Congrats to @LanceYing42 and the whole team! CogGym turns 258 experiments from 100 cog-sci papers (30+ labs!) into a living benchmark for comparing human and machine judgments, trial by trial. Paper: arxiv.org/abs/2609.21259 Platform: coggym.org

With all the advances in AI, what does it take to actually call an AI system a "thought partner"? Check out new thinking from our lab and colleagues at Stanford on meaningful human-AI -- and human-human -- thought partners.

Games have long served as a testing ground for understanding problem-solving and strategic reasoning in humans and machines, across many fields – from AI, to cognitive science, economics, and more. Yet, in life people are regularly faced not just with repeated instances of the same problem or “system of rule and reward”, but entirely new problems, new systems, new games. And when encountering new games, people not only need to decide what actions to take but whether a game is even worth playing at all! How can people think reasonably about new games for the first time, so flexibly and quickly? We take steps towards this question in our new work, out now in @Nature!

Our new @Nature paper on how people reason about novel games is out! Introducing the "Intuitive Gamer" model, a step toward AI that can decide which problems are worth solving!

Our paper on novel game reasoning is out in @Nature! Games occupy a special place in the science of intelligence. While most prior work has focused on acquiring expertise in games, here we study how people can flexibly reason about games that they have never seen before. Across a series of behavioral studies with 1,000+ participants and 100+ grid-based board games, we find that people are systematic and adaptively rational in their judgments about how fair and fun a novel game is. We explain this with the “Intuitive Gamer” cognitive model, based on principles of fast, depth-limited, and goal-directed probabilistic simulation. It captures patterns in human data well and offers insights into how people can reasonably evaluate and act when encountering new problems. This work is also a step towards thinking about AI agents that can not only solve problems but decide which problems are worth solving in the first place.

1/ How do we see 3D shape — for grasping, reaching, navigating — when the world is constantly in motion? We started with one piece of this puzzle—how the brain recovers surface geometry from dynamic input. New preprint, a joint effort from Josh Tenenbaum’s and Jim DiCarlo’s labs at MIT. (1/6) doi.org/10.64898/2026.05.13.…

Today we present a new framework for measuring human-like general intelligence in machines (what some people call AGI). Conventional AI benchmarks today assess only narrow capabilities in a limited range of human activities. We propose that a more promising way to evaluate human-like general intelligence in AI systems is through a particularly strong form of general game playing: studying how and how well they play and learn to play all conceivable human games — what we call the ``Multiverse of Human Games''. Taking a first step towards this vision, we introduce the AI GameStore, a scalable and open-ended platform that uses LLMs with humans-in-the-loop to automatically construct standardized and containerized variants of popular human games on digital gaming platforms. As a proof of concept, we generated 100 such games based on the top charts of Apple App Store and Steam, and evaluated seven frontier vision-language models (VLMs) on short episodes of play. The best models achieved less than 10% of the human average score on the majority of the games. Check out our website to play the games, see how agents play, and build agents to solve them!

Huge congratulations to @LanceYing42 and @xuanalogue on their new EMNLP paper, and invitation to present their work later this week! We’re thrilled to see this exciting work recognized. 👏 #EMNLP2025 #NLP

How do people flexibly integrate visual & textual information to draw mental inferences about agents we've never met? In a new paper led by @LanceYing42, we introduce a cognitive model that achieves this by synthesizing rational agent models on-the-fly--presented at EMNLP 2025!

How do people reason so flexibly about new problems, bringing to bear globally-relevant knowledge while staying locally-consistent? Can we engineer a system that can synthesize bespoke world models (expressed as probabilistic programs) on-the-fly?

How do people reason while still staying coherent – as if they have an internal ‘world model’ for situations they’ve never encountered? A new paper on open-world cognition (preview at the world models workshop at #ICML2025!)

Tomorrow, Oct. 8, @fchollet @mikeknoop are bringing ARC Prize live to @MIT in Boston! Thanks to Prof. Josh Tenenbaum, @mitbrainandcog, @MITCoCoSci, and AI@MIT for making this event possible. MIT students & staff - register now to get in! lu.ma/579tcudx

Ready to unlock new insights? Join this upcoming workshop and let's learn together!

I'm deeply honored to receive one of this year's @cogsci_soc Glushko Dissertation Prizes. While I'm still in a state of disbelief about the award, what I can say in no uncertain terms, is that developing these ideas with @rebecca_saxe has been immensely formative and rewarding.

People don't take a one-size-fits-all approach to planning: we change how we abstract the world to fit our goals. Our approach, Ada, integrates LLMs + formal planning to learn libraries of composable skills adapted to individual planning domains: tdy.lol/BXnzn 🧵 [1/5]