Very excited to introduce Khatam (eprint 2024/1843): a new Proximity Gaps result for Multilinear Polynomial Commitment Schemes. Not only does it reduce the size of Basefold (including over Random Foldable Codes), but it also improves Blaze, WHIR, Ligero, and others. 🧵(1/x)

Professor Siavash Shahshahani, the head of the Math Department, talks about the damage to Sharif University as a result of an American/Israeli strike. Shahshahani's students included Maryam Mirzakhani. He was a significant figure in developing the internet in Iran in the 90s.

RT @BronzyGuevara: 🔴 Just a wee reminder, if you don't like Iran's Islamic authoritarianism, it exists because the USA overthrew a secular…

A human consumes about 2,000 calories per day. Over 20 years, that’s roughly 17,000 kWh of total food energy. Training GPT-4 consumed an estimated 50 GWh of electricity. That’s 3,000 humans worth of “training energy” for a single model run. And GPT-4 is already dead. OpenAI retired GPT-4o from ChatGPT on February 13th. The model that took 50 GWh to train got less than two years of flagship status before replacement. The human you spent 17,000 kWh “training” for 20 years produces economic output for the next 40 to 60 years. The amortization window on GPT-4 was shorter than a car lease. Now look at what replaced it. GPT-5.2, released December 2025, is OpenAI’s current default. The GPT-5 series consumes an estimated 18 Wh per average query according to the University of Rhode Island’s AI Lab, up to 40 Wh for extended reasoning. That’s 8.6 times more electricity per response than GPT-4. With 2.5 billion queries hitting ChatGPT daily and GPT-5.2 now the default model, the inference math gets staggering fast. Even at a blended average well below 18 Wh, you’re looking at daily electricity consumption that could power over a million American households. This is what Altman is actually doing. OpenAI hit $13 billion in annual recurring revenue but still isn’t profitable. They need you to think of AI energy consumption as natural and inevitable, the same way you think about feeding a child, because the alternative framing is that they’re burning through enough electricity to rival small countries while racing to build 1-gigawatt Stargate data centers. The food analogy makes the energy costs feel biological and unavoidable instead of what they are: an engineering and business choice that scales with every model generation. The comparison sounds clever at a fireside chat in India. It falls apart the second you do the arithmetic.

we can write a million essays about how the future Silicon Valley wants to build is underwritten by a deep disgust with / contempt for Being A Human, or we can just let them speak for themselves

Like @davidbessis and others, I think that Hinton is wrong. To explain why, let me tell you a brief story. About a decade ago, in 2017, I developed an automated theorem-proving framework that was ultimately integrated into Mathematica (see: youtube.com/watch?v=mMaid2jY…) (1/15)

Finally out, the proof of mutual correlated agreement for RS codes, up to Johnson bound. I have let it circulate in the community about a year, but never found the time to make it public. For now without the improved bounds from the recent proximity gaps paper - but that will be upgraded soon. eprint.iacr.org/2025/2110

Super cool new work by Ron, Giacomo, Benedikt, and William - making the most efficient polynomial commitment schemes even smaller using tensor codes!

Sitting on the shoulders of giants, I am glad to announce the following paper with Eli Ben-Sasson, Dan Carmon, Swastik Kopparty, and Shubhangi Saraf: eccc.weizmann.ac.il/report/2… On the one hand, we improve the existing decoder analysis from Ben-Sasson, Carmon, Ishai, Kopparty and Saraf (BCIKS 2020), reducing it to an O(n) soundness error for correlated agreement up to the Johnson radius. In practice, it shows that degree 4 extensions of a 31 bit prime field (like M31, Babybear or Koalabear) are sufficient for FRI up to that radius, in many applications, considering that you are willing to grind. On the other hand, we provide additional counter examples that question the proximity gaps conjecture as written. Notably, over binary fields one cannot expect an O(n) error already *at* Johnson radius, rather a quadratic one. In general, proximity gaps stop at the distance where we have more than field size many proximates, meaning that we have to respect small gap to capacity. (See also the recent work of Crites and Stewart, as well as Diamond and Gruen.)