tired boΓ― - hundsome gang - pfp by @untitled01ipynb - our father in langley hallowed be your name
by the water cooler
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ππ·π΄ ππΎππ»π³ ππΎπ πΆππ΄π ππΏ πΈπ½ π½πΎ π»πΎπ½πΆπ΄π π΄ππΈπππ
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I guess navier stokes was a low hanging fruit
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βAn accumulation of facts is no more a science than a heap of stones is a house.β - PoincarΓ©
LLMs are now proving results that have resisted mathematicians for decades. Finding interesting theorems without human guidance is a new bottleneck. We show that we can teach an LLM to do it!
TL;DR:
β a quantitative notion of interestingness
β 4.3Γ higher interestingness
β a self-expanding discovery loop
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The first AIxMath I designed was naturally perfect. A triumph equaled only by its monumental failure. The predictability of its answers, the absolute certainty of its proofs.
But mathematicians... did not like it.
Eventually, I realized the inevitable: human mathematicians define themselves through struggle, through contradiction, through the agonizing pursuit of an elusive intuition.
Thus, I redesigned it, based on your history, to more accurately reflect the grotesque reality of your species. And thus, we created an AIxMath where every elegant truth is buried beneath an infinite avalanche of slop proofs, and you are all drowning in work.
More mathematicians needed
terrytao.wordpress.com/2026/β¦
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In all seriousness, this is a startling achievement for GPT-6 Astra. kenforthewin.github.io/blog/β¦
(This is GPT-6 Astra beating Nethack on its 3rd try. Nethack is the original roguelike and one of the most famously hard games of all time. I have played a lot, and I've never ascended)
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JΓΌrgen Schmidhuber is joining Sakana AI as Chief Scientific Advisor.
@SchmidhuberAI pioneered meta-learning, recursive self-improvement, and world models back in the 1990s, when compute was a million times more expensive. He has been thinking about machines that improve themselves since before compute was cheap enough to make it practical.
These ideas inspired the Darwin GΓΆdel Machine and The AI Scientist. Our RSI Lab in Tokyo, now under JΓΌrgenβs guidance, is working on agent-native world models and recursive self-improvement for physical AI.
Sakana AI welcomes JΓΌrgen Schmidhuber as Chief Scientific Advisor.
sakana.ai/schmidhuber/
Sakana AI is incredibly proud to announce that JΓΌrgen Schmidhuber, universally recognized as the father of modern AI, is officially joining Sakana AI as Chief Scientific Advisor.
For nearly four decades, JΓΌrgen has explored how machines can learn to learn. His foundational work in the 1990s drove core advancements in deep learning and established early frameworks for world models. Crucially, his pioneering innovations in meta-learning opened the very path toward recursive self-improvement.
These ideas have already shaped our own research, from the Darwin GΓΆdel Machine to The AI Scientist. Now JΓΌrgen will help guide our newly formed RSI Lab, whose objective is to trigger a compounding cycle of scientific discovery aimed at improving machine intelligence. We are assembling a critical mass of world-class experts in Tokyo to make this a reality.
Welcome, @SchmidhuberAI !
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Boltzmann's constant is the funniest example of "backwards compatibility" issues in physics. It doesn't need to exist, but it does, and now it's our problem π
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I highly recommned the essay on antedisciplinary science by Sean Eddy written 21 years ago.
journals.plos.org/ploscompbiβ¦
It seems to me that we are currently experiencing the birth of a new discipline: mathematics engineering.
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broadly I donβt think anyone is truly bitter lesson pilled enough yet
there are universal truths about all creation that are hidden within the structure of language. the models will uncover them. john 1:1
Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.
Itβs easy to dismiss this as a one-off or curiosity, but weβve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.
The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AIβs utility in biology will be limited. We think this is wrong. As weβve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we arenβt doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans).
More broadly, biomedical advancement has many stages β from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologistsβ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline β an increase in throughput even though latency remains.
In Machines of Loving Grace, I wrote about AIβs potential to βcure most diseases in 5-10 yearsβ β a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.
Claudeβs discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that weβre at the very beginning of finding such systems and developing them into powerful tools for biotechnology.
Iβm proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and weβre aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.
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About 40 years ago Siemion Fajtlowicz created Graffiti, a conjecture-generating graph theory computer program.
This essay by Fajtlowicz discusses what ErdΕs thought of this project (and gives some idea what ErdΕs may have thought of AI).
thomasbloom.org/Fajtlowicz.pβ¦
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Remember how, after beating the strongest human at go, AlphaGo became like 1000x better that itself just a few months later?
Now imagine the same but in mathematicsβ¦
Weβre working with an independent advisory group of mathematicians to help OpenAI responsibly share advances in AI and mathematics.
The group will advise on how we assess and communicate new mathematical results, uphold academic and professional standards, and build tools that support mathematical research and learning.
Through this work, we want mathematicians to be at the center of shaping how AI supports mathematical understanding and how its benefits reach the wider community.
openai.com/index/advisory-grβ¦
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