{"total":165,"rows":[{"id":"2409.12191","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","created":"2024-09-18","cited_by_count":4234,"area":"mllm"},{"id":"2312.14238","title":"InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks","created":"2023-12-21","cited_by_count":2565,"area":"mllm"},{"id":"2311.17005","title":"MVBench: A Comprehensive Multi-modal Video Understanding Benchmark","created":"2023-11-28","cited_by_count":1109,"area":"mllm"},{"id":"2403.06764","title":"An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models","created":"2024-03-11","cited_by_count":642,"area":"mllm"},{"id":"2410.04417","title":"SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference","created":"2024-10-06","cited_by_count":391,"area":"mllm"},{"id":"2409.17146","title":"Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models","created":"2024-09-25","cited_by_count":329,"area":"mllm"},{"id":"2309.11499","title":"DreamLLM: Synergistic Multimodal Comprehension and Creation","created":"2023-09-20","cited_by_count":297,"area":"mllm"},{"id":"2410.17247","title":"PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction","created":"2024-10-22","cited_by_count":294,"area":"mllm"},{"id":"2503.02175","title":"DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models","created":"2025-03-04","cited_by_count":181,"area":"mllm"},{"id":"2412.01818","title":"Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs","created":"2024-12-02","cited_by_count":157,"area":"mllm"},{"id":"2502.11494","title":"Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More","created":"2025-02-17","cited_by_count":155,"area":"mllm"},{"id":"2405.05803","title":"Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference","created":"2024-05-09","cited_by_count":139,"area":"mllm"},{"id":"2402.17766","title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","created":"2024-02-27","cited_by_count":137,"area":"mllm"},{"id":"2403.02469","title":"Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review","created":"2024-03-04","cited_by_count":125,"area":"mllm"},{"id":"2506.10967","title":"Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs","created":"2025-06-12","cited_by_count":96,"area":"mllm"},{"id":"2312.06720","title":"Audio-Visual LLM for Video Understanding","created":"2023-12-11","cited_by_count":77,"area":"mllm"},{"id":"2502.11501","title":"Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem?","created":"2025-02-17","cited_by_count":76,"area":"mllm"},{"id":"2506.13759","title":"Discrete Diffusion in Large Language and Multimodal Models: A Survey","created":"2025-06-16","cited_by_count":61,"area":"mllm"},{"id":"2406.07230","title":"Needle In A Multimodal Haystack","created":"2024-06-11","cited_by_count":58,"area":"mllm"},{"id":"2503.18278","title":"TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model","created":"2025-03-24","cited_by_count":57,"area":"mllm"},{"id":"2501.05952","title":"Scalable Vision Language Model Training via High Quality Data Curation","created":"2025-01-10","cited_by_count":55,"area":"mllm"},{"id":"2407.21439","title":"MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training","created":"2024-07-31","cited_by_count":50,"area":"mllm"},{"id":"2504.08966","title":"PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models","created":"2025-04-11","cited_by_count":49,"area":"mllm"},{"id":"2510.02912","title":"Don't Just Chase \"Highlighted Tokens\" in MLLMs: Revisiting Visual Holistic Context Retention","created":"2025-10-03","cited_by_count":47,"area":"mllm"},{"id":"2410.08584","title":"ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification","created":"2024-10-11","cited_by_count":44,"area":"mllm"},{"id":"2412.13180","title":"Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration","created":"2024-12-17","cited_by_count":41,"area":"mllm"},{"id":"2503.07588","title":"When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning","created":"2025-03-10","cited_by_count":35,"area":"mllm"},{"id":"2503.11549","title":"Similarity-Aware