Autonomous Knowledge Synthesis: Self-Improving LLM Architectures for Domain-Adaptive Intelligence (Journal of Generative Intelligence E: 3117-6429 P: 3117-6437, 2025)
The rapid evolution of Large Language Models (LLMs) has shifted focus from static knowledge processing to dynamic and autonomous knowledge synthesis. This paper introduces a futuristic yet implementable framework for self-improving, domain-adaptive LLM architectures capable of autonomously acquiring, synthesizing, refining, and validating knowledge with minimal human intervention. The research combines meta-learning, multi-agent cognition, feedback uncertainty modeling, vector-memory evolution, preference-optimized learning, and self-generated reasoning graphs. A prototype architectural pipeline—AKS-LLM (Autonomous Knowledge Synthesizer for LLMs)—is presented. Experimental simulations demonstrate that models equipped with adaptive knowledge loops outperform conventional static LLMs on domain transfer tasks by 63%, reduce hallucination by 41%, and achieve 2.7× faster convergence during incremental learning. The findings propose a foundational step toward developing self-evolving cognitive intelligence systems beyond conventional transformer limitations.