Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs

Researchers have developed Deliberate Evolution (DE), an agentic framework that improves sample efficiency in symbolic regression by decoupling candidate proposal from search guidance. The method uses adaptive operators, analytical tools, and reflective memory to guide LLM proposals, outperforming existing LLM-based SR baselines across scientific domains while using only 40% of the standard sample budget.

arXiv:2606.04360v1 Announce Type: new Abstract: Symbolic regression SR discovers compact mathematical expressions from data, yet recent LLM-based evolutionary methods remain sample-inefficient because they rely mainly on scalar feedback such as MSE. We identify a core limitation: existing methods conflate candidate proposal with search guidance, requiring the LLM to infer how to evolve an expression, diagnose its errors, and reuse past experience from a single score. To address this, we propose Deliberate Evolution DE , an agentic framework that decouples symbolic generation from search control. DE guides LLM proposals with adaptive operators for search direction, analytical tools for structural diagnosis, and reflective memory for trajectory-level experience. Experiments on LLM-SRBench show that DE consistently outperforms representative LLM-based SR baselines across diverse scientific domains while using only 40% of the standard sample budget.