VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering

• URL: http://arxiv.org/abs/2604.08549v1
• Date: Fri, 16 Jan 2026 09:08:17 GMT
• Title: VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering
• Abstract summary: We introduce VerifAI, an open-source expert system for biomedical question answering.<n>It integrates retrieval-augmented generation (RAG) with a novel post-hoc claim verification mechanism.<n>VerifAI ensures factual consistency by decomposing generated answers into atomic claims and validating them against retrieved evidence.
• Score: 0.5277756703318044
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework [60.82334952881798]
  Protein-Text Question Answering (QA) is crucial for interpreting biological sequences through natural language.<n>We propose 2D-ProteinRAG, a novel framework that empowers Large Language Models (LLMs) to operate within the gold-standard biological research workflow.<n>We show that 2D-Protein consistently achieves state-of-the-art performance, outperforming fine-tuned baselines and other RAG methods.
  arXiv  Detail & Related papers  (2026-05-17T05:03:24Z)
• NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code [49.610331036334316]
  We introduce NERFIFY, a framework that reliably converts NeRF research papers into trainable Nerfstudio plugins.<n>Code, data and implementations will be publicly released.
  arXiv  Detail & Related papers  (2026-02-28T20:57:32Z)
• MedTrust-RAG: Evidence Verification and Trust Alignment for Biomedical Question Answering [21.855579328680246]
  We propose MedTrust-Guided Iterative RAG, a framework designed to enhance factual consistency and hallucinations in medical QA.<n>First, it enforces citation-aware reasoning by requiring all generated content to be explicitly grounded in retrieved medical documents.<n>Second, it employs an iterative retrieval-verification process, where a verification agent assesses evidence adequacy.
  arXiv  Detail & Related papers  (2025-10-16T07:59:11Z)
• VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification [107.75781898355562]
  We introduce a novel framework, called VeriCite, designed to rigorously validate supporting evidence and enhance answer attribution.<n>We conduct experiments across five open-source LLMs and four datasets, demonstrating that VeriCite can significantly improve citation quality while maintaining the correctness of the answers.
  arXiv  Detail & Related papers  (2025-10-13T13:38:54Z)
• Semantic Visual Anomaly Detection and Reasoning in AI-Generated Images [96.43608872116347]
  AnomReason is a large-scale benchmark with structured annotations as quadruple textbfAnomAgent<n>AnomReason and AnomAgent serve as a foundation for measuring and improving the semantic plausibility of AI-generated images.
  arXiv  Detail & Related papers  (2025-10-11T14:09:24Z)
• Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations [49.671779378073886]
  We study question answering in the domain of radio regulations.<n>We propose a telecom-specific Retrieval-Augmented Generation (RAG) pipeline.<n>Our approach consistently improves generation accuracy across all tested models.
  arXiv  Detail & Related papers  (2025-09-11T17:43:42Z)
• MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection [84.75972919995398]
  This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles.<n>The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent, and (iv) a web-scraped data analyzer.<n>Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches.
  arXiv  Detail & Related papers  (2025-08-13T19:14:48Z)
• HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
  HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
  arXiv  Detail & Related papers  (2025-08-01T20:30:42Z)
• Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review [2.092154729589438]
  We present an agentic approach that encapsulates the hybrid RAG pipeline within an autonomous agent.<n>Our pipeline ingests bibliometric open-access data from PubMed, arXiv, and Google Scholar APIs.<n>A Llama-3.3-70B agent selects GraphRAG (translating queries to Cypher for KG) or VectorRAG (combining sparse and dense retrieval with re-ranking)
  arXiv  Detail & Related papers  (2025-07-30T18:54:15Z)
• Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG [51.120170062795566]
  We propose Divide-Then-Align (DTA) to endow RAG systems with the ability to respond with "I don't know" when the query is out of the knowledge boundary.<n>DTA balances accuracy with appropriate abstention, enhancing the reliability and trustworthiness of retrieval-augmented systems.
  arXiv  Detail & Related papers  (2025-05-27T08:21:21Z)
• Scientific QA System with Verifiable Answers [0.0]
  We introduce the VerifAI project, a pioneering open-source scientific question-answering system. The components of the system are (1) an Information Retrieval system combining semantic and lexical search techniques over scientific papers (Mistral 7B) and retrieved articles to generate claims with references to the articles from which it was derived, (2) a Retrieval-Augmented Generation (RAG) module using fine-tuned generative model (Mistral 7B) and retrieved articles to generate claims with references to the articles from which it was derived, and (3) a Verification engine, based on a fine-tuned DeBERTa
  arXiv  Detail & Related papers  (2024-07-16T08:21:02Z)
• Verif.ai: Towards an Open-Source Scientific Generative Question-Answering System with Referenced and Verifiable Answers [0.0]
  We present the current progress of the project Verifai, an open-source scientific generative question-answering system with referenced and verified answers. The components of the system are (1) an information retrieval system combining semantic and lexical search techniques over scientific papers (Mistral 7B) taking top answers and generating answers with references to the papers from which the claim was derived, and (3) a verification engine that cross-checks the generated claim and the abstract or paper from which the claim was derived.
  arXiv  Detail & Related papers  (2024-02-09T10:25:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.