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Token Reduction

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Showing 26–50 of 78 papers

TitleStatusHype
Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMsCode1
ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision TransformersCode1
Token Cropr: Faster ViTs for Quite a Few TasksCode1
Inference Optimal VLMs Need Fewer Visual Tokens and More ParametersCode1
Window Token Concatenation for Efficient Visual Large Language ModelsCode1
ZipR1: Reinforcing Token Sparsity in MLLMs—0
AdaFV: Rethinking of Visual-Language alignment for VLM acceleration—0
Astraea: A GPU-Oriented Token-wise Acceleration Framework for Video Diffusion Transformers—0
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration—0
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems—0
Deploying Foundation Model Powered Agent Services: A Survey—0
DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models—0
DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs—0
Dynamic Token Reduction during Generation for Vision Language Models—0
EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation—0
Efficient LLaMA-3.2-Vision by Trimming Cross-attended Visual Features—0
Efficient Multi-modal Large Language Models via Visual Token Grouping—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
freePruner: A Training-free Approach for Large Multimodal Model Acceleration—0
Hypernym Mercury: Token Optimization Through Semantic Field Constriction And Reconstruction From Hypernyms. A New Text Compression Method—0
ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition—0
Knowing When to Stop: Dynamic Context Cutoff for Large Language Models—0
Learning Free Token Reduction for Multi-Modal Large Language Models—0
Local Information Matters: Inference Acceleration For Grounded Conversation Generation Models Through Adaptive Local-Aware Token Pruning—0
MINT: Mitigating Hallucinations in Large Vision-Language Models via Token Reduction—0
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