Token Pruning: Your VLM but Faster","created":"2025-03-14","cited_by_count":35,"area":"mllm"},{"id":"2501.05179","title":"Global Compression Commander: Plug-and-Play Inference Acceleration for High-Resolution Large Vision-Language Models","created":"2025-01-09","cited_by_count":32,"area":"mllm"},{"id":"2506.23270","title":"Token Activation Map to Visually Explain Multimodal LLMs","created":"2025-06-29","cited_by_count":32,"area":"mllm"},{"id":"2502.17599","title":"MEDA: Dynamic KV Cache Allocation for Efficient Multimodal Long-Context Inference","created":"2025-02-24","cited_by_count":31,"area":"mllm"},{"id":"2501.02430","title":"FOLDER: Accelerating Multi-modal Large Language Models with Enhanced Performance","created":"2025-01-05","cited_by_count":30,"area":"mllm"},{"id":"2505.19147","title":"Shifting AI Efficiency From Model-Centric to Data-Centric Compression","created":"2025-05-25","cited_by_count":29,"area":"mllm"},{"id":"2410.12694","title":"VividMed: Vision Language Model with Versatile Visual Grounding for Medicine","created":"2024-10-16","cited_by_count":28,"area":"mllm"},{"id":"2412.00556","title":"Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction","created":"2024-11-30","cited_by_count":28,"area":"mllm"},{"id":"2510.17111","title":"Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey","created":"2025-10-20","cited_by_count":26,"area":"mllm"},{"id":"2404.02508","title":"VIAssist: Adapting Multi-modal Large Language Models for Users with Visual Impairments","created":"2024-04-03","cited_by_count":25,"area":"mllm"},{"id":"2503.13792","title":"Identifying and Mitigating Position Bias of Multi-image Vision-Language Models","created":"2025-03-18","cited_by_count":25,"area":"mllm"},{"id":"2507.20198","title":"A Survey of Token Compression for Efficient Multimodal Large Language Models","created":"2025-07-27","cited_by_count":25,"area":"mllm"},{"id":"2503.13108","title":"Lifting the Veil on Visual Information Flow in MLLMs: Unlocking Pathways to Faster Inference","created":"2025-03-17","cited_by_count":23,"area":"mllm"},{"id":"2510.00515","title":"Efficient Multi-modal Large Language Models via Progressive Consistency Distillation","created":"2025-10-01","cited_by_count":22,"area":"mllm"},{"id":"2503.21307","title":"InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression","created":"2025-03-27","cited_by_count":21,"area":"mllm"},{"id":"2409.09086","title":"Inf-MLLM: Efficient Streaming Inference of Multimodal Large Language Models on a Single GPU","created":"2024-09-11","cited_by_count":20,"area":"mllm"},{"id":"2505.22038","title":"Balanced Token Pruning: Accelerating Vision Language Models Beyond Local Optimization","created":"2025-05-28","cited_by_count":20,"area":"mllm"},{"id":"2507.17787","title":"Hyperbolic Deep Learning for Foundation Models: A Survey","created":"2025-07-23","cited_by_count":20,"area":"mllm"},{"id":"2512.08240","title":"Hybrid Token Compression for Vision-Language Models","created":"2025-12-09","cited_by_count":20,"area":"mllm"},{"id":"2410.14072","title":"Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers","created":"2024-10-17","cited_by_count":19,"area":"mllm"},{"id":"2507.14675","title":"Docopilot: Improving Multimodal Models for Document-Level Understanding","created":"2025-07-19","cited_by_count":19,"area":"mllm"},{"id":"2508.01548","title":"A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models","created":"2025-08-03","cited_by_count":18,"area":"mllm"},{"id":"2508.07871","title":"CATP: Contextually Adaptive Token Pruning for Efficient and Enhanced Multimodal In-Context Learning","created":"2025-08-11","cited_by_count":18,"area":"mllm"},{"id":"2504.05810","title":"PaMi-VDPO: Mitigating Video Hallucinations by Prompt-Aware Multi-Instance Video Preference