[HN Gopher] Robin: A multi-agent system for automating scientifi...
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Robin: A multi-agent system for automating scientific discovery
Author : nopinsight
Score : 98 points
Date : 2025-05-20 16:21 UTC (6 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| peterclary wrote:
| Will we have AIs doing an increasing amount of the research,
| theory and even publication, with human scientists increasingly
| relegated to doing experiments under their direction?
| lgas wrote:
| If so, it won't last long. At some point AI will be able to use
| robots to do the experiments itself.
| TechDebtDevin wrote:
| lmfao
| florbnit wrote:
| Closed loop optimization is already a thing, and you don't
| even need AI for it, just good old bayesian optimization is
| enough.
| dekhn wrote:
| In practice this turns out to be extremely challenging. I've
| been through many labs with a ton of automated stuff that
| is... constantly being worked on by a range of 3rd party
| techs, rather than actually running in response to models.
| postalrat wrote:
| It makes me wonder if there is some easily automated or
| configurable experiment is capable of revealing "new
| science".
| lamename wrote:
| Also on HN today "I got fooled by AI-for-science hype--here's
| what it taught me" https://news.ycombinator.com/item?id=44037941
| hirenj wrote:
| Not my subject area, but at least one other group looked at
| ABCA1, and judging from this abstract, it has been linked via
| GWAS already, and furthermore concludes it doesn't play a role (I
| haven't looked at the data though).
|
| I don't know, but if we were to reframe this as some software to
| take a hit from a GWAS, look up the small molecule
| inhibitor/activator for it, and then do some RNA-seq on it, I
| doubt it would gain any interest.
|
| https://iovs.arvojournals.org/article.aspx?articleid=2788418
| starlust2 wrote:
| Wouldn't the fact that another group researched ABCA1 validate
| that the assistant did find a reasonable topic to research?
|
| Ultimately we want effective treatments but the goal of the
| assistant isn't to perfectly predict solutions. Rather it's to
| reduce the overall cost and time to a solution through
| automation.
| ClaraForm wrote:
| Not if (a) it misses a line of research has been refuted 1-2
| years ago, (b) the experiments at recommends (RNA-Seq) are a
| limited resource that requires a whole lab to be setup to
| efficiently act based upon it, and (c) the result of the work
| is genetic upregulation of a gene, which could mean just
| about anything.
|
| Genetic regulation can at best let us know _involvement_ of a
| gene, but nothing about why. Some examples of why a gene
| might be involved: it's a compensation mechanism (good!), it
| modulates the timing of the actual critical processes
| (discovery worthy but treatment path neutral), it is
| causative of a disease (treatment potential found) etc...
|
| We don't need pipelines for faster scientific thinking ...
| especially if the result is experts will have to re-validate
| each finding. Most experts are anyway truly limited by access
| to models or access to materials. I certainly don't have a
| shortage of "good" ideas, and no machine will convince me
| they're wrong without doing the actual experiments. ;)
| cflyingdutchman wrote:
| This is a great framing - would you please expound on it a
| bit. Software is almost exclusively gated by the "thinking"
| step, except for very large language models, so it would be
| helpful to understand the gates ("access to models or
| access to materials") in more detail.
| ijk wrote:
| This is, I think, what I've been struggling to get across
| to people: while some domains have problems that you can
| test entirely in code, there are a lot more where the
| bottleneck is too resource-conatrained in the physical
| world to have an experiment-free researcher have any value.
|
| There's practically negative utility for detecting
| archeological sites in South America, for example: we
| already know about far more than we could hope to excavate.
| The ideas aren't the bottleneck.
|
| There's always been an element of this in AI: RL is amazing
| if you have some way to get ground truth for your problem,
| and a giant headache if you don't. And so on. But I seem to
| have trouble convincing people that sometimes the digital
| is insufficient.
| photochemsyn wrote:
| This approach is very interesting, and one attention-catching
| datum is that their proposed compound, ripasudil, is now largely
| out-of-patent with some caveats, via Google Patents and ChatGPT
| 03:
|
| > 1999 - D. Western Therapeutics Institute (DWTI) finishes the
| discovery screen that produced K-115 = ripasudil and files the
| first PCT on 4-F-isoquinoline diazepane sulfonamides. (Earliest
| composition-of-matter priority. A 20-year term from a 1999 JP
| priority date takes you to 2019 (before any extensions).
|
| > 2005 - Kowa (the licensee) files a follow-up patent covering
| the use of ripasudil for lowering intra-ocular pressure. U.S.
| counterpart US 8 193 193 issued 2012; nominal expiry 11 July
| 2026. (A method-of-use patent - can block generics in the U.S.
| even after the base substance expires).
|
| Scanning the vast library of out-of-patent pharmaceuticals for
| novel uses has great potential for curing disease and reducing
| human suffering, but the for-profit pipeline in
| academic/corporate partnerships is notoriously uninterested in
| such research because they want exclusive patents that justify
| profits well beyond a simple %-of-manufacturing cost margin.
| Indeed they'd probably try to make random patentable derivatives
| of the compound in the hope that the activity of the public
| domain substance was preserved and market that instead (see the
| Prontosil/sulfanilimide story of the 1930s, well-related in
| Thomas Hager's 2006 book "The Demon Under The Microscope).
|
| I suppose the user of these tools could restrict them to in-
| patent compounds, but that's ludicrously anti-scientific in
| outlook. In general it seems the more constraints are applied,
| the worse the performance.
|
| Another issue is this is a heavily studied area and the result is
| more incremental than novel. I'd like to see it tackle a question
| with much less background data - propose a novel, cheap, easily
| manufactured industrial catalyst for the conversion of CO2 to
| methanol.
| ankit219 wrote:
| This is very cool.
|
| One question I have in these orchestration based multi agent
| systems is the out of domain generalization. Biotech and Pharma
| is one domain where not all the latest research is out there in
| public domain (hence big labs havent trained models on it). Then,
| there are many failed approaches (internal to each lab + tribal
| knowledge) which would not be known to the world outside. In both
| these cases, any model or system would struggle to get accuracy
| (because the model is guessing on things it has no knowledge of).
| In context learning can work but it's a hit and miss with larger
| contexts. And it's a workflow + output where errors are not
| immediately obvious like coding agents. I am curious as to what
| extent do you see this helping a scientist? Put another way, do
| you see this as a co-researcher where a person can brainstorm
| with (which they currently do with chatgpt) or do you expect a
| higher involvement in their day to day workflow? Sorry if this
| question is too direct.
| greenflag wrote:
| Someone has pointed out on X/Twitter that the "novel discovery"
| made by the AI system already has an entire review article
| written about the subject [0]
|
| [0] https://x.com/wildtypehuman/status/1924858077326528991
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