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July 9, 20260 citationsOpen Access

Neuro-Symbolic Integration: Bridging Connectionist and Symbolic AI for Robust Reasoning

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RSRaj Kiran Sharma

Key Points

  • This research aims to create an architecture that merges neural and symbolic AI to enhance reasoning capabilities.
  • Developed a framework that integrates neural and symbolic AI subsystems.
  • Defined interface layers and conflict resolution protocols.
  • Analyzed emergent reasoning properties from the hybrid system.
  • The integrated framework facilitates robust reasoning across diverse tasks.
  • Found improvements in interpretability compared to pure connectionist systems.
  • Demonstrated effective conflict resolution between the AI subsystems.

Abstract

We present an architectural approach to integrating neural and symbolic AI subsystems for robust, interpretable reasoning. Pure connectionist systems excel at pattern recognition but lack systematic compositionality; symbolic systems offer logical rigour but struggle with perceptual grounding. Our neuro-symbolic integration framework defines interface layers, conflict resolution protocols, and emergent reasoning properties arising from hybrid architectures.

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Cite This Study

Raj Kiran Sharma (2026) studied this question.

synapsesocial.com/papers/6a4f3c252b81a944af575be7https://doi.org/10.5281/zenodo.21250491
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