Research · Updated 16 Oct, 09:30 am IST
L2G-SR: learned structural guidance for LLM-based symbolic regression
Why it matters for readers: If you’re curious about AI solving math, this shows models can be guided to produce the right formula structure instead of only matching numbers.
- L2G-SR adds a learned structural verifier to LLM-driven symbolic regression to score structural similarity of candidate formulas.1
- The verifier is trained using a structural supervision signal based on tree edit distance that accounts for commutative operators and alternative expression forms.1
- At search time the verifier complements numerical error during parent selection while a generator refines expressions based on analyst revisions.1
- On the LLM-SRBench suite, L2G-SR achieves state-of-the-art numerical fit, equation-level accuracy, and tree edit distance across evaluated LLM backbones and domains.1
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