Consensus AI Academic Search — Find Answers Straight from the Research Papers

Consensus is an AI academic search engine that extracts answers directly from peer-reviewed papers. Rather than handing you a list of links the way Google Schol

Consensus is an AI academic search engine that extracts answers directly from peer-reviewed papers. Rather than handing you a list of links the way Google Scholar does, it aggregates the conclusions of multiple studies around a single question: "Is this claim supported or not?" Its core difference: when you ask "Does intermittent fasting help with weight loss?", Consensus scans its database, pulls the actual findings from each paper, and then uses a "Consensus Meter" to tell you what proportion of the research supports, opposes, or takes a neutral stance. That makes it a search tool that both cuts down the time spent on literature review and lets you trace every point back to the original paper. The Fundamental Difference Between Consensus and Traditional Academic Search The unit of search in Consensus is "a paper's conclusion," not "the paper itself." Traditional tools like Google Scholar and PubMed return a list of documents that match your keywords, and it's still up to you to open each one, read the abstract, and judge its conclusion. Consensus, by contrast, takes the research question you enter and pulls the relevant finding statements directly out of each paper, presenting them side by side—compressing the two steps of "finding papers" and "reading papers to extract the key points" into one. The tool was founded in 2021 by Eric Olson and Christian Salem, and its underlying data comes from Semantic Scholar's academic corpus. According to "Semantic Scholar indexes over 200 million academic papers" (source: Allen Institute for AI / Semantic Scholar) , Consensus is able to operate on a vast, cross-disciplinary base of literature spanning medicine, psychology, economics, environmental science, and more. How the "Consensus Meter" Works The Consensus Meter quantifies the stances of multiple papers into a three-color bar of "supports / neutral / opposes." When a user poses a yes/no question (for example, "Does vitamin D supplementation reduce the risk of catching a co

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