{"total":130,"rows":[{"id":"2407.07895","title":"LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models","created":"2024-07-10","cited_by_count":584,"area":"mllm"},{"id":"2403.18814","title":"Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models","created":"2024-03-27","cited_by_count":365,"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":"2405.02246","title":"What matters when building vision-language models?","created":"2024-05-03","cited_by_count":326,"area":"mllm"},{"id":"2401.13601","title":"MM-LLMs: Recent Advances in MultiModal Large Language Models","created":"2024-01-24","cited_by_count":278,"area":"mllm"},{"id":"2403.09611","title":"MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training","created":"2024-03-14","cited_by_count":232,"area":"mllm"},{"id":"2402.12226","title":"AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling","created":"2024-02-19","cited_by_count":217,"area":"mllm"},{"id":"2405.01483","title":"MANTIS: Interleaved Multi-Image Instruction Tuning","created":"2024-05-02","cited_by_count":216,"area":"mllm"},{"id":"2406.09411","title":"MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding","created":"2024-06-13","cited_by_count":188,"area":"mllm"},{"id":"2408.12637","title":"Building and better understanding vision-language models: insights and future directions","created":"2024-08-22","cited_by_count":173,"area":"mllm"},{"id":"2501.00321","title":"OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning","created":"2024-12-31","cited_by_count":173,"area":"mllm"},{"id":"2402.12451","title":"The Revolution of Multimodal Large Language Models: A Survey","created":"2024-02-19","cited_by_count":137,"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":"2406.08394","title":"VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks","created":"2024-06-12","cited_by_count":137,"area":"mllm"},{"id":"2408.08872","title":"xGen-MM (BLIP-3): A Family of Open Large Multimodal Models","created":"2024-08-16","cited_by_count":116,"area":"mllm"},{"id":"2407.06135","title":"ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation","created":"2024-07-08","cited_by_count":114,"area":"mllm"},{"id":"2405.10739","title":"Efficient Multimodal Large Language Models: A Survey","created":"2024-05-17","cited_by_count":96,"area":"mllm"},{"id":"2408.03361","title":"GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI","created":"2024-08-06","cited_by_count":96,"area":"mllm"},{"id":"2406.11832","title":"Unveiling Encoder-Free Vision-Language Models","created":"2024-06-17","cited_by_count":94,"area":"mllm"},{"id":"2409.20566","title":"MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning","created":"2024-09-30","cited_by_count":76,"area":"mllm"},{"id":"2406.20098","title":"Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs","created":"2024-06-28","cited_by_count":74,"area":"mllm"},{"id":"2407.08303","title":"DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception","created":"2024-07-11","cited_by_count":73,"area":"mllm"},{"id":"2503.13111","title":"MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs","created":"2025-03-17","cited_by_count":73,"area":"mllm"},{"id":"2404.07214","title":"Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions","created":"2024-02-20","cited_by_count":68,"area":"mllm"},{"id":"2410.18558","title":"Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data","created":"2024-10-24","cited_by_count":67,"area":"mllm"},{"id":"2406.20076","title":"EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model","created":"2024-06-28","cited_by_count":65,"area":"mllm"},{"id":"2412.06673","title":"ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance","created":"2024-12-09","cited_by_count":64,"area":"mllm"},{"id":"2407.01509","title":"MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs","created":"2024-07-01","cited_by_count":62,"area":"mllm"},{"id":"2403.14599","title":"MyVLM: Personalizing VLMs for User-Specific Queries","created":"2024-03-21","cited_by_count":58,"area":"mllm"},{"id":"2406.07230","title":"Needle In A Multimodal Haystack","created":"2024-06-11","cited_by_count":58,"area":"mllm"},{"id":"2408.00765","title":"MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities","created":"2024-08-01","cited_by_count":57,"area":"mllm"},{"id":"2401.11944","title":"CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark","created":"2024-01-22","cited_by_count":56,"area":"mllm"},{"id":"2410.09575","title":"Reconstructive Visual Instruction