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AI · April 2, 2024

Undermind – Deep scientific paper search with adaptive LLMs

We can now build drastically higher quality search because we can use LLMs in algorithms that mimic a human's systematic research process, instead of just roughly recommending results based on semantic embeddings or term frequency. We built a deep search LLM pipeline that takes a few minutes to carefully search all the scientific literature. You describe your complex goal, as you would to a colleague. Then, we carefully search 200M+ papers. We classify the preliminary results with GPT-4. We then adapt the search goals based on relevant/irrelevant papers uncovered and continue searching,…

Undermind – Deep scientific paper search with adaptive LLMs launched on April 2, 2024 with 49 votes, #673 of 4,347 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.

Visit undermind.ai ↗Alternativestop 15% of April 2024

In plain words

Undermind is a scientific paper search tool that uses large language models to systematically explore over 200 million academic papers. Users describe their research question conversationally, and the system conducts an adaptive multi-stage search, classifying results with GPT-4 and refining queries based on relevant and irrelevant papers found. It aims to provide more comprehensive results than traditional semantic search by mimicking how researchers manually conduct literature reviews. The tool can estimate search completion and is designed for scientists seeking thorough coverage of academic literature.

written from the facts on this page · September 2026

From the sources

Undermind finds the papers others miss, reading full texts and figures, citing every source. Build command of your field with a living library as you research.

In the maker’s words, at launch

We can now build drastically higher quality search because we can use LLMs in algorithms that mimic a human's systematic research process, instead of just roughly recommending results based on semantic embeddings or term frequency. We built a deep search LLM pipeline that takes a few minutes to carefully search all…

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