AI RESEARCH
A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs
arXiv CS.CL
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ArXi:2606.04596v1 Announce Type: new Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positional bias in multi-video summarization, where the quality of a per-video summary can change with the video's input slot even when the underlying content is unchanged. We construct a benchmark from ActivityNet and News videos, covering Cooking, Domestic, Leisure, and News settings with two- and four-video inputs.