Skip to content
Log in
#graph neural networks Open access

Based on Neural and Symbolic Approaches to Program Generation and Explanation

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Explainable Artificial Intelligence (XAI)

Abstract

This paper explores a novel approach to program generation and explanation by integrating the strengths of neural networks and neuro-symbolic reasoning. Current program generation techniques often fall short due to a lack of explicit logical reasoning and interpretability. We propose a framework that combines a neural network encoder to translate problem representations into neural network states and a neuro-symbolic decoder to generate program code based on these states, leveraging knowledge graphs and logical rules. Furthermore, the neuro-symbolic decoder facilitates code explanation, providing a traceable execution path. The core claim is that this hybrid approach surpasses the limitations of single-technique methods, offering a more intelligent and explainable solution for automated program generation and understanding. The methodology outlines a system architecture, detailing the components and their interactions, and highlights the key innovations within the system.

DOI
View source

Similar papers

#graph neural networks Oct 2026

Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This article presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPPs). Unlike conventional machine learning–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data’s intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexity O(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom MPC. Compared to existing model-free MPC and deep learning neural network, the LPP-MPC has the lowest THD and root mean square error with the least computational time owing to its efficient linear structure and strong generalization capability.

Jianwu Zeng, Lizheng Cheng, V. Winstead et al. · 1 citation

Related blog posts