Jie Ma

Capturing Token Tendencies for Training-Free Token Pruning in Multimodal Large Language Models

Trend-aware Pruning is proposed, a novel framework that elevates pruning from a local snapshot decision to a temporal trajectory modeling problem, and enables a dynamic rectification mechanism that selectively reactivates "late-blooming" tokens, those initially undervalued but exhibiting rising semantic importance, thereby preventing the loss of critical visual cues.