HN Distilled

Essential insights from Hacker News discussions

AI for Scientific Search

Original Article

Hacker News Discussion

This discussion revolves around the application of AI, particularly Large Language Models (LLMs), to scientific research, with a strong focus on improving literature search, synthesis, and analysis. Several users express a desire for tools that go beyond simple search, aiming for comprehensive understanding and new insights from large bodies of research.

The Gap in AI-Powered Scientific Research Tools

A primary theme is the anticipation and desire for effective AI tools to streamline scientific literature review and analysis. Many users were initially drawn to the title of the linked article expecting a new tool, rather than a survey. They are actively looking for solutions that can manage the growing volume of scientific papers and extract meaningful insights.

  • "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." - mixedmath
  • "I was hoping for this to announce a tool for research." - gavinray
  • The conclusion of the survey itself highlights this gap: "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." - mixedmath (quoting the survey)

The Ideal Workflow for Research Synthesis

Users are articulating a highly specific and ambitious ideal workflow for engaging with scientific literature, which goes beyond basic search. This involves finding relevant papers, extracting their content and metadata, and then synthesizing this information into a comprehensive overview, often visualized through graphs or structured data.

  • "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'?" - gavinray
  • "Having a nice workflow for this would be the best thing since sliced bread for hobbyists interested in niche science topics." - gavinray
  • "My personal workflow 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 :|" - hugeBirb

Existing Tools and Approaches for AI in Research

In response to the expressed needs, several existing tools and approaches are being shared and discussed. These range from LLM-based solutions to more traditional statistical methods, with users evaluating their effectiveness and limitations.

  • LLM-based summarization and search:

    • "I personally find [o3] superior to google search almost always." - Davidzheng
    • "Linking to a nearby thread in case this is helpful: [link to related thread]" - kianN
    • "PaperAI is also an option if you prefer open-source: [link to PaperAI GitHub]" - dmezzetti
    • "Recently found https://minicule.com which is free and lets you search + import, but it focuses more on 'concept-extraction' than LLM synthesis/summary." - gavinray
    • "Check out https://elicit.com/" - AustinBGibbons
    • "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." - gavinray (commenting on Elicit)
    • "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" - tkuipers

  • Non-LLM or hybrid approaches:

    • "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." - kianN
    • "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: [link to a user's demonstration]" - gavinray (referring to kianN's tool)
    • "A while ago, I started working on two R packages for creating 'living reviews': metawoRld and DataFindR, see [link to R package overview]. 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." - andjar

The Mathematical Search Challenge

A specific sub-theme emerges from mathematicians expressing their long-standing frustration with the lack of effective search capabilities within their field, particularly for mathematical literature and concepts.

  • "I quote the conclusion of the survey: [conclusion text]. I jumped at this because I'm a mathematician who has been complaining about the lack of effective mathematical search for several years." - mixedmath
  • "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)" - Davidzheng

This discussion collectively highlights a strong demand for advanced AI solutions that can significantly improve how scientists discover, process, and synthesize information, bridging the gap between the vast amount of research available and the practical needs of researchers across disciplines.