In diseases where clinical guidelines are incomplete, contested, or mutually contradictory, knowledge completeness and dynamic conflict-aware synthesis are two safety-critical properties that standard Retrieval-Augmented Generation systems do not provide. Therefore, we present \sysname, an adaptive retrieval calibration clinical question-answering agent for ME/CFS, a disease where diagnostic frameworks coexist and major guidelines actively contradict each other on treatment. ARCagent contributes three components. First, a 1,706-chunk, 10-source knowledge base with a structured inter-guideline conflict registry spanning all active ME/CFS diagnostic frameworks. Second, a conflict-aware retrieval calibration pipeline that re-ranks retrieved evidence using query-specific focus and conflict signals. Third, a benchmark scored by LLM-as-Judge, avoiding systematic underestimation averaging 10.1 percentage points caused by keyword matching. ARCagent achieves 95.3%, outperforming all base LLMs. Code is available at https://github.com/Yukyin/ARCagent.
An adaptive memory and reflection (AMR) agentic system, a multi-agent framework in which specialized agents use dedicated memory and reflection-based feedback to retrieve relevant prior cases and improve subsequent reasoning.
Pr Murugesan, Luo-Xiao Yang, Xue-Li Chen et al.· 0 citations
This submission to the MedReason 2026 challenge is described, covering multiple-choice (MCQ) and open-ended (OE) medical visual question answering (VQA) under fully offline, containerized inference, and it is found that MCQ retrieval must compare answer \emph{semantics} rather than answer labels.
SENTRYLINE is presented, a living guideline-aware clinical question answering system that retrieves guideline passages through a vectorless hierarchical RAG pipeline and returns a role-specific answer with inline citations, factual and temporal verification reports, and drift detection notes that surface when a guideli...
Tampu Ravi Kumar, Gaurav Najpande, Muhammad Ali Khan et al.· 1 citation· ⚡1
Abstract Robust and trustworthy biomedical question answering (QA) remains a critical challenge for large language models (LLMs), especially in complex domains such as rare diseases, where information is fragmented across multiple sources. MedHopQA 2025 benchmark introduces 10 000 multi-step reasoning questions curated...
Jongmyung Jung, Hyeon Hwang, Yein Park et al.· Database: The Journal of Bio...· 0 citations
Healthcare professionals spend significant time manually retrieving information from clinical guidelines during time-sensitive scenarios. This study presents a prototype conversational AI agent that leverages Retrieval-Augmented Generation (RAG) to enable rapid, natural-language access to obstetric clinical guidelines....
Isabel Sofía Tovar Sánchez, Rubén Manrique, Nathalia Ortega et al.· 2026 IEEE Colombian Conferen...· 0 citations
ClinAgent is a conversational system based on agentic Retrieval-Augmented Generation that enables clinicians and researchers to query clinical trial information in plain language and receive grounded, up-to-date responses across multi-turn interactions, highlighting the potential of agentic AI systems to improve the ac...
Antonino Vaccarella, Riccardo Cantini, Domenico Talia et al.· 0 citations
