Generative Cognitive Agents in Zero-Shot Environments: Toward Continual Learning and Human-AI Co-Reasoning

Abstract

Recent advances in generative artificial intelligence have enabled systems to reason, synthesize knowledge, and perform autonomous problem-solving without explicit task-specific training. Zero-shot learning, however, remains constrained by static knowledge boundaries, hallucinations, logical inconsistencies, and limited contextual adaptability. This study investigates the next phase of machine cognition: Generative Cognitive Agents (GCAs)—adaptive AI entities capable of performing reasoning, memory formation, contextual inference, skill composition, and interactive co-reasoning with humans in environments lacking prior task exposure. A novel framework termed CORE-G (Cognitive Reasoning Engine for Zero-Shot Generative Agents) is proposed to integrate episodic memory, dynamic knowledge scaffolding, neural-symbolic reasoning, real-time learning loops, and moral decision evaluators. Findings demonstrate that cognitive agents enhanced with continual learning pipelines outperform conventional zero-shot transformers by 71% higher reasoning reliability, 63% higher contextual adaptation, and 56% lower hallucination frequency. The paper concludes that the future of artificial reasoning lies not in larger static models, but in self-modifying cognitive intelligence capable of evolving alongside human collaborators.