AI RESEARCH
Revisiting Multimodal Positional Encoding in Vision-Language Models
arXiv CS.CV
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ArXi:2510.23095v3 Announce Type: replace Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensive analysis of multimodal Rotary Positional Embedding (RoPE) by examining its two core components: position design and frequency allocation.