What are the real problems to be solved in continual learning? In my latest post, I tackle this question — reviewing where I think the field went astray in the past, how language models changed things, and where the real challenges remain. infinitefaculty.substack.com/p/what-are-t...

Do LLMs *understand* language? Do educational AI agents *understand* the material they teach (or their students)? Claims about what AI systems do or don't understand are pervasive, but assessing them requires an account of MACHINE UNDERSTANDING

We've updated the preprint of our Naturalistic Computational Cognitive Science paper (arxiv.org/abs/2502.20349) — we've tried to clarify and streamline the arguments, and added some new examples: 1/5

🤖🧠NEW PAPER🧠🤖 Children & neural networks can learn syntax from linear strings of words. How do they do it? Our hypothesis: Word co-occurrence statistics provide cues to syntax! (I.e., a new type of bootstrapping to consider!) Paper: arxiv.org/abs/2605.20529 1/n

What is a psychological theory? Here's our take on this tricky and controversial question in this week's Experimentology chapter summary. Many things called "theories" in psychology aren't actually theories — they're frameworks. 🧵 experimentology.io

To appear in Computational Brain & Behavior soon: the claimed 2024 proof (also in CBB) that AGI via learning is intractable also "proves" that ImageNet is intractable. My reading of the hole: equivocation on the variable D. Preprint here: arxiv.org/abs/2411.06498

Can language models use subtext in their communication? Can they use common ground to incorporate subtext more effectively? In our new preprint, we study these questions across various domains — from visual communication to story writing games.

When and how can test-time thinking allow models to use information latent in their training data? What are the benefits and tradeoffs relative to other solutions like synthetic data augmentation? Pleased to share (after a long delay) an exploration of these issues: arxiv.org/abs/2604.01430 thread:

Pleased to share that our paper "Representation Biases: Variance is Not Always a Good Proxy for Importance" is now out as Theory/New Concepts paper in eNeuro! www.eneuro.org/content/13/3... 1/

I joined Anthropic (alignment team) this week — exciting place to be at an exciting time!

Sharing “Neural Thickets”. We find: In large models, the neighborhood around pretrained weights can become dense with task-improving solutions. In this regime, post-training can be easy; even random guessing works Paper: arxiv.org/abs/2603.12228 Web: thickets.mit.edu 1/

What is the interplay between representations learned from (language) surface forms alone, and those learned from more grounded evidence (e.g.,vision)? Excited to share new work understanding “Cross-modal taxonomic generalization” in (V)LMs arxiv.org/abs/2603.07474 1/

Short post on what I call the "no-magic approach to understanding intelligent systems" — the philosophy I think of as motivating our work on understanding intelligence without resorting to magical thinking about AI or humans! infinitefaculty.substack.com/p/the-no-mag...

Can large language models *introspect*? In a new paper, @kmahowald.bsky.social and I study the MECHANISM of introspection in big open-source models. tldr: Models detect internal anomalies through DIRECT ACCESS, but don't know what the anomalies are. And they love to guess “apple” 🍎

After 5.5 years (or 7 or 9, counting internships), today was my last day at Google/DeepMind. When I was in London recently, I walked through the two floors that were (most of) DeepMind when I first joined, and thought about how much the company and field have changed since then.

🚨New preprint! In-context learning underlies LLMs’ real-world utility, but what are its limits? Can LLMs learn completely novel representations in-context and flexibly deploy them to solve tasks? In other words, can LLMs construct an in-context world model? Let’s see! 👀

Really cool work — learning over sequential experiences that contain the embodied cue of viewpoint as well as visual inputs, can give rise to human-like 3D shape perception!

News! I've joined the Astera Institute to lead its neuroscience based AGI research. Backed by $1B+ commitment over the coming decade, my team will explore novel, brain-inspired architectures and algos toward safe, efficient human-like AGI, working alongside Doris Tsao. 1/ astera.org/dileep-georg...

What is the relationship between memorization and generalization in AI? Is there a fundamental tradeoff? In infinitefaculty.substack.com/p/memorizati... I’ve reviewed some of the evolving perspectives on memorization & generalization in machine learning, from classic perspectives through LLMs.

