Multimodal large language models (MLLMs) such as GPT-4o, Gemini Pro, and Claude 3.5 have enabled unified reasoning over text and visual inputs, yet they often hallucinate in real world scenarios especially when small objects or fine spatial context are involved. We pinpoint two core causes of this failure: the absence of region-adaptive attention and inflexible token budgets that force uniform downsampling, leading to critical information loss. To overcome these limitations, we introduce Zoomer, a visual prompting framework that delivers token-efficient, detail-preserving image representations for black-box MLLMs. Zoomer integrates (1) a prompt-aware emphasis module to highlight semantically relevant regions, (2) a spatial-preserving orchestration schema to maintain object relationships, and (3) a budget-aware strategy to adaptively allocate tokens between global context and local details. Extensive experiments on nine benchmarks and three commercial MLLMs demonstrate that Zoomer boosts accuracy by up to 27% while cutting image token usage by up to 67%. Our approach establishes a principled methodology for robust, resource-aware multimodal understanding in settings where model internals are inaccessible.

翻译：诸如GPT-4o、Gemini Pro和Claude 3.5等多模态大语言模型（MLLMs）已能实现对文本与视觉输入的联合推理，但在现实场景中，尤其是在涉及小物体或精细空间上下文时，它们常常产生幻觉。我们指出导致这种失败的两个核心原因：缺乏区域自适应注意力机制以及僵化的令牌预算迫使图像进行均匀下采样，从而导致关键信息丢失。为克服这些限制，我们提出了Zoomer，一个为黑盒MLLMs提供令牌高效、细节保留的图像表示的视觉提示框架。Zoomer集成了（1）一个提示感知的强调模块，用于突出语义相关区域；（2）一个空间保持的编排方案，以维持物体间关系；（3）一个预算感知策略，用于在全局上下文与局部细节之间自适应地分配令牌。在九个基准测试和三个商用MLLMs上进行的大量实验表明，Zoomer将准确率最高提升了27%，同时将图像令牌使用量最高削减了67%。我们的方法为在模型内部不可访问的场景下实现鲁棒、资源感知的多模态理解，建立了一种原则性的方法论。