2025
Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs
NeurIPS 2025poster
In multimodal large language models (MLLMs), the length of input visual tokens is often significantly greater than that of their textual counterparts, leading to a high inference cost. Many works aim to address this issue by removing redundant visual tokens. However, current approaches either rely o…