{"total":86,"rows":[{"id":"2504.10479","title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","created":"2025-04-14","cited_by_count":1834,"area":null},{"id":"2501.13106","title":"VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding","created":"2025-01-22","cited_by_count":518,"area":null},{"id":"2502.04328","title":"Ola: Pushing the Frontiers of Omni-Modal Language Model","created":"2025-02-06","cited_by_count":83,"area":null},{"id":"2507.07999","title":"Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology","created":"2025-07-10","cited_by_count":69,"area":null},{"id":"2411.10332","title":"Number it: Temporal Grounding Videos like Flipping Manga","created":"2024-11-15","cited_by_count":65,"area":null},{"id":"2501.02885","title":"MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs","created":"2025-01-06","cited_by_count":43,"area":null},{"id":"2505.14646","title":"CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation","created":"2025-05-20","cited_by_count":43,"area":null},{"id":"2412.09604","title":"SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding","created":"2024-12-12","cited_by_count":42,"area":null},{"id":"2506.08010","title":"Vision Transformers Don't Need Trained Registers","created":"2025-06-09","cited_by_count":41,"area":null},{"id":"2504.14693","title":"Video-MMLU: A Massive Multi-Discipline Lecture Understanding Benchmark","created":"2025-04-20","cited_by_count":37,"area":null},{"id":"2508.04655","title":"X-SAM: From Segment Anything to Any Segmentation","created":"2025-08-06","cited_by_count":30,"area":null},{"id":"2603.25040","title":"Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale","created":"2026-03-26","cited_by_count":29,"area":null},{"id":"2607.05722","title":"Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding","created":"2026-07-07","cited_by_count":24,"area":null},{"id":"2601.21406","title":"Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation","created":"2026-01-29","cited_by_count":20,"area":null},{"id":"2501.13953","title":"Redundancy Principles for MLLMs Benchmarks","created":"2025-01-20","cited_by_count":19,"area":null},{"id":"2502.00392","title":"RefDrone: A Challenging Benchmark for Referring Expression Comprehension in Drone Scenes","created":"2025-02-01","cited_by_count":19,"area":null},{"id":"2511.03929","title":"NVIDIA Nemotron Nano V2 VL","created":"2025-11-06","cited_by_count":17,"area":null},{"id":"2604.04184","title":"AURA: Always-On Understanding and Real-Time Assistance via Video Streams","created":"2026-04-05","cited_by_count":16,"area":null},{"id":"2410.19314","title":"Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)","created":"2024-10-25","cited_by_count":15,"area":null},{"id":"2506.19848","title":"ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing","created":"2025-06-24","cited_by_count":15,"area":null},{"id":"2601.10129","title":"LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning","created":"2026-01-15","cited_by_count":15,"area":null},{"id":"2603.03447","title":"Proact-VL: A Proactive VideoLLM for Real-Time AI Companions","created":"2026-03-03","cited_by_count":15,"area":null},{"id":"2503.14478","title":"Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM","created":"2025-03-18","cited_by_count":14,"area":null},{"id":"2504.01001","title":"Zero-shot Benchmarking: A Framework for Flexible and Scalable Automatic Evaluation of Language Models","created":"2025-04-01","cited_by_count":14,"area":null},{"id":"2502.16435","title":"Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs","created":"2025-02-23","cited_by_count":13,"area":null},{"id":"2503.10078","title":"Image Quality Assessment: From Human to Machine Preference","created":"2025-03-13","cited_by_count":12,"area":null},{"id":"2602.02140","title":"Quantifying the Gap between Understanding and Generation within Unified Multimodal Models","created":"2026-02-02","cited_by_count":12,"area":null},{"id":"2605.04128","title":"JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation","created":"2026-05-05","cited_by_count":12,"area":null},{"id":"2511.23386","title":"VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction","created":"2025-11-28","cited_by_count":10,"area":null},{"id":"2504.15707","title":"RePOPE: Impact of Annotation Errors on the POPE Benchmark","created":"2025-04-22","cited_by_count":9,"area":null},{"id":"2503.18422","title":"Breaking the Encoder Barrier for Seamless Video-Language Understanding","created":"2025-03-24","cited_by_count":8,"area":null},{"id":"2512.04810","title":"EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture","created":"2025-12-04","cited_by_count":8,"area":null},{"id":"2512.04763","title":"MemLoRA: Distilling Expert Adapters for On-Device Memory Systems","created":"2025-12-04","cited_by_count":7,"area":null},{"id":"2503.09499","title":"MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?","created":"2025-03-12","cited_by_count":6,"area":null},{"id":"2601.01373","title":"UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models","created":"2026-01-04","cited_by_count":6,"area":null},{"id":"2509.02949","title":"ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly","created":"2025-09-03","cited_by_count":5,"area":null},{"id":"2606.04261","title":"Can Generalist Agents Automate Data Curation?","created":"2026-06-02","cited_by_count":5,"area":null},{"id":"2610.02076","title":"LLM-as-Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them","created":"2026-10-01","cited_by_count":4,"area":null},{"id":"2503.00059","title":"Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models","created":"2025-02-27","cited_by_count":3,"area":null},{"id":"2504.01792","title":"UniViTAR: Unified Vision Transformer with Native Resolution","created":"2025-04-02","cited_by_count":3,"area":null},{"id":"2506.09081","title":"FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation","created":"2025-06-10","cited_by_count":3,"area":null},{"id":"2512.22334","title":"SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence","created":"2025-12-26","cited_by_count":3,"area":null},{"id":"2603.00461","title":"ReMoT: Reinforcement Learning with Motion Contrast Triplets","created":"2026-02-28","cited_by_count":3,"area":null},{"id":"2606.17574","title":"DeepInsight: A Unified Evaluation Infrastructure Across the Physical AI