Learning","created":"2025-04-08","cited_by_count":17,"area":"mllm"},{"id":"2508.13305","title":"Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving","created":"2025-08-18","cited_by_count":16,"area":"mllm"},{"id":"2602.02951","title":"N\\\"uwa: Mending the Spatial Integrity Torn by VLM Token Pruning","created":"2026-02-03","cited_by_count":15,"area":"mllm"},{"id":"2602.19870","title":"ApET: Approximation-Error Guided Token Compression for Efficient VLMs","created":"2026-02-23","cited_by_count":12,"area":"mllm"},{"id":"2508.00553","title":"HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models","created":"2025-08-01","cited_by_count":11,"area":"mllm"},{"id":"2510.16598","title":"VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Multimodal LLMs","created":"2025-10-18","cited_by_count":11,"area":"mllm"},{"id":"2512.06328","title":"ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models","created":"2025-12-06","cited_by_count":11,"area":"mllm"},{"id":"2512.20561","title":"FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models","created":"2025-12-23","cited_by_count":11,"area":"mllm"},{"id":"2603.07135","title":"The Model Knows Which Tokens Matter: Automatic Token Selection via Noise Gating","created":"2026-03-07","cited_by_count":11,"area":"mllm"},{"id":"2601.03928","title":"FocusUI: Efficient UI Grounding via Position-Preserving Visual Token Selection","created":"2026-01-07","cited_by_count":10,"area":"mllm"},{"id":"2405.18937","title":"Kestrel: 3D Multimodal LLM for Part-Aware Grounded Description","created":"2024-05-29","cited_by_count":9,"area":"mllm"},{"id":"2501.15021","title":"AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models","created":"2025-01-25","cited_by_count":9,"area":"mllm"},{"id":"2408.07146","title":"Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces","created":"2024-08-13","cited_by_count":8,"area":"mllm"},{"id":"2508.03351","title":"SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models","created":"2025-08-05","cited_by_count":8,"area":"mllm"},{"id":"2505.18757","title":"ToDRE: Effective Visual Token Pruning via Token Diversity and Task Relevance","created":"2025-05-24","cited_by_count":7,"area":"mllm"},{"id":"2507.10069","title":"ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism","created":"2025-07-14","cited_by_count":7,"area":"mllm"},{"id":"2603.01236","title":"AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models","created":"2026-03-01","cited_by_count":7,"area":"mllm"},{"id":"2312.13103","title":"Exploring Multimodal Large Language Models for Radiology Report Error-checking","created":"2023-12-20","cited_by_count":6,"area":"mllm"},{"id":"2501.07802","title":"Visual Language Models as Operator Agents in the Space Domain","created":"2025-01-14","cited_by_count":6,"area":"mllm"},{"id":"2502.15210","title":"PairBench: Are Vision-Language Models Reliable at Comparing What They See?","created":"2025-02-21","cited_by_count":6,"area":"mllm"},{"id":"2503.13440","title":"MaTVLM: Hybrid Mamba-Transformer for Efficient Vision-Language Modeling","created":"2025-03-17","cited_by_count":6,"area":"mllm"},{"id":"2504.02349","title":"Large (Vision) Language Models are Unsupervised In-Context Learners","created":"2025-04-03","cited_by_count":6,"area":"mllm"},{"id":"2512.20276","title":"ActionFlow: A Pipelined Action Acceleration for Vision Language Models on Edge","created":"2025-12-23","cited_by_count":6,"area":"mllm"},{"id":"2602.12957","title":"HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding","created":"2026-02-13","cited_by_count":6,"area":"mllm"},{"id":"2603.01083","title":"Can Vision Language Models Assess Graphic Design Aesthetics? A Benchmark, Evaluation, and Dataset Perspective","created":"2026-03-01","cited_by_count":6,"area":"mllm"},{"id":"2602.00746","title":"Can Vision-Language Models Handle Long-Context Code? An Empirical Study on Visual Compression","created":"2026-01-31","cited_by_count":5,"area":"mllm"},{"id":"2602.13315","title":"IDPruner: