WorldMind Evidence vs Traditional RAG
How Entity Extraction Works
WorldMind Evidence extracts three key entities from your query to ensure precise matching with clinical evidence:
Endpoint
The clinical measurement being queried
Population
The patient group being studied
Timepoint
When the measurement was taken
Why this matters: By matching these entities, WorldMind Evidence ensures answers are based on evidence that precisely matches your query's clinical context, reducing false positives by 4x compared to traditional RAG.
About Our Research
This proof-of-concept implements the Licensing Oracle framework from our paper "Stemming Hallucination in Language Models Using a Licensing Oracle" (arxiv.org/abs/2511.06073). The paper introduces a quality control mechanism that evaluates queries before generating answers, abstaining when confidence is too low. This POC extends the theoretical framework with entity-based validation—extracting clinical entities (endpoint, population, timepoint) and matching them against structured evidence holons. The result is a system that achieves 4x fewer false positives than traditional RAG while maintaining full source traceability. The entity extraction layer addresses the "subtle misbinding" problem where queries have high similarity to evidence but don't precisely match the clinical context.
Read the Full PaperTeam
Richard Ackermann
Founder of WorldMind and co-author of the Licensing Oracle framework, with 30+ years of experience in regulated domains including large-scale assessment systems.
LinkedIn ProfileDr. Simeon Emanuilov
PhD, AWS Certified. Led technical implementation and AWS architecture for this proof-of-concept, focusing on serverless design and production readiness.
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Extracted Entities (Schema Validation)
Licensing Decision
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