More info here: https://www.undermind.ai/static/Undermind_whitepaper.pdf
3-6 minutes for results takes getting used to, but we've found most people don't complain if it solves a problem that is actually impossible to solve without hours of digging, ie if you use it on something truly hard. Low latency is more crucial for public search engines like Google (0.5s delay -> 20% traffic loss) where there are convenient, fast alternatives.
Preliminary search is a blend of semantic embeddings on 100M+ papers and keyword search, citation links, etc. Reasonably accurate, but full of noise for complex queries.
https://www.undermind.ai/home/
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...
https://www.undermind.ai/home/
Undermind is a deep, systematic search engine to discover incredibly complex topics in scientific papers.We optimized everything for accuracy and comprehensiveness. That’s the bottom line that matters to scientists, doctors, etc. (not compute cost/time required).How it works: You type in what you’re looking for, in all its complexity - whatever you need to solve your problem. Then, we find...
https://app.undermind.ai/home/
Built an automated system to run a deep search of ArXiv and carefully find all the precise papers that exist on a complex topic.It's different from simple RAG because it searches, classifies, and adapts based on relevant papers it uncovers, and then continues until it finds every paper on a topic (trying to mimic the human research process). Benchmarked 10x higher accuracy and total retrieval...
We should track how experienced scientists switch fields, since this is probably a strong signal for important science.
I tracked author movement between fields from all ArXiv papers over the last two decades. The results reveal some obvious trends (ie. the growth of AI), and can more generally provide valuable context for career choices.