Self-Healing Cognitive Node Resolution System in Multi-Agent Systems

Abstract

The invention relates to a self-healing cognitive node solution system that detects, causally decomposes, and autonomously corrects cognitive deadlock, hallucination propagation, conflict of intent, and context poisoning problems that occur during runtime in large language model (LLM) based multi-agent systems (MAS) in the fields of machine learning, artificial intelligence, data analysis, large language model-based multi-agent systems, and metacognitive repair architectures, based on internal representation behaviors. The system in question can be used in verification, feedback, and cognitive integrity preservation applications on platforms incorporating artificial intelligence techniques and analytical methodologies, as well as in telecommunication operators and enterprise AI infrastructures.