Title: Visual Structures Help Visual Reasoning:  Addressing the Binding Problem in LVLMs
Venue: NeurIPS 2025
Accepted: yes
Keywords: Visual Reasoning

Large Vision-Language Models

Cognitive Science

Binding Problem
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n-\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.\n\nQUESTIONS:\nSee above the remarks. \n\nTypo:\nLine 154: Everthg\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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)\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nNo. 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?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nDiscussed", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}