Semantic Head Specialization Guides Hybrid ViT Attention for Multimodal LLMs

Abstract:Hybrid attention dominates frontier LLMs, yet Vision Transformers (ViTs) in multimodal LLMs lack a satisfactory hybrid design, with no consensus on why certain attention patterns work better. To fill this gap, we study ViT attention heads and find they differentiate into object- and background-specialist roles, a pattern most pronounced under full attention; we call this Semantic Head Specialization (SHS). We propose SHS-Index to quantify this specialization, show that it distinguishes full-attention from chunk-window ViTs, and find that it strongly tracks downstream benchmark performance. We then identify three structural factors that shape SHS---window interaction, token serialization, and local softmax allocation---and use them as design principles for hybrid attention. Guided by these factors, we design Ariadne Attention, a hybrid that matches full attention on 22 image and video tasks at 6.5x less attention compute. Our findings establish head specialization as a measurable property for diagnosing and designing principled hybrid ViT attention at the multimodal-LLM scale.

Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)

arXiv:2608.28383 [cs.CV]

(or arXiv:2608.28383v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.28383 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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