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AI · alternatives · 2026

24 alternatives to 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 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… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Deep search of all ML papers launched in 2024; newer entries below may have overtaken it.

  1. 1IM

    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…

    2024 · papermatch.mitanshu.tech · its alternatives →

  2. 2

    OpenScholarXIV 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

    2024 · github.com · its alternatives →

  3. 3
  4. 4

    Discover, read, write and manage research with ease using AI

    2024 · paperguide.ai · its alternatives →

  5. 5

    I 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...

    2024 · embedding.io · its alternatives →

  6. 6

    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&#x2F;irrelevant papers uncovered and continue searching,…

    2024 · undermind.ai · its alternatives →

  7. 7

    Not 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…

    2025 · github.com · its alternatives →

  8. 8

    Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning. Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that…

    2024 · github.com · its alternatives →

  9. 9

    Hi HN! Open Paper allows you to annotate and understand your research papers with an AI copilot. I wanted a paper-reading assistant that let met actually dig into papers in-place while benefiting from AI tooling. When it comes to studying, I'm not a fan of the typical chat interfaces that obfuscate uploaded files and turn them into black box 'content'. Hence, I've implemented a split-pane view. To mitigate the risk of hallucinations, all AI responses come with grounded citations that refer back to the source material in-flow. Some feature highlights: - Actually read your papers in one place…

    2025 · openpaper.ai · its alternatives →

  10. 10BA
  11. 11

    Hello HN, Talk2Arxiv is a small open-source RAG application I've been building for a few weeks. To use it just prepend any arxiv.org link with 'talk2' to load the paper into a responsive RAG chat application (e.g. www.arxiv.org&#x2F;abs&#x2F;1706.03762 -> www.talk2arxiv.org&#x2F;abs&#x2F;1706.03762). All implementation details are in the GitHub. Currently, because I've opted to extract text from the PDF of the paper rather than reading the LaTeX source code (since I wanted to build a more generic PDF RAG in the process), it struggles with symbolic text &#x2F; mathematics, and sometimes fails…

    2023 · github.com · its alternatives →

  12. 12

    AI-assisted research paper searching tool

    2025 · arxivsub.comfyai.app · its alternatives →

  13. 13

    We built PageIndex, a document indexing system that turns documents into hierarchical search trees to support reasoning-based RAG. Traditional vector-based RAG often struggles with retrieval accuracy because it optimizes for similarity, not relevance. But what we really need in retrieval is relevance — which requires reasoning. When working with professional documents that demand domain expertise and multi-step reasoning, vector-based RAG and similarity search often fall short. So we started exploring a more reasoning-driven approach to RAG. Reasoning-based RAG enables LLMs to think and…

    2025 · github.com · its alternatives →

  14. 14

    Hey HN, I built PaperLens ([thepaperlens.com](https:&#x2F;&#x2F;thepaperlens.com&#x2F;)) after one too many frustrating sessions trying to find what I actually needed in academic research. Whether I was doing a literature review, trying to quickly find a solid source to back up a point in a discussion, or just attempting to understand a complex subject, traditional keyword searches often felt like searching for a needle in a haystack. PaperLens uses RAG (Retrieval-Augmented Generation) and the latest AI models to provide more precise, semantic search results. It also has an AI chatbot that…

    2025 · thepaperlens.com · its alternatives →

  15. 15

    I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…

    2023 · github.com · its alternatives →

  16. 16OA

    Hi HN, I built an open-source AI agent that has already indexed and can search the entire Epstein files, roughly 100M words of publicly released documents. The goal was simple: make a large, messy corpus of PDFs and text files immediately searchable in a precise way, without relying on keyword search or bloated prompts. What it does: - The full dataset is already indexed - You can ask natural language questions - Answers are grounded and include direct references to source documents - Supports both exact text lookup and semantic search Discussion around these files is often fragmented. This…

    Jan 2026 · epstein.trynia.ai · its alternatives →

  17. 17

    An experimental AI-organized search results page

    2025 · blog.google · its alternatives →

  18. 18

    Command line interface for managing papers from arXiv on a local machine. - njhofmann/arXivist

    2020 · github.com · its alternatives →

  19. 19

    Generate AI summaries for research papers and articles

    2022 · scholarcy.com · its alternatives →

  20. 20

    Generated-app

    2025 · ragdaily.com · its alternatives →

  21. 21AF

    Hey HN! I've always found it hard to keep up with the latest AI research, so I built Arxiv Feed! https:&#x2F;&#x2F;arxiv-feed.vercel.app&#x2F; It's basically a feed of AI research papers + a one-liner explaining what problem its solving, etc. You can also click on any paper to get a TL;DR. Right now, I've only indexed a few hundred large language model papers, but will expand to indexing AI papers in other topics. Thinking of also adding a way for people to up-vote&#x2F;down-vote papers. Would love to hear any thoughts&#x2F;feedback! :D Thanks!

    2023 · arxiv-feed.vercel.app · its alternatives →

  22. 22

    Hi HN, As a researcher, I often found myself struggling with the limitations of keyword-based search when exploring PubMed papers. To address this, I created PubMed Search (https:&#x2F;&#x2F;www.pubmedisearch.com&#x2F;), a tool that leverages a vector database to enable semantic search across medical research literature. Some key features: * Daily updates to ensure access to the latest articles * Semantic search using latest & greatest embedding models * Some additional useful info about the papers (tldr, journal, publication date, etc.) Hope you find it useful!

    2024 · pubmedisearch.com · its alternatives →

  23. 23RH

    Hi Hacker News! I’m a Bayesian statistician that has been working on applying hierarchical mixture models (originally developed for genomics) to structure text data, and in the process, used these models to build (what started as a personal) tool for conducting literature reviews and deep research. My literature review process starts with a broad search to find a few key papers&#x2F;groups, and from there expands along their citation networks. I needed to conduct a few rounds of literature reviews during the course of my research and decided to build a tool to facilitate this process. The…

    Oct 2025 · sturdystatistics.com · its alternatives →

  24. 24

    When I read papers, I have to jump between multiple tabs to find the dataset, code, videos, peer reviews, and so on. I tried to fix this with this project. It started as a project just for papers on arXiv, but after its initial success on Twitter (got like 1.9k views: the most I have gotten for a post), I have now expanded it to include other openly available papers from PubMed Central, bioRxiv, medRxiv, and eLife. These papers have been linked with their genes, proteins, diseases, drugs, clinical trials, 3D protein structures, code, and cited and similar papers. This project now has four…

    Jul 2026 · tomesphere.com · its alternatives →

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