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.

翻译：尽管大规模视觉语言模型（LVLMs）取得了进展，但其视觉推理能力常受限于绑定问题：即未能可靠地将感知特征与其正确的视觉指代对象关联起来。这一限制导致了在计数、视觉搜索、场景描述和空间关系理解等任务中持续存在的错误。一个关键因素是，当前LVLMs主要并行处理视觉特征，缺乏基于空间定位的序列注意力机制。本文提出了增强推理的视觉输入结构（VISER），这是一种简单有效的方法，通过低层次空间结构增强视觉输入，并搭配鼓励序列化、空间感知解析的文本提示。我们通过实证证明，仅使用单次查询推理，即可在核心视觉推理任务上实现显著的性能提升。具体而言，VISER将GPT-4o在视觉搜索、计数和空间关系任务上的性能分别提高了25.0%、26.8%和9.5%，并在二维数据集上将场景描述的编辑距离误差降低了0.32。此外，我们发现视觉修改对这些增益至关重要；纯文本策略（包括思维链提示）不仅不足，甚至可能降低性能。VISER强调了视觉输入设计相对于纯语言推理策略的重要性，并表明视觉结构化是增强LVLMs组合与空间推理能力的强大且通用的方法。