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,…
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
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 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, repeating in a scalable, structured exploration. Because of the classification accuracy, we can track this process statistically to predict what fraction of relevant papers have been discovered so far at any point, and know when the search is complete. There's more explanation of techniques/benchmarks in the whitepaper on our homepage. We want to optimize the workflow for researchers in ML, biotech, medicine, etc, and would love critical feedback and suggestions. One major challenge is getting users to accurately describe their search goal, including everything implicitly in their head (instead of keyword phrasing). Another is how to differentiate what's happening behind the scenes, and manage expectations on timing (it's ~3-6 minutes). Also, of course, how to optimally present the results.
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