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该论文提出了一种用于多模态情感推理的智能体框架，通过模态感知智能体、证据引用、验证阶段和目标化提示补丁实现无需更新权重的提示空间适配，在MER2025数据集上优于冻结静态流水线。
BiCFlow-MER proposes a conditional-flow framework for audio-text multimodal emotion recognition that disentangles affective evidence from speaker and lexical factors, achieving state-of-the-art results on IEMOCAP, MELD, and CASE benchmarks.
This paper proposes an Affect-Prototype-Conditioned Fusion (APCF) framework for open-vocabulary multimodal emotion recognition with incomplete modalities, using an affect-prototype library to guide feature fusion and an LLM decoder for generating natural language emotion labels.
The paper proposes Light-MER, a lightweight multimodal emotion recognition framework that uses knowledge distillation from an 8B teacher model to a sub-1B student, achieving state-of-the-art performance with significantly higher inference efficiency, challenging the necessity of models larger than 1B parameters.
This paper introduces MER-R1, a reinforcement learning framework that synergizes fast and slow thinking for multimodal emotion recognition. It achieves state-of-the-art performance by jointly optimizing recall and precision through dual-objective disentanglement and slow-fast confidence calibration.