Researchers presented the CLEAR framework for verifying medical data

Researchers introduced CLEAR — an agent framework designed to resolve contradictions in medical data when working with large language models. The system is developed to combat hallucinations and inaccuracies that arise when using RAG architectures in critical areas.

The algorithm works through three independent channels of information acquisition: parametric knowledge of the model itself, local medical corpora, and dynamically extracted data from external sources. The aggregator verifies candidate answers by matching them with the evidence base and checking the quality of each source for data agreement or conflict.

The adjudication module makes a decision to retain or correct the answer using override-guard and challenge-audit mechanisms. In case of detecting irreconcilable contradictions, the system automatically initiates targeted re-search and re-verification procedures, which increases the actual accuracy of conclusions.

Using CLEAR allows minimizing the risks associated with the obsolescence of knowledge in LLM weights. Unlike standard RAG systems, which often take contradictory data for truth, this framework provides multi-level evidence verification, which is critical for clinical recommendations and diagnosis.