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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.
This paper identifies a 'positional copying' shortcut where small language models answer arithmetic questions by copying the last number before the answer delimiter, bypassing actual reasoning. This effect explains why shuffling CoT steps retains performance; it accounts for 89-92% of teacher-forcing accuracy in 1-3B models on GSM8K.
SDSR proposes lightweight self-describing structured data with dual-layer guidance to exploit LLM primacy bias, achieving 100% routing accuracy without vector DBs.