3 papers
cs.CV2026
DiffPrune: differentiable information throttling for token pruning in vision-language models
Landi He, Mingde Yao, Shawn Young +1
Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. The key is to learn a score that measures whether a token…
cs.CV2026
Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models
Landi He, Mingde Yao, Shawn Young +1
Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. Existing methods typically rely on Gumbel-Softmax to appro…
cs.CL2026
Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety
Qian Shen, Fanghua Cao, Min Yao +3
Large Language Models (LLMs) are widely applied in educational practices, such as for generating children's stories. However, the generated stories are often too difficult for chil…