Stack","created":"2026-06-16","cited_by_count":3,"area":null},{"id":"2503.14559","title":"Squeeze Out Tokens from Sample for Finer-Grained Data Governance","created":"2025-03-18","cited_by_count":2,"area":null},{"id":"2511.22172","title":"Guiding the Inner Eye: A Framework for Hierarchical and Flexible Visual Grounded Reasoning","created":"2025-11-27","cited_by_count":2,"area":null},{"id":"2604.10784","title":"TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training","created":"2026-04-12","cited_by_count":2,"area":null},{"id":"2606.12300","title":"Natural-Language Temporal Grounding in Hour-Long Videos is a Search Problem: A Benchmark and Empirical Decomposition","created":"2026-06-10","cited_by_count":2,"area":null},{"id":"2607.08522","title":"Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data","created":"2026-07-09","cited_by_count":2,"area":null},{"id":"2609.22452","title":"Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models","created":"2026-09-18","cited_by_count":2,"area":null},{"id":"2503.13383","title":"Cream of the Crop: Harvesting Rich, Scalable and Transferable Multi-Modal Data for Instruction Fine-Tuning","created":"2025-03-17","cited_by_count":1,"area":null},{"id":"2505.14638","title":"Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference","created":"2025-05-20","cited_by_count":1,"area":null},{"id":"2508.06553","title":"Static and Plugged: Make Embodied Evaluation Simple","created":"2025-08-06","cited_by_count":1,"area":null},{"id":"2509.01610","title":"Improving Large Vision and Language Models by Learning from a Panel of Peers","created":"2025-09-01","cited_by_count":1,"area":null},{"id":"2510.12126","title":"MetaCaptioner: Towards Generalist Visual Captioning with Open-source Suites","created":"2025-10-14","cited_by_count":1,"area":null},{"id":"2511.05883","title":"Unveiling Modality Bias: Automated Sample-Specific Analysis for Multimodal Misinformation Benchmarks","created":"2025-11-08","cited_by_count":1,"area":null},{"id":"2511.22341","title":"Unexplored flaws in multiple-choice VQA make benchmarking unreliable","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2603.13418","title":"GPrune-LLM: Generalization-Aware Structured Pruning for Large Language Models","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2604.04780","title":"CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models","created":"2026-04-06","cited_by_count":1,"area":null},{"id":"2605.15691","title":"SEED: Targeted Data Selection by Weighted Independent Set","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2605.16893","title":"NGM: A Plug-and-Play Training-Free Memory Module for LLMs","created":"2026-05-16","cited_by_count":1,"area":null},{"id":"2605.21988","title":"Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learning","created":"2026-05-21","cited_by_count":1,"area":null},{"id":"2606.29531","title":"MotionAtlas: Detailed Region Captioning for Motion-Centric Videos","created":"2026-06-28","cited_by_count":1,"area":null},{"id":"2609.02231","title":"PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment","created":"2026-09-02","cited_by_count":1,"area":null},{"id":"2609.05141","title":"SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding","created":"2026-09-04","cited_by_count":1,"area":null},{"id":"2503.06542","title":"ARMOR: Empowering Multimodal Understanding Model with Interleaved Multimodal Generation Capability","created":"2025-03-09","cited_by_count":0,"area":null},{"id":"2510.06071","title":"Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2601.02888","title":"RPIQ: Residual-Projected Multi-Collaboration Closed-Loop and Single Instance Quantization for Visually Impaired Assistance","created":"2026-01-06","cited_by_count":0,"area":null},{"id":"2603.23160","title":"UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2603.26055","title":"Pioneering Perceptual Video Fluency Assessment: A Novel Task with Benchmark Dataset and Baseline","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2604.02371","title":"Internalized Reasoning for Long-Context Visual Document Understanding","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2605.17273","title":"Position: State-of-the-Art Claims Require State-of-the-Art Evidence","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2605.18714","title":"Semantic Generative Tuning for Unified Multimodal Models","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.21479","title":"WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2605.28604","title":"Mining Multi-Modality Spatio-Temporal Cues for Video Important Person Identification","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.07161","title":"TraRA: Trajectory-level Recognition Aggregation for Video Text Spotting in Urban Surveillance","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2606.11231","title":"CFCamo: A Counterfactual Detect-or-Abstain Framework for Camouflaged Object Detection","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.21657","title":"Chehre: An Emoji-Prompted Dataset to Explore Perceptual Flexibility in Video Language Models","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2606.24774","title":"Revealing Training Data Exposure in Vision Language Large Models via Parameter Gradients","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2608.11692","title":"HUGIN: Enhancing Vision-Language Planning for Autonomous Logistics Sorting","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2609.01315","title":"A Composable Evaluation System for Reproducible Omni-Modal Foundation Model Evaluation","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.22910","title":"When Should a VLM Look? Paying Only for Visual Calls That Were Needed and Used","created":"2026-09-19","cited_by_count":0,"area":null},{"id":"2609.25131","title":"Entropy Can Flow, or It Can Guide. Be Entropy. LEDFlow: Introducing Entropy-guided Generation Order into Uniform Discrete Flow","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2609.36224","title":"Mutually Adversarial Self-Training with Evolving Data for Unified Multimodal Models","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.16735","title":"VAttnPO: Visual Attention-Guided Policy Optimization","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.18535","title":"Meta-Video: Efficient Standardized Evaluation for Video Understanding","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}