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arXiv Machine Learning

DIGEST: Discovery-Guided LLM Tuning for Symbolic Regression

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.

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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.

By Yating Ling, Wenjing Cun, Zhitang Chen
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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.

By Xinyu Pang, Zhanke Zhou, Xuan Li, Fangrui Lv, Shanshan Wei, Sen Cui, Bo Han, Changshui Zhang
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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.

By Peiyi Zheng, Yanming Kang, Hans De Sterck, Giang Tran
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arXiv AI
Jun 30

Evolutional Math: Cross-Validated Island-Model Genetic Programming for Interpretable Symbolic Regression on Small, Wide Datasets

arXiv:2606. 28381v1 Announce Type: cross Abstract: Symbolic regression via genetic programming routinely fails on small, wide datasets - a regime common in clinical-trial monitoring, biostatistics, and engineering pilot studies - by converging on bloated, overfit expressions that exploit correlation rather than prediction.

By Artem Andrianov (Cyntegrity Germany GmbH, Hofheim am Taunus, Germany)
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arXiv AI
Aug 3

Library Reachability in LSR-Synth: How Anti-Memorization Design Changes the Measurement of Symbolic Discovery

arXiv:2607. 28684v1 Announce Type: new Abstract: Existing benchmarks for scientific equation discovery are largely composed of well-known equations available in the public domain, making it difficult to determine whether a model is discovering laws from data or merely recalling answers from its training corpus.

By Zhan'ao Yao, Liang Yin, Zhihao Gao, Boxuan Zhang, Xiaoyu Wu, Linjing Li, Rongyan Wang, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu
llmsbenchmarks
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