arXiv:2602.23561v2 Announce Type: replace-cross Abstract: Symbolic regression (SR) has gained recent traction in AI-driven scientific discovery for learning closed-form physical laws. Yet existing methods are dominated by heuristic search or data-intensive approaches that often assume low-noise regimes and lack principled uncertainty quantification, while fully probabilistic SR formulations remain scarce. We introduce a scalable probabilistic framework for SR, VaSST, based on variational inference. VaSST uses soft symbolic trees, a continuous relaxation of symbolic expression trees in which di
The continuous drive for AI in scientific discovery necessitates advancements in methods that can robustly identify underlying physical laws, especially as AI applications mature. This development represents a key step in making AI-driven science more reliable and broadly applicable.
This development offers a principled and scalable probabilistic framework for symbolic regression, moving beyond heuristic methods to enable more reliable and interpretable AI-driven scientific discovery. It addresses a critical void in current AI approaches by offering robust uncertainty quantification, essential for high-stakes scientific applications.
The ability to perform symbolic regression with principled uncertainty allows AI systems to not only propose equations but also quantify their confidence, leading to more trustworthy and actionable scientific insights. This shifts the focus from purely predictive models to models that can explain their outputs with statistical rigor.
- · AI researchers
- · Scientific discovery platforms
- · AI-driven drug discovery
- · Materials science
- · Heuristic symbolic regression methods
- · Data-intensive, low-noise assumption models
- · Black-box AI approaches in science
More accurate and interpretable physical laws are derived from experimental data, accelerating scientific hypothesis generation.
New scientific theories and engineering principles emerge faster, leading to breakthroughs in various fields.
The development of 'AI scientists' becomes more feasible, capable of autonomously formulating and validating scientific theories with quantified certainty.
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Read at arXiv cs.LG