Tuning","created":"2024-10-12","cited_by_count":55,"area":"mllm"},{"id":"2406.11271","title":"MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens","created":"2024-06-17","cited_by_count":50,"area":"mllm"},{"id":"2406.15334","title":"Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning","created":"2024-06-21","cited_by_count":50,"area":"mllm"},{"id":"2405.08807","title":"SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation","created":"2024-05-14","cited_by_count":48,"area":"mllm"},{"id":"2402.13220","title":"How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts","created":"2024-02-20","cited_by_count":47,"area":"mllm"},{"id":"2406.05127","title":"Towards Semantic Equivalence of Tokenization in Multimodal LLM","created":"2024-06-07","cited_by_count":45,"area":"mllm"},{"id":"2411.17762","title":"MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding","created":"2024-11-26","cited_by_count":44,"area":"mllm"},{"id":"2506.18898","title":"Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations","created":"2025-06-23","cited_by_count":44,"area":"mllm"},{"id":"2502.06788","title":"EVEv2: Improved Baselines for Encoder-Free Vision-Language Models","created":"2025-02-10","cited_by_count":41,"area":"mllm"},{"id":"2406.01920","title":"CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models","created":"2024-06-04","cited_by_count":38,"area":"mllm"},{"id":"2410.10855","title":"Core Knowledge Deficits in Multi-Modal Language Models","created":"2024-10-06","cited_by_count":38,"area":"mllm"},{"id":"2406.07904","title":"Grounding Multimodal Large Language Models in Actions","created":"2024-06-12","cited_by_count":35,"area":"mllm"},{"id":"2410.17637","title":"MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models","created":"2024-10-23","cited_by_count":34,"area":"mllm"},{"id":"2410.10139","title":"MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models","created":"2024-10-14","cited_by_count":33,"area":"mllm"},{"id":"2405.15232","title":"DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception","created":"2024-05-24","cited_by_count":32,"area":"mllm"},{"id":"2406.14544","title":"Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs","created":"2024-06-20","cited_by_count":32,"area":"mllm"},{"id":"2510.08485","title":"InstructX: Towards Unified Visual Editing with MLLM Guidance","created":"2025-10-09","cited_by_count":32,"area":"mllm"},{"id":"2605.12500","title":"SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture","created":"2026-05-12","cited_by_count":32,"area":"mllm"},{"id":"2411.18499","title":"OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation","created":"2024-11-27","cited_by_count":31,"area":"mllm"},{"id":"2402.06599","title":"On the Out-Of-Distribution Generalization of Multimodal Large Language Models","created":"2024-02-09","cited_by_count":30,"area":"mllm"},{"id":"2405.01926","title":"Auto-Encoding Morph-Tokens for Multimodal LLM","created":"2024-05-03","cited_by_count":30,"area":"mllm"},{"id":"2504.14666","title":"Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens","created":"2025-04-20","cited_by_count":28,"area":"mllm"},{"id":"2403.12736","title":"Towards Multimodal In-Context Learning for Vision & Language Models","created":"2024-03-19","cited_by_count":25,"area":"mllm"},{"id":"2407.17453","title":"VILA$^2$: VILA Augmented VILA","created":"2024-07-24","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":"2403.09193","title":"Can We Talk Models Into Seeing the World Differently?","created":"2024-03-14","cited_by_count":23,"area":"mllm"},{"id":"2411.02564","title":"Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models","created":"2024-11-04","cited_by_count":22,"area":"mllm"},{"id":"2501.05901","title":"Valley2: Exploring Multimodal Models with Scalable Vision-Language Design","created":"2025-01-10","cited_by_count":21,"area":"mllm"},{"id":"2410.16983","title":"Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models","created":"2024-10-22","cited_by_count":20,"area":"mllm"},{"id":"2507.20519","title":"AgroBench: Vision-Language Model Benchmark in Agriculture","created":"2025-07-28","cited_by_count":20,"area":"mllm"},{"id":"2410.10798","title":"MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling","created":"2024-10-14","cited_by_count":19,"area":"mllm"},{"id":"2410.01744","title":"Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks","created":"2024-10-02","cited_by_count":18,"area":"mllm"},{"id":"2412.07589","title":"DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation","created":"2024-12-10","cited_by_count":18,"area":"mllm"},{"id":"2502.12148","title":"HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation","created":"2025-02-17","cited_by_count":18,"area":"mllm"},{"id":"2404.14715","title":"FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction","created":"2024-04-23","cited_by_count":17,"area":"mllm"},{"id":"2502.11925","title":"GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs","created":"2025-02-17","cited_by_count":17,"area":"mllm"},{"id":"2404.13874","title":"VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models","created":"2024-04-22","cited_by_count":16,"area":"mllm"},{"id":"2407.08583","title":"The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective","created":"2024-07-11","cited_by_count":15,"area":"mllm"},{"id":"2412.05818","title":"SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation","created":"2024-12-08","cited_by_count":15,"area":"mllm"},{"id":"2410.05970","title":"PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling","created":"2024-10-08","cited_by_count":14,"area":"mllm"},{"id":"2410.13859","title":"$\\gamma-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models","created":"2024-10-17","cited_by_count":14,"area":"mllm"},{"id":"2509.18582","title":"The