MMTR-Bench shows that current MLLMs face significant difficulty reconstructing masked text from visual context, especially at sentence and paragraph lengths.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
dataset 1polarities
use dataset 1representative citing papers
ERASE prunes 85% of vision tokens in Qwen2.5-VL-7B while retaining 89.46% accuracy, outperforming prior methods that retain only 78.1%.
citing papers explorer
-
Can MLLMs "Read" What is Missing?
MMTR-Bench shows that current MLLMs face significant difficulty reconstructing masked text from visual context, especially at sentence and paragraph lengths.
-
ERASE: Eliminating Redundant Visual Tokens via Adaptive Two-Stage Token Pruning
ERASE prunes 85% of vision tokens in Qwen2.5-VL-7B while retaining 89.46% accuracy, outperforming prior methods that retain only 78.1%.