The field of symbolic regression and program analysis is moving towards developing more advanced and efficient techniques for discovering mathematical expressions and reasoning about program behaviors. One of the key areas of focus is on improving the scalability and performance of symbolic execution, which is a powerful program analysis technique that can formally reason about the correctness of program behaviors and detect software bugs. Researchers are also exploring new approaches to symbolic regression, such as using pre-training frameworks and modular representations, to improve the accuracy and interpretability of discovered expressions. Additionally, there is a growing need for standardized benchmarks and evaluation metrics to compare and improve the performance of different symbolic regression algorithms. Noteworthy papers in this area include:

• Thinking Outside the Template with Modular GP-GOMEA, which presents a modular representation for GP-GOMEA that allows multiple trees to be evolved simultaneously, enhancing interpretability and accuracy.
• Advancing Symbolic Discovery on Unsupervised Data: A Pre-training Framework for Non-degenerate Implicit Equation Discovery, which introduces a novel pre-training framework to discover implicit equations from unsupervised data, effectively tackling the problem of degenerate solutions.
• Empc: Effective Path Prioritization for Symbolic Execution with Path Cover, which proposes a novel path prioritization technique that leverages a small subset of paths as a minimum path cover, significantly improving code coverage and bug findings.