AI RESEARCH
Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
arXiv CS.CV
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ArXi:2605.18160v1 Announce Type: new In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information. The dominant connector-based paradigm projects visual features into textual sequence, enabling unified multimodal alignment and reasoning within a generative architecture.