arXiv:2606.04360v1 Announce Type: cross 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 c
Ongoing research into more efficient and robust LLM applications is actively addressing current limitations in complex problem-solving like symbolic regression, driven by the rapid evolution of agentic AI frameworks.
This development proposes a more sample-efficient approach to symbolic regression using LLMs, which could significantly accelerate scientific discovery and the development of more robust AI systems by improving how LLMs learn and generalize from data.
The proposed 'Deliberate Evolution' framework decouples generation from search guidance in LLM-based symbolic regression, potentially leading to more accurate and less data-intensive discovery of mathematical expressions.
- · AI researchers
- · Scientific discovery platforms
- · Machine learning solution providers
- · Data-driven industries
- · Inefficient LLM-based SR methods
- · Computational processes requiring large datasets for SR
Improved efficiency in symbolic regression enables faster development of explanatory AI models for complex systems.
Accelerated scientific research through automated discovery of underlying mathematical principles and physical laws.
New classes of AI applications become feasible due to the ability to quickly derive compact, interpretable models from limited data.
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Read at arXiv cs.LG