WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning.

arXiv:2607.28418v2 Announce Type: replace Abstract: Pruning is a promising approach for improving the efficiency of large language models (LLMs). Static structured pruning is hardware-friendly, but its input-agnostic computation allocation often degrades model