arXiv AI
Jun 9
FunctionEvolve: Structure-Guided Symbolic Regression with LLMs
arXiv:2606. 07704v1 Announce Type: cross Abstract: Symbolic regression aims to uncover explicit scientific laws from data.
EvoMO-SR: Multiobjective LLM-based Evolution of Symbolic Expressions with substructure guidance
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arXiv AI
Jun 30
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
arXiv:2605. 23272v2 Announce Type: replace-cross Abstract: Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data.
arXiv Machine Learning
Aug 27
InsightSR: Refining Symbolic Regression Search Spaces via Parallel Semantic and Structural LLM Guidance
InsightSR is a new framework that integrates Large Language Models (LLMs) with the PySR genetic programming engine to refine symbolic regression search spaces. It employs two LLM-guided pathways: a Semantic Seed Pathway that generates dimensionally consistent functional skeletons, and a Structural Feature Pathway that suggests nonlinear feature transformations. Over successive iterations, these pathways expand the input space and shift the search toward shallow, semantically informed trees, with a feedback loop that evaluates and refines candidate features. The method achieves a 95% exact recovery rate on the Feynman benchmark and 80.18% accuracy on the LLM-SRBench LSR-Transform task, outperforming existing genetic programming and neural-symbolic approaches while preserving strong out-of-distribution generalization.
arXiv Machine Learning
Sep 21
MOSAIC-SR: Transformer-Guided Symbolic Regression for Scientific Equation Recovery
MOSAIC‑SR is a new symbolic regression method that combines a pretrained Transformer with search‑based refinement. The Transformer generates multiple initial sketches, which seed searches that jointly recover equation structure and constants using scale‑aware optimization and symbolic repair. On the SRSD‑Feynman dataset and six other benchmarks, MOSAIC‑SR achieves the highest symbolic solution rate and ranks among the top two in predictive accuracy, even when irrelevant dummy variables are present.
arXiv AI
Aug 3
MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models
arXiv:2607. 29561v1 Announce Type: cross Abstract: Symbolic Regression (SR) aims to discover analytical equations from observational data and plays a central role in scientific modeling.
arXiv Machine Learning
Aug 31
Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests
The paper presents a probabilistic symbolic regression framework that models mathematical expressions as ensembles of symbolic trees, using a regularizing prior to control complexity and an Occam’s window-based posterior to capture uncertainty across plausible models. It provides theoretical guarantees on posterior concentration, including near‑parametric rates when an exact finite formula exists and oracle results under misspecification. Empirical results show the method outperforms state‑of‑the‑art competitors in predictive accuracy, symbolic complexity, and structural recovery on benchmark scientific equations and a materials discovery task.
arXiv Machine Learning
Jun 9
Decomposable Neuro Symbolic Regression
arXiv:2511. 04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data.
arXiv Machine Learning
Aug 6
DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery
arXiv:2608. 05120v1 Announce Type: new Abstract: Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes.
Hugging Face Trending Papers
Aug 5
DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery
Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes. Symbolic regression (SR) has emerged as a powerful data-driven approach for identifying interpretable kinetic models, but usually operates without domain knowledge, often exploring physicochemically implausible models.
arXiv Machine Learning
Sep 7
SMILE: Bridging Continuous Optimization and Discrete Symbolic Recovery
SMILE (Sine, Multiplication, Identity, Logarithm, Exponential) is a hybrid framework that merges continuous gradient-based optimization with discrete symbolic recovery for symbolic regression. It operates in three stages: structural analysis to uncover the compositional hierarchy of the target expression, continuous optimization to learn parameters of a network using interpretable activations, and symbolic recovery via structured pruning, coefficient optimization, and rounding to produce a compact expression with exact symbolic constants. Evaluated on SRBench, SMILE achieves the highest symbolic solution rate under high noise, remains on the Pareto front of accuracy versus complexity, and recovers simpler expressions much faster than competing methods.
arXiv Machine Learning
Jun 4
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
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.
arXiv Machine Learning
Jul 14
LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation
arXiv:2607. 10546v1 Announce Type: new Abstract: Discovering governing partial differential equations (PDEs) from noisy observational data is a fundamental challenge in scientific machine learning.