RISR: Residual-Informed Scientific Equation Discovery with Large Language Models

The paper introduces RISR, a residual‑informed approach for symbolic regression that leverages error patterns to guide formula discovery. A residual encoder transforms aligned inputs, targets, predictions, and residuals into tokens that condition a language model to propose candidate equations, while a dual‑view relational encoder predicts the utility of corrections. Evaluated on the LLM‑SRBench, RISR achieves higher in‑distribution and out‑of‑distribution accuracy than baseline methods, demonstrating that incorporating residual information can improve numerical equation recovery.

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