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MOSAIC-SR: Transformer-Guided Symbolic Regression for Scientific Equation Recovery

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Symbolic regression aims to recover closed-form equations from observations, providing interpretable models for scientific discovery. Existing approaches struggle to combine flexible structural search with efficient inference. Search-based methods can refine expression structure but often rely on costly combinatorial optimization with random initialization. Pretrained neural models generate formulas almost instantly, but their predictions often contain symbolic errors. We introduce MOSAIC-SR, which uses a pretrained Transformer to propose multiple initial sketches. These sketches initialize se

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