Hallucinations in AI agents’ instances where generated outputs deviate from factual or intended information pose significant risks in high-stakes domains such as autonomous decision-making, medical diagnostics, and legal analysis. This research presents a predictive modeling framework for the autonomous detection and correction of AI-agent hallucinations using transformer-based architectures. The proposed method integrates multi-stage attention mechanisms, semantic consistency scoring, and contextual anomaly detection to identify hallucination patterns in real-time. A corrective submodule, trained via supervised fine-tuning and reinforcement learning from human feedback (RLHF), dynamically adjusts outputs toward verifiable ground truth without requiring human intervention. Experiments conducted on benchmark datasets across open-domain QA, dialogue systems, and multimodal reasoning tasks show a substantial reduction in hallucination rates while preserving fluency and relevance. The findings highlight the potential of transformer-driven predictive models to improve the trustworthiness and reliability of autonomous AI agents in critical applications.
Perumalsamy et al. (Mon,) studied this question.