Enhancing SLM’s With Neuro-Symbolic Reasoning, Experience Buffers, Memory Consolidation and Hyperdimensional Computing

Introduction

Artificial intelligence has grown exponentially over the past few decades, with neural networks dominating the space due to their powerful ability to learn from vast amounts of data. However, they often lack interpretability and struggle with reasoning tasks that require more than pattern recognition.

On the other hand symbolic AI, which ruled the AI landscape in earlier times excels at handling rules, logic and abstract reasoning but it often falls short when dealing with noisy, real-world data. A promising frontier in AI development is the integration of these two approaches in turn leading to what is now known as neuro-symbolic computing.

In this article, we will take an in-depth look at an advanced neuro-symbolic system that not only blends the best of neural and symbolic reasoning but also incorporates hyperdimensional computing (HDC), experience buffers and memory consolidation to make small neural models far more capable and efficient. These enhancements enable small models to effectively “learn from experience” and adapt their symbolic reasoning based on past interactions.