
@letonyoi
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researcher on quantum error correction
Paris
Joined May 2009
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Certainly the most surprising result in my career so far: a new qLDPC decoder that I thought had no chance to work!
We call it the Frontier decoder, joint work with Rüdiger Urbanke (1/4)
what a nightmare
Banger AI takeover story
lesswrong.com/posts/3375SZNG…
"We have entire institutions that were supposed to be focused on innovation... that cannot adapt meaningfully to a world with AI."
sadly true
AI is an apocalypse in the sense that it reveals all. Or it reveals much.
We have mathematicians arguing that solving the problems that they themselves identified as the goals (in their funding applications and talks) never mattered.
We have the finest literary people in France who were collectively fooled by a novel written by AI. None of them would ever accept AI authorship.
We have been told for decades that without copyright, there could be no progress... and all of a sudden, we have these machines producing copyright-free content, often breaking new ground.
We have entire institutions that were supposed to be focused on innovation... that cannot adapt meaningfully to a world with AI.
We are left wondering. What is actual progress and what is mere signalling. How people react to AI tells us much.
this is how it starts
Effective Immediately, all organizations using PHP for their systems are advised to be prepared for an update addressing two critical vulnerabilities. PHP is widely used for web applications. These vulnerabilities often provide a direct path to the server's file system or database. If an immediate update is not feasible, it is recommend that organizations take the system offline until it can be fully mitigated. NSA will follow up shortly with more specific guidance.
Interesting development for quantum supremacy:
this paper gives a polynomial classical algorithm for the original boson sampling proposal of Aaronson-Arkhipov for any constant-fraction loss, ie any implementation without fault tolerance (1/2)
arxiv.org/abs/2610.27065
1) This is remarkable.
2) If mathematicians can find such simplified proofs (with the help of AI) so soon after the first proof was published, I don't see why future AI couldn't do that on its own in the near future.
Awesome work!
And they even use our Frontier decoder ;-)
Congrats to quantinuum on seeing color code lifetime improve 12x going from d=3 to d=5 and ~12x again from d=5 to d=7: cdn.prod.website-files.com/6…
Notes:
- This suggests practical error rates reached at d≈21; need more qubits
- The 20ms cycle time is brutally slow; need more speed
I don’t know where we got the idea that humans were super smart.
Reading paper reviews these days and seeing a lot of "under the stated assumption". Are there any human reviewers who really write this?
imagine in 6 months, when the next internal models at frontier labs will write these 5-page proofs from the book directly
5 page proof of no Siegel zeroes by Ben Green 🤯
hexagonmath.org/pdf/2610.003…
It may surprise outsiders that many academic papers can be considered as curiosities. But this is a small price to pay to get true advances sometimes.
It may surprise those outside number theory to learn that, for decades, people had written several papers that assumed a Siegel zero exists:
Famously, in 1982 Heath-Brown proved the twin prime conjecture in this hypothetical world.
Now in light of the Siegel zero disproof, a collection of the literature has now become something of a historical curiosity.
londmathsoc.onlinelibrary.wi…
At least no one is claiming that OpenAI stole their ideas on this one.
TERENCE TAO:
“OpenAI did make a major breakthrough [on quasi-Riemann Hypothesis] the biggest advance on the subject in 50 years. A massive new zero-free region, very clever new technique.
Well, it’s sort of a combination of three or four existing ideas, arranged in a very unusual way. It’s not something I think any single mathematician alive would have come up with.
I think if you got the right people to work together and locked them in a room, they might have come up with this after lots of trial and error.”
Nice to see some mathematicians excited by AI results!
On 6 October, James Maynard was nervously checking his phone under the table throughout dinner. He’d heard rumors of a tsunami heading for the shores of math. When he checked in one last time from bed, the swell struck: OpenAI had released more than 700 math papers, generated with an advanced version of its AI model. They appeared to topple hundreds of long-standing problems, forcing researchers to face the reality that no problem in math is off-limits for machines.
At first, Maynard, a mathematician at the University of Oxford and recipient of the Fields Medal, math’s most prestigious prize, feared for colleagues whose work would be upended. But then he spotted a paper that offered progress on one of his favorite pastimes: the Riemann hypothesis, a puzzle about prime numbers and one of seven famed Millennium Prize Problems, often considered the most formidable in all of math. “I was like a kid at Christmas,” Maynard says. “Even if it was some scary monster giving me a gift, I was just so excited.”
