Satire, a form of artistic expression combining humor with implicit critique, holds significant social value by illuminating societal issues. Despite its cultural and societal significance, satire comprehension, particularly in purely visual forms, remains a challenging task for current vision-language models. This task requires not only detecting satire but also deciphering its nuanced meaning and identifying the implicated entities. Existing models often fail to effectively integrate local entity relationships with global context, leading to misinterpretation, comprehension biases, and hallucinations. To address these limitations, we propose SatireDecoder, a training-free framework designed to enhance satirical image comprehension. Our approach proposes a multi-agent system performing visual cascaded decoupling to decompose images into fine-grained local and global semantic representations. In addition, we introduce a chain-of-thought reasoning strategy guided by uncertainty analysis, which breaks down the complex satire comprehension process into sequential subtasks with minimized uncertainty. Our method significantly improves interpretive accuracy while reducing hallucinations. Experimental results validate that SatireDecoder outperforms existing baselines in comprehending visual satire, offering a promising direction for vision-language reasoning in nuanced, high-level semantic tasks.

翻译：讽刺作为一种结合幽默与隐性批判的艺术表达形式，通过揭示社会问题具有重要的社会价值。尽管讽刺在文化和社会层面意义重大，但其理解——尤其是纯视觉形式——对当前的视觉-语言模型仍是一项挑战性任务。该任务不仅需要检测讽刺元素，还需解读其微妙含义并识别所涉及实体。现有模型往往难以有效整合局部实体关系与全局上下文，导致误读、理解偏差和幻觉生成。为克服这些局限，我们提出SatireDecoder，一种无需训练的框架，旨在增强讽刺图像理解能力。该方法采用多智能体系统执行视觉级联解耦，将图像分解为细粒度的局部与全局语义表征。此外，我们引入基于不确定性分析的思维链推理策略，将复杂的讽刺理解过程分解为不确定性最小化的序列子任务。本方法在显著提升解释准确性的同时有效减少了幻觉现象。实验结果表明，SatireDecoder在理解视觉讽刺方面优于现有基线模型，为视觉-语言推理在微妙且高层级的语义任务中提供了新的研究方向。