Deep search of all ML papers
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 compared to Google Scholar for a median search (whitepaper on website). Also knows when it is complete, and misses virtually nothing (< 3% or so, once it's converged). Website has a free trial and a…
In plain words
Deep Search of All ML Papers is an automated research tool that searches ArXiv to comprehensively find papers on complex machine learning topics. Unlike standard search tools, it iteratively searches, classifies, and adapts based on discovered papers to mimic human research workflows. The system identifies when its search is complete and retrieves papers with high accuracy. It is designed for researchers who need thorough, exhaustive literature reviews rather than quick surface-level results. A free trial and example search reports are available on the website.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 compared to Google Scholar for a median search (whitepaper on website). Also knows when it is complete, and misses virtually nothing (< 3% or so, once it's converged). Website has a free trial and a bunch of example search reports. Want feedback and suggestions.
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