Perceptual Projection Pruning: Diversity-Aware Video Token Pruning for Multimodal Large Language Models

Applying Multimodal Large Language Models (MLLMs) tolong-form video is challenged by the substantial computational cost ofprocessing long token sequences. Current training-free reduction meth-ods present a difficult trade-off between efficient and content diversity.Query-focused approaches are efficient but sacrifice content diversity,which is critical for general-purpose understanding and open-ended dia-logue. Conversely, methods that preserve diversity by operating on high-dimensional features often incur significant computational overhead.We introduce Perceptual Projection Pruning (P 3 ), a training-free frame-work that tries to address this trade-off. Our approach is based on the hy-pothesis that a low-dimensional yet informative representation can serveas an efficient proxy for diversity-aware visual token pruning. We firstproject high-dimensional features into a 3D perceptual space by comput-ing three complementary saliency axes: dynamic (motion), regional (con-textual contrast), and focal (local complexity). This representation thenserves as a foundation for a subsequent diversity-aware pruning strat-egy. Experiments across SOTA MLLMs show our method retains nearly95% of the vanilla model’s performance while pruning up to 75% of to-kens. Our work demonstrates that a carefully designed low-dimensionalrepresentation is highly effective for guiding diversity-aware visual tokenpruning in MLLMs.