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cs.CV2026

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

Jie Ma, Zhike Qiu, Jie Gao +4

The paper introduces Trend-aware Pruning, a training‑free method that models the temporal dynamics of attention to selectively keep visual tokens that become important in deeper la…

cs.CV2026

Compression and Retrieval: Implicit Memory Retrieval for Video World Models

Zhan Peng, Jie Ma, Huiqiang Sun +6

Video world models hold promise for simulating interactive environments, yet maintaining consistent long-term memory across complex camera trajectories remains a critical challenge…

cs.CV2026

Look Less, Reason More: Block-wise Attention Skipping for Efficient Multimodal LLMs

Jie Ma, Zhike Qiu, Jiayi Ji +2

Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computational cost of self-attention over long visual token sequences. However…

cs.CV2026

DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Driving

Chenxu Dang, Sining Ang, Yongkang Li +7

Vision-Language-Action (VLA) models for autonomous driving increasingly adopt generative planners trained with imitation learning followed by reinforcement learning. Diffusion-base…

cs.CV2026

SparseOccVLA: Bridging Occupancy and Vision-Language Models via Sparse Queries for Unified 4D Scene Understanding and Planning

Chenxu Dang, Jie Wang, Guang Li +6

In autonomous driving, Vision Language Models (VLMs) excel at high-level reasoning , whereas semantic occupancy provides fine-grained details. Despite significant progress in indiv…