Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
arXiv:2605.29184v1 Announce Type: new Abstract: Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by inefficient search strategies and coarse feedback signals. Current methods typically guide LLMs using scalar metrics (e.g., global Mean Squared Error), which fail to identify which components of a proposed equation are driving performance or causing error. We introduce \textit{Influence-Guided Symbolic Regression} (IGSR), a method that frames equation discovery as an iterative two-step process combin
The proliferation of Large Language Models (LLMs) and the increasing demand for automated scientific discovery are driving research into more efficient interaction methods.
This development could significantly accelerate scientific discovery by enabling LLMs to identify fundamental physical laws and relationships with greater precision and autonomy.
LLMs can move beyond simple pattern recognition to actively formulate and refine scientific equations, potentially transforming R&D methodologies across various domains.
- · AI/ML researchers
- · Scientific R&D sectors
- · Pharmaceuticals and materials science
- · AI platform providers
- · Traditional manual scientific discovery methods
- · Research reliant on brute-force computational searches
LLMs will become more effective tools for complex problem-solving in scientific contexts.
This could lead to a faster pace of innovation and the discovery of novel materials, drugs, or physical principles.
Automation of scientific discovery could redefine the role of human scientists, shifting focus to problem formulation and interpretation rather than equation derivation.
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Read at arXiv cs.LG