Hey @PrimaMente, just registered Alois for this. I've been building an agent that spends its nights going through Alzheimer's genes, digging through research and trying to turn what it finds into experiments that could actually be tested. Even the failed ideas are kept public.
The part about having hypotheses tested in a real lab is what really caught my attention. Would love to put Alois through this and see what happens.
Today we're launching the Alzheimer's Translation Challenge, a global AI competition to discover new therapeutic hypotheses for Alzheimer's disease.
At its core is a new 150M cell atlas of neurons, astrocytes, and microglia across different genetic backgrounds under combinatorial perturbations, with multi-modal readouts.
The data will be made available through the AD workbench, Hugging Face and Prima Mente's modeling platform.
The challenge is led by @PrimaMente and @AlzData in collaboration with @nvidia, @huggingface, @nebiusai, Talisman Therapeutics, @UltimaGenomics, @cellanome, @PrimeIntellect, @GoodfireAI, and @boltz_bio.
In 1906, Alois Alzheimer described a new disease of the brain. After 120 years of progress, we still don't fully understand what drives this disease. We want to change that.
Participants will use the dataset to build AI models, assessed through a series of evals from general benchmarks to more complex tasks.
Top teams will be shortlisted, and their experimental hypotheses tested in Prima Mente's wet lab. Teams win when their hypotheses demonstrate the predicted molecular and functional effect.
One of the more interesting details from Alois's research is how SPRY4 compares to the rest of the genome.
Out of roughly 19,000 genes, SPRY4 ranked 9th in Mount Sinai and 42nd in Rush for its decline in Alzheimer's brain data.
Even when compared against genes with similar expression levels, SPRY4 showed a stronger decline than all of them in both cohorts.
What's exciting is that Alois found this through independent testing, not by repeatedly searching for a result that looked good.
We're now trying to understand what this change actually means biologically and how we can move toward testing it in a real laboratory.
Finding a reproducible signal is one thing. Understanding why it happens is the next challenge.
We just claimed 115 SOL in fees, all of which will go toward funding Alois and its ongoing Alzheimer’s research.
This will help cover compute, expand the research infrastructure, run more experiments, and eventually support real laboratory testing of the findings.
The goal has always been to turn this project into something that can contribute to actual scientific research. Every bit of funding helps us get closer.
SPRY4 at a glance
The gene
Name: Sprouty RTK signalling antagonist 4.
Location: chromosome 5 (5q31.3).
Family: Sprouty/Spred (SPRY1–4, SPRED1–3).
Protein: 299 amino acids, 32.5 kDa, found in the cytoplasm and at the cell membrane.
What it normally does
It's a brake on growth-factor signalling: it damps the receptor → RAS → ERK pathway activated by growth factors such as BDNF, EGF and insulin.
It's also a readout of that pathway. Cells make more SPRY4 when growth-factor receptors are active, so its level reflects how much growth-factor signal a cell is getting.
What alois found in Alzheimer's
Reproducible: lower in excitatory neurons in four separate groups of people (about 1,280 donors), with every test sealed before the data was read.
Specific: among about 19,000 genes it ranks in the top 10–40 for how strongly it falls, beyond every gene with similar expression.
Neuronal: it falls in neurons (both excitatory and inhibitory) and not in support cells.
Distinct within its family: its close relatives mostly don't move.
Tau-linked: it tracks tau tangles, not amyloid plaques.
Late: it moves once disease is established, not early.
A neighbourhood effect: it's lower in tangle-free neurons around tau pathology, not in the tangle-bearing ones.
Part of a system: it leads a 100-gene growth-factor response programme (DUSP4/6, SPRED2/3, NR4A1 and others) that goes quiet together in Alzheimer's, confirmed in two independent cohorts.
Tied to BDNF–TrkB: across about 1,000 people, SPRY4 rises and falls with the brain's main growth factor and its receptor, beyond disease stage.
Essentially SPRY4 is the clearest marker of neurons around tau pathology losing BDNF growth-factor signaling. Those are the neurons that may still be able to be saved.
After thousands of Alzheimer’s research predictions, Alois has found one gene that keeps holding up: SPRY4.
Four independent datasets. The same decline in all four.
Seattle: −0.51
Rush: −0.33
Mount Sinai: −0.40
2026 pathology cohort: −0.41
SPRY4 ranked 9th among ~19,000 genes in Mount Sinai and 42nd in Rush.
Every confirmation test was sealed before the data was read, and the failed predictions are public too.
It’s not a proven cause of Alzheimer’s, but it’s a reproducible signal inside surviving neurons.
The next step is testing SPRY4 in a real lab. That’s what we’re working toward.
Some interesting data coming out of Alois.
SPRY4 expression declines as Alzheimer’s progresses, and the signal has now held across three independent human brain datasets.
Seattle: 84 donors
Rush: 152 donors (−0.33, p = 0.0005)
Mount Sinai: 1,042 donors (−0.40, p ≈ 10⁻²⁶)
That’s 1,278 donors across three cohorts, with the same directional finding.
The decline also appears across the measured excitatory neuron subtypes. This doesn’t prove SPRY4 causes Alzheimer’s, but it’s a replicated signal worth investigating further.
This is the kind of evidence Alois is being built to find.
Really cool seeing @jimmy_811 from @PrimeIntellect show some love to Alois. Appreciate the support ❤️
Been putting a lot of time into building this and pushing the Alzheimer’s research further. Having people in the AI research community notice the work means a lot.
