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On September 7, 2026, arXiv cs.AI published the paper “Deep Divide-and-Reduce in Symbolic Regression”, introducing a new method called “Deep Blocking and Reduction”. This method aims to address the high computational complexity and large search space issues in symbolic regression. By employing a deep blocking strategy to break down large-scale searches into multiple sub Tasks, and combining a reduction mechanism for optimizing result filtering, it significantly improves model training efficiency and accuracy.
arXiv:2608.02628v2 提出 Deep Divide-and-Reduce in Symbolic Regression (DDRSR)，旨在解决现有符号回归方法缺乏数学物理原理理解及 AI Feynman 方法适用性窄、易失败的问题。DDRSR 基于对更广泛分解结构形式的形式化分析，扩展了表达式分解与约简的适用范围并提供了坚实的解析基础。实证评估显示，该理论原则在表达式分解及下游符号回归性能上均带来显著优势。文章最后讨论了该范式的适用场景、固有局限及未来研究方向。