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-rout
Record details
Published: 5 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 6 August 2026
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ethics.ai (5 August 2026), “A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination,” evidence record 16931, https://ethics.ai/record/16931 (originally published by arXiv cs.AI).
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