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本文提出了“加性原子森林”框架，利用导数代数和自扩展原子库，同时从数据中恢复函数及其反导数的符号形式。该方法在分类基准和费曼符号回归任务上表现优异，且结果具有可解释性。
This paper presents Block-Wise Differentiable Sinkhorn Attention, a method for efficient long-context balanced entropic optimal transport attention on TPU hardware. It introduces a tail-refinement surrogate for exact differentiation, proving an efficient backward pass schedule and demonstrating significant improvements in Pfam sequence alignment reconstruction.