{"total":119,"rows":[{"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":"2407.11691","title":"VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models","created":"2024-07-16","cited_by_count":455,"area":null},{"id":"2406.04325","title":"ShareGPT4Video: Improving Video Understanding and Generation with Better Captions","created":"2024-06-06","cited_by_count":388,"area":null},{"id":"2407.14505","title":"T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation","created":"2024-07-19","cited_by_count":185,"area":null},{"id":"2501.12380","title":"MMVU: Measuring Expert-Level Multi-Discipline Video Understanding","created":"2025-01-21","cited_by_count":137,"area":null},{"id":"2406.16020","title":"AudioBench: A Universal Benchmark for Audio Large Language Models","created":"2024-06-23","cited_by_count":128,"area":null},{"id":"2603.14482","title":"V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning","created":"2026-03-15","cited_by_count":117,"area":null},{"id":"2411.04998","title":"HourVideo: 1-Hour Video-Language Understanding","created":"2024-11-07","cited_by_count":113,"area":null},{"id":"2410.10818","title":"TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models","created":"2024-10-14","cited_by_count":97,"area":null},{"id":"2410.03290","title":"Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models","created":"2024-10-04","cited_by_count":91,"area":null},{"id":"2409.18111","title":"E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding","created":"2024-09-26","cited_by_count":70,"area":null},{"id":"2506.09344","title":"Ming-Omni: A Unified Multimodal Model for Perception and Generation","created":"2025-06-11","cited_by_count":66,"area":null},{"id":"2505.05467","title":"StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant","created":"2025-05-08","cited_by_count":63,"area":null},{"id":"2603.20633","title":"Seed1.8 Model Card: Towards Generalized Real-World Agency","created":"2026-03-21","cited_by_count":50,"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":"2502.12081","title":"Unhackable Temporal Rewarding for Scalable Video MLLMs","created":"2025-02-17","cited_by_count":33,"area":null},{"id":"2502.12084","title":"VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues","created":"2025-02-17","cited_by_count":33,"area":null},{"id":"2503.18943","title":"SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding","created":"2025-03-24","cited_by_count":30,"area":null},{"id":"2408.04223","title":"VideoQA in the Era of LLMs: An Empirical Study","created":"2024-08-08","cited_by_count":29,"area":null},{"id":"2405.03194","title":"CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario","created":"2024-05-06","cited_by_count":27,"area":null},{"id":"2412.18619","title":"Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey","created":"2024-12-16","cited_by_count":27,"area":null},{"id":"2512.21334","title":"Streaming Video Instruction Tuning","created":"2025-12-24","cited_by_count":26,"area":null},{"id":"2603.19217","title":"LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs","created":"2026-03-19","cited_by_count":24,"area":null},{"id":"2412.01800","title":"PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos","created":"2024-12-02","cited_by_count":22,"area":null},{"id":"2411.16771","title":"VidHal: Benchmarking Temporal Hallucinations in Vision LLMs","created":"2024-11-25","cited_by_count":19,"area":null},{"id":"2412.05185","title":"LinVT: Empower Your Image-level Large Language Model to Understand Videos","created":"2024-12-06","cited_by_count":19,"area":null},{"id":"2411.12951","title":"On the Consistency of Video Large Language Models in Temporal Comprehension","created":"2024-11-20","cited_by_count":18,"area":null},{"id":"2502.11664","title":"VRoPE: Rotary Position Embedding for Video Large Language Models","created":"2025-02-17","cited_by_count":18,"area":null},{"id":"2510.07550","title":"TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility","created":"2025-10-08","cited_by_count":18,"area":null},{"id":"2503.14378","title":"Impossible Videos","created":"2025-03-18","cited_by_count":16,"area":null},{"id":"2510.06077","title":"When Thinking Drifts: Evidential Grounding for Robust Video Reasoning","created":"2025-10-07","cited_by_count":16,"area":null},{"id":"2412.09601","title":"TimeRefine: Temporal Grounding with Time Refining Video LLM","created":"2024-12-12","cited_by_count":15,"area":null},{"id":"2606.14777","title":"JoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence","created":"2026-06-10","cited_by_count":14,"area":null},{"id":"2509.11866","title":"Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding","created":"2025-09-15","cited_by_count":13,"area":null},{"id":"2510.18915","title":"UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in