是的，Florne，我下次会做得更好！用于幽默表情包检测的代理反馈推理
Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection
AI总结：
Florne通过闭环反馈机制提升表情包幽默检测的适应性和解释质量。
摘要：
幽默的表情包融合了视觉和文本线索来传达讽刺、讽刺或社会评论，这对AI系统来说是一个独特的挑战，因为这些系统必须解读意图而不是表面相关性。现有的多模态或提示基于模型生成幽默的解释，但它们处于开环中，缺乏在做出预测后批评或改进其推理的能力。我们提出了Florne，一个代理反馈推理框架，将表情包的理解视为在学习期间的闭环过程和推理期间的开环过程。在闭环中，一个推理代理由裁判批评；错误和语义反馈被转换为控制信号，并存储在反馈指导的非参数知识库中。在推理期间，模型从该知识库中检索类似的裁判经验，并利用它们来调节其提示，使推理更加完善，而无需微调。在PrideMM数据集上，Florne在预测性能和解释质量上都优于静态多模态基线，表明反馈调节提示是实现适应性表情包幽默理解的可行路径。
英文摘要：
Humorous memes blend visual and textual cues to convey irony, satire, or social commentary, posing unique challenges for AI systems that must interpret intent rather than surface correlations. Existing multimodal or prompting-based models generate explanations for humor but operate in an open loop,lacking the ability to critique or refine their reasoning once a prediction is made. We propose FLoReNce, an agentic feedback reasoning framework that treats meme understanding as a closed-loop process during learning and an open-loop process during inference. In the closed loop, a reasoning agent is critiqued by a judge; the error and semantic feedback are converted into control signals and stored in a feedback-informed, non-parametric knowledge base. At inference, the model retrieves similar judged experiences from this KB and uses them to modulate its prompt, enabling better, self-aligned reasoning without finetuning. On the PrideMM dataset, FLoReNce improves both predictive performance and explanation quality over static multimodal baselines, showing that feedback-regulated prompting is a viable path to adaptive meme humor understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
