'Shape" "cone" "calor ("stope": "oylnderr" "color" 'eyen's. ("shape": "foru" "calor" "orange"]]
VISER
Row: 3: [''hape" "toras" "coler" "blue), ("shape" "Shape"""rerat" "calerti"gnean"" "shheee""ppnere" "caler" "Groen"])
Standard prompt sequentially based on rows"
mathod
Row (rshaset:/towee "color: (greer"))
Figure 21: An example of a 3D scene description task and the improvement gained by applying VISER. The VLM incorrectly adds an extra object, counting three green toruses instead of two, which aren't part of the scene. In contrast, our proposed method accurately describes only the objects present.
E.2 Object Distribution (Entropy) Analysis
To probe another failure case, we conducted a controlled analysis targeting such potential failure modes. Specifically, we evaluated performance with respect to object distributions, which quanti- fies how widely objects are spread across rows. Low entropy indicates objects are concentrated in one row, while high entropy corresponds uniform spread. illustrated in Table 11, VISER more effective when objects distributed across the image (high entropy), where scaffolding pro-
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Standard prompt
Baseline
Image
VISER
left-above
Standard prompt "scan sequentially based on rows"
Our method
Figure 22: An example spatial relationship task and the effect of applying our propsed method the VLMs output. As shown in the figure, adding grid lines assists the VLM in selecting the correct choice.
Standard prompt
Baseline
right
Image
VISER
b.
left
Standard prompt "scan sequentially based on rows"
Our method
Figure 23: An example of a 3D spatial relationship task and the impact of applying VISER on the VLMs output. As shown in the figure, adding grid lines improves the VLMs ability to select the correct choice.
vides meaningful visual separation. However, when most objects are located in single region (low entropy), scaffolds are less helpful, sometimes even harmful. This exposes second failure case:
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Standard prompt
Baseline
A. Front
Row Image
VISER
Standard prompt "scan sequentially based on rows"
method
Figure 24: An example from a natural dataset for the spatial relationship task and the impact of applying VISER on the VLMs output. The question asks about the spatial relationship between the jar and glasses from the cameras perspective. As shown in the figure, applying VISER helps the VLM answer the question correctly.
Table 10:
Aceuracy of GPT-4o Qwen2.5-Vi VI. on the counting task across different levels of average line-object intersection (ranging from 0.00.2 to 0.81.0).
Model
Method
0.0-0.2
0.2-0.4
0.4-0.6
0.6-0.8
0.8-1.0
Baseline
9.50
12.00
7.00
3.50
3.50
GPT-4o
VISER
26.00
26.00
22.00
14.00
13.00
Baseline 3.00
4.00
10.50
13.50
18.00
Qwen2.5-VL
VISER
37.00
34.50
17.50
5.00
9.00
ineffective intervention when object positioning limits scaffold utility. An example of this synthetic dataset is illustrated in Figure 25b.
Table 11: Accuracy of GPT-4o and Qwen2.5-VV VL on the counting task across different levels of object spatial entropy (ranging from 0.000.50 to 1.752.00).
Model
Method
0.00-0.50
0.50-1.00
1.00-1.25
1.25-1.50
1.50-1.75
1.75-2.00
Baseline
29.00
30.00
27.00
33.00
38.00
37.00
GPT-4o
VISER
48.00
54.00
62.00
62.00
69.00
68.00
Baseline 14.00
16.00
17.00
16.00
7.00
5.00
Qwen2.5-VL
VISER
1.00
15.00
16.00
26.00
33.00
36.00
Our primary experiments are conducted on randomly generated datasets with naturally high entropy (1.9) and low average interference 0. (74),,, where method is most effective.
F Attention visualization
In this section, we analyze and visualize the attention maps corresponding to the generated outputs from both the baseline and our proposed method using the Owenn25-vVL--B-Insttri [41] model.
Figure 26 shows the results of VISER, where we instruct the model to "Sean sequentially based on horizontal lines exist in the image," encouraging it to analyze each row independently. The
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Object-Line Interaction
Object Distribution Analysis
0.4 0.8 0.1
0.00 0.50
1.25 1.50
1.75 2.00
(a)
(b)
Figure 25: Visualization of failure cases in the 2D counting task. Subfigure (a) shows examples with varying levels of object-line interference, while Subfigure (b) illustrates different object spatial distributions.
extracted attention maps for each predicted numerical value confirm this approach, demonstrating that the model attends more strongly to the corresponding spatial regions. For comparison, Figure 27 presents the results from the baseline model, which was given the same instruction. In this case, the attention is less focused, and the predicted numbers do not align clearly with their respective regions. This shows that adding the horizontal line guidance enhanced the model's 's ability to focus on each region independently, resulting in improved performance and greater precision.
By applying VISER, the model guided focus sequentially on smaller, more localized regions within the image. This enables it to more accurately identify and attend objects in specific areas. The localized approach allows for more detailed and focused analysis, leading to better alignment between the predicted numerical values and the corresponding spatial regions. In contrast, the base- line method attempts to attend to the entire image in a single step, without such localized guidance. This broader attention scope reduces the model's ability focus specific regions, causing diffi- culty with precise object identification and reducing accuracy. The model fails to effectively resolve finer details and spatial relationships within the image. To further illustrate this, Figure 28 shows the baselines predicted numbers, which attempt to attend to all circles simultaneously. Due to the high number of circles, the model fails answer the question correctly. Conversely, Figure 26 demon- strates that by incorporating explicit sequential reasoning, VISER allows the model to count objects in each region more accurately and aggregate the results more effectively.
Reeporac:
To counn numberof corles image, we wewillscan sequentially based borizontal lines
Fire Horizoneal Line. There The crirkk..
Image
Qwen2.5-V
Ovepss
Scoond Horizontal Line: There cindes.
Third Hertsonel Line. There circles
Fouch Honsonal
Image
sumnup 2.
Scan sequestiiaaly basd luchunta image.
Figure 26: Visualization 2D attention maps generated by our proposed method using the Qwen2.5- "L-7B-Innsrrue model counting task. The attention patterns demonstrate that the predicted numerical values for each row attend more strongly to their corresponding spatial regions, indicating improved alignment between predictions and visual content.
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Resooner:
Nags
dedemminn the mumber of cinkk image. In's the sorns ireo fire imaginary hernomed rowe.
Top Row (Row 12 There are cirdea.
Image
Dupet
Second Row (Row 2) There circles
Thin
There circle..
Poreet
(Row
Gocse of teage ieaginury
sum up the toral number cirdes Tora Number Cirdes 4-1+3-13
Sys
Figure 27: Visualization of 2D attention maps generated by the baseline method using the Qwen2.5- VL-7B-Instruct model on the same counting task. Although provided with the same instruction to scan the image row by row, the attention maps exhibit less focus, and the predicted numerical values show weaker alignment with their corresponding spatial regions. Additionally, the numerical predictions lack proper row-region binding, and the predictions for the third and fourth rows are incorrect.
