Tsendsuren

Research scientist at Google DeepMind | previously at Microsoft Research and Postdoc at UMass. Views are my own. Most tweets in Mongolian 🇲🇳.

[Amin Vahdat @AminVahdat]

Thrilled to share a big milestone from our AI & Infrastructure team at @Google.

Today we’re expanding our 8th generation TPU portfolio with:
• TPU 8i Max: Built for massive context windows and the highest compute density per rack
• TPU 8tx: Purpose-built for secure, confidential inference

This marks the first time we’ve introduced four distinct TPU systems in a single generation, in one year.

For developers, hardware is only as good as the software stack. And we want you to be able to build with TPUs as easily and efficiently as we do at Google. You can now:
↳ Run native PyTorch & Hugging Face models via TorchTPU (zero code rewrites)
↳ Cut KV transfer latency >90% with open-source TPU Sync
↳ Eliminate idle compute waste with Agent Compute Engine

Read more about our newest TPUs on our blog: https://t.co/8eaFhq3jhi

I look forward to connecting with builders, researchers, engineers and organizations pushing the boundaries of AI and computing!

[Tsendsuren @TsendeeMTS]

To me, scaling sounds a bit lazy but if you have money, why not. Innovation wins in long term. RSI so far has been around what researcher have done already, i.e. helping pretrain another LLM.

[Richard Sutton @RichardSSutton]

Brute force computation is not enough for intelligence. Clever new methods are needed. The science of AI is far from finished. IMHO

[Tsendsuren @TsendeeMTS]

@TEKnologyy @841io Awesome! Looking forward to reading it.

[Tsendsuren @TsendeeMTS]

LLM memory atm is akin to stone age. LLM is learning to harness, i.e taking notes for itself. Humans as an individual takes notes too or writes book or as a society harness collective mind and stores information in culture and legacy. But unlike LLM, we internalize them quickly.

[Anthropic @AnthropicAI]

Memory is what allows Claude to learn from experience. In a new Engineering Blog post, we share how “dreaming” helps Claude organize what it’s learned, correct its mistakes, and find what it needs more easily the next time. https://t.co/uUEN4bNkr9

[Tsendsuren @TsendeeMTS]

@841io @TEKnologyy Very interesting!

[Kazuki Irie @kzkirie]

Lovely! "Weight-space programming" is such an important concept in DL; we covered it 4 times in my Yale course this semester!

1. Gradient descent as a weight-space programmer.
2. Fast weight programming.
3. Metalearning: GD writes learning algorithms
4. Self-referential WM & RSI

[Kazuki Irie @kzkirie]

Needless to say, we also covered the neuroscience connection.

More in the primer paper: https://t.co/qADEQMHtlQ

[Tsendsuren @TsendeeMTS]

I love neural nets updating their own weights!! That was the whole idea around MetaNets and Fast-Weights. We are about see another regime of training data and scaling of point-wise weight update mechanisms.

[Rikiya Takehi @rikiyatakehi]

We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.

We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: https://t.co/bOWCcFhCVq

[Google DeepMind @GoogleDeepMind]

Gemini 4 Carbon is our next frontier model.

It’s state-of-the-art across cybersecurity and long-horizon capabilities.

[Google DeepMind @GoogleDeepMind]

This is Genie 4, our most realistic and interactive world model yet. 🌐

Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.

[Konsti Wohlwend @konstiwohlwend]

The complexity theory result is actually huge.

P problems can be solved in polynomial time. It was widely believed that NP-complete problems require exponential time.

P = NP states that NP-complete problems can be solved in polynomial time. OpenAI didn't prove that. But, they proved that (some) NP-complete problems can be solved faster than exponential time, meaning:

- P = NP, or
- the complexity of NP problems is a "middle thing" between polynomial and exponential, eg. O(n^log n)

If it survives peer reviews, this could be one of the biggest CS breakthroughs in decades.

[Tsendsuren @TsendeeMTS]

@rosstaylor90 Thanks for going into the details. The trade-offs make a lot of sense. I imagine that there is a different co-ordination regimes we can find across tasks and along optimization horizon. And yes, maybe building a collective harness can help break the prior.

[Tsendsuren @TsendeeMTS]

Isn’t this expected as pre-training data has such information?

[Nathan Benaich @nathanbenaich]

this work from @GenReasoning is fascinating - training agent organizations to work as a collective

“During training, organisations develop additional layers of hierarchy, adopt different divisions of labour across tasks, and devote more resources to peer review. Agents also increasingly reason about their teammates and offer help. In safety evaluations, however, they become less likely to report problems to human overseers.”

[Superman @thesupermannx]

Samsung open-sourced a method that shrinks 13B parameter LLM into less than 1 GB.