Harmonizing Importance and Diversity in Visual Token Pruning for MLLMs","created":"2026-02-10","cited_by_count":5,"area":"mllm"},{"id":"2604.00886","title":"PixelPrune: Pixel-Level Adaptive Visual Token Reduction via Predictive Coding","created":"2026-04-01","cited_by_count":4,"area":"mllm"},{"id":"2409.11919","title":"LLM-wrapper: Black-Box Semantic-Aware Adaptation of Vision-Language Models for Referring Expression Comprehension","created":"2024-09-18","cited_by_count":3,"area":"mllm"},{"id":"2409.20018","title":"Visual Context Window Extension: A New Perspective for Long Video Understanding","created":"2024-09-30","cited_by_count":3,"area":"mllm"},{"id":"2502.07409","title":"MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification","created":"2025-02-11","cited_by_count":3,"area":"mllm"},{"id":"2507.20630","title":"TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model","created":"2025-07-28","cited_by_count":3,"area":"mllm"},{"id":"2508.17243","title":"CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models","created":"2025-08-24","cited_by_count":3,"area":"mllm"},{"id":"2509.11662","title":"MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs","created":"2025-09-15","cited_by_count":3,"area":"mllm"},{"id":"2511.15098","title":"A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models","created":"2025-11-19","cited_by_count":3,"area":"mllm"},{"id":"2511.16786","title":"Revisiting Multimodal KV Cache Compression: A Frequency-Domain-Guided Outlier-KV-Aware Approach","created":"2025-11-20","cited_by_count":3,"area":"mllm"},{"id":"2601.19908","title":"CHIME: Chiplet-based Heterogeneous Near-Memory Acceleration for Edge Multimodal LLM Inference","created":"2025-12-12","cited_by_count":3,"area":"mllm"},{"id":"2602.05809","title":"Focus-Scan-Refine: From Human Visual Perception to Efficient Visual Token Pruning","created":"2026-02-05","cited_by_count":3,"area":"mllm"},{"id":"2604.02570","title":"WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models","created":"2026-04-02","cited_by_count":3,"area":"mllm"},{"id":"2604.17915","title":"OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models","created":"2026-04-20","cited_by_count":3,"area":"mllm"},{"id":"2605.29657","title":"OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning","created":"2026-05-28","cited_by_count":3,"area":"mllm"},{"id":"2606.12412","title":"Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models","created":"2026-06-10","cited_by_count":3,"area":"mllm"},{"id":"2606.18681","title":"Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs","created":"2026-06-17","cited_by_count":3,"area":"mllm"},{"id":"2505.15816","title":"Streamline Without Sacrifice -- Squeeze out Computation Redundancy in LMM","created":"2025-05-21","cited_by_count":2,"area":"mllm"},{"id":"2506.01097","title":"Task-Related Token Compression in Multimodal Large Language Models from an Explainability Perspective","created":"2025-06-01","cited_by_count":2,"area":"mllm"},{"id":"2506.03928","title":"Vision Remember: Recovering Visual Information in Efficient LVLM with Vision Feature Resampling","created":"2025-06-04","cited_by_count":2,"area":"mllm"},{"id":"2507.02909","title":"Fine-grained Token Allocation Via Operation Pruning for Efficient MLLMs","created":"2025-06-24","cited_by_count":2,"area":"mllm"},{"id":"2507.02948","title":"DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction","created":"2025-06-28","cited_by_count":2,"area":"mllm"},{"id":"2507.08505","title":"Efficient Deployment of Vision-Language Models on Mobile Devices: A Case Study on OnePlus 13R","created":"2025-07-11","cited_by_count":2,"area":"mllm"},{"id":"2508.02134","title":"Free-MoRef: Instantly Multiplexing Context Perception Capabilities of Video-MLLMs within Single Inference","created":"2025-08-04","cited_by_count":2,"area":"mllm"}],"next_cursor":"100"}