A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compresses heterogeneous search failures into a scalar score and a single prompt. We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views. A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory. During search, evaluator feedback characterizes reliability and productivity, updates role-level utilities, and routes elite motifs, failure traces, and validity diagnostics to d...
| Authors | Wenxiao Zhao, Dong Liu, Kaiyi Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 August 2026 |
| Last updated at source | 6 August 2026 |
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