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DASS: A Solver-Agnostic Dynamic Auxiliary Search Strategy for Symbolic Regression

NeurIPS 2026 · Probabilistic and scientific modeling › Molecules and proteins · approach: Statistical analysis of ML practice

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Proposes DASS, a solver-agnostic dynamic auxiliary search strategy that can be plugged into existing symbolic regression solvers to improve the accuracy-efficiency trade-off.

Problem

Symbolic regression suffers from an enormous search space and combinatorial explosion, making it hard to balance accuracy and algorithmic efficiency.

Approach

Instead of only designing more complex solver-specific algorithms, adds a dynamic auxiliary search strategy that is agnostic to the underlying SR solver, guiding the search independently of solver internals.

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