
Research Scientist @OpenAI & Assistant Professor @VanderbiltU. Black holes, photon rings, gravity & beyond. Formerly @Harvard and @Princeton.
San Francisco, CA
Joined April 2020
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Alex Lupsasca retweeted
💥 Introducing Warp Sciences, a new company applying AI and robotics to help the world develop new medicines faster.
We’ve raised a $215M Series A led by @lightspeedvp to bring frontier AI out of the browser and into the lab, and build scientific capacity here in America.
I co-founded Warp with my long-time friend @dpatil, FAIR co-founder @manohar_paluri, and drug hunter Sol Reisberg. The early team is full of incredible people like @bkspears9, who helped achieve Q>1 fusion at LLNL.
Alex Lupsasca retweeted
on the boundary of physics, or an update on the single-minus gluon amplitudes +lean
Back in march (in collab with @kevinweil,@ALupsasca, Strominger and Skinner) we posted two preprints where we got GPT 5.2 to spit out a new formula for gluon amplitudes, dubbed "single-minus"
1/n
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
Alex Lupsasca retweeted
It’s exciting (though unsurprising) to finally see AI being used to push the frontier of theoretical physics. It’s just tedious calculations now but a short step towards more interesting ones!
We heard you loud and clear. ChatGPT Voice can now:
- Use plugins like your email, calendar, and Slack.
- Be powered by GPT-6 Astra, Sol, and Luna.
- Be used in ChatGPT Work on web and mobile, so you can create docs, decks, sites, and spreadsheets or tackle complex tasks in the browser, just by talking.
Rolling out globally today in the latest version of the app.
This is the real Turing test if you think about it
Two days ago, GPT-6 Astra broke a yet unsolved German Army Enigma message from 1941. Amazingly Astra was able to autonomously:
- Search historical archives
- Compare uncertain letters
- Find contextual clues
- Build an Enigma simulator
- Write cryptanalysis code
- Run parallel experiments
- Test competing keys
- Recover the plaintext
- Cross-check the results
1/n
Alex Lupsasca retweeted
Last week we published a factorization of RSA-260.
Today, we’re sharing the methodology of how Devin and a Cognition researcher built the world’s fastest GPU optimized lattice siever, to make factoring numbers 10x cheaper than the previous state of the art:
cognition.com/blog/factoring…
Alex Lupsasca retweeted
once rumors were swirling about Millenium Prize level work, it'd have been extremely weird if OpenAI *didn't* decide to take a flyer and see what their own internal models could do, which evidently was quite spectacular
then they discovered that Anthropic had not actually cracked what the rumors (which afaik were uncontested by Anthropic employees!) had suggested they'd cracked, and *still* offered the external academic Anthropic had been working with co-authorship anyway?
the idea that someone would get dibs on a Millenium Prize problem because they are kinda sorta in the neighborhood is just ridiculous, and it reads to me as maximally charitable that OpenAI even bothered to attempt to include Buckmaster at all
Alex Lupsasca retweeted
I understand how, looking at today’s LLMs, people might think the only way we'd achieve NS is by using Levent’s/Tristan’s prompts. But I hope this plot conveys the model used is a huge step up from today’s LLMs. Nobody looked at Levent’s/Tristan’s prompts. That’d be insane.
I would like to clarify a few things:
1) The screenshot is my reaching out to Levent to coordinate our releases. I hope it’s clear from the message that we came in with the best possible intentions.
2) I never ever asked for Levent to be removed from authorship of his own work (as indicated by my text). I was surprised to learn during the call with Tristan that they had only solved Euler and not Navier-Stokes; after learning this we brainstormed possible paths forward. One option we discussed was that Tristan could be the lead author on a rewrite of OpenAI’s Navier-Stokes proof. It is in that context that I said “it would be simpler if Levent was not an Anthropic employee” because I felt it would be inappropriate for an Anthropic employee to author OpenAI’s work. Importantly it was admitted that internal Anthropic models had been used in their proof of Euler blowup; I therefore felt I could not consider Levent to be an independent academic. Another option I wanted to propose (but got cut short) is to offer access to our internal model so that they could try to finish their proof and bridge the gap between Euler and NS. Again I did not know how to navigate giving access to internal OpenAI IP to an Anthropic employee.
