{"total":18,"rows":[{"id":"2403.00476","title":"TempCompass: Do Video LLMs Really Understand Videos?","created":"2024-03-01","cited_by_count":363,"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":"2411.04998","title":"HourVideo: 1-Hour Video-Language Understanding","created":"2024-11-07","cited_by_count":113,"area":null},{"id":"2502.04144","title":"HD-EPIC: A Highly-Detailed Egocentric Video Dataset","created":"2025-02-06","cited_by_count":104,"area":null},{"id":"2403.10228","title":"HawkEye: Training Video-Text LLMs for Grounding Text in Videos","created":"2024-03-15","cited_by_count":86,"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":"2507.09313","title":"ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models","created":"2025-07-12","cited_by_count":27,"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":"2411.12951","title":"On the Consistency of Video Large Language Models in Temporal Comprehension","created":"2024-11-20","cited_by_count":18,"area":null},{"id":"2412.02071","title":"Progress-Aware Video Frame Captioning","created":"2024-12-03","cited_by_count":11,"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":"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":"2605.07568","title":"Tracing the Arrow of Time: Diagnosing Temporal Information Flow in Video-LLMs","created":"2026-05-08","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":"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":"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":"2609.26233","title":"The Temporal Moderation Gap: Text-to-Video Safety Filters Are Blind to Harm in Motion","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.32462","title":"Can Motion-Language Models Ground Structure? STRIDE for Evaluating the Evaluators","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}