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…
Deep search of all ML papers launched on January 24, 2024 with 109 votes, #519 of 4,438 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
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
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
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…
Does a similar job
all alternatives →- IMI made a website to semantically search ArXiv papers2024 · papermatch.mitanshu.tech · ▲324
As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
ScholArxiv – an open-source, aesthetic, minimal research paper explorer2024 · github.com · ▲147OpenScholarXIV is an open-source, aesthetic, minimal and AI powered app that allows users to search, read, bookmark, share, download and view summaries of academic papers from the arXiv repository. - ScholarXIV/OpenScholarXIV
Turn any website into a knowledge base for LLMs2024 · embedding.io · ▲305I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
Undermind – Deep scientific paper search with adaptive LLMs2024 · undermind.ai · ▲49We 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,…
PageIndex – Vectorless RAG2025 · github.com · ▲192Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning…
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