AI RESEARCH
Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference
arXiv CS.CV
•
ArXi:2606.01711v1 Announce Type: new 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, existing methods overlook the positional and attentional consistency between the full and reduced sequences, resulting in a distorted representation.