STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery
The article introduces STRIDE, a self-reflective agent framework designed to enhance the reliability of automatic equation discovery. It addresses limitations in existing systems that often misjudge useful equations and accumulate redundant information. STRIDE improves the process by integrating data-aware generation and feedback mechanisms, leading to better accuracy and robustness in symbolic regression tasks.
- ▪STRIDE coordinates data-aware generation, mixed-fitting evaluation, and critic-executor repair.
- ▪The framework enhances the reliability of equation discovery by providing shared feedback on fitted scores and candidate behavior.
- ▪Experiments show that STRIDE improves accuracy and out-of-distribution robustness across multiple LLM backbones.
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Computer Science > Artificial Intelligence arXiv:2605.17790 (cs) [Submitted on 18 May 2026] Title:STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery Authors:Jiarui Su, Songjun Tu, Bei Sun, Xiaojun Liang View a PDF of the paper titled STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery, by Jiarui Su and 3 other authors View PDF HTML (experimental) 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.
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