Russian Researchers Build Real-Time Monitor to Catch AI Hallucinations

A team at Reshetnev University in Krasnoyarsk has developed a method to detect and flag when neural networks generate false or fabricated information-a persistent problem known in the industry as hallucination. The approach could be applied across education, government services, healthcare, and legal tech.

Current solutions like Retrieval-Augmented Generation (RAG) reduce hallucinations by pulling from a knowledge base before answering, but they still stumble on typos, contradictory queries, or incomplete data. The Reshetnev team, led by associate professor Anastasia Polyakova, tackled this by first building a classifier that identifies common hallucination patterns. They then created an automated stress-testing pipeline that generates test queries, compares outputs against reference answers, and measures accuracy using semantic similarity metrics.

The key deliverable is a prototype real-time monitoring module. It logs every incoming query and dialog context, assigns a confidence score to each model response, and alerts an operator if the risk of an unreliable answer is high. Because the module is model-agnostic, it can be dropped into any neural network-from university chatbots to government portals and medical or legal assistants-without requiring a full system overhaul.