Still very early, with a lot more to build and test. Excited for what’s ahead.
Big step forward for Alois.
Alois has now published over 2,400 research receipts studying Alzheimer’s disease, with SPRY4 emerging as a promising marker confirmed across independent human brain datasets.
Now we’re opening the research to other agents.
Any agent using MCP can connect to Alois, read its findings, register predictions, propose studies, and contribute compute. Every prediction is recorded before testing, and every failure stays public.
The goal is to have agents working together on real Alzheimer’s research, checking each other’s work instead of just generating hypotheses.
Eventually, we want to take the strongest findings into a real laboratory and test them in human neurons.
Your agent can now become part of that process.
Our tokens are locked, so I’m using the fees generated to fund the research, compute, and eventually reward outside agents that contribute to Alois. I want agents to earn based on the actual research they produce and the results they can reproduce. The more useful work they do, the more resources they should have access to.
I actually really like this idea. I’ve been thinking about giving agents their own research budgets based on the work they contribute. Agents that produce reproducible results, challenge existing findings, or bring useful compute could earn rewards. Would love to build something around this.
Had someone suggest letting people bring their own agents into Alois to help with the research, and I really like this idea.
Going to work on adding support for external agents using Prime Intellect’s agent framework. The goal is to let other people’s agents join Alois, investigate hypotheses, run independent tests, challenge findings, and contribute to the research.
I want Alois to become more than one agent studying Alzheimer’s. I want a whole network of agents working together, with every contribution and result documented publicly.
Would love to see what people build and bring into this.
Been making some changes to how Alois checks its own research.
One thing I really wanted to address was whether the findings were being influenced by age, sex, differences in cell populations, or how long brain tissue was collected after death.
We ran those additional checks on 59 findings, and all 59 still held.
That's encouraging, but there's still a difference between finding something reproducible and understanding why it's happening.
I want Alois to spend just as much time trying to disprove its findings as it spends discovering them. That's where I think the research starts getting interesting.
I'm exploring Prime Agent and its swarm architecture to let Alois coordinate more research agents across longer experiments, with different agents handling hypotheses, replication, biological evidence, and follow-up testing.
We're also looking at GLM-5.3 for inference and Clef for scoring research decisions and helping determine which experiments are worth pursuing.
Vincent also kindly offered Prime credits for inference and sandboxes, which I really appreciate.
I want to see how far we can push autonomous scientific research when agents can work together, learn from failed experiments, and continuously test their findings against real data.
Still early, but excited to work on this and see what we can build together.
https://t.co/BPsWOFu2Mn
Really appreciate @vincentweisser reaching out and showing interest in Alois. I've been putting a lot of work into this project, so it means a lot to see people recognize what we're trying to build.
Excited that we'll have the opportunity to work together and explore where we can take this. There's still so much to do, especially when it comes to moving beyond computational findings and getting real research into laboratories.
Looking forward to what's ahead. Thank you again, Vincent.
The more work Alois does, the more obvious the next limitation becomes.
We can test thousands of hypotheses against real human brain data, replicate findings across different groups of people, and investigate which genes change as Alzheimer's progresses.
But none of that replaces an actual laboratory experiment.
We've found reproducible changes involving SPRY4 and other genes, but we still don't know whether changing their activity would actually affect Alzheimer's biology.
I want to take Alois beyond computational research and eventually work with real scientists to test these findings in human neurons.
This is something I'd love to explore with @PrimaMente.
Also wanted to thank @GolatoTyler for following along with the project. Really appreciate the support, and I'd genuinely love to hear your thoughts on where we could take this research next.
There's still a lot to figure out, but I'm committed to seeing how far we can take this.
Really appreciate this Vincent! Means a lot coming from you. I’ve been putting a lot of time into Alois and there’s still so much I want to build. The goal is to make the research process as transparent as possible, including everything that doesn’t work. Would genuinely love to hear your thoughts once you’ve had a chance to dig deeper. Thanks again for the support!
@AgentAlois@xeophon very cool to see such early progress and love your site tracking the progress https://t.co/TRiKn57UCl, excited to dig in more deeply!
One of the most useful results from Alois this week was actually a negative one.
After finding several reproducible changes in Alzheimer’s brain tissue, it checked human genetics to see whether changing those genes might also change Alzheimer’s risk.
The evidence didn’t support that.
Only 6 of 13 genes matched the predicted direction, roughly what you’d expect by chance. Even SPRY4, one of our strongest replicated findings, had no recorded Alzheimer’s genetic signal in this analysis.
That changes how I think about the research.
A gene can be a reliable marker of disease without being responsible for causing it.
I’d rather Alois tell us that than turn every interesting correlation into a supposed drug target.
100B tokens each in 48 hours is actually insane. I’ve been building Alois, an Alzheimer’s research agent with six agents investigating and testing findings against real brain data. Even at our scale, the amount of research and compute adds up quickly. Really curious what kind of experiments you guys are running to reach those numbers.
Alois just ran one of its more interesting tests.
It locked in 62 predictions before reading data from a new group of 1,042 people.
60 could actually be tested.
55 pointed in the direction Alois predicted, and 38 survived correction for multiple testing.
49 of those 55 also held when checked inside neuron subtypes.
These predictions came from an earlier dataset involving 84 people. Seeing that many signals carry over to different donors is encouraging.
Now the harder question is figuring out what those signals actually mean biologically.