AI RESEARCH
CPC-VAR:Continual Personalized and Compositional Generation in Visual Autoregressive Models
arXiv CS.CV
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ArXi:2605.19750v1 Announce Type: new Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation. Despite their strong generative capability, existing VAR-based personalization methods remain limited to static settings, failing to accommodate evolving user demands. In particular, sequential concept learning leads to severe catastrophic forgetting, while multi-concept synthesis often suffers from feature entanglement and attribute inconsistency.