Omni Models","created":"2025-10-21","cited_by_count":13,"area":null},{"id":"2412.02071","title":"Progress-Aware Video Frame Captioning","created":"2024-12-03","cited_by_count":11,"area":null},{"id":"2505.22810","title":"VidText: Towards Comprehensive Evaluation for Video Text Understanding","created":"2025-05-28","cited_by_count":11,"area":null},{"id":"2602.00288","title":"TimeBlind: A Spatio-Temporal Compositionality Benchmark for Video LLMs","created":"2026-01-30","cited_by_count":11,"area":null},{"id":"2602.13191","title":"CoPE-VideoLM: Leveraging Codec Primitives For Efficient Video Language Modeling","created":"2026-02-13","cited_by_count":11,"area":null},{"id":"2410.05714","title":"Enhancing Temporal Modeling of Video LLMs via Time Gating","created":"2024-10-08","cited_by_count":10,"area":null},{"id":"2504.05046","title":"MotionPRO: Exploring the Role of Pressure in Human MoCap and Beyond","created":"2025-04-07","cited_by_count":10,"area":null},{"id":"2512.04643","title":"SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding","created":"2025-12-04","cited_by_count":10,"area":null},{"id":"2607.14935","title":"VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding","created":"2026-07-16","cited_by_count":10,"area":null},{"id":"2407.12508","title":"MERLIN: Multimodal Embedding Refinement via LLM-based Iterative Navigation for Text-Video Retrieval-Rerank Pipeline","created":"2024-07-17","cited_by_count":9,"area":null},{"id":"2505.20124","title":"TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos","created":"2025-05-26","cited_by_count":9,"area":null},{"id":"2504.09282","title":"VideoAds for Fast-Paced Video Understanding","created":"2025-04-12","cited_by_count":8,"area":null},{"id":"2506.21742","title":"VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues","created":"2025-06-26","cited_by_count":8,"area":null},{"id":"2509.26636","title":"AccidentBench: Benchmarking Multimodal Understanding and Reasoning in Vehicle Accidents and Beyond","created":"2025-09-30","cited_by_count":8,"area":null},{"id":"2406.09390","title":"LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living","created":"2024-06-13","cited_by_count":7,"area":null},{"id":"2412.17637","title":"SCBench: A Sports Commentary Benchmark for Video LLMs","created":"2024-12-23","cited_by_count":7,"area":null},{"id":"2504.17828","title":"VEU-Bench: Towards Comprehensive Understanding of Video Editing","created":"2025-04-24","cited_by_count":7,"area":null},{"id":"2505.23617","title":"One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory","created":"2025-05-29","cited_by_count":7,"area":null},{"id":"2606.02569","title":"AdaCodec: A Predictive Visual Code for Video MLLMs","created":"2026-06-01","cited_by_count":7,"area":null},{"id":"2503.09949","title":"UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?","created":"2025-03-13","cited_by_count":6,"area":null},{"id":"2504.20384","title":"FiLA-Video: Spatio-Temporal Compression for Fine-Grained Long Video Understanding","created":"2025-04-29","cited_by_count":6,"area":null},{"id":"2412.11906","title":"PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension","created":"2024-12-16","cited_by_count":5,"area":null},{"id":"2502.02487","title":"Hier-EgoPack: Hierarchical Egocentric Video Understanding with Diverse Task Perspectives","created":"2025-02-04","cited_by_count":5,"area":null},{"id":"2504.05925","title":"SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation","created":"2025-04-08","cited_by_count":4,"area":null},{"id":"2506.12992","title":"SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models","created":"2025-06-15","cited_by_count":4,"area":null},{"id":"2509.19245","title":"ConViS-Bench: Estimating Video Similarity Through Semantic Concepts","created":"2025-09-23","cited_by_count":4,"area":null},{"id":"2512.14870","title":"HERBench: A Benchmark for Multi-Evidence Integration in Video Question Answering","created":"2025-12-16","cited_by_count":4,"area":null},{"id":"2601.04778","title":"CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models","created":"2026-01-08","cited_by_count":4,"area":null},{"id":"2601.06559","title":"ArrowGEV: Grounding Events in Video via Learning the Arrow of Time","created":"2026-01-10","cited_by_count":4,"area":null},{"id":"2603.14935","title":"Video-CoE: Reinforcing Video Event Prediction via Chain of Events","created":"2026-03-16","cited_by_count":4,"area":null},{"id":"2605.13803","title":"EvoGround: Self-Evolving Video Agents for Video Temporal Grounding","created":"2026-05-13","cited_by_count":4,"area":null},{"id":"2606.07639","title":"MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention","created":"2026-06-01","cited_by_count":4,"area":null},{"id":"2509.24563","title":"NeMo: Needle in a Montage