Show HN: Deep search of all ML papers (app.undermind.ai)
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 bunch of example search reports. Want feedback and suggestions.
33 comments
[ 3.4 ms ] story [ 77.7 ms ] threadIf you can find a way to make the results closer to real-time, this will be a really popular product.
https://chat.openai.com/g/g-dGz4aw9iA-research-refiner - the free version (just ChatGPT Plus subscription needed)
Yeah I get you're technically paying for the "advanced" search but it still leaves a bad taste in the mouth because this service's entire existence depends on open source knowledge.
P.S. hiding pricing behind registration isn't cool
Since this only supports arXiv, and not paper repositories from other industries.
I was curious how this was measured since benchmarking accuracy for LLMs is tough. Found this in the paper: "This classification accuracy was benchmarked by manually analyzing over 400 papers across a range of representative searches, and comparing the human evaluation to the language model’s judgment"
I'm skeptical that their dataset of 400 papers with 3 classification labels (highly relevant, closely related, or ignorable) is large enough to represent the diversity of queries they're going to get from users. To be clear, I don't think this undermine's (haha) the value of what they've built, still very cool.
Are you filtering users however? I cannot sign up in a personal capacity with a GMail email. The page raises this error: “Please use a valid institutional or company email address.”
1. The hyperbolic claims are going to be off-putting to some. You’ve “solved” ML search? 50x better than Google scholar on a metric no one’s been benchmarking against? Consider your audience and what they would find credible.
2. The UX needs work. To give one aesthetic example, in the results there are large, brightly colored, red and green circles that are used inconsistently, and they clash the palette. This stuff can affect how sticky your service is.
3. Don’t restrict signup by email domain. This is nuts. Never add friction to gaining customer relationships. If you’re capacity constrained limit the trial. If you’re trying to segment the market there are better ways.
4. The name “Undermind”, is not working to my ear. It’s worth changing. At least find a product person whose opinion you respect and ask their take.
5. I think a lot of people here would be willing to give you useful technical feedback on the architecture and approach if more information were shared about how the service works, but I didn’t notice that was available.
hm.
If OP wants to grow their project, those 4 has to be the first to target.