Sarcasm detection is a crucial yet challenging Natural Language Processing task. Existing Large Language Model methods are often limited by single-perspective analysis, static reasoning pathways, and a susceptibility to hallucination when processing complex ironic rhetoric, which impacts their accuracy and reliability. To address these challenges, we propose **SEVADE**, a novel **S**elf-**Ev**olving multi-agent **A**nalysis framework with **D**ecoupled **E**valuation for hallucination-resistant sarcasm detection. The core of our framework is a Dynamic Agentive Reasoning Engine (DARE), which utilizes a team of specialized agents grounded in linguistic theory to perform a multifaceted deconstruction of the text and generate a structured reasoning chain. Subsequently, a separate lightweight rationale adjudicator (RA) performs the final classification based solely on this reasoning chain. This decoupled architecture is designed to mitigate the risk of hallucination by separating complex reasoning from the final judgment. Extensive experiments on four benchmark datasets demonstrate that our framework achieves state-of-the-art performance, with average improvements of **6.75%** in Accuracy and **6.29%** in Macro-F1 score.

翻译：反讽检测是一项关键但极具挑战性的自然语言处理任务。现有的大型语言模型方法在处理复杂的反讽修辞时，通常受限于单一视角分析、静态推理路径以及易产生幻觉，这影响了其准确性和可靠性。为解决这些挑战，我们提出了**SEVADE**，一种新颖的**自演进多智能体分析**框架，采用**解耦评估**以实现抗幻觉的反讽检测。我们框架的核心是一个动态智能体推理引擎，它利用一个基于语言学理论的专业智能体团队，对文本进行多方面的解构并生成结构化的推理链。随后，一个独立的轻量级理据裁决器仅基于此推理链执行最终分类。这种解耦架构旨在通过将复杂推理与最终判断分离来降低幻觉风险。在四个基准数据集上的大量实验表明，我们的框架实现了最先进的性能，在准确率上平均提升了**6.75%**，在宏平均F1分数上平均提升了**6.29%**。