While multimodal large language models have advanced across text, image, and audio, personalization research has remained primarily vision-language, with unified omnimodal benchmarking that jointly covers text, image, and audio still limited, and lacking the methodological rigor to account for absent-persona scenarios or systematic grounding studies. We introduce Omni-Persona, the first comprehensive benchmark for omnimodal personalization. We formalize the task as cross-modal routing over the Persona Modality Graph, encompassing 4 task groups and 18 fine-grained tasks across ~750 items. To rigorously diagnose grounding behavior, we propose Calibrated Accuracy (Cal), which jointly evaluates correct grounding and appropriate abstention, incorporating absent-persona queries within a unified evaluation framework. On our dedicated experiments, three diagnostic findings emerge: (i) recent open-weight models show a consistent audio-vs-visual grounding gap that RLVR partially narrows via dense rule-based supervision; (ii) recall score and parameter scale are incomplete diagnostics, since strong recall can coexist with absent-persona hallucination and larger models do not always achieve higher Cal, exposing calibration as a separate evaluation axis; and (iii) SFT is limited by the scalability of ground-truth annotation. RLVR improves recall but not calibrated abstention under our reward design, leaving Cal at or below the base model. Omni-Persona thus serves as a diagnostic framework that surfaces the pitfalls of omnimodal personalization, guiding future post-training and reward design.

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