A Modular Neurosymbolic Framework for General-Purpose Reasoning: Bridging Symbolic and Deep Learning for Interpretable AI
Abhishek Pankaj Tiwari · 2025
Modern AI systems excel at perception tasks yet continue to struggle with general-purpose reasoning and interpretability, two pillars of trustworthy, human-aligned intelligence. We present NeuroLogicX, a modular neurosymbolic framework that demonstrates the potential for interpretable AI through its design, combining symbolic logic with deep learning components. Unlike task-specific hybrids, NeuroLogicX cleanly separates perception, reasoning, and explanation into independent modules, each connected via transparent interfaces. Our experimental evaluation on bAbI reasoning tasks shows that NeuroLogicX achieves competitive performance (94.2% accuracy) while maintaining complete reasoning transparency, outperforming pure neural baselines (87.3% accuracy) and rule-based systems (91.1% accuracy) with statistical significance (p<0.001). The modular design enables symbolic rules to guide and interact with learned neural representations, supporting both logical rigor and adaptive learning. While our evaluation demonstrates proof-of-concept viability on structured reasoning tasks, broader evaluation is needed to establish generalizability. By unifying deep learning with structured logic under a modular design, NeuroLogicX contributes toward building more transparent and auditable AI systems.