这篇论文提出了RDS Fusion，不用微调就能在讽刺检测上达到和微调BERTweet一样的水平，还在难数据集上比很多监督方法强。
RDS Fusion是一种混合神经符号架构，通过压缩链式思维（CoT）推理轨迹实现零样本讽刺检测，无需监督微调。在TweetEval测试集（N=734）上，RDS取得78.1%准确率和0.777宏F1，匹配微调BERTweet的性能上限。在严重不平衡的iSarcasm数据集上，其冻结CoT管道过滤了22.5%的分布外幻觉，零样本宏F1达0.6726，Ironic F1为0.4821，优于多个有监督SemEval集成模型。统计消融实验表明，仅当三种信号完全融合时才产生显著改善（p=0.005）。
Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts
Large Language Models (LLMs) natively default to literal semantic interpretations, making zero-shot irony detection a persistent challenge. We introduce the Robust Dual-Signal (RDS) Fusion framework, a hybrid neuro-symbolic architecture that compresses Chain-of-Thought (CoT) reasoning trajectories without Supervised Fine-Tuning (SFT). Evaluated on a strictly held-out TweetEval test set (N=734), RDS achieves 78.1% accuracy and a Macro F1 of 0.777, matching the absolute performance ceiling of the fine-tuned BERTweet. On the heavily imbalanced iSarcasm dataset, the frozen CoT pipeline filters 22.5% of out-of-distribution hallucinations, yielding a zero-shot Macro F1 of 0.6726 and Ironic F1 of 0.4821, outperforming multiple heavily supervised SemEval transformer ensembles. A statistical ablation confirms this structural synergy: adding the symbolic prior to the neural baseline yields no significant gain (p = 0.242), and the marginal benefit of adding the CoT pipeline to that prior is heavily compressed (p = 0.149). Only the complete, concurrent fusion of all three signals achieves a statistically validated improvement over the baseline (p = 0.005).