Light-MER: Compressing an 8B Emotion Teacher into an 855M Student Using Hidden-State Geometry Rather Than Answers

Light-MER is a multimodal emotion recognition compression work by the University of Glasgow, the Institute of Computing Technology of the Chinese Academy of Sciences, and Shandong University. Accepted to the ACM MM 2026 main conference, it compresses an 8B teacher model into an 855M student model, with the training signal drawn from hidden-state geometry rather than answers. The work targets the deployment cost of generative multimodal emotion recognition, with the goal of enabling the model to output paragraphs that explain emotions.