Existing referring Audio-Visual Segmentation (Ref-AVS) benchmarks primarily focus on explicit expressions whose referents can be resolved through direct visual, acoustic, or linguistic correspondence. Consequently, they provide limited coverage of implicit references that require multi-step reasoning over multimodal evidence. To bridge this gap, we introduce **Reasoning Ref-AVS**, a new task that requires models to infer the referred object from audio-visual-language cues before generating its segmentation mask. We further construct **ReAVS**, a comprehensive dataset with manually annotated referring expressions, multimodal cues, object masks, and reasoning chains, covering diverse reasoning spanning diverse reasoning patterns. Evaluations of representative Ref-AVS models on ReAVS reveal substantial performance degradation when direct cross-modal correspondence is insufficient, exposing a clear gap between multimodal recognition and referent reasoning. Motivated by these challenges, we propose **Re2Seg**, an omni-modal framework that performs object-centric evidence encoding, multi-hop reasoning, and reasoning-conditioned mask decoding. Experiments demonstrate that Re2Seg achieves stronger segmentation performance under reasoning-intensive references and provides interpretable object-inference traces. These results highlight the importance of learning to reason before masking for bridging the gap between multimodal recognition and referring segmentation.