arXiv AI
SRHarness: A Harness for Agentic Symbolic Regression
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A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
arXiv:2608. 04872v1 Announce Type: cross Abstract: 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.
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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.
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Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
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AutoSR: Automatic Symbolic Regression by Searching Research States
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