
The Frontier
Coreset pruning cuts 80% of VLM tokens, keeps accuracy intact
Vision-language models are drowning in their own pixels, and a Chinese Academy of Sciences team has a training-free fix: cut up to 80% of the visual tokens a multimodal model has to chew through — and lose nothing. The CAS Institute of Automation's Zidong Taichu team has released GMC (Grounded Message Coreset Pruning), a training-free method that cuts up to 80.2% of a vision-language model's visual tokens while retaining 97.78% of full-model capability — and in its most aggressive…










