arXiv AI October 9, 2026 By Jinghua Hou, Zhe Liu, Hengshuang Zhao

DVD: Dynamic Vector Decoding for Efficient MLLM-based Perception

The paper introduces DVD, a dynamic vector decoding method that unifies 2D and 3D perception tasks for multimodal large language models. DVD transforms various perceptual representations—such as 2D bounding boxes, masks, and 3D bounding boxes—into 1D vector sequences, maps them to compact discrete tokens, and then decodes them back to the original formats via a lightweight de-tokenizer. Experiments on RefCOCO, SUN‑RGBD, KITTI, Hypersim, and nuScenes show that DVD outperforms existing methods while significantly reducing token overhead and inference latency.

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llms agents computer-vision robotics efficiency multimodal benchmarks

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