{"total":169,"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":"2511.21631","title":"Qwen3-VL Technical Report","created":"2025-11-26","cited_by_count":2793,"area":"mllm"},{"id":"2503.01743","title":"Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs","created":"2025-03-03","cited_by_count":521,"area":"mllm"},{"id":"2607.02770","title":"Gemma 4 Technical Report","created":"2026-07-02","cited_by_count":259,"area":"mllm"},{"id":"2509.23661","title":"LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training","created":"2025-09-28","cited_by_count":244,"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":"2505.16933","title":"LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning","created":"2025-05-22","cited_by_count":148,"area":"mllm"},{"id":"2502.10391","title":"MM-RLHF: The Next Step Forward in Multimodal LLM Alignment","created":"2025-02-14","cited_by_count":83,"area":"mllm"},{"id":"2411.15296","title":"MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs","created":"2024-11-22","cited_by_count":72,"area":"mllm"},{"id":"2502.13059","title":"SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models","created":"2025-02-18","cited_by_count":66,"area":"mllm"},{"id":"2410.16153","title":"Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages","created":"2024-10-21","cited_by_count":64,"area":"mllm"},{"id":"2410.06625","title":"ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time","created":"2024-10-09","cited_by_count":60,"area":"mllm"},{"id":"2501.02189","title":"A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges","created":"2025-01-04","cited_by_count":47,"area":"mllm"},{"id":"2502.14191","title":"Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision Language Models","created":"2025-02-20","cited_by_count":44,"area":"mllm"},{"id":"2503.13646","title":"Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos","created":"2025-03-17","cited_by_count":44,"area":"mllm"},{"id":"2505.18657","title":"MLLMs are Deeply Affected by Modality Bias","created":"2025-05-24","cited_by_count":40,"area":"mllm"},{"id":"2503.10582","title":"VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search","created":"2025-03-13","cited_by_count":35,"area":"mllm"},{"id":"2509.13317","title":"3D Aware Region Prompted Vision Language Model","created":"2025-09-16","cited_by_count":33,"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":"2501.03225","title":"Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation","created":"2025-01-06","cited_by_count":30,"area":"mllm"},{"id":"2503.14607","title":"Can Large Vision Language Models Read Maps Like a Human?","created":"2025-03-18","cited_by_count":30,"area":"mllm"},{"id":"2511.04655","title":"Benchmark Designers Should \"Train on the Test Set\" to Expose Exploitable Non-Visual Shortcuts","created":"2025-11-06","cited_by_count":29,"area":"mllm"},{"id":"2410.13824","title":"Harnessing Webpage UIs for Text-Rich Visual Understanding","created":"2024-10-17","cited_by_count":28,"area":"mllm"},{"id":"2410.08474","title":"SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models","created":"2024-10-11","cited_by_count":24,"area":"mllm"},{"id":"2510.10991","title":"A Survey on Agentic Multimodal Large Language Models","created":"2025-10-13","cited_by_count":23,"area":"mllm"},{"id":"2412.08687","title":"VisionArena: 230K Real World User-VLM Conversations with Preference Labels","created":"2024-12-11","cited_by_count":22,"area":"mllm"},{"id":"2512.21675","title":"UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture","created":"2025-12-25","cited_by_count":22,"area":"mllm"},{"id":"2507.20519","title":"AgroBench: Vision-Language Model Benchmark in Agriculture","created":"2025-07-28","cited_by_count":20,"area":"mllm"},{"id":"2410.21311","title":"MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding","created":"2024-10-25","cited_by_count":19,"area":"mllm"},{"id":"2505.21076","title":"DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding","created":"2025-05-27","cited_by_count":18,"area":"mllm"},{"id":"2508.10552","title":"When Language Overrules: Revealing Text Dominance in Multimodal Large Language Models","created":"2025-08-14","cited_by_count":18,"area":"mllm"},{"id":"2512.22615","title":"Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone","created":"2025-12-27","cited_by_count":18,"area":"mllm"},{"id":"2505.20236","title":"Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models","created":"2025-05-26","cited_by_count":16,"area":"mllm"},{"id":"2411.01492","title":"EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark","created":"2024-11-03","cited_by_count":15,"area":"mllm"},{"id":"2503.08741","title":"Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis","created":"2025-03-11","cited_by_count":14,"area":"mllm"},{"id":"2505.16416","title":"Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models","created":"2025-05-22","cited_by_count":14,"area":"mllm"},{"id":"2512.04686","title":"Towards