Photographer Eye: Teaching Multimodal Large Language Models to Understand Image Aesthetics like Photographers","created":"2025-09-23","cited_by_count":13,"area":"mllm"},{"id":"2603.06577","title":"Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion","created":"2026-03-06","cited_by_count":13,"area":"mllm"},{"id":"2406.05814","title":"TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models","created":"2024-06-09","cited_by_count":12,"area":"mllm"},{"id":"2502.17297","title":"Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts","created":"2025-02-24","cited_by_count":12,"area":"mllm"},{"id":"2402.13607","title":"CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models","created":"2024-02-21","cited_by_count":11,"area":"mllm"},{"id":"2410.06555","title":"ING-VP: MLLMs cannot Play Easy Vision-based Games Yet","created":"2024-10-09","cited_by_count":11,"area":"mllm"},{"id":"2506.07227","title":"Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning","created":"2025-06-08","cited_by_count":11,"area":"mllm"},{"id":"2409.02101","title":"Towards Real-World Adverse Weather Image Restoration: Enhancing Clearness and Semantics with Vision-Language Models","created":"2024-09-03","cited_by_count":10,"area":"mllm"},{"id":"2410.04778","title":"MM-R$^3$: On (In-)Consistency of Vision-Language Models (VLMs)","created":"2024-10-07","cited_by_count":10,"area":"mllm"},{"id":"2411.11909","title":"SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization","created":"2024-11-17","cited_by_count":10,"area":"mllm"},{"id":"2412.16158","title":"HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding","created":"2024-12-20","cited_by_count":10,"area":"mllm"},{"id":"2504.02862","title":"Towards Understanding How Knowledge Evolves in Large Vision-Language Models","created":"2025-03-31","cited_by_count":10,"area":"mllm"},{"id":"2407.03604","title":"Modality-Specialized Synergizers for Interleaved Vision-Language Generalists","created":"2024-07-04","cited_by_count":9,"area":"mllm"},{"id":"2407.17379","title":"MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models","created":"2024-07-24","cited_by_count":9,"area":"mllm"},{"id":"2501.00958","title":"2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining","created":"2025-01-01","cited_by_count":9,"area":"mllm"},{"id":"2506.01480","title":"Janus-Pro-R1: Advancing Collaborative Visual Comprehension and Generation via Reinforcement Learning","created":"2025-06-02","cited_by_count":9,"area":"mllm"},{"id":"2405.10311","title":"UniRAG: Universal Retrieval Augmentation for Large Vision Language Models","created":"2024-05-16","cited_by_count":8,"area":"mllm"},{"id":"2409.09564","title":"TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings","created":"2024-09-15","cited_by_count":8,"area":"mllm"},{"id":"2409.14993","title":"Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unification","created":"2024-09-23","cited_by_count":8,"area":"mllm"},{"id":"2506.07963","title":"SUDER: Self-Improving Unified Large Multimodal Models for Understanding and Generation with Dual Self-Rewards","created":"2025-06-09","cited_by_count":8,"area":"mllm"},{"id":"2508.03142","title":"UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying","created":"2025-08-05","cited_by_count":7,"area":"mllm"},{"id":"2508.20181","title":"Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization","created":"2025-08-27","cited_by_count":7,"area":"mllm"},{"id":"2411.13317","title":"Teaching VLMs to Localize Specific Objects from In-context Examples","created":"2024-11-20","cited_by_count":6,"area":"mllm"},{"id":"2402.12195","title":"Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion","created":"2024-02-19","cited_by_count":5,"area":"mllm"},{"id":"2503.16566","title":"REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models","created":"2025-03-20","cited_by_count":5,"area":"mllm"},{"id":"2406.11262","title":"Generative Visual Instruction Tuning","created":"2024-06-17","cited_by_count":4,"area":"mllm"},{"id":"2407.15819","title":"Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight","created":"2024-07-22","cited_by_count":4,"area":"mllm"}],"next_cursor":"100"}