Atcentis
Image
Resporac: Number Jhins//tsess
Figure 28: Visualization of the 2D attention maps generated by the baseline method using the --------B--nsttrrtt
Qwen2.5-VL-7B Instruct model on counting task. The attention maps reveal broader, less fo- cused attention pattern, with the model attempting to attend to all regions simultaneously. This lack of localized attention results in incorrect predictions, evidenced by the model's 's failure properly align with the specific spatial regions in the image, leading to errors in counting.
Response:
Mags
To coum the sumber of achere the imags acan sqquenially loaod herizontal linc.
1 Fire now (lne i) There oppire.
Image
Qwan2.5.VL
Osapet
2 Second line (lne i) There sipheres.
3 Third line (Jine 3). There apheree.
Fourth tine (Ine The
Coust of
dicondl sumber of Number Spheres Sid 0-12
Seansegurnialyy
Figure 29: Visualization 3D attention maps generated by our proposed method using the Qwen2.5- --s-rnnst model on a counting task. The attention patterns demonstrate that the predicted numerical values for each row attend more strongly to their corresponding spatial regions, indicating improved alignment between predictions and visual content.
G Computational cost
To ensure a fair comparison of computational cost, measured the average number of output tokens generated across different tasks. Specifically, we compared three methods using the GPT-4o model: the Bascline, our proposed method (VISER), and Chain-of- Thoougt (CoT) prompting strategy. Table 12 reports the average token count for each method across four task types, each evaluated on 100 samples.
30

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Table 12: Comparison of average generated tokens for VISER, CoT, and the Baseline across differ- tasks using the GPT-4o model.
Method
Visual Search
Counting
Scene Desc.
Spatial Rel.
Baseline
83.88
11.83
61.77
8.43
CoT
90.48
103.12
12.52 62.13
VISER
153.39
40.94
62.67
10.50
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Reviews:
[{"review_id": "40phH5wKTX", "reviewer": "Reviewer_PsPb", "summary": "This paper tackles the persistent binding problem in Vision-Language Models (VLMs): the challenge of reliably associating visual features (like shape and color) and spatial properties (like location) with the correct objects in complex scenes. The authors propose a simple but effective approach: augmenting input images with explicit spatial structures (e.g., horizontal lines) and pairing this with a textual prompt encouraging sequential, spatially-aware parsing (e.g., “Scan the image sequentially based on horizontal lines”). The method is shown to significantly improve performance across visual reasoning tasks—including visual search, counting, scene description, and spatial relationship understanding—on both synthetic and real-world datasets. Notably, the gains are robust across multiple VLM architectures (e.g., GPT-4o, Claude 3.5-sonnet, Qwen2.5-VL), and the intervention is both model-agnostic and computationally lightweight. The paper also discusses neuroscientific inspiration and positions this visual intervention as a practical step towards better compositional visual reasoning in current VLMs.", "questions": "1. Analyze how performance varies with the number and density of lines (or other scaffold structures). Is there an optimal trade-off between cognitive load reduction and potential visual clutter?\n\n2. Could you provide further analysis or case studies on natural images? For instance, does the method generalize to more varied, unstructured real-world scenes, or is it limited to synthetic data?\n\n3. Please discuss and visualize representative failure cases. For example, when does the intervention not help (or even hurt), and why?\n\n4. Some of the plots are hard to read (e.g., Figure 3 and 4 in Appendix). Please improve the clarity. \n\nI would be willing to reconsider my rating especially if question 1 and 3 are well-addressed.", "limitations": "The paper discusses several limitations, including reduced gains on natural images and the risk of adversarial misuse. The discussion is generally adequate, but the paper would benefit from more detail on when/where the intervention fails.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The authors benchmark across diverse VLMs (proprietary and open-source) and multiple tasks (search, counting, scene description, spatial reasoning).\n2. The approach is motivated with cognitive/neuroscience background and illustrated with intuitive figures.\n3. Addresses a widely acknowledged challenge (binding problem) in a practical, easily-adoptable way.\n\nWeaknesses:\n1. The impact of key hyperparameters (e.g., number/density of lines) is not systematically explored. How to derive the optimal scaffold configuration is not explained or discussed.\n2. Some plots, especially in the appendix (e.g., Figures 3 and 4), are hard to read due to cluttered labeling or small font size. Improving figure clarity would benefit the paper.\n3. The paper could benefit from deeper qualitative analysis and more extensive discussion of failure modes.", "was_revised": true, "final_justification": "Resolved Issues:\n - Hyperparameter sensitivity (Q1/W1): The authors provided additional experiments varying both the number and thickness of lines across tasks and models. Results demonstrate robustness in the 2–6 line range and up to 5 px thickness, confirming that the method is not overly sensitive to hyperparameters. This addressed my earlier concern.\n - Use of natural images (Q2): The authors clarified that their paper already includes real-world datasets (“Learning To Count Everything,” “Spatial-MM”), and supplemented this with explanations of why synthetic data remains useful for controlled analysis. This alleviates my concern that the method may be overly synthetic-focused.\n - Clarity of figures (Q4/W2): The authors acknowledged the issue and committed to improving the clarity of figures in the final version.\n\nPartially Resolved Issues:\n - Failure mode analysis (Q3/W3): The authors performed additional controlled experiments analyzing line-object interference and object distribution entropy. These analyses clearly identify when scaffolding is beneficial (low interference, high entropy) and when it may hurt performance (dense overlap, clustered objects). While this represents a meaningful step forward, the analysis remains somewhat limited in scope and primarily synthetic. Still, it increases transparency about the method’s limitations.\n\nOverall I think the authors addressed most of my concerns during the rebuttal period. I increase my rating to 4 and I tend to think it's a borderline paper where strengths outweigh weaknesses.", "rebuttal": "We sincerely thank you for your detailed and constructive review.\n\n# **Q1 & W1**\n\n> The impact of key hyperparameters (e.g., number/density of lines) is not systematically explored.\n\n**Response:**\n\nWe acknowledge that our study did not systematically explore the full space of scaffold configurations. Our goal was to design a **simple**, **effective-enough** structure that could be **generally applicable** across diverse tasks, rather than task-specific tuning. The number of lines was selected using a **rule-of-thumb heuristic**:\n\n* Too few lines (e.g., 1–2) yield coarse regions, offering little improvement over the raw image for guiding sequential attention.\n\n* Too many lines risk **visual clutter** and **interference artifacts**, potentially hindering reasoning.\n\nWe originally used **3 horizontal lines** with **1 px thickness** across all tasks. To address the reviewer’s concern, we conducted additional experiments varying both the **number** and **thickness** of lines across three tasks and two representative models, one **closed-source (GPT-4o)** and one **open-source (Qwen2.5-VL)**. Each experimental setup contains **300 samples**, allowing for consistent comparison across configurations.