7 papers
Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs
Peiran Yin, Hao Wu, Zihao Yang +6
Visual token pruning for multimodal large language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned performance on common VQA benchmark…
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs
Yongxin Zheng, Hao Wu, Fang Li +8
Multi-view representations enable pretrained image and video vision-language models to understand 3D scenes, but their long visual token sequences make inference costly. Redundancy…
MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs
Xudong Wang, Hao Wu, Haozhe Hu +5
Multimodal large language models (MLLMs) incur substantial inference costs when processing long visual-textual sequences. While existing operation compression methods exploit modal…
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Haozhe Hu, Hao Wu, Peiran Yin +3
Pruning is a promising approach for improving the efficiency of large language models (LLMs). Static structured pruning is hardware-friendly, but its input-agnostic computation all…
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
Haozhe Hu, Hao Wu, Anhao Zhao +4
Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove computation across tokens, layers…
From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models
Junlong Tong, Zilong Wang, YuJie Ren +4
Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To…
Results of the 2024 CommonRoad Motion Planning Competition for Autonomous Vehicles
Yanliang Huang, Xia Yan, Peiran Yin +5
Over the past decade, a wide range of motion planning approaches for autonomous vehicles has been developed to handle increasingly complex traffic scenarios. However, these approac…