STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery
For researchers using LLMs to discover symbolic equations from data, STRIDE addresses reliability issues in existing generation-centered loops.
STRIDE introduces a self-reflective agent framework for LLM-based symbolic equation discovery that improves accuracy, out-of-distribution robustness, and structural recovery across multiple benchmarks and LLM backbones.
LLM-based equation discovery offers a promising route to recovering symbolic laws from data, but many systems still rely on generation-centered loops that propose candidates, fit parameters, score results, and reuse selected examples. Such loops can misjudge useful skeletons under unreliable fitting, discard near-correct equations that require repair, and accumulate redundant memories that provide limited guidance. We propose STRIDE, a self-reflective agent framework that improves reliability by coordinating data-aware generation, mixed-fitting evaluation, critic--executor repair, and diversity-preserving semantic memory. By turning fitted scores and candidate behavior into shared feedback, STRIDE enables equations to be proposed, assessed, refined, and reused within a closed-loop discovery process. Experiments on representative symbolic-regression benchmarks and LSR-Synth suites show that STRIDE improves accuracy, OOD robustness, and structural recovery across multiple LLM backbones, with ablations and analyses confirming the contribution of its core components.