STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery

• URL: http://arxiv.org/abs/2605.17790v1
• Date: Mon, 18 May 2026 03:14:32 GMT
• Title: STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery
• Abstract summary: LLM-based equation discovery offers a promising route to recovering symbolic laws from data.<n>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.
• Score: 6.0574120466674986
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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