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A Theoretical Framework for Self-Directed Knowledge Acquisition in Agentic Large Language Models

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Overview

Large Language Models (LLMs) possess remarkable generative capabilities but are fundamentally constrained by their static, pre-trained knowledge. This paper introduces a novel theoretical architectural framework for an agentic LLM system designed for self-directed knowledge acquisition.

The proposed system aims to autonomously identify its knowledge gaps, explore external information sources such as the World Wide Web, rigorously validate acquired data, and integrate new, verified knowledge into an accessible, modifiable external repository—without direct human intervention in the core acquisition loop and without altering the LLM’s underlying parametric weights.

Key Components

  • Curiosity Service: Identifying knowledge lacunae
  • Subconscious Mind: Temporary concept storage
  • Agentic Web Exploration: Information retrieval module
  • Ingestion and Processing: Data extraction unit
  • Validation Pipeline: Multi-stage data integrity verification
  • Data Admissibility Rules: Filtering engine
  • Long-Term Memory: Graph-Retrieval Augmented Generation (Graph-RAG) for persistent knowledge integration

Impact

This framework positions itself as a roadmap for future research in continuously learning AI systems, with foundational aspects prototypable using current technologies.