Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference 事件

Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference arXiv:2606.01711v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computational complexity arising from the vast number of visual tokens incurs significant memory and latency bottlenecks. While visual token reduction (VTR) strategies have been explored to mitigate this burden,