AI RESEARCH
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
arXiv CS.AI
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ArXi:2605.23272v1 Announce Type: cross Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data. Most existing SR methods function within a bi-level optimization framework: an outer loop that searches for the discrete equation structure, and an inner loop that optimizes the continuous parameters of that structure. Crucially, parameter-fitting quality directly determines a structure's score and thus the outer-loop search.