It's called "LittleBit"

Instead of storing AI weights as standard numbers, they used latent factorization to crush them down to extreme sub-1-bit levels.

In some configurations, they hit 0.1 bits per weight.

But here is where the architecture gets crazy.

When you compress an AI this much, you can stop doing math.

Instead of forcing the hardware to do heavy floating-point multiplication, LittleBit replaces the core computation with a bitwise XOR operation.

It swaps complex matrix math for basic sign flips.

The results rewrite the rules of model deployment:

• Unlocks a massive 11.6x inference speedup relative to standard FP16 models.

• Radically reduces memory footprint and loading bandwidth.

• Maintains robustness in extreme sub-0.5 bit regimes where previous compression methods catastrophically fail.

This is not a clever optimization.

It is the blueprint for running massive, state-of-the-art AI locally on cheap, resource-constrained devices.

[Valerio Capraro @ValerioCapraro]

BREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification.

The most important article to read today is not one of OpenAI’s 700 AI-generated math papers.

It is this other paper, making a deep and worrying point:

A Lean-verified proof does not automatically validate the proof written in natural language, nor does it mean that the formal statement captures the intended theorem.

During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely.

It can hallucinate another theorem.

Lean correctly verifies the result.
But the proved result may no longer be what the paper claims.

This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution.

They identify at least two mismatches between the written intermediate results and their Lean counterparts:

One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result.

A pressure-flux estimate is obtained through a different bound, and proved through a different argument.

It is not clear whether these mismatches invalidate the entire proof.

But they raise an important issue.

OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving.

Epistemia at scale.

*
Paper in the first reply

[Tsendsuren @TsendeeMTS]

This is the way. I am a big believer in hybrid computing. This is much more efficient, accessible and secure.

[Marina Mogilko @siliconvalleymm]

BREAKING: @JensenHuang just said @nvidia and @Microsoft have "reinvented the personal computer completely."

Two things change:
1 - Your computer becomes the home for your agents.
2 - Your AI runs partly on your machine and partly in the cloud.

Right now your laptop sits on your desk all day and waits for you. Huang wants it to keep working while you're out. You give an agent a task, and it uses your apps and files on its own. You check in from your phone. He's said he'll text his agents on WhatsApp and they'll text back.

The agent lives on a computer you own, at home or on your desk, and runs around the clock.

I like the idea. Now I want to see one agent finish one boring, real task for me from start to finish.

[Satya Nadella @satyanadella]

Today marks a new chapter for Windows, as we bring unmetered intelligence to every desk and every home, and make every PC a place where agents can work securely on your behalf.

Some highlights of what we announced:

• MAI-Code-1.1 Flash: 137B parameter coding model w/ 256K context window, which is now optimized to run on your PC!

• GitHub Copilot now hands off work to local models like MAI-Code-1.1 Flash, helping projects cost a lot less without sacrificing quality.

• With Hybrid Intelligence, Copilot can now take action directly on the PC and keep sensitive work on your device.

• And with Code in Copilot, you can essentially build any software you need on your desktop, without any cloud token spend, and it’s just super at it. You’re no longer limited to what’s in an app store! Your PC becomes an infinite software factory.

• Security is foundational to all this, which is why we are also bringing together Windows and Agent 365 so agents can work within secure boundaries on-device, including MXC a local sandbox for agent execution. Windows becomes your secure agent box!

• All this comes to life on a new generation of devices, like Surface Laptop Ultra, powered by NVIDIA RTX Spark.

Can’t wait to see what you build with all this.

[Michig ᠮᠢᠰᠾᠢᠭ @mishig25]

openai/math: 722 papers from an internal OpenAI model, 372 results

I built a search engine for it 🧮

• plain English: "how many shuffles randomize a deck?"
• names/jargon: "UGC", "Kaplansky"
• filter: Lean-verified, negative results, reasoning traces

https://t.co/apv11SKnEx

[Sebastian Thrun @SebastianThrun]

1/ Introducing PhilosophyBench from @StanfordAILab @StanfordHCI, the first independent, large-scale benchmark for evaluating AI’s philosophical capabilities.

https://t.co/csZ86gugw6

[Tsendsuren @TsendeeMTS]

Some RL plots are more fun to watch

[Fuli Luo @_LuoFuli]

Nearly half a year of silence. We spent it studying one problem: how far RL can scale.

MiMo-V2.6 is in the middle of its RL run right now. Three things we scaled: compute (~2B tokens per step, 1568 prompts × 16 rollouts, fully async), environments and harnesses (multi-task agentic RL, mixed across multiple harnesses in one run), and grader compute (agentic in-group credit assignment, with test-case and rubric-based rewards). We'll open-source the details piece by piece over the coming weeks.

Streaming the run: https://t.co/ZSxahzJRju