AURORA:Action Unit-Reinforced Latent Reasoning for Multimodal Emotion Recognition

Open-vocabulary multimodal emotion recognition (OV-MER) aims to predict flexible emotion expressions from video, audio, and language signals. Despite recent advances, existing OV-MER methods remain predominantly text-supervised: models are optimized to generate rationales and emotion labels, while reinforcement learning rewards only the final textual outputs. Such output-level supervision provides limited guidance for preserving fine-grained facial evidence, causing subtle muscle activations, temporal expression dynamics, and co-occurring facial movements to be diluted during language generation. We propose AURORA, a latent reasoning framework that explicitly grounds OV-MER in facial Action Units (AUs). AURORA combines frame-wise AU prediction with a continuous latent reasoning stage supervised to reconstruct the projected visual tokens of an AU-selected facial frame. This reconstruction objective encourages the latent states to retain facial evidence before emotion prediction. A second stage uses standard Group-Relative Policy Optimization (GRPO) with emotion-wheel, output-format, and AU-consistency rewards for task-level alignment. Experiments on OV-MERD+ and MER-UniBench demonstrate that AURORA achieves state-of-the-art performance, reaching a mean score of 84.87 on MER-UniBench when built on Qwen3-VL-8B.