Evidence receipt / recommendation
Published · transcript-backedMentions personal use of systematic literature view API.
17 Jun 2026 The Cognitive Revolution Radically Better Reasoning: Elicit's Andreas Stuhlmüller & Jungwon Byun on World Models for Research
“When I run systematic literature views, often I kind of use the systematic literature view API and I iterate using like various other models on the protocol for a while, and then I run it in the background and retrieve it.”
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Everything needed to verify it.
- Speaker
- Andreas Stuhlmüller
- Attribution
- Verified speaker
- Claim type
- recommendation
- Recorded
- 17 Jun 2026
- Publisher
- The Cognitive Revolution
Transcript context
…Yeah, cool. That in and of itself is an interesting reflection of how frequently you're swapping things out and how dynamic and competitive the environment is. Maybe three more questions, if you will. One, do you have plans to expose elicit as a tool for random people's cloud codes to use? That could be by allowing them to do it via API if they have an account, or even more broadly, it could be like through a sort of 402 code type thing, a 0.xyz I've recently been exploring as a way to get just pay per use access to a bunch of different tools. Yes, no, why not? No, I mean, it's already, so we haven't been advertising it that much, but we already have an MCP and API. A lot of people use the API. People can check it out at docs.elicit.com. And a lot of the work I do with Elicit is through the API. When I run systematic literature views, often I kind of use the systematic literature view API and I iterate using like various other models on the protocol for a while, and then I run it in the background and retrieve it. So I think that is an important use case that we'd really like to support for not everything has to happen through the interface and I think more and more will happen through APIs. Yeah, okay, cool. I'm sorry I missed that in my prep, but I'll again point my at the documentation. How about, so I'm a little bit of an AI for science mini arc right now. I'd be interested in your take on other big picture approaches to AI for science. You guys are obviously coming at it with a very systematic reasoning angle. There is the sort of close the loop angle where we'll empower these models to actually run experiments through a cloud lab or whatever, and then they'll be getting feedback from reality. That seems like it could go somewhere quite interesting. Then there's of course training models on other modalities of data. We've of course seen how proteins fold and There's what's the, what if I do this perturbation to a cell, like what's its next state going to be? Or you can go on and on in that domain. Interesting takes that you think might be non-consensus that you'd like to share.…
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