Multimodal sarcasm detection requires reasoning over cross-modal incongruities between literal expression and intended meaning, yet the specific analytical perspectives needed vary across samples due to the diversity of sarcastic mechanisms. While recent methods make this analytical process explicit, they still rely on fixed, predefined perspectives that operate independently under hand-crafted routing rules. We argue that multimodal sarcasm detection instead calls for self-elicited multi-perspective reasoning, where a model autonomously generates the perspectives needed for each sample and progressively integrates them into a coherent analysis. To realize this goal, we propose ProCrit, a Proposal-Critic two-agent framework with a proposal agent for multi-perspective reasoning and a critic agent for external evaluation and targeted revision guidance. First, to overcome the lack of process-level supervision in existing sarcasm datasets, ProCrit synthesizes process-level reasoning annotations through a dynamic-role agentic rollout: a strong vision-language model sequentially spawns analytical roles within a shared context, and the resulting multi-role trajectories are flattened into sequences that preserve cross-perspective dependencies while enabling efficient autoregressive generation. Second, to improve reasoning reliability, ProCrit adopts a draft-critique-revise paradigm in which an independent critic identifies reasoning deficiencies and provides targeted natural-language feedback for directed revision. Finally, we develop a mutual-refinement training framework that jointly optimizes proposal drafting and feedback-guided revision via dual-stage reinforcement learning, while refining the critic agent according to the actual effectiveness of its feedback. Experiments on three widely used benchmarks demonstrate the effectiveness of ProCrit.

翻译：多模态讽刺检测需要对字面表达与隐含意图之间的跨模态不一致进行推理，然而，由于讽刺机制的多样性，每个样本所需的具体分析视角各不相同。尽管现有方法使这一分析过程显式化，但仍依赖固定且预定义的视角，这些视角在手工制定的路由规则下独立运行。我们认为，多模态讽刺检测应当采用自诱发的多视角推理，即模型自主为每个样本生成所需视角，并逐步将其整合为连贯的分析。为实现这一目标，我们提出ProCrit，一个提案-批评双智能体框架，其中提案智能体负责多视角推理，批评智能体负责外部评估与定向修订指导。首先，为克服现有讽刺数据集在过程级监督方面的缺失，ProCrit通过动态角色智能体滚动合成过程级推理标注：一个强大的视觉语言模型在共享上下文内依次生成分析角色，并将得到的多角色轨迹展平为序列，以保留跨视角依赖关系，同时支持高效的自回归生成。其次，为提升推理可靠性，ProCrit采用草稿-批评-修订范式，由独立批评智能体识别推理缺陷，并提供定向的自然语言反馈以指导修订。最后，我们开发了一个联合优化训练框架，通过双阶段强化学习协同优化提案草稿生成与反馈引导修订，同时根据批评智能体反馈的实际效用对其加以改进。在三个广泛使用的基准数据集上的实验验证了ProCrit的有效性。