AI RESEARCH
AffectVerse: Emotional World Models for Multimodal Affective Computing
arXiv CS.CV
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ArXi:2605.19950v1 Announce Type: new Humans infer emotions by integrating observed multimodal cues with expectations about how affective states may unfold. Existing multimodal large language models (MLLMs), however, often treat emotion recognition as static fusion over complete audiovisual-text inputs, leaving affective dynamics implicit. We propose AffectVerse, a Qwen2.5-Omni-based model equipped with an Emotion World Module (EWM), an action-free representation-level module for short-horizon latent affective prediction.