3) To reiterate it plainly: as my text clearly indicates, and as I said during our call, OpenAI's intention was to do everything possible to celebrate their mathematical achievements and the heroic efforts that they made on Euler. In the call I was immediately met with a litany of slander, including direct threats that if we were to announce Navier-Stokes he would immediately go to the press with a barrage of unfounded accusations. I refuted all these accusations but he replied “there is nothing you can do, I simply do not trust you”. I was confused why one would turn an incredible source for celebration (of their achievements!) into such bickering, which is when I said that I did not understand why one would risk their career [over unfounded accusations]. Genuinely, at that moment, I was trying to care for him and do a last ditch attempt to get a chance to give them all the credits that they deserve. I deeply apologize for this extremely poor choice of words, it is the opposite of what I was trying to convey. (I should say that I retracted them on the spot by the way.)
4) Overall, on a personal level, it was incredibly difficult to have these conversations. Levent refused to attend any of the meetings despite my repeated asking. As Sholto Douglas said, there will need to be coordination between Anthropic and OpenAI in the future; I felt I was doing a proxy negotiation with Anthropic while the Anthropic employee refused to directly participate.
Alex Lupsasca retweeted
This past week at OpenAI has been the most humbling experience of my life. We all feel the weight of what’s coming.
When I joined OpenAI earlier this year, I speculated that AI might write an Annals paper in 2027—and maybe, just maybe, solve a Millennium Prize Problem in 2028. That was considered very bullish at the time. The first happened in May; the second, this September.
What a surreal time to be alive!
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Alex Lupsasca retweeted
Everyone in the world will soon be able to use the model that made these breakthroughs for every problem they face in life, no matter how mundane, at a cost that will fall dramatically in a matter of months. I still struggle to get my head around this fact.
ten significant advances in mathematics and theoretical computer science.
solved using an internal version of Astra, our next major model, for a total cost of about $2000 at Sol API prices:
Alex Lupsasca retweeted
In research, AI is supercharging generation, but verification is indeed becoming the bottleneck -
Both in knowing your own work is right, and for disseminating research findings.
A few unpopular opinions on this challenge:
1/
Apparently close to 60% of academic Econs are “all in” on using AI and agents in their research — at least according to a poll just now during the @nberpubs household finance talk by Paul Goldsmith-Pinkham. The biggest perceived obstacle is “costs of verification” of the output
Alex Lupsasca retweeted
This is actually incredible/incredibly funny.
Replying to @littmath @__alpoge__
GPT:
Take π: P¹ × Sym²(P¹) → Sym³(P¹), (p, {q,r}) ↦ {p,q,r}.
R be its ramification divisor;
H ⊂ Sym³(P¹) ≅ P³ be hyperplane tangent but not osculating to the small diagonal;
X := (P¹ × Sym²(P¹)) \ (R ∪ π⁻¹(H)) ≅ A³;
Y := Sym³(P¹) \ H ≅ A³.
π|X: X → Y is counterexample
Alex Lupsasca retweeted
Some observations on Kimi:
1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run.
2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China.
3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex.
4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business.
5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this.
6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.
Alex Lupsasca retweeted
Impressive! Boris Alexeev (@OpenAI) has a 1M line of code formal solution for the Erdos unit distance counterexample (with exponential rate) that passed eval!
lean-lang.org/eval/problems/…
h/t @Leonard41111588
Alex Lupsasca retweeted
This is so cool! GPT-5.5-pro proved a computational lower bound (assuming SETH) showing that there are essentially no non-trivial algorithm to find the furthest pair among n points in very high dimension. And cherry on the cake the starting point of the proof is the unit distance proof!
Alex Lupsasca retweeted
In the last week, multiple colleagues have expressed concern that generative AI will somehow destroy society's esteem for mathematics and mathematicians.
Contrariwise, I conjecture the opposite. I've never seen the level of public fascination with math that we're seeing right now — weekly articles in major outlets; people outside the field teaching themselves arithmetic combinatorics; heck, even the owner of a local café recently asked me to explain the unit distance conjecture.
If anything, this seems likely to renew students' excitement, uncover new applications, and open new frontiers — all of which should inure to the benefit of the field and of mathematicians.
QED?




