\n\n_Table 1: Performance of GPT-4o and Qwen2.5-VL across different tasks with varying numbers of horizontal lines._\n\n| Model | Task | Baseline | 1 Line | 2 Lines | 3 Lines | 4 Lines | 5 Lines | 6 Lines | 9 Lines | 12 Lines |\n| -------------- | ------------------------ | -------- | ------ | --------- | --------- | ------- | ------- | ------- | ------- | -------- |\n| **GPT-4o** | Counting (Acc.) | 10.67 | 35.00 | 33.67 | **45.67** | 23.00 | 17.00 | 16.67 | 17.67 | 23.00 |\n| | Visual Search (Acc.) | 50.54 | 66.06 | 63.83 | 74.09 | 69.53 | **74.72** | 66.61 | 65.79 | 65.59 |\n| | Scene Desc. (Edit Dist.) | 2.26 | 1.97 | 2.02 | 2.06 | **1.80** | 2.03 | 2.02 | 2.23 | 2.46 |\n| **Qwen2.5-VL** | Counting (Acc.) | 5.33 | 13.33 | 19.33 | **39.67** | 25.67 | 33.33 | 28.67 | 27.67 | 20.00 |\n| | Visual Search (Acc.) | 40.80 | 36.99 | **55.87** | 50.03 | 49.11 | 47.92 | 53.22 | 52.42 | 53.70 |\n| | Scene Desc. (Edit Dist.) | 9.64 | **8.61** | 8.78 | 8.81 | 9.21 | 9.61 | 10.39 | 10.95 | 10.24 |\n\n_Table 2: Performance of GPT-4o and Qwen2.5-VL across different tasks with varying line thicknesses (in pixels) in our method._\n\n| Model | Task | Baseline | 1 px | 2 px | 3 px | 4 px | 5 px |\n| -------------- | ------------------------ | -------- | --------- | --------- | -------- | ----- | ----- |\n| **GPT-4o** | Counting (Acc.) | 10.67 | 42.00 | 51.67 | 51.67 | **55.67** | 51.33 |\n| | Visual Search (Acc.) | 50.54 | **72.08** | 71.23 | 71.67 | 67.74 | 67.11 |\n| | Scene Desc. (Edit Dist.) | 2.26 | 2.00 | 2.07 | **1.98** | 2.04 | 2.12 |\n| **Qwen2.5-VL** | Counting (Acc.) | 5.33 | **41.33** | 35.00 | 38.00 | 39.00 | 38.00 |\n| | Visual Search (Acc.) | 40.80 | **52.58** | 45.93 | 46.41 | 44.27 | 45.84 |\n| | Scene Desc. (Edit Dist.) | 9.64 | 8.83 | **8.63** | 8.88 | 8.93 | 9.52 |\n\nWe find that performance generally **drops at extremes** (very few or very many lines), while remaining **robust in the 2–6 line range**. A similar trend is seen for **line thickness**, where results remain consistently strong up to 5 px.\n\nSome **task-specific patterns** are also observed: for example, in Scene Description, increasing the number of lines tends to increase edit distance (lower is better), suggesting that excessive scaffolding may interfere with free-form language output.\nThese findings support our original heuristic and show that our method is **not highly sensitive** to scaffold hyperparameters, and performs **consistently across a wide range** of settings. The optimal configuration may vary slightly by task or model, but no drastic tuning is needed to achieve substantial gains.\n\n# **Q2**\n\n> Could you provide further analysis on natural images? For instance, does the method generalize to more varied, unstructured real-world scenes, or is it limited to synthetic data?\n\n**Response:**\n\nWe appreciate the reviewer's concern. In fact, we have already included multiple experiments on natural images in our paper. For instance, see Section 4.1, Tables 2 and 4, where we show the effectiveness of our method on two real-world datasets, “Learning To Count Everything” and “Spatial-MM”. These datasets include a diverse set of labeled images of unstructured scenes, e.g., counting the number of fish in a marine scene or the number of balloons in the sky. Some of our figures (e.g., Figure 1 in the main paper and Figures 14 and 19 in the supplementary material) also include real examples from these datasets. That being said, synthetic datasets offer advantages—for example, certain experiments, such as precise 2D manipulations, are only feasible with synthetic data due to its high degree of controllability.\n\nMore precisely, synthetic datasets allow for controlled variation and targeted analysis of model behavior, for example, the hyperparameter study discussed previously. These analyses would not be possible on natural data due to its complexity and annotation limitations.\n\nPlease also visit our response to Reviewer *memf*, in which we further evaluate our method on additional benchmarks involving natural images.\n\n# **Q3 & W3**\n\n> The paper could benefit from deeper qualitative analysis and more extensive discussion of failure modes.\n\n> Discuss and visualize representative failure cases. For example, when does the intervention not help (or even hurt), and why?\n\n**Response:**\n\nWe appreciate the reviewer’s suggestion to include failure cases and more extensive qualitative analysis. Our method places the added lines in a fixed configuration that is independent of image content. While this design ensures consistency and simplicity, it can introduce limitations. For instance, added lines may intersect with important objects, potentially introducing ambiguity about those objects. Alternatively, objects may lie in regions where the added lines provide little to no benefit in decomposing the task. To address this, we conduct two additional controlled experiments on the 2D counting task, targeting conditions where our intervention may help or hurt.\n\n### Varying the degree of object-boundary interaction\n\nWe synthetically vary the level of object-line interference in scenes with circular objects and group data into bins (0.0–0.2: low interference; 0.8–1.0: high interference).\n\n_Table 3: Accuracy of GPT-4o and Qwen2.5-VL on the counting task across different levels of average line-object intersection (ranging from 0.0–0.2 to 0.8–1.0)._\n\n| **Model** | **Method** | **0.0–0.2** | **0.2–0.4** | **0.4–0.6** | **0.6–0.8** | **0.8–1.0** |\n| ---------- | ---------- | ----------- | ----------- | ----------- | ----------- | ----------- |\n| GPT-4o | Baseline | 9.50 | 12.00 | 7.00 | 3.50 | 3.50 |\n| | Ours | 26.00 | 26.00 | 22.00 | 14.00 | 13.00 |\n| Qwen2.5-VL | Baseline | 3.00 | 4.00 | 10.50 | 13.50 | 18.00 |\n| | Ours | 37.00 | 34.50 | 17.50 | 5.00 | 9.00 |\n\n\n**Insights:** Our method improves performance significantly in low-interference cases. However, in high-interference scenarios (e.g., dense line-object overlaps), the benefit diminishes. This highlights a key failure mode: **when scaffolding introduce ambiguity in object detection**. For example, overlapping lines can cause objects to be either **double-counted** (across both lines) or **missed entirely** (not clearly belonging to either region), leading to reduced model accuracy.\n\n---\n\n### Object Distribution (Entropy) Analysis\nHere, we vary how objects are distributed across image rows. Low entropy indicates objects are concentrated in one row, while high entropy corresponds to a uniform spread.\n\n_Table 4: Accuracy of GPT-4o and Qwen2.5-VL on the counting task across different levels of object spatial entropy (ranging from 0.00–0.50 to 1.75–2.00)._\n\n| **Model** | **Method** | **0.00–0.50** | **0.50–1.00** | **1.00–1.25** | **1.25–1.50** | **1.50–1.75** | **1.75–2.00** |\n| ---------- | ---------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| GPT-4o | Baseline | 29.00 | 30.00 | 27.00 | 33.00 | 38.00 | 37.00 |\n| | Ours | 48.00 | 54.00 | 62.00 | 62.00 | 69.00 | 68.00 |\n| Qwen2.5-VL | Baseline | 14.00 | 16.00 | 17.00 | 16.00 | 7.00 | 5.00 |\n| | Ours | 1.00 | 15.00 | 16.00 | 26.00 | 33.00 | 36.00 |\n\n**Insights:** Our method is more effective when objects are distributed across the image (high entropy), where scaffolding provides meaningful visual separation. However, when most objects are located in a single region (low entropy), scaffolds are less helpful, sometimes even harmful. This exposes a second failure case: ineffective intervention when object positioning limits scaffold utility. \n\n---\n\nOur primary experiments are conducted on randomly generated datasets with naturally high entropy (\\~1.9) and low average interference (\\~0.174), where our method is most effective.