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ARIA Project Status

Last updated: May 5, 2026

What Was Built

Multi-agent pipeline in agent/agent.py with 5 stages:

  1. Query Architect: generates 5 MeSH-optimised PubMed queries via Qwen2.5-72B on AMD MI300X
  2. Literature Scout: fetches from PubMed and Europe PMC in parallel, deduplicates by PMID
  3. PRISMA Filter: automatic inclusion/exclusion screening with one-line reasons, user can override any decision
  4. Evidence Synthesiser: structured synthesis with Background, Key Findings, Level of Evidence, Conflicting Evidence, Research Gaps, Clinical Implications
  5. Citation Builder: formatted references with PMID, synthesis runs on PRISMA-included papers only

Additional Features

  • SSE streaming: real-time 5-stage progress bar with percentage
  • PRISMA-style paper screening: automated include/exclude with rationale, user override buttons
  • Evidence confidence scoring: green/yellow/red badges on each synthesis section with hover tooltips
  • Abstract viewer: click any citation to expand full abstract inline
  • PDF export: download formatted report via ReportLab
  • Selective literature review: checkboxes on citations, user picks papers, generates focused academic paragraph
  • Predictive model: constructive and destructive forecasts as a post-synthesis stage
  • Evidence comparison table: LLM extracts structured data table with real author names, no duplicates
  • Follow-up Q&A: ask follow-up questions after synthesis, answered using already-fetched papers
  • Query refinement suggestions: 3 AI-generated follow-up research questions based on synthesis gaps
  • Session history: queries saved to sessions.json, reloadable from sidebar
  • Rate limit retry logic: automatic backoff on API errors
  • SSL patch for PubMed and Europe PMC Entrez on corporate/university networks

Tech Stack

  • LLM: Qwen2.5-72B-Instruct on AMD MI300X via vLLM 0.17.1
  • Agent Framework: LangGraph + LangChain
  • Literature Retrieval: BioPython Entrez / PubMed NCBI + Europe PMC
  • Web Framework: Flask with SSE streaming
  • PDF: ReportLab
  • Frontend: HTML, CSS, vanilla JS
  • Runtime: Windows 11, Python 3.11, RTX 3060 12GB (local) + AMD MI300X 192GB (inference)

API Endpoints

  • GET / � main UI
  • GET /stream � SSE pipeline stream
  • POST /query � standard pipeline (fallback)
  • POST /score � confidence scoring
  • POST /predict � predictive model
  • POST /selective-review � focused literature review from selected papers
  • POST /extract-table � evidence comparison table
  • POST /export-pdf � PDF report download
  • POST /followup � follow-up question against existing synthesis
  • POST /suggest-queries � 3 AI-generated follow-up research questions
  • GET /sessions � load query history
  • POST /sessions/save � save completed query

Environment

  • VLLM_BASE_URL: set via HF Space environment variable
  • VLLM_API_KEY: EMPTY
  • Python venv at ./venv
  • Start server: venv\Scripts\activate && python app.py
  • AMD MI300X: DigitalOcean droplet, 192GB VRAM

Deployment

What Remains

  1. Demo video (under 5 minutes)
  2. Final lablab.ai submission