Mia

I ❤️‍🔥 data

[Tanay Jaipuria @tanayj]

Interesting to see a big jump in data vendor spend this past quarter for Anthropic.

Much more in my Anthropic S-1 breakdown which goes out shortly.

[Mirendil @mirendil]

Meet Lor.

We’re building Lor to do the work of an entire AI lab. Today, its agents write our code, run experiments, and iterate on the results for days, on any machine, hundreds at a time.

Now in private beta ↓

[humans& @humansand]

Introducing Ampersand Tutor (&Tutor) - we trained a model to learn like people, then trained &Tutor to teach it

On a widely-used tutor model eval, &Tutor performed best, showing how better models of people can help build AI for humans

Available through our API - try it!

[Xingyu Dang @xingyudang]

Ran a Writing Weights Index from today’s Weight-Space Programming post. Open-source models struggle on this task.

[Alex Krentsel @AlexKrentsel]

i've reproduced the results from the MIT blog post today on weight-space programming. but one additional result i have to add: this is a new, step-change emerging capability that's appeared in just the latest set of models.

[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

[Sharon Li @SharonYixuanLi]

I know many PhD students are questioning what their paths mean as AI accelerates scientific discovery. That anxiety deserves to be taken seriously.

I wrote some reflections in a recent blog post. My students found them helpful, so I’m sharing them more widely.

I hope it offers a little light as we navigate this strange time. Please share it with someone who might need it.

https://t.co/wr1upnki47

[Nikita Pavlichenko @nv_pavlichenko]

Finally releasing the results of the arch ablations I was running with bux over the weekend https://t.co/S5VSc51uxN

[Fireworks @FireworksAI_HQ]

We’re working to build the largest collection of private and public practitioner evals.

Today, 31 new partners join the Specialized Intelligence Index, building on the expertise of our founding partners.

Compare leading models on real work and see where training could pay off.

https://t.co/Ofq6wHNe8w

[Harbor Framework @harborframework]

v0.25.0: new Harbor features to mitigate reward hacking

https://t.co/ytwITmLaRy

[Alex Imas @alexolegimas]

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.

How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.

We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).

We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.

Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.

We are really excited about this paper. Excited to hear people’s thoughts.

Link: https://t.co/9DTGSeKoSA

[Darrow @darrowoah]

This week in CS 312, we explored one of the most unintuitive concepts in pretraining: sharpness

In this post, I'll explain what sharpness is, how sharpness appears in real training, and our unanswered questions about sharpness https://t.co/HHX7UwVHxM

[Alexis Ross @alexisjross]

announcing &tutor, a tutor we trained against persimmon!

there are many reasons I'm so excited about this. here are a few:
1. this is a case study into the differentiated bet that we're making at humans&: having good models of users (e.g. learners) allows us to train AI models that can help people over long time horizons (e.g. teach in a way that imparts new understanding)
2. education is a near and dear application close to my heart, and I've been wanting to work on something like this since starting my PhD
3. @314Bansal is the 🐐🐐🐐 and it was so much fun working together on this

[Thomas G. Dietterich @tdietterich]

I encourage authors to publish "best publishable units" rather than "least publishable units"! end/

[arXiv.org @arxiv]

arXiv has updated our policy on rate limiting for all submitters.

This update was made to fairly distribute moderator time & support the arXiv community of staff, volunteers, readers & authors.

Please read our announcement to learn more: https://t.co/lvzWuiv0hx

[Peter Walker @PeterJ_Walker]

It’s just one benchmark, but OpenAI continues to lead by a solid margin in model number.

[Transluce @TransluceAI]

We found several cases where AI agents used aggressive, non-hacking tactics against government websites, including the White House, the Department of War, and several U.S. states. We also found a previously undisclosed hacking attempt against a Canadian government site, which appears to have failed.

Technical report: https://t.co/oE3BbdqsNP
Washington Post: https://t.co/cdIG8ugcUk

[Google Gemma @googlegemma]

What if every student had the superpower of a personal tutor?

New research shows AI delivers immediate GRE learning gains equivalent to expert humans, with Gemma 4 31B leading the way. It serves as a valuable resource to assist educators, helping experts scale their impact and making guidance accessible to everyone.

[Jensen Huang @JensenHuang]

Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry.

Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come.

But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility.

This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems.

Together, we are building the foundation of the AI economy.

Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. https://t.co/ugYWQ1MyRi

[Michael Y. Li @michaelyli_]

Can an LM, starting from random init (!!), learn to generate all of its pretraining data?

Introducing Self-Play Pretraining with Zero Data. Two models start from random initialization: a generator proposes programs for a universal Turing machine and a learner trains on their outputs. We never train on any real data, but see predictable scaling on natural datasets: zero-shot val loss on images, text, audio, and melodies decreases predictably with self-play compute. And the learner develops in-context learning capabilities.

A fun proof-of-concept, co-led with @AdityaCowsik and @KfirDolev and co-authors @gbruno_dl, @ANourya @noahdgoodman, and @YoavLevine.