> **VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering**
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> View PDF HTML (experimental) Abstract:We introduce VerifAI, an open-source expert system for biomedical question answering that integrates retrieval-augmented generation (RAG) with a novel post-hoc claim verification mechanism. Unlike standard RAG systems, VerifAI ensures factual consistency by decomposing generated answers into atomic claims and validating them against retrieved evidence using a fine-tuned natural language inference (NLI) engine. The system comprises three modular components: (1) a hybrid Information Retrieval (IR) module optimized for biomedical queries (MAP@10 of 42.7%), (2) a citation-aware Generative Component fine-tuned on a custom dataset to produce referenced answers, and (3) a Verification Component that detects hallucinations with state-of-the-art accuracy, outperforming GPT-4 on the HealthVer benchmark. Evaluations demonstrate that VerifAI significantly reduces hallucinated citations compared to zero-shot baselines and provides a transparent, verifiable lineage for every claim. The full pipeline, including code, models, and datasets, is open-sourced to facilitate reliable AI deployment in high-stakes domains. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2604.08549 [cs.IR] (or arXiv:2604.08549v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2604.08549 arXiv-issued DOI via DataCite Journal reference: Sumitted to IEEE Access,2026 Submission history From: Nikola Milošević Dr [view email] [v1] Fri, 16 Jan 2026 09:08:17 UTC (2,978 KB)

tl;dr: VerifAI is an open-source expert system for biomedical question answering that integrates retrieval-augmented generation with a novel post-hoc claim verification mechanism to ensure factual consistency.