A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs 事件

A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs arXiv:2606.04596v1 Announce Type: new Abstract: 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