AAAI 2026technical0 citations
CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models
Jingyao Li, Jingyun Wang, Molin Tan, Haochen Wang, Cilin Yan, Likun Shi, Jiayin Cai, Xiaolong Jiang
Abstract
Cross-Video Reasoning (CVR) presents a significant challenge in video understanding, which requires simultaneous understanding of multiple videos to aggregate and compare information across groups of videos. Most existing video understanding benchmarks focus on single-video analysis, failing to assess the ability of multimodal large language models (MLLMs) to simultaneously reason over various videos. Recent benchmarks evaluate MLLMs
BibTeX
@inproceedings{aaai2026_crossvidacompreh,
title = {CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models},
author = {Jingyao Li and Jingyun Wang and Molin Tan and Haochen Wang and Cilin Yan and Likun Shi and Jiayin Cai and Xiaolong Jiang and Yao Hu},
booktitle = {AAAI 2026},
year = {2026}
}