exploring, experimenting, investing
Palo Alto, CA
Joined March 2019
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a peak inside the inner workings of math ai
Some more details on our work @HarmonicMath : arxiv.org/abs/2510.01346
By the way, we’re hiring Research Engineers. This is a high-leverage opportunity to join a small and world-class team. High agency is required. DM me leads
We entered First Proof Batch 3 as the only open-weight model: Kimi K3, running on the open-source Humanize harness.
No full solves yet, but real partial progress on a few problems and plenty of lessons. Here's what we learned along the way.
markkim.bearblog.dev/first-p…
A huge thank you to the @DeepInfra team. They stood up two 8xB300 nodes on a day's notice when our cluster went down. Our submission would not have happened without them, and we're incredibly grateful!
We have an incredible team working on interesting projects, including MAP (mapmath.org/). If you're interested in working with us, please reach out!
We also have a ton of compute to put behind the work.
when your two worlds come together unexpectedly, incredible things happen!
humanfia 🤝 princeton
We have now also completed a full Lean formalization of the Hamilton-Perelman proof of Thurston’s Geometrization conjecture!
Ben, Yuan, Ziyang and I completed this in collaboration with the Humanfia team at NVIDIA @juihuichung @LigengZhu
Mark Kim retweeted
🚀 Humanfia × Geometrization: Perelman's Proof, Now Fully Formalized in Lean
The Humanfia team and Yuan Liao, Ziyang Qin, Ayush Khaitan , Ben Chow, and have completed a full Lean formalization of the Hamilton–Perelman proof of Thurston's Geometrization conjecture! It goes beyond Poincaré and classifies every 3-manifold. Mathematicians needed years to digest this proof. Now a machine has verified it line by line within weeks!
The progress of Humanize agentic math:
26-07: Perfect score in IMO 2026
26-08: 670/672 in PutnamBench
26-09: 100% in PutnamBench and 1st place in LeanEval (Surpassed by Axiom later)
26-10: 1st place again in LeanEval with upgraded Humanize workflow
26-10: Completed the Lean formalization of the Hamilton–Perelman proof of Thurston's Geometrization conjecture.
We are increasingly convinced: when everyone has tokens of similar intelligence, the agent workflow is what decides how far AI can go in frontier mathematics.
Tokens are「transforming」 the math.
🌌Geometry library: github.com/qinz1yang/differe…
⚙️ Humanize: github.com/humanfia/humanize
#AI #LLM #Humanize #Mathematics #Lean4 #Agent #Humanfia #Geometrization
Mark Kim retweeted
Two years ago I argued we needed an autoformalizer.
Now the time has come to formalize all of mathematics.
We need to unite behind a single Mathematics Autoformalization Project that turns all known mathematics into one Lean library.
@jdlichtman @patrickshafto
Mark Kim retweeted
🚀 Kernel Design Agents to Optimize Kimi Delta Attention (upto 2.96x)
When the KDA (Kernel Design Agents) project was initiated, its name happened to coincide with another KDA (Kimi Delta Attention). More than once, people asked us , "Can we use KDA to write a version of KDA?" Today, right at a moment with a full moon good for shooting, we are thrilled to share the latest results of KDA(gent)-v0.6: using KDA(gent) to optimize KDA(ttn)!
KDA-v0.6 achieves this through
* Humanize2 Flow & LLM Collaboration Optimization: Introducing multi-round iterative evolution with Flame Chase, GPT-5.6-Sol, and Fable-5, paired with an automated Workspace cleanup mechanism to significantly broaden the operator search space.
* Multilingual Domain-Specific Skill Stack Support: Fully supporting CuTe-DSL, CUDA C++, as well as recent Agent-Native CAKE IR and TIRx. Combined with customized diagnostic tools (such as CuTe's dedicated IKET pipeline analyzer and TIRx's CPU-side numerical simulation and static checks), achieving deep performance bottleneck diagnostics.
* Self-Evolving Kernel Wiki: Building a dynamically updated knowledge base that automatically cleans up invalid/erroneous implementations, corrects classification tags, and streamlines search indexing, significantly improving the Agent's knowledge retrieval quality and code generation accuracy.
