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EditSR proposes a two-layer framework combining a neural symbolic regression model with an edit-based Rectifier to efficiently rectify structural errors in generated expressions, reducing error accumulation and improving recovery of complex symbolic structures with limited extra cost.
A comprehensive survey on uncertainty quantification in symbolic regression, reviewing frequentist, Bayesian, and model selection approaches to address the lack of reliability support in real-world decision processes.
Deliberate Evolution (DE) is an agentic framework that improves LLM-based symbolic regression by decoupling candidate generation from search control, using adaptive operators, structural diagnosis tools, and reflective memory to achieve better results with only 40% of the standard sample budget.
This paper presents 'Additive Atomic Forests,' a framework for simultaneous symbolic recovery of functions and their antiderivatives using derivative algebra and self-expanding atom libraries. The method achieves strong performance on classification benchmarks and Feynman symbolic regression tasks while offering interpretable results.
This paper introduces DoLQ, a multi-agent framework that uses Large Language Models to perform both qualitative and quantitative evaluations for discovering ordinary differential equations from observational data.