\n\n### Summary of Limitations\n\nWe acknowledge that our method, while effective in many settings, may be sensitive to visual layout and task-specific characteristics in some cases. A fixed scaffolding structure may get lower performance when interference of lines and objects is too high, or when clustered positioning limits the spatial separation benefits of scaffolds—though in most cases, it still outperforms the baseline. Future work may explore *ensemble-based reasoning* with diverse scaffolds (e.g., varied line positions, density, colors) to increase robustness and reduce sensitivity to edge cases.\n\n# **Q4 & W2**\n\n> Some of the plots are hard to read (e.g., Figure 3 and 4 in Appendix). Please improve the clarity.\n\n**Response:**\n\nThank you for pointing this out. We acknowledge that Figures 3 and 4 in the Appendix could be clearer. We will revise these plots in the final version to improve readability,  including larger font sizes, clearer legends, and better color contrast."}, {"review_id": "SdA770HjSb", "reviewer": "Reviewer_vhup", "summary": "The paper proposes a mechanism to deal with the binding problem, which involves associating visual features with the right object in the image in VLMs. The mechanism augments the image with horizontal lines and then prompts the model to scan the image sequentially based on the horizontal lines. Through extensive experiments on visual reasoning tasks demanding sequential processing, like visual search, counting, scene description, and spatial relationship understanding the authors demonstrate the effectiveness of the method.", "questions": "1. Why is the performance of the finetuned model lower than the base model for tasks except counting? Can the authors share more details about the finetuned model (e.g. datasets, training recipe etc)?\n\n2. Why is the performance of Llama4 nearly zero for 2d visual search, unlike for the case of 3d? Also why does this approach not improve the performance of Llama4 on scene description task?\n\n3. How does the baseline, which is asked to imagine horizontal lines without drawing explicitly on the image like in Figure 21 perform on the tasks?\n\n4. Does simply drawing horizontal lines and asking to scan the image accordingly improve performance on the visual analogy tasks in [1]?\n\n[1] - Declan Campbell, Sunayana Rane, Tyler Giallanza, Camillo Nicolò De Sabbata, Kia Ghods, Amogh Joshi, Alexander Ku, Steven Frankland, Tom Griffiths, Jonathan D Cohen, et al. Understanding the limits of vision language models through the lens of the binding problem. Advances in Neural Information Processing Systems, 37:113436–113460, 2024.", "limitations": "Discussed", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is well written.\n2. A visual prompting mechanism based on drawing horizontal lines and asking the model to scan it sequentially is proposed.\n3. The mechanism shows strong improvements by reducing the binding problem in VLMs on several visual reasoning tasks. \n\nWeaknesses:\n\n1. The paper provides no reasoning behind why they chose 3 horizontal lines. In fact if all the objects lie between two lines, then this approach won’t lead to any improvements in performance. \n\n2. It seems the instruction and the visual structures (e.g grid lines for spatial reasoning vs horizontal lines for others) is kinda task-specific, raising questions about the generality of the approach.", "was_revised": true, "final_justification": "The authors' rebuttal has addressed my concerns and questions, so I raised my score.", "rebuttal": "We sincerely thank you for your detailed and constructive review.\n\n# **W1**\n> The paper provides no reasoning behind why they chose 3 horizontal lines. In fact if all the objects lie between two lines, then this approach won’t lead to any improvements in performance.\n\n**Response:**\n\nWe appreciate the reviewer’s critical perspective on the choice of 3 horizontal lines. Our choice of using **3 horizontal lines** was motivated by a **simple heuristic**: too few lines produce regions that are too coarse for effective reasoning, while too many lines risk introducing clutter and interfering with the visual content.\n\nTo address this concern more directly, we conducted **additional ablation experiments** varying the **number of scaffold lines** (1–12) across three tasks and two models (GPT-4o and Qwen2.5-VL), using **300 samples per configuration**. As shown in the table below, performance tends to **peak around 3–4 lines** and **degrades at both extremes**, confirming that neither too sparse nor too dense scaffolding is ideal.\n\n| Model | Task | Baseline | 1 Line | 2 Lines | 3 Lines | 4 Lines | 5 Lines | 6 Lines | 9 Lines | 12 Lines |\n| -------------- | ------------------------ | -------- | ------ | --------- | --------- | ------- | ------- | ------- | ------- | -------- |\n| **GPT-4o** | Counting (Acc.) | 10.67 | 35.00 | 33.67 | **45.67** | 23.00 | 17.00 | 16.67 | 17.67 | 23.00 |\n| | Visual Search (Acc.) | 50.54 | 66.06 | 63.83 | 74.09 | 69.53 | **74.72** | 66.61 | 65.79 | 65.59 |\n| | Scene Desc. (Edit Dist.) | 2.26 | 1.97 | 2.02 | 2.06 | **1.80** | 2.03 | 2.02 | 2.23 | 2.46 |\n| **Qwen2.5-VL** | Counting (Acc.) | 5.33 | 13.33 | 19.33 | **39.67** | 25.67 | 33.33 | 28.67 | 27.67 | 20.00 |\n| | Visual Search (Acc.) | 40.80 | 36.99 | **55.87** | 50.03 | 49.11 | 47.92 | 53.22 | 52.42 | 53.70 |\n| | Scene Desc. (Edit Dist.) | 9.64 | **8.61** | 8.78 | 8.81 | 9.21 | 9.61 | 10.39 | 10.95 | 10.24 |\n\nThese results demonstrate that our method is **robust across a range of line counts**, and that our originally chosen configuration of 3 lines offers a strong tradeoff between structure and interference. Importantly, it suggests that **precise tuning of line number is not critical** for achieving performance gains.\n\nTo address the second concern, we conducted a **controlled analysis** targeting such potential **failure modes**. Specifically, we evaluated performance with respect to **object distributions**, which quantifies how widely objects are spread across rows. Our findings show that when entropy is high (i.e., objects are uniformly distributed across rows), our method delivers substantial improvements. Conversely, when entropy is low (e.g., objects concentrated in a single row), the performance gains diminish.\n\n| **Model** | **Method** | **0.00–0.50** | **0.50–1.00** | **1.00–1.25** | **1.25–1.50** | **1.50–1.75** | **1.75–2.00** |\n| ---------- | ---------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| GPT-4o | Baseline | 29.00 | 30.00 | 27.00 | 33.00 | 38.00 | 37.00 |\n| | Ours | 48.00 | 54.00 | 62.00 | 62.00 | 69.00 | 68.00 |\n| Qwen2.5-VL | Baseline | 14.00 | 16.00 | 17.00 | 16.00 | 7.00 | 5.00 |\n| | Ours | 1.00 | 15.00 | 16.00 | 26.00 | 33.00 | 36.00 |\n\nThis analysis confirms the reviewer’s intuition: **object distribution matters**, and poorly placed scaffolds may hinder performance. However, it's important to note that even in some low-entropy settings, our method still outperforms the baseline. We outperform the baseline across all entropy ranges in GPT-4o, even when objects lie between two lines. In Qwen, the baseline only leads in one low-entropy case; elsewhere, our method matches or outperforms it. We also outperform baselines on the natural dataset (Tables 2 and 4), confirming strong generalization.\n\n# **W2**\n> It seems the instruction and the visual structures is kinda task-specific, raising questions about the generality of the approach.