MLLM | JJ's Blog

📝 LEARNPRUNER: RETHINKING ATTENTION-BASED TOKEN PRUNING IN VISION LANGUAGE MODELS - ICLR'26

📝 DOCPRUNE: Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning - CVPR'26

📝 Empirical Recipes for Efficient and Compact Vision-Language Models

📝 Variation-aware Vision Token Dropping for Faster Large Vision-Language Models - CVPR'26

📝 PRUNE REDUNDANCY, PRESERVE ESSENCE: VISION TOKEN COMPRESSION IN VLMS VIA SYNERGISTIC IMPORTANCE–DIVERSITY - ICLR'26

📝 MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs - ICLR'26

📝 ONE PATCH DOESN’T FIT ALL: ADAPTIVE PATCHING FOR NATIVE-RESOLUTION MULTIMODAL LARGE LANGUAGE MODELS - ICLR'26

📝 Matryoshka Multimodal Models - ICLR'25

📝 Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models - EMNLP'25

📝 VLCACHE: Computing 2% Vision Tokens and Reusing 98% for Vision–Language Inference

📝 Accelerating Streaming Video Large Language Models via Hierarchical Token Compression - CVPR'26

📝 HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices

📝 VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning - NeurIPS'25

📝 Empower Vision Applications with LoRA LMM - Eurosys'25

📝 Venus: An Efficient Edge Memory-and-Retrieval System for VLM-based Online Video Understanding - INFOCOM'26

📝 RServe: Overlapping Encoding and Prefill for Efficient LMM Inference

📝 ModServe - Modality- and Stage-Aware Resource Disaggregation for Scalable Multimodal Model Serving - SoCC'25

📝 Efficiently Serving Large Multimodal Models Using EPD Disaggregation - ICML'25

📝 ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism - NeurIPS'25 Oral

📝 Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More - EMNLP'25

📝 MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders -CVPR 2025

📝 Efficient Multi-modal Large Language Models via Progressive Consistency Distillation - NeurIPS'25

📝 ViSpec: Accelerating Vision-Language Models with Vision-Aware Speculative Decoding - NeurIPS'25

📝 QSVD: Efficient Low-rank Approximation for Unified Query-Key-Value Weight Compression in Low-Precision Vision-Language Models - NeurIPS'25 Spotlight

📝 SPECVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning - EMNLP'25

📝 Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs - *NeurIPS'25