The release comes less than 1 month after OpenAI announced a solution to the Navier-Stokes equations, another Millennium Prize Problem. OpenAI’s latest proof solves only a weaker version of the Riemann hypothesis. But Maynard, who made the most recent notable Riemann advance in 2024, says it is “groundbreaking” and likely to have payoffs for other problems across math. “Within analytic number theory, it’s obviously the best headline result for the past 100 years in the field.”
Learn more: scim.ag/4s9F8Ip
Funny that Scott gets introduced as « ex-OpenAI employee »
Ex-OpenAI researcher Scott Aaronson admits AI one-shotted open problems from his 25-year-old papers, while accelerating the atrophy of his own mathematical skills:
“This summer I fed in some of the open problems from my papers 25 years ago, and just boom, boom, boom. It solved them in a one-shot way."
"These are problems that probably I could have solved if I had spent enough time, more like 100 hours at the least. But these days I don’t really get contiguous hours to think."
"AI accelerates the atrophying of my own skills. If it’s something you could work out in some hours, but GPT can just work it out for you right now, that is very tempting to do, and I often do it."
@UTAustin
Extraordinary interview, highly recommended!
Replying to @AndrewCurran_
Anthony Leverrier retweeted
I cannot let a steam engine analogy go by without bringing up John Wilkinson, perhaps the most underappreciated engineer of the Industrial Revolution.
Watt had figured out how to dramatically improve the steam engine, but he spent years struggling to actually manufacture one. The problem was embarrassingly simple: nobody could make a sufficiently round cylinder.
The casting and machining tolerances were atrocious. Watt complained that one 18-inch cylinder was out of round by as much as THREE-EIGHTHS OF AN INCH. Steam leaked past the piston. He tried packing the gaps with cork, oiled rags, paper, and apparently even old hats. Nothing worked particularly well.
Enter John Wilkinson, a British ironmaster whose immediate concern was manufacturing better cannons.
At the time, iron cannons were commonly cast around a core, introducing defects and misalignment that could make the barrels inaccurate or dangerously weak. Wilkinson developed a process for boring cannons from solid castings, and in 1774 patented a vastly improved boring machine.
The key innovation was almost comically mechanical: a massive, rigid boring bar, supported at both ends, that could cut a genuinely straight, circular hole instead of following the irregularities of the casting.
He adapted this technology to Watt's cylinders. By 1776, Matthew Boulton was boasting that Wilkinson had bored a FIFTY-INCH cylinder accurate to within the thickness of an old shilling.
Suddenly, the steam engine was a commercially viable machine.
And the story gets better. Wilkinson became one of Watt's first customers, buying an engine to drive the blast machinery at his own ironworks. The steam engine was now helping manufacture the iron that made more steam engines possible.
I find this whole episode absolutely extraordinary. The Industrial Revolution was, for a critical period, bottlenecked on the ability to make a sufficiently round hole.
We remember Watt, and rightly so. We remember the invention of the steam engine and the subsequent development of thermodynamics. But the man who made the machinery that made the whole thing possible is practically a footnote.
I think we consistently underestimate how much of scientific progress is actually downstream of advances in tooling, measurement, and manufacturing precision. It's not just that technology precedes theory. Sometimes the decisive breakthrough is that someone finally figures out how to build the thing the theorists have been talking about for twenty years.
Anyway, I think John Wilkinson deserves considerably more credit for modern civilization than he gets.
a few thoughts:
1. pretraining science is kind of like how people built quite good steam engines before they understood thermodynamics. “Science owes more to the steam engine than the steam engine owes to science.”-(L.J. Henderson). if nick is right, which i am pretty sure he is, progress points in the direction of more interpretability, and pretraining was "slouching towards monosemanticity". my guess would be that major breakthroughs in interp also advance capabilities
2. nick is a good friend of mine, safe to say my origin story at openai wouldn't have happened if i hadn't met him, and it's fascinating watching him operate and seeing the early drafts evolve into this. he's the most profoundly happy guy I know- not driven by guilt, activism, or religious terror, but by fascination. he of course understands the stakes of understanding neural networks, but it doesn't seem like the core reason he does it: he just loves the neural nets. afaict he's posting this from some beautiful hideaway in tokyo. there's a lesson in there for all the +EV maxers. i feel very grateful to have met him
3. i do think it's crazy how few people across the world were staring at neural nets growing: most have moved to a different level of behaviorist analysis, thinking about reward functions and test scores and whatnot. it does not bode that well for the major model companies, oai included, that fundamental breakthroughs like this are coming from outside. we've become too much of a pressure cooker focusing on local incentives, studying narrow tracks under old paradigms, mostly doing engineering, rather than fundamental research, and implicitly hoping the next model will figure it out






