\n\n**Response:**\n\nThank you for raising this important point. While certain visual structures (e.g., grids for spatial tasks) may appear task-specific, our goal was to test whether **a simple, generic scaffold** could improve performance **across diverse tasks** without per-task tuning. We used this same structure in most experiments to emphasize generality. \n\nFor the spatial relation task, we included a grid only because the task’s **answer space is symmetric across directions** (left, right, above, below), making a grid more natural. However, even **horizontal lines alone still yield notable gains**: on the synthetic spatial relation task with GPT-4o, accuracy improves from **43.00%** (baseline) to **48.50%** (horizontal) and **52.50%** (grid); similarly, on the natural spatial relation task, accuracy goes from **69.39%** to **76.92%** and **77.43%**, respectively. \n\nWe acknowledge that while our method is effective in many settings, it can be **sensitive to layout and task characteristics**  in some cases. As a next step, we plan to explore a *wisdom-of-the-crowd* strategy with **diverse scaffolds** (e.g., varying line position, density, and color) to increase robustness and reduce input-specific failure cases.\n\n# **Q1**\n> Why is the performance of the finetuned model lower than the base model for tasks except counting? Can the authors share more details about the finetuned model (e.g. datasets, training recipe etc)?\n\n**Response:**\n\nMulberry is a fine-tuned variant of the Qwen2.5-VL model, specifically optimized for general-purpose visual reasoning. Its training dataset spans a broad range of vision-language tasks, including several that overlap with our tasks—such as spatial relationship understanding, counting, and scene description. The fine-tuning strategy employed for Mulberry incorporates Monte Carlo Tree Search (MCTS) and its cooperative variant (coMCTS), as outlined in the original paper.\n\nThe reason Mulberry does not consistently outperform the baseline across all tasks is likely due to its broad task coverage during training, which may lead to trade-offs in performance on some specialized tasks.\n\n# **Q2**\n> Why is the performance of Llama4 nearly zero for 2d visual search, unlike for the case of 3d? Also why does this approach not improve the performance of Llama4 on scene description task?\n\n**Response:**\n\nLLama4 performs poorly on 2D visual search but shows stronger results in the invisible condition, likely due to its strong bias toward answering “no” when uncertain (lines 186–188). To address this asymmetry, we report the harmonic mean of visible and invisible accuracies for a more balanced assessment.\n\nThe model performs better in the 3D visual search task, likely due to the presence of richer spatial structure—such as depth, shading—that enhance the model’s ability to detect low-level visual features. These additional cues likely increase feature entropy by introducing more visual diversity across objects, which helps reduce representational interference and improves the model’s capacity to bind features correctly. This stands in contrast to the 2D setting, where the stimuli are more abstract and symbol-like, leading to lower entropy and higher susceptibility to binding errors.\n\n| Model      | Dataset | Method   | Visible | Invisible  | Harmonic |\n| ---------- | --------- | -------- | -------    | ---------  | -------- | \n| llama-4    | 2D        | Baseline | 0.00    | **100.00**      | 0.00     | \n|            | 2D        | Ours     | **0.75** | 97.50     | **1.00** | \n|            | 3D        | Baseline | 41.00     | **98.00** | 56.00     | \n|            | 3D        | Ours     | **45.50** | 93.00     | **59.00** |\n\nHowever, the lack of improvement in the scene description task reflects a broader issue: LLama4 is generally weaker and less stable across multi-object reasoning tasks. It struggles with compositionality and consistent feature binding, which our method depends on. This is also evident in its limited gains on spatial relation reasoning in Table 4. Overall, due to its brittle internal representations, This model is less amenable to prompting-based enhancements in complex reasoning scenarios.\n\n# **Q3**\n> How does the baseline, which is asked to imagine horizontal lines without drawing explicitly on the image perform on the tasks?\n\n**Response:**\n\nTo address your question, we evaluated GPT-4o on all tasks using 2D images. As detailed in the table below, our method outperforms the imaginary-baseline method across every task, yielding an 8.5% gain in spatial relation accuracy, a 13.31% improvement in counting, a 0.12 reduction in edit distance for scene description, and a 0.05 increase in the harmonic mean for visual search.\n\n| Model      | Method    | Counting  | Spatial Relationship | Visual Search | Scene Description | \n| ---------- | --------  | --------- | -------------------- | ------------- | ----------------- | \n| GPT-4o     | Baseline  | 16.85     | 43.00                | 0.48          | 1.84              |\n|            | Imaginary | 18.24     | 44.00                | 0.68          | 1.75              |\n|            | Ours      | **31.55** | **52.50**            | **0.73**      | **1.63**          |\n\n# **Q4**\n> Does simply drawing horizontal lines and asking to scan the image accordingly improve performance on the visual analogy tasks?\n\n**Response:**\n\nRegarding the visual analogy task, we evaluated two models—GPT-4o, the strongest closed‑source baseline, and Qwen2.5-VL, the leading open‑source variant in our evaluations. We achieved 100% accuracy using both the baseline and our proposed approach for GPT-4o, demonstrating exemplary performance on the visual analogy benchmarks. We observed a significant improvement in accuracy with Qwen, which increased from 72.0 % to 77.0 %.\n\n| Model      | Method   | Accuracy |\n| ---------- | -------- | --------- |\n| GPT-4o     | Baseline | **100** |\n|            | Ours     | **100** |\n| Qwen2.5-VL | Baseline | 72.0 |\n|            | Ours     | **77.0** |"}, {"review_id": "yaBYorUCdY", "reviewer": "Reviewer_memf", "summary": "The authors introduce a visual prompting method that involves 1) drawing lines in an image; 2) asking a vision+language model to undertake a task with a \"hint\" of the augmentation. Surprisingly, this approach improves performance for closed+open models across-the-board in traditionally tricky perceptual tasks like counting and visual search.", "questions": "- can you report numbers on broader benchmarks using your method to check to see if it degrades performance for other tasks?\n- can you conduct an error analysis to understand for which instances this helps/hurts, and see if this is consistent across models?\n- can you report some variance-accounting information (e.g., p-values or dataset size information) to build confidence in the stability of your reported evaluations?", "limitations": "No. The authors do not have a limitations section, and the discussion section does not discuss limitations of the work (beyond alluding to the idea that their visual prompting is so simple and so effective that it could cause some non-specific harm (L321)). Can you be 1) more specific about the potential harms caused by your method?; and 2) touch on the concrete limitations of the work?", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The authors consider a breadth of models and perceptual tasks, and show improvements in most cases.\n- The method is simple and effective\n- Results are reported for perceptual tasks that span both synthetically generated geometries, as well as photographic images.