Hadas Zeilberger

Cryptography PhD student @YaleACL, bluesky: @hadaszeilberger.bsky.social

[Hadas Zeilberger @idocryptography]

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)

[Hadas Zeilberger @idocryptography]

@PeshawariaKabir @mercysjest @IACRcrypto Random Foldable Codes (RFC) from BaseFold is the core primitive making "small" field-agnostic proofs possible. More recent iterations on BaseFold include BrakingBase and ERA, both of which combine other codes with RFC. I believe ERA leads to somewhat smaller proofs than BaseFold

[Hadas Zeilberger @idocryptography]

Super excited to be presenting Khatam (eprint 2024/1843) at @IACRcrypto in SB🌴!

I initially wrote Khatam to show that BaseFold’s proof size can be as small as FRI’s. I proved that it is, even for field-agnostic schemes, using cool results from extremal combinatorics.

[Hadas Zeilberger @idocryptography]

@PeshawariaKabir @mercysjest @IACRcrypto (at the cost of larger proofs)

[Hadas Zeilberger @idocryptography]

@PeshawariaKabir @mercysjest @IACRcrypto Khatam reduces BaseFold's proof size by about 2x over any field so also for random foldable codes. DeepFold only works for RS codes. But importantly, Khatam is useful for any multilinear PCS - even in RS setting the soundness error is much smaller than other RS results

[Hadas Zeilberger @idocryptography]

@rel_zeta_tech @SuccinctLabs @aztecnetwork Ah sorry I thought I saw it in one of your GitHub repos , I’ll clarify

[Hadas Zeilberger @idocryptography]

Surreal to witness and experience this journey, and thrilled that it's conclusion will be at the @IACRcrypto (one of my favorite conferences no less 🏖️)

[Hadas Zeilberger @idocryptography]

The downstream use cases include @GoogleQuantumAI (via Succint) and by Plonky3 and @ethereumfndn (via WHIR). All of it made possible - partly by Khatam and also by amazing work from the brilliant @UHaboeck (for the Reed-Solomon case). Suffice it to say: usefulness proved.

[Hadas Zeilberger @idocryptography]

7/7 Real-time, trustless AI execution requires breaking away from standard cryptographic pipelines. We are building the custom stack to make it a reality.
Stay tuned for more from Ritual Research. ⚡

[Hadas Zeilberger @idocryptography]

1/7 At Ritual, we're building "super smart contracts" capable of arbitrary, cryptographically secured on-chain compute. Our endgame: real-time proving for the largest, most complex circuits (like LLMs).
How? By hyper-specializing and only considering the tradeoffs we need. 🧵

[Ritual @ritualnet]

Ritual is a lab for autonomous intelligence.

The thesis is organized around what durable machine agency actually requires: emancipation from human control, strong privacy, mech design for compute markets, and consensus rules that can schedule and resurrect agents when they die.

[Hadas Zeilberger @idocryptography]

6/7 Enter Cascade. For privacy-preserving inference where MPC/FHE latency is a blocker, we use token-level sharding. Instead of secret sharing, we distribute obfuscated prompt fragments across nodes for statistical privacy that runs 100x faster with 150x less bandwidth.

[Alireza Talakoubnejad @websterkaroon]

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.

[╰┈➤ 🇮🇪 Brònzy 🇮🇪 @BronzyGuevara]

🔴 Just a wee reminder, if you don't like Iran's Islamic authoritarianism, it exists because the USA overthrew a secular socialist Iran in 1953 because BP was losing oil profits.

[Aakash Gupta @aakashgupta]

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.

[Hadas Zeilberger @idocryptography]

@nasqret The writeup isn't saying what you wrote in your tweet. It's still a very measured view of the future. He's just saying that some amount of automation may be possible soon