Alternatives
Products that do what Deep search of all ML papers does
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…
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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
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We're excited to release PaperQA2, an open source RAG library specialized to work with the scientific literature. We've seen some really compelling results with it (https://paper.wikicrow.ai), like superhuman performance at question answering and summarization when compared with expert scientists. PaperQA2 is a major overhaul of our prior PaperQA system, it includes automatically obtained rich metadata for each paper, a CLI to work with local papers directly, a local full-text search engine for keywords searches over PDF files, a state-of-the-art algorithm for LLM-based re-ranking…
2024 · github.com
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The project aims to organize computer science research in a logical, simple, and easy-to-follow way. It is designed to help us find papers worth reading first. I started building Trending Papers because following computer science research has become increasingly hard as the pace of innovation accelerates. The number of new articles on Arxiv has grown at 27% CAGR for the past 20 years. 240 new papers have been filed daily on average over the past 12 months. And the number is growing: last month, there were well over 300 new papers on average every single day. The system is based on some…
2023 · trendingpapers.com
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2018 · semanticscholar.org
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As a researcher, I created SmartXiv to solve a problem I faced every day: keeping up with the overwhelming number of research papers uploaded to arXiv. With over 1000 new papers each day, finding the most relevant research was time-consuming and exhausting. I needed a smarter way to stay updated. What SmartXiv Does • Personalized Recommendations: Using advanced AI, SmartXiv analyzes your interests and sends you daily emails with research papers that align with your preferences. •Efficient Research: By curating the latest papers for you, SmartXiv saves you hours of research. • Fully…
2024 · smartxiv.com
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I created this tool over the weekend because, as someone interested in AI and technology, I find many research papers on arXiv fascinating but often incredibly dense and difficult to understand due to the heavy jargon and technical language. I wanted to make these complex topics more accessible to laypeople like myself by converting the topics described in these papers into full-length books that break down key concepts, making cutting-edge research easier to grasp. You can think of it as generating prerequisite reading materials before being able to read the actual paper. Here are some…
2024 · instabooks.ai
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I’ve been frustrated with PDFs and found arXiv HTML lacking, so I built a fully interactive paper reader. Features: • Hover references, citations, equations • Light/dark mode • Auto-generated dependency graphs for definitions/lemmas/theorems • Table of contents that syncs with scroll • Highlighting + annotations • “Copy raw LaTeX” anywhere Featured paper: Video models are zero-shot learners and reasoners (Veo 3) https://www.sciencestack.ai/arxiv/2509.20328v2
Nov 2025 · sciencestack.ai
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Citrus Search is a similarity-based search engine for scientific literature: select a paper, start the search and jump right to the heart of your research domain. Get an overview of important contributions from seminal papers to the state of the art. Searching for research with text queries can cumbersome: queries of just a couple of words are not a very high-fidelity signal, leading to lots of false positives in the search results. Also, you run the risk of missing papers which simply use different taxonomy than your query. This is where similarity-based search can be a great complement:…
2024 · citrus-search.com
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After getting frustrated with macOS's Spotlight search, e.g., typing "driver license" doesn't give me anything unless the file name matches exactly, I thought, why not index my entire Documents folder? This way, I can find that one PDF or image buried deep in subfolders using natural language queries. So I built SmartSearch; it uses SentenceTransformers for embeddings and FAISS for fast similarity search. Best of all, it runs locally on your computer. Github: https://github.com/neberej/smart-search/ Demo:…
2025 · github.com
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Dear HN Community, I am a long time fan and first-time contributor. I just launched a developer focused semantic search platform and wanted to share it with the community. The idea is simple: upload structured or unstructured documents, select the fields you want to index and tag as metadata, and instantly get a clean search API you can use in your own app. Here is what it currently supports: - Manage your own tenants and projects - Upload .json and .txt files (support for .pdf, .docx, .xlsx, .yml, etc. coming soon) - Expose 3 APIs: search, upload document (embeddings), and delete document -…
2025 · aisearch.vpuna.com
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