\n\n\nWeaknesses:\n\n- While there are improvements with this method on average, the variance seems high, e.g., for llama4 in 3d visual search, performance drops slightly in most cases.\n\n- It would have been nice to include performance beyond these tasks on more standard benchmarks --- do you lose anything for, e.g., MMBench or similar? While this is a helpful augmentation for the small set of counting/perception focused tasks, it's not clear if this augmentation could hurt for other tasks, thus limiting the impact of this work\n\n- No error analysis is presented so it's not clear on which instances this method helps.\n\n- It was a bit confusing why the Mulberry model got a separate section in 4.7 rather than being a comparison point in all of the results tables. The authors did not fine-tune this model, and thus, the experimental setup seems to be the same.\n\n- Dataset statistics are not provided, and thus, it's not clear how stable the reported accuracies are. Table 2 has a lot of \".00\"s in it, which makes me wonder if this dataset has exactly 100 datapoints per row? If this is the case, I wonder if there's a better way to present these results instead of this large wall of numbers? (e.g., a plot showing the accuracy as difficulty goes up, and reporting the final average number which is taken over many more datapoints and is thus more numerically stable? There are also a few bolding problems in the tables, e.g., table 2 has 54 bolded when 56 should be bolded.)\n\n- The prompting method is somewhat ad-hoc, e.g., a grid is used for a spatial relationship task whereas 3 lines are used elsewhere.\n\n\nOverall, the idea is clever+simple, and the experiments form a strong basis for an interesting contribution. However, there are a number of clear-cut experiments/empirical that I would have hoped to see from a work like this (i.e., a prompting contribution), e.g., quantitative error analyses (e.g., do the modifications consistently improve performance on the same instances across models?), practical considerations (e.g., does drawing the lines in the image hurt performance more broadly?), etc.  . These pieces, coupled with some presentation concerns temper my excitement about the work.\n\n\n(typo: typo L154 everythig -> everything)", "was_revised": true, "final_justification": "The authors undertook my requested experiments by 1) reporting numbers on broader benchmarks; 2) conducting an error analysis; and 3) adding variance-accounting information, so I raised my score.", "rebuttal": "We sincerely thank you for your detailed and constructive review.\n\n# **Q1 & W2**\n> It would have been nice to include performance beyond these tasks on more standard benchmarks.\n\n**Response:**\n\nWe evaluated our method on two reasoning-focused benchmarks: MMBench [1] and PHYBench [2]. For MMBench, we include all its reasoning tasks, which covers Attribute, Logical, and Relation Reasoning. PHYBench is a benchmark for physical commonsense reasoning with a focus on visual understanding, and includes the categories: Dynamics, Scene Understanding, Object Relationships, and Object Property. All results were obtained on the validation split. More information about these tasks is available in the respective benchmark papers.\n\n**MMBench Accuracy (%):**\n\n| Model      | Method   | Attribute | Logical     | Relation  |\n| ---------- | -------- | --------- | --------- | --------- |\n| GPT-4o     | Baseline | **88.67** | 78.25     | 75.17     |\n|            | Ours     | 88.00     | **82.75** | **81.12** |\n| Qwen2.5-VL | Baseline | 80.67     | **80.00** | 82.99     |\n|            | Ours     | **82.33** | **80.00** | **83.33** |\n\n**PHYBench Accuracy (%):**\n\n | **Model**    | **Method** | **Dynamics** | **Scene Understanding** | **Object Relationships** | **Object Property** | **Overall** |\n|--------------|------------|--------------|---------------------------|---------------------------|----------------------|-------------|\n| **GPT-4o**   | Baseline   | 35.00        | 33.33                     | **75.00**                | 55.00               | 55.68       |\n|              | Ours       | **40.00**    | **41.67**                 | **75.00**                | **70.00**           | **_61.37_** |\n| **Qwen2.5-VL** | Baseline | **40.00**    | **50.00**                 | 61.11                    | 60.00               | 54.55       |\n|              | Ours       | **40.00**    | **50.00**                 | **72.22**                | **70.00**           | **_61.36_** |\n\n\nThese results demonstrate that our method performs robustly across a variety of tasks and datasets. Its effectiveness on both standard and physically grounded benchmarks highlights the generality and versatility of our approach in diverse reasoning settings.\n\n# **Q2 & W3 & Limitations**\n> Can you conduct an error analysis to understand for which instances this helps/hurts, and see if this is consistent across models?\n> Can you be 1) more specific about the potential harms caused by your method?; and 2) touch on the concrete limitations of the work?\n\n**Response:**\n\n**We thank the reviewer for the valuable suggestions regarding error analysis and limitations. We agree that such analysis can offer deeper insights into the strengths and weaknesses of LVLMs equipped with our strategy.**\n\n### Error Analysis: Line Interference\n\nTo understand when our method helps or hurts, we conducted a controlled experiment on the 2D counting task. We varied the *amount of line-object interference* (measured as the normalized intersection area), keeping the number and size of objects constant.\n\n| Model | Method | 0.0–0.2 | 0.2–0.4 | 0.4–0.6 | 0.6–0.8 | 0.8–1.0 |\n| -------------- | -------- | ------- | ------- | ------- | ------- | ------- |\n| **GPT-4o** | Baseline | 9.5 | 12.0 | 7.0 | 3.5 | 3.5 |\n| | Ours | 26.0 | 26.0 | 22.0 | 14.0 | 13.0 |\n| **Qwen2.5-VL** | Baseline | 3.0 | 4.0 | 10.5 | 13.5 | 18.0 |\n| | Ours | 37.0 | 34.5 | 17.5 | 5.0 | 9.0 |\n\nWe observe that performance decreases with increasing overlap, which is expected. In our main datasets, the average interference is \\~0.174 (i.e., low), minimizing this concern.\n\n### Error Analysis: Object Distribution across partitions\n\nWe also tested the effect of object distribution by varying the *entropy* of object locations across image rows.\n\n| Model | Method | 0.00–0.50 | 0.50–1.00 | 1.00–1.25 | 1.25–1.50 | 1.50–1.75 | 1.75–2.00 |\n| -------------- | -------- | --------- | --------- | --------- | --------- | --------- | --------- |\n| **GPT-4o** | Baseline | 29.0 | 30.0 | 27.0 | 33.0 | 38.0 | 37.0 |\n| | Ours | 48.0 | 54.0 | 62.0 | 62.0 | 69.0 | 68.0 |\n| **Qwen2.5-VL** | Baseline | 14.0 | 16.0 | 17.0 | 16.0 | 7.0 | 5.0 |\n| | Ours | 1.0 | 15.0 | 16.0 | 26.0 | 33.0 | 36.0 |\n\nFor our method, performance improves with increasing entropy; denser layouts hinder the visual scaffolding's effectiveness. Again, our main datasets in the paper naturally exhibit high entropy (\\~1.9).\n\n### Limitations and Harms\n\nWe acknowledge that our method may be sensitive to certain image or task properties, especially when lines intersect objects—causing undercounting or overcounting during row-by-row scanning. A promising direction is **ensemble-based scaffolding**, using diverse line types (e.g., grids, varied colors/thicknesses), which could improve robustness and reduce corner-case failures. \n\n# **Q3 & W1 & W5**\n\n> Dataset statistics are not provided, and thus, it's not clear how stable the reported accuracies are. Table 2 has a lot of \".00\"s in it, which makes me wonder if this dataset has exactly 100 datapoints per row? If this is the case, I wonder if there's a better way to present these results instead of this large wall of numbers?