Cross-View Point Correspondence in Vision-Language Models","created":"2025-12-04","cited_by_count":14,"area":"mllm"},{"id":"2603.06148","title":"VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models","created":"2026-03-06","cited_by_count":14,"area":"mllm"},{"id":"2509.03986","title":"Promptception: How Sensitive Are Large Multimodal Models to Prompts?","created":"2025-09-04","cited_by_count":13,"area":"mllm"},{"id":"2605.15864","title":"Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination","created":"2026-05-15","cited_by_count":13,"area":"mllm"},{"id":"2504.08851","title":"Mimic In-Context Learning for Multimodal Tasks","created":"2025-04-11","cited_by_count":12,"area":"mllm"},{"id":"2412.06263","title":"iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models","created":"2024-12-09","cited_by_count":11,"area":"mllm"},{"id":"2505.15952","title":"VideoGameQA-Bench: Evaluating Vision-Language Models for Video Game Quality Assurance","created":"2025-05-21","cited_by_count":11,"area":"mllm"},{"id":"2501.07244","title":"Can Vision-Language Models Evaluate Handwritten Math?","created":"2025-01-13","cited_by_count":10,"area":"mllm"},{"id":"2506.15681","title":"GenRecal: Generation after Recalibration from Large to Small Vision-Language Models","created":"2025-06-18","cited_by_count":10,"area":"mllm"},{"id":"2512.15713","title":"DiffusionVL: Translating Any Autoregressive Models into Diffusion Vision Language Models","created":"2025-12-17","cited_by_count":10,"area":"mllm"},{"id":"2602.07026","title":"Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models","created":"2026-02-02","cited_by_count":10,"area":"mllm"},{"id":"2509.09307","title":"Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization","created":"2025-09-11","cited_by_count":9,"area":"mllm"},{"id":"2510.16926","title":"Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input","created":"2025-10-19","cited_by_count":9,"area":"mllm"},{"id":"2512.02895","title":"MindGPT-4ov: An Enhanced MLLM via a Multi-Stage Post-Training Paradigm","created":"2025-12-02","cited_by_count":9,"area":"mllm"},{"id":"2604.06832","title":"Fast-dVLM: Efficient Block-Diffusion VLM via Direct Conversion from Autoregressive VLM","created":"2026-04-08","cited_by_count":9,"area":"mllm"},{"id":"2605.15677","title":"VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing","created":"2026-05-15","cited_by_count":9,"area":"mllm"},{"id":"2609.00111","title":"Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving","created":"2026-08-31","cited_by_count":9,"area":"mllm"},{"id":"2510.19307","title":"Unified Reinforcement and Imitation Learning for Vision-Language Models","created":"2025-10-22","cited_by_count":8,"area":"mllm"},{"id":"2511.00846","title":"OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks","created":"2025-11-02","cited_by_count":8,"area":"mllm"},{"id":"2602.01816","title":"Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies","created":"2026-02-02","cited_by_count":8,"area":"mllm"},{"id":"2602.02537","title":"WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models","created":"2026-01-28","cited_by_count":8,"area":"mllm"},{"id":"2604.28123","title":"Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL","created":"2026-04-30","cited_by_count":8,"area":"mllm"},{"id":"2606.09393","title":"CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning","created":"2026-06-08","cited_by_count":8,"area":"mllm"},{"id":"2606.10400","title":"Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark","created":"2026-06-09","cited_by_count":8,"area":"mllm"},{"id":"2503.21699","title":"MAVERIX: Multimodal Audio-Visual Evaluation and Recognition IndeX","created":"2025-03-27","cited_by_count":7,"area":"mllm"},{"id":"2509.00419","title":"LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression","created":"2025-08-30","cited_by_count":7,"area":"mllm"},{"id":"2510.23479","title":"MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal Understanding","created":"2025-10-27","cited_by_count":7,"area":"mllm"},{"id":"2510.26865","title":"Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench","created":"2025-10-30","cited_by_count":7,"area":"mllm"},{"id":"2512.08923","title":"Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs","created":"2025-12-09","cited_by_count":7,"area":"mllm"},{"id":"2603.06569","title":"Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders","created":"2026-03-06","cited_by_count":7,"area":"mllm"},{"id":"2604.12358","title":"Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding","created":"2026-04-14","cited_by_count":7,"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":"2508.18179","title":"SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models","created":"2025-08-25","cited_by_count":6,"area":"mllm"},{"id":"2512.22238","title":"Masking