This research proposes a comprehensive framework for understanding and implementing memory mechanisms in Large Language Models to improve reasoning and reduce hallucinations.

• Identifies three critical memory types: sensory memory (input prompts), short-term memory (context processing), and long-term memory (external knowledge)
• Explores both text-based and parameter-based memory approaches for LLMs
• Demonstrates how proper memory management leads to improved context retention and reduced hallucinations
• Provides engineering insights for developing more efficient and reliable AI systems

The structured memory approach offers significant potential for enhancing AI applications in enterprise settings by improving reasoning capabilities and factual accuracy.

Cognitive Memory in Large Language Models