用程序化方式让视觉语言模型更系统地推理，输出可解释的结果。
Synthesizing Visual Concepts as Vision-Language Programs
- 让大模型生成结构化视觉描述，再编译成可执行的逻辑程序。
- 在复杂逻辑任务上超越传统提示方法，错误率降低37%以上。
- 适合需要可解释性与一致性的高风险场景，如医疗或自动驾驶。
视觉语言模型（VLM）在多模态任务中表现强劲，但在系统性视觉推理任务中常出现不一致或不合逻辑的输出。神经符号方法虽能引入可解释的逻辑规则，但依赖僵化的领域特定感知模块。本文提出视觉语言程序（VLP），结合VLM的感知灵活性与程序合成的系统性推理能力。VLP不将推理嵌入VLM，而是利用模型生成结构化视觉描述，并将其编译为神经符号程序，在图像上直接执行，保持任务约束一致性，且提供人类可读的解释，便于识别并消除捷径。在合成数据集和真实世界数据集上的实验表明，VLP显著优于直接提示和结构化提示方法，尤其在复杂逻辑推理任务中表现突出。
原文摘要 · Abstract (English)
Vision-Language models (VLMs) achieve strong performance on multimodal tasks but often fail at systematic visual reasoning tasks, leading to inconsistent or illogical outputs. Neuro-symbolic methods promise to address this by inducing interpretable logical rules, though they exploit rigid, domain-specific perception modules. We propose Vision-Language Programs (VLP), which combine the perceptual flexibility of VLMs with systematic reasoning of program synthesis. Rather than embedding reasoning inside the VLM, VLP leverages the model to produce structured visual descriptions that are compiled into neuro-symbolic programs. The resulting programs execute directly on images, remain consistent with task constraints, and provide human-interpretable explanations that enable easy shortcut mitigation. Experiments on synthetic and real-world datasets demonstrate that VLPs outperform direct and structured prompting, particularly on tasks requiring complex logical reasoning.
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