Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression 事件

Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression arXiv:2602.08885v5 Announce Type: replace-cross Abstract: Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much more efficient than the predominant genetic programming SR methods, but currently struggles to scale to realistic scientific complexity. We find that a key obstacle is the lack of a fast reduction of equivalent