Ai2 Scholar QA: Organized Literature Synthesis with Attribution
About
Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We introduce Ai2 Scholar QA, a free online scientific question answering application. To facilitate research, we make our entire pipeline public: as a customizable open-source Python package and interactive web app, along with paper indexes accessible through public APIs and downloadable datasets. We describe our system in detail and present experiments analyzing its key design decisions. In an evaluation on a recent scientific QA benchmark, we find that Ai2 Scholar QA outperforms competing systems.
Amanpreet Singh, Joseph Chee Chang, Chloe Anastasiades, Dany Haddad, Aakanksha Naik, Amber Tanaka, Angele Zamarron, Cecile Nguyen, Jena D. Hwang, Jason Dunkleberger, Matt Latzke, Smita Rao, Jaron Lochner, Rob Evans, Rodney Kinney, Daniel S. Weld, Doug Downey, Sergey Feldman• 2025
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Deep Research | ResearchQA | Score75 | 42 | |
| Long-form research | DRB | Score36.1 | 39 | |
| Deep Research | HealthBench | Score32 | 38 | |
| Science Question Answering | ResearchQA | Accuracy (ResearchQA)75 | 37 | |
| Deep Research Report Generation | DRB | Overall Score36.1 | 24 | |
| Search-based Question Answering | SQA v2 | Overall Score87.7 | 21 | |
| Aggregate Deep Research Performance | SQA, ResearchQA, and DRB v2 | Average Score66.3 | 21 | |
| Deep Research | HealthBench ResearchQA DRB Macro Average | Average Score47.7 | 21 | |
| Deep Research | DeepResearchBench (DRB) | Overall Score36.1 | 21 | |
| Deep Research | SQA v2 | Score87.7 | 18 |
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