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最近，来自加州大学圣克鲁兹分校、乔治·梅森大学和Datadog的研究人员发现：在心算任务中，几乎所有实际的数学计算都集中在序列的最后一个token上完成，而不是分散在所有token中。 这意味着，相较于在Transformer和多层感知机（MLP）中常见的全局信息访问——即每个token在预测时都能查询并利用整个上文信息——在诸如心算这样的特定任务中，全局访问其实并不是必需的。