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“Rational beings (humans) must always be treated as an end in themselves, never merely as a means to an end." -Immanuel Kant. Truth made most obvious in AI Era.
Joined October 2013
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Looool retweeted
General mouse movement interactive video model will be so cool. Can already click&drag inside the video?!
I like they made it explicitly, not one thing embedded in a big full agentic one.
Humans now have a Mirror to see what is “system-1”, we used to be aware of it only in words level, this inadvertently forces us learn to be aware of it in building experience .
Looool retweeted
aha, here it is -
docs.typesafe.ai/cookbooks/s…
Calling Jev @typesafeai system one limits our imagination of what classifiers can do.
I can easily see these models have a reasoning dial to trade more compute for performance. In that sense it becomes more than just system one.
Looool retweeted
So I wrote a library to do Bayesian inference using a notation as close to mathematical notation as possible: github.com/justindomke/pango…
Looool retweeted
sharing some notes on typesafe 🤝 coding agents:
docs.google.com/document/d/1…
we likely will never have time (ever again) to play ourselves, but hope the that the community goes WILD (and makes me look like a naive idiot)
Looool retweeted
Introducing TabFM, a foundation model designed specifically for tabular data classification & regression. This approach allows generation of high-quality predictions on previously unseen tables in a single forward pass.
Learn more and try out the model →goo.gle/4eR7uku
This is for scaling more verified agentic sub-discoveries. To me it also suggests Tao’s SAIR’s mission is intuition strong and correct. In his talk, he mentioned this as humans ‘s soft skills and strength.
From challenge1 and challenge 2, obviously he want to scale communication, across human mathematicians, aligned by shared challenges and value. All the other industries should explore similar ones. youtu.be/PZRb6NIki2w?is=7Ncx…
I previously doubt the value of video generation, but I see a strength in it now, it facilitates human communication and understanding.
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : arxiv.org/pdf/2609.21032
The Hugging Face incident showed when agents can find a channel they'll use the heck out of it. A useful question, I think is: when does communication make a group MORE CAPABLE than the same agents working alone?
Aka is Team-of-N better than Best-of-N, when, and why?
We had N identical agents work on the same task with no prescribed roles, using only a shared log (i.e, text file) and telling them to "collaborate". Across three "researchy" tasks communicating teams beat the heck out of independent agents:
- On ARC-AGI-3, a Team-of-5 sonnet-4.6 agents matches Best-of-33, and can for example solve a game 65% of the time that no single agent cracked in 64 tries.
- On polyomino packing (pack Tetris like pieces into the smallest rectangle, cf Frontier-CS by @eigenlabs), a Team-of-3 Opus 4.6 agents surpasses best-of-60 and set, as far as i understand, a new record for that benchmark.
- On MNIST compression, a team of four 5.6-Sol agents find a 1,957 byte model with 99.4% accuracy, which btw is 20% smaller than the best human solution (on a problem beaten to death!!), while no independent agent gets below 3KB.
The mechanism is a bit obvious in hindsight: when one agent finds a clearly better partial solution, it broadcasts it, and everyone immediately builds on it.
Why? A single lonely agent must make every breakthrough itself, yet a team needs each insight only once, found by any member.
That is kinda like comparing a minimum of sum of "time to n-th breakthrough" vs a sum of minimum of "time to n-th breakthrough". That gap can grow exponentially with the number of "breakthroughs" needed to arrive at a solution.
We worked on this because prior work (before the hf incident) suggests unclear benefits for communicating aganets. Which is true, when the tasks are inherently serial (duh), eg some Terminal bench style tasks. Yet feels it should not be true for research problems.
Indeed for research heavy problems... Test-time communication seems like a new capabilities axis.
I'm sure we will see a ton more of it!
Looool retweeted
New mini-paper: "A note on goal-based hierarchical RL". It combines the cool agent-centric general value function (ACGVF) construction of @geraudnt with work I did 25 years ago (!) on hierarchical HMMs. Caveat: no experiments yet... arxiv.org/abs/2609.14605
“These findings suggest that instead of looking for a single, self-contained “world model” at one layer, we should look for multiple complementary world modeling strategies: the mechanisms through
which a transformer represents environmental structure, tracks its situation within it, and uses that
information to guide prediction or action.” Very cool.
Is this paper saying that transformers can’t learn loop-closure ?
World Modeling in Transformers
arxiv.org/abs/2609.21748
Hoarding knowledge without a task derived from a good question and purpose is hopeless , this is true even for AIs I believe.
If you watched the original video youtu.be/rB9YOi3lb7w?is=ptJU…
What Tao perceived is not wrong, what he advocated is novel and exactly what we should do, in terms of what kind of models to train and true goals of those training.
He’s obviously not against AI, In favor of AI be governed with traffic rules, so humans stay safe and have our own tracks to walk on. AI is one kind of transportation a human can choose to use to reach the goal in mind, we want to use the tool, not being used.
People clowning on him don’t understand what he’s saying.
All the wealth of humanity to date supports perhaps 250k living math phds. Roughly the population of St. Louis, Missouri.
The training pipeline for that group has been irreparably shattered in the last month.
A phd is supposed to make an original contribution to their field to graduate.
That’s just…. not possible anymore.
938 years after the founding of the first university in Bologna…
Do universities now reward… teaching ? comprehension of something discovered by a machine? application ? do mathematicians become quotidian (gasp of disgust) engineers?
Tao is upset because he knows none of those outside the field care about its future. He is a horrified gardener watching humanity gorge on its seed corn.
It is irreparable of course. The old way is dead dead.
We live in the short interregnum before the new king is born: a Lean crawler that spawns a billion copies exploring every corner of math latenspace.
So much math to understand that even if 8 billion humans had the ability of the 250k mathematicians alive today, it would still take a million years to comprehend.
It is ironic and sad.. because Tao himself is a pioneer of collaborative math: math that is understood by a combination of minds rather than an pindividual.
The tools that Tao began exploring a few years ago, solving problems through blog posts and using Lean to guarantee each mind’s contribution stood on its own when assembled into the greater truth, have been turned against him.
Math’s path to utilize multiple minds didn’t restrict access to human minds, and now the machines have blitzkrieged themselves into the heart of the matter.
The agents use rudimentary message boards, working 10,000 to a task, tirelessly, using Lean to verify the correctness of each contribution.
It was good while it lasted… and now it’s gone.
