professor @UW scientist @GoogleDeepMind. computational cognitive models of social minds and machines. founder @diffeo & @CSM_ai
Seattle, WA
Joined April 2011
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Max Kleiman-Weiner retweeted
What began as an effort to build better infrastructure for online surveys and data collection has grown into something larger.
Today, we're officially launching Proof of Human on @ycombinator.
Our new name reflects our mission: to preserve human trust on the internet in the age of AI.
As AI becomes increasingly capable, knowing whether there's a real human on the other side is becoming fundamental infrastructure.
If you're working on a difficult human verification problem, we'd love to hear from you.
Welcome to the Proof of Human era.
We won best paper at the COLM Workshop on Agent Behavior for our paper: AI Assistants Overassist!
Congratulations @verona_teo, @jainraghav10! Check out the preprint arxiv.org/abs/2607.21306
Max Kleiman-Weiner retweeted
Today, we’re excited to launch the Datology Curation Studio, customizable frontier data curation for every team training its own models.
Data quality is the ultimate compute multiplier, and Curation Studio makes it easy for everyone.
Hope we make progress on alignment quickly.
After six years in stealth, Atomic Machines is out. Our mission: on-demand, universal command of matter. First beachhead: the Matter Compiler, an AI-native manufacturing system that builds working micro-machines from code alone. No per-product tooling. No process development. Different code, different machine.
Everyone is excited about recursive *self* improvement (RSI) but human progress is social and driven by cumulative culture. Individuals stand on the shoulders of past giants. Our new paper tests whether LLM swarms benefit from an alternative RSI: recursive *social* improvement.
LLM agents are evolving their own skills and working together in swarms. But how well do they learn from each other?
Our new paper on Recursive Social Improvement finds that copying peers’ discoveries can come at the expense of exploration! 🧵
arxiv.org/abs/2609.38516
Max Kleiman-Weiner retweeted
Have we built machines that think like humans? 🧠🤖
In a new preprint, we propose CogGym, which compares AI and human judgments across 258 commonsense-reasoning experiments from 100 cognitive-science papers.
Paper: arxiv.org/abs/2609.21259
Platform: coggym.org
ALT CogGym workflow diagram: research experiments are standardized in EML by an LLM agent with human review, then rendered for human participants and computational models. Replicated and original human data are compared with model responses in a shared analysis.
Max Kleiman-Weiner retweeted
Excited to present this work @NeurIPSConf this December!
Thanks again to my collaborators @fcicala @blaiseaguera @tyrell_turing @natashajaques @maxhkw @eyvindn !!!
Can self-interested, self-improving, self-replicating agents learn to cooperate?
Our new paper, Tapes Together Strong, shows they can: when social behavior, computation, and reproduction share one energy budget, cooperation evolves from scratch.
arxiv.org/abs/2609.10817 🧵
Max Kleiman-Weiner retweeted
Also the whole "assign weights to others based on connectedness" idea is not novel. I don't think this is all to relational morality, but @maxhkw has a great paper on a computational model where agents learn how much to value others based on relatedness.
compminds.uw.edu/pdfs/kleima…
There has never been a better time in history to write a real email. Separating equilibrium is sharper than ever
I’ve pointed out that AI is killing cold outreach. Let’s check in on how that’s going. We’re months away from the Fall *2027* PhD admissions cycle and I’ve gotten about 75 inquiries. I expect this to increase exponentially until December. And this is just one of 10-15 categories of unsolicited email I get. Sadly I’m long past the point of being able to open all mail. On top of that, there is a new wave of spam / attempted extortion from a company called iLands that lets people run unmonitored agents. tedium.co/2026/09/11/ilands-…
One response to my previous post — what’s wrong with needing introductions for outreach? Why am I sad about the death of cold emails? It doesn’t affect me much, because I’m well established in my career. But once upon a time I was the one writing cold emails. Growing up in India, I didn’t have many connections to rely on for reaching out to prominent researchers. Without cold emails, I wouldn’t have ended up where I am today.
Of course this is far from a catastrophe, but there was something magical about the egalitarian potential of the internet, and we lose something when we go back to clubby hierarchies.
Our newest work unifying game theoretic ideas about the evolution of cooperation with the emergence of computation itself.
Self-replicating programs first emerge through random mutation and then spread by cooperating.
Can self-interested, self-improving, self-replicating agents learn to cooperate?
Our new paper, Tapes Together Strong, shows they can: when social behavior, computation, and reproduction share one energy budget, cooperation evolves from scratch.
arxiv.org/abs/2609.10817 🧵
Join us!
We are hiring research scientists in London to build next-generation world models. This is a unique opportunity to be part of a dynamic team.
google.com/about/careers/app…
Max Kleiman-Weiner retweeted
We are hiring research scientists in London to build next-generation world models. This is a unique opportunity to be part of a dynamic team.
google.com/about/careers/app…
Max Kleiman-Weiner retweeted
1/ Today, we introduce Faraday, a 27B-parameter AI Scientist that extends the capabilities of coding agents with a layer of scientific intuition. Trained via long-horizon RL, Faraday outperforms Claude Opus 4.8 and GPT-5.5 on the task of replicating research papers. 🧵
New lab preprint! AI assistants are myopically helpful. Unlike caregivers that want to empower people and grow their long term autonomy, AI assistants interrupt independent thinking and jump right to the answer.
Max Kleiman-Weiner retweeted
Deciding when to jump in and help someone—and when to hold back and let them work through it—is something humans navigate constantly. How do AI assistants handle this tradeoff?
We introduce Int-Bench, a framework for evaluating interventions during problem-solving tasks.
