Show HN: 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, 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.