Scientific QA

Scientific question answering studies how AI can answer questions about research literature, scientific data, and technical subjects in ways that are accurate and verifiable. Common approaches combine retrieval-augmented generation with sparse and dense search, reranking, citation checks, and structured representations such as knowledge graphs; multimodal systems also interpret figures, tables, and other scientific documents. Benchmarks increasingly test multi-hop reasoning, quantitative tasks, evidence location, citation quality, and whether systems abstain when answers are unsupported. A central challenge is that fluent answers and strong aggregate scores do not guarantee faithful use of evidence, especially when questions require synthesis across sources or modalities.