It was a record (still now?) arxiv.org/abs/1806.05393

This is going to be a super fun workshop on scaling & learning dynamics & optimization! Please consider submitting your best work there!

The originality and the depth of science are really impressive. High thinking, signal to flop ratio. Congrats Damien, Elliot, Courtney et al.

Mathematicians 🤝AI researchers arxiv.org/abs/2601.22401. Our take on AI solving Erdos problems: * Many "Open" problems are actually just obscure: many cases the AI didn't find something new, only rediscovered solutions buried in the literature. We present our systematic approach to reporting AI results on Erdos. * The real bottleneck is still human labor, e.g. we spent lots of time filtering out technically correct but meaningless solutions (AI missed Erdos’s original intent). * Acceleration in solving low-hanging fruits is real, but we also need to highlight the many more misses that require human auditing. Clear research directions ahead though, and we feel optimistic about drastically increasing the signal-to-noise ratio. More to come!

I am hiring a student researcher to work with our team in Montreal on LLMs architecture and pre-training in spring-summer 2026, if you're excited to push the frontier of research forward, join us to help keeping the TPUs warm. fill out this form: forms.gle/1AfdyCbzjdKi2yAb7

First scaling law: performance follows a power law of compute, with its exponent governed by science and engineering. Second scaling law: the total improvement of this law follows a power law of resource, with its exponent governed by vision and conviction.

Great talk from ⁦@ShikaiQiu⁩ on scaling collapse, which can be used as a sensitive diagnostic for model specification (LR, etc) at small scale that transfers to large scales! arxiv.org/abs/2507.02119

come and enjoy the blessing from universality

Learning rate schedule is a big mystery in machine learning that is underinvested in both theory and practice. Shikai did an awesome job in revealing that simple quadratic models can indeed capture many key insights from big ones.

While scaling laws typically predict the final loss, we show in our ICML oral paper that good scaling rules enable accurate predictions of entire loss curves of larger models from smaller ones! w/@Locchiu, @andrewgwils, J. Pennington, A. Agarwala: arxiv.org/abs/2507.02119 1/10

Really impressive work! Changing the exponent is highly nontrivial.

Wow, didn’t think I’d see this conjecture solved in my lifetime terrytao.wordpress.com/2025/…

Very cool! Congrats and thanks for trusting our codebase! @peterjliu (creator of nanodo), @ARomanNovak ,@_katieeverett (pre-)lead of the codebase.

It’s extremely exciting to see the frontiers of science and practice merge, yet equally disheartening to see when they become frontier of geopolitics. What wisdoms do AI offer to human?

Curious to hear thoughts about levels of AGI in math. My take (unchanged since march 2023): L1: solve IMO L3: breakthroughs in 2 areas (e.g., analysis/PDEs, algebra/number theory, geometry/topology), each ~ one Annals/JAMS/Acta paper L5: solve a Millennium problem L6: ...

“勿以恶小而为之，勿以善小而不为。惟贤惟德，能服于人” ("Do not do evil just because it is small; do not ignore doing good just because it is small. Only by being virtuous and moral can one be respected by others.") learned this in middle school, never forgot, passed it to my kids

If you miss the NYTimes needle, especially one that is statistically uniform (blog.alexalemi.com/a-degree-…), you can use this page: alexalemi.com/random/electio… I whipped together to reason about the correlations between the swing states tonight as results come in.

Lgtm

Two amazing accounts with high signal I have been impressed by recently on this app is: @cloneofsimo and @kellerjordan0 Just unprompted thought :)

“It’s the people, not the projects …”