[HN Gopher] KumoRFM: A Foundation Model for In-Context Learning ...
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KumoRFM: A Foundation Model for In-Context Learning on Relational
Data
Author : cliffly
Score : 99 points
Date : 2025-05-23 06:50 UTC (16 hours ago)
(HTM) web link (kumo.ai)
(TXT) w3m dump (kumo.ai)
| simplesort wrote:
| Jure Leskovec was my Professor at Stanford a few years back, cool
| to see he's behind this.
|
| He seemed like a good guy and got the sense that he was destined
| to do something big
| stuartjohnson12 wrote:
| Vid is a good friend of mine and he's wicked smart and also a
| very solid guy I adore.
|
| I'm also guessing at some point he will probably read this
| comment, so hey Vid! See you at the next VRSA meetup!
| andraz wrote:
| Wickedly smart team indeed!
| Rohitcss wrote:
| A real-time in-context label generator. Nice...
| bookworm123 wrote:
| I feel like this is the next big thing for AI, having the ability
| to interact with any sort of structured dataset out of the box.
| Very cool project!
| perbu wrote:
| I'll suspect it'll be more like the next little thing. Most of
| don't interact that much with structured data, so the
| applications will be very specific.
|
| However, the algo-trading crowd, will likely be very interested
| in this. They deal with structured data all day and it would
| surprise me if most of them don't already have things like this
| working in their networks. They seem to be very secretive,
| though, so we're not gonna hear much.
| cliffly wrote:
| We all interact with structured data models constantly, like
| literally thousands of times each day, just indirectly.
|
| Every single credit card purchase gets classified by a model
| as fraud or ok. When you go to Netflix and see recommended
| movies, it's all predictions on structured data. Every single
| post in every social media feed is there because a model
| predicted you'd like it.
|
| Realistically, it might be more like 10s of thousands or even
| hundreds of thousands of predictions that we engage with in a
| day.
|
| If reality matches the benchmarks for this model, it can kick
| off a whole new category of models that can potentially be
| bigger than LLMs
| gk1 wrote:
| Structured data = relational data
|
| This has more applications than you might first think.
| hbarka wrote:
| Does AI for relational data work the same way as token
| predictions does for LLM AI?
| tinyoli wrote:
| Strange that they do not compare it against TabFN, which is
| another foundation model for tabular data.
| (https://github.com/PriorLabs/TabPFN)
| profjure wrote:
| TabPFN is an amazing innovation. But there are some crucial
| differences in model capabilities that make it hard for a fair
| comparison.
|
| TabPFN can only operate on a single small table. But real-world
| datasets are actually multi-table and to make accurate
| prediction you need to capture signal from multiple tables (for
| example, customers, products, purchases).
|
| So, the comparison to TabPFN would be unfair as it would only
| use data from a single table and that would lead to bad
| performance of TabPFN.
| SubiculumCode wrote:
| So suppose I've got a database of behavioral and neuroimaging
| data from a research study on autism. Is this something that can
| be used to predict diagnosis from the other data fields?
| profjure wrote:
| Yes, I think this would work. For example, you'd organize the
| data into 3 tables: patients, behaviors and images. The
| patients table would have a partially filled-out "diagnosis"
| column. The model would then predict diagnosis of not-yet-
| diagnosed patients based on the patterns in data fields of
| previously diagnosed patients.
| EGreg wrote:
| So can this be used to predict patterns for traffic, restaurant
| table availability, and your customers' demand for things based
| on other customers?
| autorinalagist wrote:
| Hey! I'm one of the engineers who worked on this project.
|
| These are all problems that KumoRFM is able to solve given that
| you have the right relational data of course! So e.g. for
| predicting restaurant table availability you would need at
| least an occupancy table which records how many seats were
| available historically and you can predict its future entries.
|
| But you can also add more relevant data without joining into a
| single table, so you can add a restaurants table, a holiday-
| calendar table, weather patterns, etc. and KumoRFM should take
| it all into account when predicting.
| nsbk wrote:
| Interesting timing, they have recently reached out to my $dayjob.
| We will be probably be running a workshop on our (massive)
| dataset with them. I'd like to evaluate the performance of a
| couple of analytical models we've manually built against whatever
| this model can do based on some prompts. Exciting times!
| dcrimp wrote:
| interesting! Super cool idea to augment software built with
| traditional DBs
|
| I had some thoughts [1] around a concept similar to this a while
| ago, although it was much less refined. My thinking was around
| whether or not we could have a neural net remember a relational
| database schema, and be able to be queried for facts it knows,
| and facts it might predict.
|
| This seems like a much more sensical (and actualised) stab at
| this kinda concept.
|
| [1]: dancrimp.nz/2024/11/01/semantic-db/
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