Neural Symbolic Regression (NSR) uses neural networks as functional preconditioners to learn smooth, noise‑robust approximations of target functions in an interaction‑aware nonlinear feature space. A subsequent LASSO step extracts sparse, interpretable closed‑form expressions, while distributed hyperparameter optimization with Ray Tune and ASHA scheduling improves predictive accuracy and symbolic fidelity. Experiments on the Nguyen benchmark demonstrate that NSR outperforms SINDy and untuned neural baselines in RMSE, noise robustness, and out‑of‑distribution generalization, with ablation studies highlighting the importance of feature interactions, neural depth, and tuning strategies.
By Ravi Kumar U, Sumitra S
arXiv:2511. 04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data.
By Giorgio Morales, John W. Sheppard
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.
By Peiyi Zheng, Yanming Kang, Hans De Sterck, Giang Tran
arXiv:2606. 07704v1 Announce Type: cross Abstract: Symbolic regression aims to uncover explicit scientific laws from data.
By Zeyu Xia, Jun Zhu, Dong Yan
arXiv:2610.19942v1 Announce Type: new
Abstract: We introduce MorphingNet, a network for symbolic regression (SR) whose units change their function continuously. Instead of searching over expressions...
By Jincheng Gao, Jiahe Yi, Shuo Sun
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.
By Boxiao Wang, Kai Li, Zhiwei Chen, Yang Huang, Runxiang Wang, Ziwen Zhang, Yifan Zhang, Jian Cheng
arXiv:2609.36187v1 Announce Type: new
Abstract: Symbolic Regression (SR) is a data-driven method for scientific discovery which searches for interpretable analytical relationships within data. Recent...
By Cristina Rossetti, Anna V. Kononova, Thomas B\"ack, Fei Liu, Niki Van Stein
arXiv:2608. 02628v1 Announce Type: cross Abstract: Symbolic regression (SR) is the task of discovering underlying patterns from data and representing them using mathematical expressions.
By Yusong Deng, Yanjie Li, Weijun Li
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.
By Yating Ling, Wenjing Cun, Zhitang Chen
The paper introduces an exhaustive dataset of univariate canonical skeletons for neural symbolic regression, generated deterministically via a constrained context-free grammar. These skeletons serve as generic structural representations that eliminate redundant expressions and provide uniform operator distribution, thereby avoiding simplicity bias. The authors also present a sampling strategy for clean numerical data and benchmark 150 SR problems across three tiers, demonstrating challenges for current state‑of‑the‑art methods.
By Giorgio Morales, Anik\'o Ek\'art
DIGEST is a new method for symbolic regression that combines a lightweight LLM with Genetic Programming. It uses a pretrained decoder block as a symbolic policy to seed GP populations, providing flexible generation of candidate expressions. The approach also feeds high‑quality GP discoveries back into the LLM to refine future generations, leading to improved recovery rates and faster search times on benchmark datasets.
By Kaifeng Jin, Ruizhong Qiu, Zihao Li, Xiao Lin, Dongqi Fu, Yinglong Xia, Hong Li, Hanghang Tong
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.
By Somjit Roy, Pritam Dey, Bani K. Mallick, Debdeep Pati