Despite progress in multimodal sarcasm detection, existing datasets and methods predominantly focus on single-image scenarios, overlooking potential semantic and affective relations across multiple images. This leaves a gap in modeling cases where sarcasm is triggered by multi-image cues in real-world settings. To bridge this gap, we introduce MMSD3.0, a new benchmark composed entirely of multi-image samples curated from tweets and Amazon reviews. We further propose the Cross-Image Reasoning Model (CIRM), which performs targeted cross-image sequence modeling to capture latent inter-image connections. In addition, we introduce a relevance-guided, fine-grained cross-modal fusion mechanism based on text-image correspondence to reduce information loss during integration. We establish a comprehensive suite of strong and representative baselines and conduct extensive experiments, showing that MMSD3.0 is an effective and reliable benchmark that better reflects real-world conditions. Moreover, CIRM demonstrates state-of-the-art performance across MMSD, MMSD2.0 and MMSD3.0, validating its effectiveness in both single-image and multi-image scenarios. Dataset and code are publicly available at https://github.com/ZHCMOONWIND/MMSD3.0.