StudSar: A Neural Associative Memory System for Artificial Intelligence

Abstract

StudSar is a novel neural associative memory system engineered to emulate human-like mechanisms for forming, storing, and retrieving memories in artificial intelligence (AI), addressing critical limitations in existing memory models. Inspired by human learning strategies, StudSar processes extensive textual data through a structured workflow that segments inputs into semantically rich blocks, generating 384-dimensional embeddings using the ‘all-MiniLM-L6-v2’ model from the Sentence Transformers library. These embeddings serve as associative markers, enabling real-time knowledge integration and precise, similarity-based retrieval via a custom StudSar Neural network implemented in PyTorch. Through iterative refinements, StudSar has evolved to incorporate advanced features, including dynamic memory updates, enhanced contextual handling, and metadata integration, such as emotional tags (e.g., “curiosity”), reputation scores, and usage frequency; mimicking human memory dynamics where frequently accessed information is reinforced. Unlike conventional AI assistants, which struggle to accurately link to specific fragments within large inputs, particularly as data scales, StudSar excels at pinpointing exact information with context-aware precision, even in expansive corpora. StudSar introduces Perfect Unified Memory, consolidating model knowledge, user documents, and metadata into a single store, eliminating the need for external vector databases. It also incorporates Native Emotions for affective tagging, Dynamic Reputations for real-time recall probability adjustments based on user feedback, and Total Persistence for saving and reloading entire memory states, ensuring scalability and high retrieval accuracy (cosine similarities of 0.665–0.798 for routine queries and 0.393–0.579 for challenging tasks). Unlike conventional AI assistants, which struggle to accurately link to specific fragments within large inputs, StudSar excels at pinpointing exact information with context-aware precision, even in expansive corpora. This paper elucidates StudSar’s architecture, detailing its five-stage pipeline: text segmentation, embedding generation, marker creation, network integration, and query-driven retrieval. Experimental results demonstrate robust retrieval accuracy, persistent memory across sessions, and adaptability to new data, validated through tests on diverse queries and metadata-driven scenarios. StudSar’s scalability and modular design position it as a transformative contribution to next-generation AI systems, with applications in conversational agents, personalized learning platforms, and knowledge management. By bridging intuitive human memory processes with technical innovation, StudSar lays a foundation for advanced cognitive features, such as emotional state modeling and memory consolidation, paving the way for AI systems that more closely emulate human intelligence.