Omni-Persona: Systematic Benchmarking and Improving Omnimodal Personalization

• URL: http://arxiv.org/abs/2605.09996v1
• Date: Mon, 11 May 2026 05:11:22 GMT
• Title: Omni-Persona: Systematic Benchmarking and Improving Omnimodal Personalization
• Abstract summary: We introduce Omni-Persona, the first comprehensive benchmark for omnimodal personalization.<n>We formalize the task as cross-modal routing over the emphPersona Modality Graph, encompassing 4 task groups and 18 fine-grained tasks across $sim750$ items.
• Score: 47.84878018101177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \emph{Persona Modality Graph}, encompassing 4 task groups and 18 fine-grained tasks across ${\sim}750$ items. To rigorously diagnose grounding behavior, we propose \emph{Calibrated Accuracy ($\mathrm{Cal}$)}, which jointly rewards correct grounding and appropriate abstention, incorporating absent-persona queries within a unified evaluation framework. On our dedicated experiments, three diagnostic findings emerge: (i) open-source models show a consistent audio-vs-visual grounding gap that RLVR partially narrows via dense rule-based supervision; (ii) answerable recall and parameter scale are incomplete diagnostics, since strong recall can coexist with absent-persona hallucination and larger models do not always achieve higher $\mathrm{Cal}$, exposing calibration as a separate evaluation axis; and (iii) SFT is bounded by the difficulty of constructing annotated ground-truth supervision at scale, while RLVR generalizes more consistently through outcome-level verifiable feedback yet drifts toward conservative behavior and lower generation quality under our reward design. 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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