\n\n> Can you report some variance-accounting information (e.g., p-values or dataset size information) to build confidence in the stability of your reported evaluations?\n\n**Response:**\n\n### Dataset Statistics and Table Presentation\n\nThank you for pointing this out. We include detailed dataset statistics in the supplementary material, under the section *\"Benchmark and score details\"* (line 49). For each task, we report the number of samples and their configurations (e.g., number of objects per image, object types, triplet structure, etc.).\n\nWe agree that Table 2 can be improved visually. In the final version, we plan to revise it using summary plots (e.g., accuracy vs. difficulty level) and report aggregated values to better highlight trends. We’ll also correct formatting issues (e.g., bolding errors in Table 2, such as \"54\" instead of the correct \"56\").\n\n---\n\n### Performance Variance and Statistical Significance\n\nWe acknowledge the importance of demonstrating statistical stability. Each reported value reflects the **average over a substantial number of samples per configuration** (typically 50–100), ensuring baseline numerical reliability.\n\nTo reduce non-deterministic effects and ensure reproducibility, we follow best practices by using **greedy decoding (temperature = 0)**, as suggested in the paper *The Good, The Bad, and The Greedy* [3], ensuring consistent output per input sample.\n\nRather than relying on repeated trials of a single setup, we emphasize robustness through a **diverse evaluation across tasks and models**, including both 2D and 3D scenes, synthetic and real data, and open- and closed-source VLMs. Across **120 pairwise experimental comparisons** between our method and the baseline (excluding supplementary material), our approach **outperforms in 103 cases**, with only **17 minor drops**, 9 of which stem from **LLaMA4**, a model found to be less tolerant in general.\n\nTo assess the significance of this result, we conducted a one-sided binomial sign test on the 113 non-tied cases (96 wins vs. 17 losses). The resulting p-value is approximately 7.4 × 10⁻¹⁵, strongly rejecting the null hypothesis that our method performs no better than the baseline.\n\nThe remaining 7 cases were ties, typically arising in scenarios with either zero (mostly observed with LLaMA4) or saturated performance, where both methods yield identical outcomes. \n\nFinally, we agree that adding statistical intervals would strengthen the comparisons. In the final revision, we plan to include **confidence intervals using bootstrap sampling** (for static datasets) and **repeated generation** (for synthetic datasets) to better reflect evaluation stability. \n\n# **W4**\n> It was a bit confusing why the Mulberry model got a separate section in 4.7.\n\n**Response:**\n\nWe placed Mulberry in a separate section (4.7) to emphasize a different comparison goal: evaluating whether our training-free, input-level method can match or outperform models improved through fine-tuning. The main tables focus only on base models with and without our method, so including Mulberry there would mix fundamentally different settings. This separation underscores that our lightweight approach achieves competitive or superior results without any additional training. We will make this motivation clearer in the camera-ready version.\n\n# **W6**\n> The prompting method is somewhat ad-hoc, e.g., a grid is used for a spatial relationship task whereas 3 lines are used elsewhere.\n\n**Response:**\n\nThank you for the feedback. We agree that visual structuring is not identical across all tasks. Our main goal was to show that even a simple visual structure (three horizontal lines) can significantly improve reasoning without tuning it for each task. We used this same structure across most experiments to emphasize generality. For the spatial relation task, we used a grid because the answer space is symmetric across four directions (left, right, above, below), making a grid a more natural fit. However, horizontal lines are also effective in this case: on the synthetic spatial relation task with GPT-4o, accuracy improves from **43.00%** (baseline) to **48.50%** (horizontal) and **52.50%** (grid); similarly, on the natural spatial relation task, accuracy goes from **69.39%** to **76.92%** and **77.43%**, respectively.\n\n# References\n\n[1] Liu, Yuan, et al. \"Mmbench: Is your multi-modal model an all-around player?.\" European conference on computer vision. Cham: Springer Nature Switzerland, 2024.\n\n[2] Chow, Wei, et al. \"Physbench: Benchmarking and enhancing vision-language models for physical world understanding.\" arXiv preprint arXiv:2501.16411 (2025).\n\n[3] Song, Yifan, et al. \"The good, the bad, and the greedy: Evaluation of llms should not ignore non-determinism.\" arXiv preprint arXiv:2407.10457 (2024)."}, {"review_id": "3zLKn03ZpT", "reviewer": "Reviewer_6z5n", "summary": "The work investigates the very interesting binding problem in neural networks, and more specificcally in vision-language models. The paper introduces a method combining simple visual scaffolding using horizontal lines, with a targeted textual prompt to encourage sequential, spatially-aware processing. The results show consistent performance improvements across visual search, counting, scene description, and spatial relationship understanding both in synthetic and natural datasets.", "questions": "See above the remarks. \n\nTypo:\nLine 154: Everthg", "limitations": "The limitations of the work could be grouped in a separate section including the discussion on when to expect lesser performance gains. The limitations could be tight to the ideas for future refinements, improvements and alternative ways to solve the binding problem.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "-\tThe paper covers a very pertinent problem of neural networks, i.e.,  the binding problem described in Von Der Malsburg 1994 and clearly defined by Greff et al. arXiv:2012.05208 2020. Research efforts aimed at addressing this issue are encouraged and greatly valued.\n-\tThe paper proposes a simple but original training-free approach that could be refined in the future.\n-\tThe results obtained on synthetic and natural images are very convincing.  \n-\tMany tasks are evaluated including visual search, counting, scene description and spatial relationship processing.\n-\tThe model operates without any additional supervision. \n-\tThe paper opens many venues for future research, including video processing. \n\n\nWeaknesses \n\t\n- The authors structure the image with horizontal lines though there are many different ways to structure an image and afterwards sequentially scanning the image: The paper could benefit from experiments with alternative ways of structuring and scanning the image.\n- The authors best add a short separate paragraph that describes the Simple baseline. They might give it another name as readers might confuse it with your approach which at places you call “simple”.\n-\tThe authors speak about refinement of their method in the future. Perhaps they could give some directions or alternative approaches that could be investigated in the future. There could also be a short discussion on the potential impact of their work. \n-\tIndicate more clearly in the tables which results are obtained on the synthetic datasets.\n-\tElaborate more on the (lack of additional) computational cost.", "was_revised": true, "final_justification": "I am satisfied with the answers to my questions, so I keep my (good) score.", "rebuttal": "We sincerely appreciate your thoughtful and encouraging review, as well as the constructive suggestions for improving and extending our work.