AI RESEARCH
Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
arXiv CS.AI
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ArXi:2602.08885v5 Announce Type: replace-cross Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much 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 expressions to a concise normalized form. Amortized SR has addressed this with general-purpose Computer Algebra Systems (CAS) like SymPy, but the high computational cost severely limits.