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Researchers from Jilin University systematically evaluate positional bias in multi-video summarization using MLLMs, constructing a benchmark from ActivityNet and News videos and assessing nine models with metrics including Coverage, Directional Positional Bias, and Middle-Edge Gap. Results show positional effects are domain- and model-dependent, and increasing visual or generation budget does not uniformly resolve the imbalance.