Omni-Perception Policy Optimization for Multimodal Emotion Reasoning

Abstract

Recent Omni-MLLMs are driving a paradigm shift in multimodal emotion recognition from label-only prediction toward *Multimodal Emotion Reasoning* (MER), where models output both emotions and textual explanations grounded in visual, acoustic, and linguistic signals. However, we show that current emotion-oriented Omni-MLLMs still lack *reliable omni-modal perception*: they (i) underutilize multimodal cues in their reasoning trajectories and (ii) exhibit unfaithful behavior, often hallucinating modality-specific statements from other modalities. Building on these insights, we propose **OPPO** (**O**mni-**P**erception **P**olicy **O**ptimization), a reinforcement learning framework that explicitly optimizes multimodal perception. First, an Omni-Perception Reward decomposes ground-truth reasoning into fine-grained visual, acoustic, and emotion cues and rewards trajectories that semantically recover these cues. Second, an Omni-Perception Loss compares the policy under full and unimodally masked inputs, applying a KL penalty only to modality-specific evidence tokens to suppress cross-modal hallucination. We further introduce *MEP-Bench*, a diagnostic benchmark that quantifies *utilization* and *faithfulness*. Experiments show that OPPO achieves state-of-the-art performance on MER-UniBench and substantially improves utilization and faithfulness scores on MEP-Bench, highlighting the importance of sufficient and faithful omni perception for multimodal emotion reasoning. The code is provided in the Supplementary Materials.