A Survey on Socratic Meta-Cognition for LLM Truth Verification and related Frameworks

The proliferation of large language models (LLMs) across high-stakes domains necessitates a paradigm shift in how their truthfulness and reliability are assessed. This paper surveys the core challenges of LLM untruthfulness, specifically addressing the phenomena of hallucinations, pervasive bias, and the fundamental epistemological problem of a lack of a single ground truth. It proposes that Socratic metacognition—an integrated approach combining the introspective self-regulation of metacognition with the critical, question-based inquiry of the Socratic method—offers a robust solution. The report delineates the theoretical foundations of both human and computational metacognition, operationalizes the Socratic method for artificial intelligence (AI), and synthesizes existing architectural and conceptual frameworks. By examining models such as the Metacognitive Integrated Dual-Cycle Architecture (MIDCA) and the SocraticAI multi-agent system, a unified conceptual framework is proposed. This framework envisions a self-regulating system that uses a structured, question-based dialogue to identify and rectify its own logical inconsistencies and factual inaccuracies. The paper's contribution is a synthesis of disparate research fields, demonstrating a path toward building more reliable, transparent, and trustworthy LLMs that can navigate complex, ambiguous information spaces with a greater degree of verifiability.