STRIDE: Self-Reflective AI Framework for Equation Discovery
A new framework called STRIDE has been introduced by researchers to enhance the dependability of equation discovery using LLMs. Unlike conventional loops that generate candidates, adjust parameters, evaluate outcomes, and recycle chosen examples—which can misidentify valuable structures due to unreliable fitting, overlook nearly accurate equations, and build unnecessary memories—STRIDE integrates data-aware generation, mixed-fitting assessment, critic-executor correction, and diversity-preserving semantic memory. By converting fitted scores and candidate actions into collective feedback, it facilitates a closed-loop process for proposing, evaluating, refining, and reusing equations. Tests on symbolic-regression benchmarks and LSR-Synth suites indicate that STRIDE boosts accuracy, robustness against out-of-distribution data, and overall reliability. This research addresses significant challenges in AI-driven scientific discoveries, specifically in recovering symbolic laws from data. The findings are available on arXiv with the identifier 2605.17790.
Key facts
- STRIDE is a self-reflective agent framework for LLM-based equation discovery.
- It improves reliability by coordinating data-aware generation, mixed-fitting evaluation, critic-executor repair, and diversity-preserving semantic memory.
- Traditional generation-centered loops can misjudge skeletons, discard near-correct equations, and accumulate redundant memories.
- STRIDE uses shared feedback from fitted scores and candidate behavior.
- Experiments on symbolic-regression benchmarks and LSR-Synth suites show improved accuracy and OOD robustness.
- The framework is designed to recover symbolic laws from data.
- Published on arXiv with identifier 2605.17790.
- STRIDE enables a closed-loop discovery process.
Entities
Institutions
- arXiv