LLM-ACES is a closed-loop method that combines LLM-proposed operator priors with disagreement-driven adaptive data acquisition to discover governing ODEs, reporting lowest median NMSE and 46-52% symbolic accuracy on 122 systems.
Symbolicgpt: A generative trans- former model for symbolic regression
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UNVERDICTED 6representative citing papers
EditSR improves neural symbolic regression accuracy on complex expressions by pretraining an edit-based rectifier on state-transition correction chains that enforce syntactic validity and condition edits only on the current expression state.
SDE recovers closed-form PMFs for discrete distributions via evolutionary search guided by domain priors, recovering all benchmark families with accurate parameters and improving mixture fits on real data.
FePySR uses a neural network to pre-extract valid features before PySR search, recovering more equations than baselines on benchmarks and identifying governing ODEs in 24 of 100 biological cases where PySR finds none.
GPT-4 models rediscover Langmuir isotherms and produce fits on Nikuradse pipe-flow data via iterative chain-of-thought prompting with scientific context and external code feedback.
ChatSR aligns scientific data encoders with LLMs to produce formulas that fit data and satisfy explicit priors, reporting SOTA results on 13 symbolic regression benchmarks plus zero-shot handling of unseen prior types.
citing papers explorer
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LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search
LLM-ACES is a closed-loop method that combines LLM-proposed operator priors with disagreement-driven adaptive data acquisition to discover governing ODEs, reporting lowest median NMSE and 46-52% symbolic accuracy on 122 systems.
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EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
EditSR improves neural symbolic regression accuracy on complex expressions by pretraining an edit-based rectifier on state-transition correction chains that enforce syntactic validity and condition edits only on the current expression state.
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Symbolic Density Estimation for Discrete Distributions
SDE recovers closed-form PMFs for discrete distributions via evolutionary search guided by domain priors, recovering all benchmark families with accurate parameters and improving mixture fits on real data.
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FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
FePySR uses a neural network to pre-extract valid features before PySR search, recovering more equations than baselines on benchmarks and identifying governing ODEs in 24 of 100 biological cases where PySR finds none.
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In Context Learning and Reasoning for Symbolic Regression with Large Language Models
GPT-4 models rediscover Langmuir isotherms and produce fits on Nikuradse pipe-flow data via iterative chain-of-thought prompting with scientific context and external code feedback.
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ChatSR: Multimodal Large Language Models for Scientific Formula Discovery
ChatSR aligns scientific data encoders with LLMs to produce formulas that fit data and satisfy explicit priors, reporting SOTA results on 13 symbolic regression benchmarks plus zero-shot handling of unseen prior types.