\n\n# **W1**\n\n> The authors structure the image with horizontal lines, though there are many different ways to structure an image and afterwards sequentially scanning the image. The paper could benefit from experiments with alternative ways of structuring and scanning the image.\n\n**Response:**\n\nOur goal in this work was to propose a **simple, generally applicable scaffolding strategy** that could support visual reasoning across tasks, without relying on complex or highly specialized structures. Rather than exhaustively exploring all possible structuring and scanning schemes, we prioritized a design that balances **practicality**, **clarity**, and **broad effectiveness**. The number of lines was selected based on a **rule-of-thumb**: too few lines yield overly coarse regions that do little to guide attention, while too many may lead to **visual clutter** and degrade performance due to unnecessary complexity. We employed a grid specifically for spatial reasoning tasks, as it aligns well with the directional nature of the problem. However, our experiments show that even **horizontal lines alone** are effective in such tasks.\n\nAs detailed in our response to Reviewer **PsPb**, we conducted additional experiments varying the **number** and **thickness** of lines across multiple tasks and models. Results show that the method is **not highly sensitive** to the exact scaffold configuration and maintains performance across a wide hyperparameter range. \n\n\n\n# **W2 & W4**\n\n> The authors best add a short separate paragraph that describes the Simple baseline. They might give it another name as readers might confuse it with your approach which at places you call “simple”.\n\n> Indicate more clearly in the tables which results are obtained on the synthetic datasets.\n\n**Response:**\n\nWe thank the reviewer for this helpful suggestion. We agree that the term \"Simple baseline\" may cause confusion, particularly since \"simple\" is also used to describe aspects of our own approach. To improve clarity, we will revise the manuscript to include a short standalone paragraph describing the baseline, and we will rename our method to \"Visual Input Structure for Enhanced Reasoning (VISER)\" to ensure a clear distinction.\n\nWe will also revise the tables to more clearly indicate which results are obtained on the **synthetic** datasets.\n\n\n# **W3**\n\n> The authors speak about refinement of their method in the future. Perhaps they could give some directions or alternative approaches that could be investigated in the future. There could also be a short discussion on the potential impact of their work.\n\n**Response:**\n\n### Ensemble of Different Configurations:\nIn addition to the future directions already discussed, one interesting refinement would be to move beyond a single static visual scaffold. Inspired by ensemble-style reasoning approaches, one could generate multiple versions of the same input using a variety of simple visual structures (such as different numbers of lines, gridlines, varied line positions, colors, or thickness) and then query the model on each version. Each version may guide the model to reason in a slightly different way. By aggregating the responses and selecting the most common answer, we can reduce the risk of failure from any one structure that may not suit a particular input. This \"wisdom of the crowd\" strategy could make the method more robust and flexible and help avoid situations where a static structure becomes a bottleneck on certain inputs.\n\n\n**Theoretical Analysis of Line-Induced Inductive Biases:**\n\nWhile our method empirically improves performance by structuring the image into line-based partitions and we also analyzed how line-based structuring changes the model’s attention maps compared to unstructured input (Subsection F of supplementary material), a deeper theoretical or mechanistic understanding remains open. \n\n**Robustness:**\n\nMethods that introduce structure into the image—such as ours—may inadvertently expose the model to adversarial attacks that exploit this added structure to mislead the model's reasoning. As such, analyzing and improving the robustness of structure-augmented approaches represents a valuable direction for future work.\n \n**Possible Impact:**\n\nOur method also offers practical benefits for applications like assistive vision, robotics, and education, while also raising important considerations for AI safety and transparency, especially given its effectiveness without requiring model access.\n\n# **W5**\n\n> Elaborate more on the (lack of additional) computational cost.\n\n**Response:**\n\nTo provide a fair comparison of computational cost, we measured the average number of generated output tokens across tasks. Specifically, we compared the baseline model, our method, and a Chain-of-Thought (CoT) prompting strategy, all using the GPT-4o model. The table below reports these values across four task types, each evaluated on 100 samples.\n\n**Average Output Token Counts (GPT-4o):**\n\n|  Method  |  Visual Search  |  Counting  |  Scene Desc. |  Spatial Rel.  |\n| -------- | ------------- | --------- | ----------------- | ---------------- |\n| Baseline   | 83.88         | 11.83     | 61.77             | 8.43             |\n| CoT      | 90.48         | 103.12    | 62.13             | 12.52             |\n| Ours   | 153.39     | 40.94 | 62.67         | 10.50         |\n\nThese results show that our method generates on par or fewer tokens than CoT in most tasks while remaining close to the baseline. We believe this reflects a favorable trade-off between reasoning effectiveness and computational cost."}]

Author rebuttal:

Meta-review: The paper proposes a training-free visual scaffolding approach—adding simple spatial structure (e.g., horizontal lines) to images and pairing it with a sequential “scan by rows/grid” prompt—to mitigate binding failures in VLMs. Across counting, visual search, scene description, and spatial relations, the intervention yields consistent gains on synthetic and natural data and works across both closed- and open-weight models. 

Strengths include a simple, model-agnostic mechanism that improves multiple perceptual reasoning tasks (with some natural-image results) and a practical framing tied to the binding problem. Initial weaknesses flagged by reviewers were the lightness of the contribution, limited ablations/error analysis, uncertainty about generality/hyperparameters, figure clarity, and whether the scaffold hurts other benchmarks.  

In rebuttal, the authors added systematic ablations on line count and thickness showing robustness, analyzed failure modes via line-object interference and spatial entropy, and reported broader results on MMBench/PHYBench and additional statistical tests. They also evaluated visual analogies and showed a small improvement on RAVEN by prompting models to use the existing grid structure, and noted minor compute overhead versus CoT. These additions materially strengthen the paper and address most factual/methodological concerns. 

After discussion, two reviewers increased to borderline-accept and one remained positive; the remaining reservations concern conceptual simplicity and presentation polish rather than correctness. There is no fundamental flaw: the effect is real (replicated across models/tasks), limitations are now documented (e.g., interference/high clustering), and the scope is appropriately framed as a practical input-design lever rather than a new learning algorithm. 

Given all this, the AC recommends the paper be accepted, with a request to incorporate the new analyses, clarify baselines/naming, tighten figures/tables, and make the limitations/failure modes prominent in the camera-ready.
Decision: Accept (poster)