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Multi-agent AI (LangGraph) to audit AI papers for reproducibility

PaperPulse, a browser-integrated multi-agent AI system built on a LangGraph orchestrator, audits scientific papers, news articles and web content for methodological rigor, statistical validity and code-to-paper alignment. The system runs four specialized agents in sequence — a Universal Extractor, a GitHub/Code Grounder, a Statistical Critic and a Citation-Grounded Synthesizer — behind a FastAPI backend, with a Chrome extension sending the active tab's URL and rendering the resulting structured JSON report as Markdown. PaperPulse positions itself against generic "Chat with PDF" wrappers by cross-referencing claims, searching GitHub for official repositories and flagging reproducibility gaps with exact citations.

read1 min views3 publishedNov 15, 2026
Multi-agent AI (LangGraph) to audit AI papers for reproducibility
Image: Michielbdejong (auto-discovered)

PaperPulse is a browser-integrated, multi-agent AI system that automatically audits scientific papers, news articles, and web content for methodological rigor, statistical validity, and code-to-paper alignment.

Unlike generic "Chat with PDF" wrappers, PaperPulse orchestrates specialized AI agents to cross-reference claims, search GitHub for official repositories, and flag reproducibility gaps with exact citations.

graph TD
    A[Chrome Extension: Active Tab] -->|Universal URL| B(FastAPI Backend)
    B --> C{LangGraph Orchestrator}
    C --> D[Agent 1: Universal Extractor]
    D --> E[Agent 2: GitHub/Code Grounder]
    E --> F[Agent 3: Statistical Critic]
    F --> G[Agent 4: Citation-Grounded Synthesizer]
    G --> H[(Structured JSON Report)]
    H -->|Rendered Markdown| A
    
    style C fill:#4f46e5,stroke:#312e81,stroke-width:2px,color:#fff
    style A fill:#0f172a,stroke:#334155,stroke-width:2px,color:#fff
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