ARIA - Automated Research Intelligence Agent

ARIA (Autonomous Research Intelligence Agent) is a multi-agent pipeline that turns a clinical question into a rigorously synthesized, citation-backed answer, then interrogates its own sources for integrity issues most researchers never have time to check by hand. The core pipeline runs five stages: a Query Architect generates MeSH-optimized search strategies, a Literature Scout retrieves matching papers from PubMed and Europe PMC, a PRISMA Filter screens for relevance, an Evidence Synthesizer builds a structured, cited synthesis, and a Citation Builder formats references. The result is a clinical-grade literature review complete with a comparison table, PRISMA exclusion log, and predictive forecasting of where the evidence is heading. What sets ARIA apart is its Integrity Audit layer, five independent detection agents that stress-test the synthesis itself: a Citation Ghost Detector flags citations that don't actually support their attached claims, a Methodology Drift Tracker catches when a paper's results section doesn't match its stated methods, a Confidence Calibration Check flags language stronger or weaker than the underlying data supports, a Cross-Paper Contradiction Finder surfaces where cited studies genuinely conflict rather than just differ in topic, and a Reproducibility Score grades whether a study reports enough detail to be redone. Each runs in parallel against the same papers already retrieved, no extra fetching, and renders as color-coded, explainable flags across two views: internal consistency and external validity. Built on LangGraph for orchestration and Qwen2.5-72B served via vLLM on an AMD MI300X, ARIA treats research synthesis and research auditing as one system, not two separate tools.