AI for Scientific Search
omarsar | 125 points | 12day ago | arxiv.org
gavinray|12day ago
I was hoping for this to announce a tool for research.
Anyone know of the best way to do something like:
"Find most relevant papers related to topic XYZ, download them, extract metadata, generate big-picture summary and entity-relationship graph"?
Having a nice workflow for this would be the best thing since sliced bread for hobbyists interested in niche science topics.
Recently found https://minicule.com which is free and lets you search + import, but it focuses more on "concept-extraction" than LLM synthesis/summary.
andjar|12day ago
A while ago, I started working on two R packages for creating 'living reviews': metawoRld and DataFindR, see https://andjar.github.io/metawoRld/articles/conceptual_overv... . You do the broad literature search yourself, but the idea is to use LLMs to select relevant studies and perform data extraction in a structured, reproducible manner. The extracted data is stored in a git repository for collaboration and version tracking, with automated validation and website generation for presenting results.
TechDebtDevin|12day ago
"Structured and Reproducable"
dmezzetti|12day ago
PaperAI is also an option if you prefer open-source: https://github.com/neuml/paperai
Disclaimer: I'm the primary author of this project.
AustinBGibbons|12day ago
Check out https://elicit.com/
gavinray|12day ago
Seems potentially useful, thanks! Only drawback I can see is the small number of papers provided by the free plan, but that's reasonable I suppose.
kianN|12day ago
I built a public literature review search tool for some graduate student friends that became pretty popular in the Santa Barbara area. It actually does exactly what you are describing.
It’s not neural network based: it leverages hierarchical mixture models to give a statistical overview of the data. It lets you build these analysis graphs via search or citation networks.
Example: https://platform.sturdystatistics.com/deepdive?search_type=e...
gavinray|12day ago
This is genuinely incredible, tried it using a recent-ish paper on the pharmacology and mechanisms of the Androgen Receptor and my mind is blown:
https://platform.sturdystatistics.com/deepdive?fast=1&q=http...
hugeBirb|12day ago
I've been trying to tackle this exact problem. Current process is to use exa.ai to collect a wide breadth of research papers. Do a summarization pass and convert to markdown. Search for more specific terms then give the relevant papers/context to Gemini 2.5 pro and say give me a summary. Looking for very specific resources and to be honest it's been a terrible process :|
kianN|12day ago
Linking to a nearby thread in case this is helpful: https://news.ycombinator.com/item?id=44457928
matt1|7day ago
My site, https://www.emergentmind.com, is exactly for this. It surfaces trending AI/ML/CS papers, summarizes them, links to social commentary, lets you read and download papers, links to topics, and more. Would love any feedback you have!
tkuipers|12day ago
I’ve found a lot of success with https://www.undermind.ai/ though I’m not sure it has the graph you’re looking for
gavinray|12day ago
This also looks excellent, thank you!
sergeim19|12day ago
Hi, I'm the creator of https://tatevlab.com. It does something similar + aiming to be something like a "spotify" for research papers (currently working on a feature to allow creating and sharing personal collections). It summarizes papers based on practical potential and you can find papers based on similarity. Feedback is welcome.
Metacelsus|12day ago
https://platform.futurehouse.org/
gavinray|11day ago
Their Chemistry LLM that's an iteration of ChemCrow is really useful, thank you!
whattheheckheck|12day ago
Connectedpapers.com
tough|12day ago
emergentmind is pretty good
mixedmath|12day ago
From the title, I had thought that this would be a new tool for searching science, such as searching the arxiv. But this is actually a survey.
I quote the conclusion of the survey:
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In conclusion, rapid advancements in artificial intelligence, particularly large language models like OpenAI-o1 and DeepSeek-R1, have demonstrated substantial potential in areas such as logical reasoning and experimental coding. These developments have sparked increasing interest in applying AI to scientific research. However, despite the growing potential of AI in this domain, there is a lack of comprehensive surveys that consolidate current knowledge, hindering further progress. This paper addresses this gap by providing a detailed survey and unified framework for AI4Research. Our contributions include a systematic taxonomy for classifying AI4Research tasks, identification of key research gaps and future directions, and a compilation of open-source resources to support the community. We believe this work will enhance our understanding of AI’s role in research and serve as a catalyst for future advancements in the field.
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I jumped at this because I'm a mathematician who has been complaining about the lack of effective mathematical search for several years.
Davidzheng|12day ago
How do you view o3? I personally find it superior to google search almost always. Do you find that it often misses key references? (also mathematician)
mixedmath|8day ago
Google is completely inadequate at mathematical search. But here is a concrete problem that no search seems to handle: given some complicated integral (say, some contour integral involving a K-Bessel function), find where it appears in the literature.
Most search will totally fail, because this is made of math symbols. Embedding-based search will give various related things involving, say, integrals and Bessel functions. But then I end up opening Gradshteyn and Ryzhik and trying to find where in this book the relevant terrible integrals appear.
This is a common experience for analytic number theorists. And it's a lousy experience.
masterjack|12day ago
Have you found https://sugaku.net/ useful? It’s focused on math research
BrtByte|11day ago
This paper is more of a meta-level overview than a hands-on solution