KDAgent ultimately optimized the KDAttn kernel to 2.96x of FlashKDA, and we face a lot of unexpected hacks during the optimization. More details shared in our blogs nvlabs.github.io/kda/blog/20…
Release results of KDA for KDA: github.com/NVlabs/kda/tree/2…
Community & Production Deployment:
All kernels are open-sourced for community verification! For deploying NVIDIA Agentic CUDA acceleration in production environments, we recommend using the production-level version rigorously verified by FlashInfer.
By the Team: Dongyun Zou, Yixin Dong, Hongyi Jin, Junxian Guo, Yahui Cui, Avery Huang, Zihao Ye, Junru Shao, Changye Li, Zijian Zhang, Sihao Liu, Song Bian, Ligeng Zhu.
The man, the legend!
For those of you following the recent developments around Navier–Stokes, this should be an incredible lecture.
This Friday at Harvard, Javier Gómez-Serrano, who has been working with Google DeepMind on the problem, will present and analyze OpenAI’s recent breakthrough.
I spoke with him today, and he’s currently working through the solution in preparation for the lecture. This will be a good one.
For those of you following the recent developments around Navier–Stokes, this should be an incredible lecture.
This Friday at Harvard, Javier Gómez-Serrano, who has been working with Google DeepMind on the problem, will present and analyze OpenAI’s recent breakthrough.
I spoke with him today, and he’s currently working through the solution in preparation for the lecture. This will be a good one.
Carlota Perez covers this well in Technological Revolutions and Financial Capital.
Every technological revolution since 1771 has run roughly half a century.
Installation is fast because financial capital rushes into the first half of the S-curve, causing a bubble.
Deployment is slow because every business has to assess the cost of adopting the new technology and the economy built around the old paradigm has to be rebuilt.
Sam Altman admits he was wrong on AI's timeline and says society and the economy will adapt more slowly
"I thought when we got to GPT-4, which was back in 2023, that very quickly after that there was going to be much more disruption, software businesses up for grabs right away, than it turned out to be."
"I think I was wrong about a few things, but one in terms of the speed: the economy just has so much inertia."
"People keep doing the same things, buying from the same company, wanting to use their tools the same way. I think this is actually a positive in many ways, and it's going to make this big transition go smoother and slower. I'm grateful for it."
"But it means we've all been too ambitious on timelines. Even with this incredible technology, society and the economy will adapt more slowly."
It makes a ton of sense once you connect it to what neural networks actually do.
They capture data through successive layers of mathematical equations.
Recent progress in AI has made “mathematizing” the world dramatically easier and cheaper.
If we can mathematize human behavior at a sufficiently granular level, we can, in theory, build accurate human simulations. Those simulations become the building blocks for simulating the world itself.
We’re moving from top-down models to agent-based ones.
markkim.bearblog.dev/mathema…
markkim.bearblog.dev/superin…
Had a similar experience with Simile where the longer you dwell on the simulation concept, the more you realize that there's something here.
And didn't realize Andrej was involved, wild.
It crystallized when I learned SharkNinja is experimenting with AI-generated "synthetic personas" even before doing focus groups. The leading consumer products team, at least on the public markets, doesn't even turn to real humans first.
We'll see how effective this type of research is by 4Q26 when the first synthetic-influenced products land. The future is strange.
Wrote this last February after seeing a demo of TTT being applied to video.
markkim.bearblog.dev/test-ti…
Test-time training shifts compute from pre-training to adaptation at inference. It handles distribution shifts more efficiently and amortizes training cost across the model's entire lifetime.
‼️huge ssi news.
ilya is about to take his first tentative steps out of the age of research and back into the age of scale. it’s time to smell what ssi is cooking.
ssi have built a small reasoning engine that can compete with much larger training runs because his data is better curated to meta learning. but, more importantly.
we’re about to step into the era of TTT (test time training. gradient descent happening in real time to solve your problems). so instead of a context window you get actual learning.
and because it’s so sample efficient it can be trained on hard to verify tasks that other paradigms can’t touch. everyone else’s weights are frozen, they struggle out of distribution. ssi have created something that has a bundle of knowledge but can truly learn in real time and use that to your advantage.
current approaches are trying to hack their way to ‘learn’ with memory tricks, this thing will updates its weights, remember key lessons, and finally feel like a human level reasoner. this is a huge paradigm shift from the king. early results are very impressive. we can stop watching memento on repeat.
it’s learning all the way down, the descent is real.