[논문리뷰] Unified Spatio-Temporal Token Scoring for Efficient Video VLMsarXiv에 게시된 'Unified Spatio-Temporal Token Scoring for Efficient Video VLMs' 논문에 대한 자세한 리뷰입니다.#Review#Token Pruning#Video-Language Models (VLMs)#Computational Efficiency#Spatio-Temporal Scoring#Vision Transformers (ViT)#Large Language Models (LLM)#End-to-End Training2026년 3월 18일댓글 수 로딩 중
[논문리뷰] Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language ModelsarXiv에 게시된 'Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models' 논문에 대한 자세한 리뷰입니다.#Review#Multimodal Large Language Models (MLLMs)#Token Pruning#Graph-Structured Pruning (GSP)#Query-Conditioned Semantic Pruning (QCSP)#Determinantal Point Processes (DPP)#Model Efficiency#Visual Redundancy2025년 12월 1일댓글 수 로딩 중
[논문리뷰] Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal ModelsShijie Dong이 arXiv에 게시한 'Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models' 논문에 대한 자세한 리뷰입니다.#Review#Large Multimodal Models#Visual Token Compression#Token Pruning#Benchmark#Efficiency#Inference Latency#Multimodal LLMs2025년 11월 9일댓글 수 로딩 중
[논문리뷰] Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-TuningYue Min이 arXiv에 게시한 'Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning' 논문에 대한 자세한 리뷰입니다.#Review#LLM SFT#Data Pruning#Sample Pruning#Token Pruning#Error-Uncertainty Plane#Q-Tuning#Data Efficiency#Dynamic Pruning2025년 10월 1일댓글 수 로딩 중