STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917506595160064 |
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| author | Su, Jiarui Tu, Songjun Sun, Bei Liang, Xiaojun |
| author_facet | Su, Jiarui Tu, Songjun Sun, Bei Liang, Xiaojun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_17790 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery Su, Jiarui Tu, Songjun Sun, Bei Liang, Xiaojun Artificial Intelligence 68T05, 68T20, 68T42, 68W30 I.2.6; I.2.8; I.1.2 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. |
| title | STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery |
| topic | Artificial Intelligence 68T05, 68T20, 68T42, 68W30 I.2.6; I.2.8; I.1.2 |
| url | https://arxiv.org/abs/2605.17790 |