Teacher and Reinforcing Student for Distilling Vision-Language Models","created":"2025-12-23","cited_by_count":6,"area":"mllm"},{"id":"2602.12705","title":"MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs","created":"2026-02-13","cited_by_count":6,"area":"mllm"},{"id":"2604.08896","title":"GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing","created":"2026-04-10","cited_by_count":6,"area":"mllm"},{"id":"2507.02279","title":"LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models","created":"2025-07-03","cited_by_count":5,"area":"mllm"},{"id":"2509.19018","title":"OmniBridge: Unified Multimodal Understanding, Generation, and Retrieval via Latent Space Alignment","created":"2025-09-23","cited_by_count":5,"area":"mllm"},{"id":"2511.20272","title":"VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs","created":"2025-11-25","cited_by_count":5,"area":"mllm"},{"id":"2511.20515","title":"HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning","created":"2025-11-25","cited_by_count":5,"area":"mllm"},{"id":"2606.13870","title":"Mirage Probes: How Vision Models Fake Visual Understanding","created":"2026-06-11","cited_by_count":5,"area":"mllm"},{"id":"2410.12329","title":"Understanding the Role of LLMs in Multimodal Evaluation Benchmarks","created":"2024-10-16","cited_by_count":4,"area":"mllm"},{"id":"2503.10857","title":"Towards Understanding Graphical Perception in Large Multimodal Models","created":"2025-03-13","cited_by_count":4,"area":"mllm"},{"id":"2503.18484","title":"Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning","created":"2025-03-24","cited_by_count":4,"area":"mllm"},{"id":"2505.09498","title":"Flash-VL 2B: Optimizing Vision-Language Model Performance for Ultra-Low Latency and High Throughput","created":"2025-05-14","cited_by_count":4,"area":"mllm"},{"id":"2507.22037","title":"Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security","created":"2025-07-29","cited_by_count":4,"area":"mllm"},{"id":"2508.19111","title":"Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs","created":"2025-08-26","cited_by_count":4,"area":"mllm"},{"id":"2510.11496","title":"AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model","created":"2025-10-13","cited_by_count":4,"area":"mllm"},{"id":"2602.11824","title":"Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models","created":"2026-02-12","cited_by_count":4,"area":"mllm"},{"id":"2603.17508","title":"Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation","created":"2026-03-18","cited_by_count":4,"area":"mllm"},{"id":"2604.06714","title":"Steering the Verifiability of Multimodal AI Hallucinations","created":"2026-04-08","cited_by_count":4,"area":"mllm"},{"id":"2604.17274","title":"Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models","created":"2026-04-19","cited_by_count":4,"area":"mllm"},{"id":"2604.18164","title":"MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge","created":"2026-04-20","cited_by_count":4,"area":"mllm"},{"id":"2607.23125","title":"Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation","created":"2026-07-25","cited_by_count":4,"area":"mllm"},{"id":"2607.24957","title":"PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models","created":"2026-07-27","cited_by_count":4,"area":"mllm"},{"id":"2501.19017","title":"Benchmarking Gaslighting Negation Attacks Against Multimodal Large Language Models","created":"2025-01-31","cited_by_count":3,"area":"mllm"},{"id":"2502.13487","title":"Transferring Textual Preferences to Vision-Language Understanding through Model Merging","created":"2025-02-19","cited_by_count":3,"area":"mllm"},{"id":"2503.22655","title":"Text-Only Data Synthesis for Vision Language Model Training","created":"2025-03-28","cited_by_count":3,"area":"mllm"},{"id":"2506.05439","title":"LLMs Can Compensate for Deficiencies in Visual Representations","created":"2025-06-05","cited_by_count":3,"area":"mllm"},{"id":"2510.00063","title":"AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy","created":"2025-09-29","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":"2512.15949","title":"The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs","created":"2025-12-17","cited_by_count":3,"area":"mllm"},{"id":"2602.00414","title":"Toward Autonomous Laboratory Safety Monitoring with Vision Language Models: Learning to See Hazards Through Scene Structure","created":"2026-01-31","cited_by_count":3,"area":"mllm"},{"id":"2602.19058","title":"Do LLMs and VLMs Share Neurons for Inference? Evidence and Mechanisms of Cross-Modal Transfer","created":"2026-02-22","cited_by_count":3,"area":"mllm"}],"next_cursor":"100"}