Neuro-Symbolic Reasoning: Performance, Challenges, and Benchmarks: A Systematic Literature Review

Abstract

Traditional AI struggles with interpretability and generalisation in complex reasoning tasks, limiting its effectiveness in domains like healthcare and robotics. This systematic review aims to evaluate Neuro-Symbolic Reasoning (NeSy) frameworks, which integrate symbolic reasoning with neural networks to address these challenges. 28 empirical studies (2017–2024) were analysed from arXiv, IEEE Xplore, PubMed, and conferences, using a PRISMA-guided methodology with inclusion criteria focusing on NeSy frameworks, performance, and scalability. Results show NeSy systems achieve a mean accuracy of 93.00% (SD 5.35%) across visual reasoning, NLP, robotics, and healthcare, outperforming neural baselines by 26.00% on average (SD 18.29%). Methodologies like pLogicNet, DiffLogic, and NSFR enhance generalisation, e.g., in spatial reasoning tasks. However, computational inefficiencies and explainability gaps persist (mean quality score 7.53/9, SD 1.04). NeSyBench, using datasets like MIMIC-III and CLEVR, and NeSyEval for standardised metrics (accuracy, F1-score, interpretability), was proposed to refine NeSy systems. This review provides a roadmap for developing interpretable, scalable AI, advancing applications in diagnostics and autonomous systems.