12 articles tagged with #symbolic-regression. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed new methods for extracting symbolic formulas from Kolmogorov-Arnold Networks (KANs), addressing a key bottleneck in making AI models more interpretable. The proposed Greedy in-context Symbolic Regression (GSR) and Gated Matching Pursuit (GMP) methods achieved up to 99.8% reduction in test error while improving robustness.
Researchers have developed a new framework that uses large language models to guide symbolic regression in discovering interpretable physical laws from high-dimensional materials data. The method reduces the search space by approximately 10^5 times compared to traditional approaches and successfully identified novel formulas for key properties of perovskite materials.
Researchers propose symbolic intermediaries—compact mathematical expressions derived from symbolic regression—to bridge the gap between Large Language Models and physics simulators by converting continuous numerical outputs into interpretable symbolic forms. LLM-based agents using this interface outperformed genetic algorithms by 19-53% on mechanism synthesis tasks, demonstrating that translating simulator behavior into symbolic language enables grounded geometric reasoning without model retraining.
Researchers introduce Mixture of Concept Bottleneck Experts (M-CBE), a framework that enhances interpretable AI by allowing multiple expert expressions to map concepts to predictions rather than a single predetermined function. The approach combines Linear M-CBE and Symbolic M-CBE variants to improve both accuracy and adaptability while maintaining human-understandable decision-making processes.
Researchers introduce SimpliPy, a rule-based simplification engine that accelerates symbolic regression by 100x compared to SymPy, enabling the amortized neural symbolic regression method Flash-ANSR to match state-of-the-art genetic programming approaches while producing more concise expressions.
Researchers introduce Influence-Guided Symbolic Regression (IGSR), a novel framework combining LLMs with Monte Carlo Tree Search to discover scientific equations more efficiently. The method uses granular influence scores to evaluate which components of equations contribute to accuracy, enabling systematic refinement. The approach demonstrated genuine discovery potential by identifying a novel relationship between DNA methylation and RNA Polymerase II pausing that was subsequently validated experimentally.
Researchers demonstrate that recombination-based operators in Cartesian Genetic Programming can achieve competitive performance when combined with proper hyperparameter optimization, challenging the long-held assumption that mutation-only approaches are superior for symbolic regression tasks.
Researchers introduced ECSEL, an explainable classification method that learns symbolic equations to create interpretable machine learning models. The approach outperforms competing symbolic regression methods on benchmarks while maintaining computational efficiency and classification accuracy comparable to traditional ML models.
Researchers propose GESR, a genetic programming method that uses BERT language models to intelligently guide mutations and crossovers in symbolic regression tasks, rather than relying on random evolutionary processes. The approach significantly improves computational efficiency compared to traditional genetic programming algorithms while maintaining strong performance across multiple regression problems.
Researchers introduce DoLQ, a new method that combines large language models with symbolic regression to discover ordinary differential equations from observational data. The approach integrates both qualitative physical reasoning and quantitative metrics through a multi-agent architecture, demonstrating superior performance over existing methods in recovering accurate symbolic equations.
Researchers have developed an open hypothesis-learning framework that combines symbolic regression with large language models to autonomously discover physical laws from scanning probe microscopy experiments. Rather than optimizing within predefined objectives, the system generates and evaluates candidate physical models directly from experimental data, demonstrating success in characterizing ferroelectric domain switching behavior.
Researchers developed a physics-constrained machine learning framework that uses genetic programming to automatically discover closed-form mathematical equations for modeling water retention in porous materials with complex pore structures. The approach represents mathematical expressions as binary trees and incorporates physical constraints to ensure scientifically valid solutions.