for Video-Language Understanding","created":"2025-09-29","cited_by_count":3,"area":null},{"id":"2602.22779","title":"TrajTok: Learning Trajectory Tokens enables better Video Understanding","created":"2026-02-26","cited_by_count":3,"area":null},{"id":"2605.21931","title":"EvoVid: Temporal-Centric Self-Evolution for Video Large Language Models","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2606.02443","title":"PaSBench-Video: A Streaming Video Benchmark for Proactive Safety Warning","created":"2026-06-01","cited_by_count":3,"area":null},{"id":"2503.09994","title":"TIME: Temporal-Sensitive Multi-Dimensional Instruction Tuning and Robust Benchmarking for Video-LLMs","created":"2025-03-13","cited_by_count":2,"area":null},{"id":"2503.24008","title":"H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding","created":"2025-03-31","cited_by_count":2,"area":null},{"id":"2509.17901","title":"Do Modern Video-LLMs Need to Listen? A Benchmark Audit and Scalable Remedy","created":"2025-09-22","cited_by_count":2,"area":null},{"id":"2512.13281","title":"VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?","created":"2025-12-15","cited_by_count":2,"area":null},{"id":"2605.22678","title":"Swift Sampling: Selecting Temporal Surprises via Taylor Series","created":"2026-05-21","cited_by_count":2,"area":null},{"id":"2605.22823","title":"Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs","created":"2026-05-21","cited_by_count":2,"area":null},{"id":"2605.26584","title":"O-MARC: Omni Memory-Augmented Compression Distillation for Efficient Video Understanding","created":"2026-05-26","cited_by_count":2,"area":null},{"id":"2608.07014","title":"Stable Curves, Unstable Items: Item-Level Scaling Heterogeneity in Video LLMs","created":"2026-08-07","cited_by_count":2,"area":null},{"id":"2408.07867","title":"Continuous Perception Matters: Diagnosing Temporal Integration Failures in Multimodal Models","created":"2024-08-15","cited_by_count":1,"area":null},{"id":"2408.11347","title":"Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments","created":"2024-08-21","cited_by_count":1,"area":null},{"id":"2503.18507","title":"Can Text-to-Video Generation help Video-Language Alignment?","created":"2025-03-24","cited_by_count":1,"area":null},{"id":"2505.21068","title":"Predicting Implicit Arguments in Procedural Video Instructions","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2601.03781","title":"MVP: Enhancing Video Large Language Models via Self-supervised Masked Video Prediction","created":"2026-01-07","cited_by_count":1,"area":null},{"id":"2601.06106","title":"Judge Model for Large-scale Multimodality Benchmarks","created":"2026-01-03","cited_by_count":1,"area":null},{"id":"2602.05215","title":"E.M.Ground: A Temporal Grounding Vid-LLM with Holistic Event Perception and Matching","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2602.11244","title":"Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2604.08209","title":"OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering","created":"2026-04-09","cited_by_count":1,"area":null},{"id":"2604.12944","title":"Distorted or Fabricated? A Survey on Hallucination in Video LLMs","created":"2026-04-14","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":"2605.22907","title":"VideoOdyssey: A Benchmark for Ultra-Long-Context and Omni-Modal Video Understanding","created":"2026-05-21","cited_by_count":1,"area":null},{"id":"2606.30026","title":"MuseBench: Benchmarking Intent-Level Audiovisual Arts Understanding in MLLMs","created":"2026-06-29","cited_by_count":1,"area":null},{"id":"2608.03160","title":"Caved or Convinced: Temporal Sampling Gates Claim Deference in Video Large Language Models","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2503.21190","title":"Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2505.24346","title":"VUDG: A Dataset for Video Understanding Domain Generalization","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2507.19420","title":"CircuitProbe: Tracing Visual Temporal Evidence Flow in Video Language Models","created":"2025-07-25","cited_by_count":0,"area":null},{"id":"2510.03955","title":"Harnessing Synthetic Preference Data for Enhancing Temporal Understanding of Video-LLMs","created":"2025-10-04","cited_by_count":0,"area":null},{"id":"2510.06512","title":"LogSTOP: Temporal Scores over Prediction Sequences for Matching and Retrieval","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2603.20354","title":"Leum-VL Technical Report","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2605.09904","title":"TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.23045","title":"The TIME Machine: On The Power of Motion for Efficient Perception","created":"2026-05-21","cited_by_count":0,"area":null}],"next_cursor":"100"}