DrugClaw and DrugAudit: A Primary-Source-Grounded Agent and Authority-Aware Benchmark for Drug-Information Question Answering
arXiv:2606.01434v1 Announce Type: new Abstract: Drug-information question answering is a high-stakes setting where hallucinated facts can mislead clinical decision-making and the provenance of each cited fact matters as much as the fact itself. We present DrugClaw, a multi-agent retrieval-augmented system that queries a registry of drug and pharmacovigilance skills via a reflection-driven state-machine workflow and returns answers grounded in primary regulatory or peer-reviewed records. We also contribute DrugAudit, a 3,772-item authority-aware benchmark with an evaluation panel that scores up
The increasing sophistication of AI models and the critical need for verifiable, non-hallucinated information in high-stakes fields like medicine demand agentic solutions with robust grounding.
This development addresses the critical challenge of AI hallucination in sensitive areas, improving reliability and trustworthiness for medical and pharmaceutical applications.
The introduction of primary-source-grounded AI agents and authority-aware benchmarks sets a new standard for drug-information QA, moving beyond simple factual recall to provenance verification.
- · Pharmaceutical industry
- · Healthcare providers
- · Patients
- · AI safety and ethics researchers
- · AI models prone to hallucination without grounding
- · Generic, unverified drug information sources
Improved accuracy and reliability of AI-driven drug information, reducing medication errors.
Accelerated drug discovery and development through more efficient and trustworthy information retrieval.
Potential for new regulatory frameworks and certifications for AI agents operating in high-stakes informational domains.
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