Research Microscope
Built at Meta Global AI Developer Hackathon · Oct 26, 2026 · Remote

Research Microscope is a reader for scientific papers that helps researchers understand them, not just find or summarize them. Papers now arrive faster than we can read them: arXiv alone received over 40,000 new submissions in September 2026, and on a single day in October over 700 AI-written math manuscripts were released at once. AI helps us produce more papers; we believe it should also help us understand them. Terence Tao calls this "Math 2.0". We think every field deserves it. How it works: - Map of the field. Ask a question in plain words. We search ~8M papers (SPECTER2 embeddings + FAISS + the Semantic Scholar citation graph) and draw a map of the field by year, with groups named by Muse and papers ranked by closeness to your question, so you know where to start. - Scan or Read. Scan gives the abstract, figures and conclusions; Read opens the full paper in a clean reader. - Symbols. Hover any symbol and every use lights up, including inside equations; the margin quotes where the author defined it. - Gap notes. Where the author says "it's easy to see", Muse writes a short explanation grounded in this paper and in passages from other papers (nearly 300,000 research passages), each line labeled AI-generated, From this paper, or From other papers. - Live equations. Our engine reads an equation, and Muse Wonder writes the code for an interactive instrument under a strict contract. A separate verifier checks it against the paper's claim and a sandbox runs it. Today 9 of our 11 widgets pass. - Explain. After a section, you explain it in your own words; Muse checks every claim against the author's sentences and other papers and answers Matches / Contradicts / No support / You missed, with quotes. On our test set of 137 hand-written claims the verdict was right 99% of the time. Today we focus on computer science and math. Next: a research hub with weekly spaced questions on what you read, and connections across fields. Attribution: metadata from Semantic Scholar; papers from arXiv, always linked to the source.