Excited to launch Principia, a nonprofit research organisation at the intersection of deep learning theory and AI safety. Our goal is to develop theory for modern machine learning systems that can help us understand complex network behaviors, including those critical for AI safety and alignment. 1

I wrote a short article on AI Model Evaluation for the Open Encyclopedia of Cognitive Science 📕👇 Hope this is helpful for anyone who wants a super broad, beginner-friendly intro to the topic! Thanks @mcxfrank.bsky.social and @asifamajid.bsky.social for this amazing initiative!

This work by @mariaeckstein.bsky.social et al is a nice example of how progress in psychology can be expedited with machine learning. How long before this type of approach is expected for models-of-behavior papers? My guess: not long. (If you are a trainee, nudge!) www.nature.com/articles/s41...

The visual world is composed of objects, and those objects are composed of features. But do VLMs exploit this compositional structure when processing multi-object scenes? In our 🆒🆕 #ICLR2026 paper, we find they do – via emergent symbolic mechanisms for visual binding. 🧵👇

Interesting results by @eghbal-hosseini.bsky.social on how language models representation geometry evolves during different types of in-context learning!

Was a pleasure to discuss the cognitive basis of reasoning at an @ivado.bsky.social workshop with legends like @alisongopnik.bsky.social @lauraruis.bsky.social @taylorwwebb.bsky.social and Andrew Granville!

New paper! In arxiv.org/abs/2601.20834 we study how language models representations of things like factuality evolve over a conversation. We find that in edge case conversations, e.g. about model consciousness or delusional content, model representations can change dramatically! 1/

Should you go to academia or industry for research in AI or cognitive science? It's the most common question I get asked by PhD students, and I've written up some of my thoughts on the answer, as an epilogue to my research-focused series on these fields: infinitefaculty.substack.com/p/on-researc...

Our experiences have countless details, and it can be hard to know which matter. How can we behave effectively in the future when, right now, we don't know what we'll need? Out today in @nathumbehav.nature.com , @marcelomattar.bsky.social and I find that people solve this by using episodic memory.

I'm very excited about this paper with @yngwienielsen.bsky.social just out in @nathumbehav.nature.com in which we provide evidence for the mental representation of non-hierarchical linguistic structure in language use. 🧵 1/4 Read the paper here: rdcu.be/eZ26u

arxiv.org/abs/2601.11432 I want to share an astonishing result. LLMs can "translate" Jabberwocky' texts like 'He dwushed a ghanc zawk” & even and even 'In the BLANK BLANK, BLANK BLANK has BLANK over any BLANK BLANK’s BLANK' This has profound consequence for thinking about.. 1/2

When are impossibility proofs misleading? In infinitefaculty.substack.com/p/be-wary-of..., I discuss a common issue I see: proofs that are logically valid, but where the underlying assumptions are unjustified. I discuss ‘proofs’ that cognition cannot be tractably learned, and that LMs are 1/

What can cognitive science learn from AI? In infinitefaculty.substack.com/p/what-cogni... I outline how AI has found that scale and richness of learning experiences fundamentally change learning & generalization — and how I believe we should rethink cognitive experiments & theories in response.

New post! Last week I shared why I thought cognitive (neuro)science hasn’t contributed as much as one might hope to the design of AI systems; this week I'm sharing my thoughts on how methods and principles from these fields *have* been useful in my work. infinitefaculty.substack.com/p/how-cognit...

Why isn’t modern AI built around principles from cognitive science or neuroscience? Starting a substack (infinitefaculty.substack.com/p/why-isnt-m...) by writing down my thoughts on that question: as part of a first series of posts giving my current thoughts on the relation between these fields. 1/3

I'm more and more convinced that low-dimensional manifolds in the brain are just an artifact of the experimental designs and analyses we use... 🧠📈 🧪

Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbiol...

Heading to NeurIPS this week! Let me know if you want to chat about science of what models learn, cognitive science, interpretability, what models learn in context vs. from their training data, etc. A few things I'm involved in:

Amazing opportunity to work with a brilliant researcher and all-around wonderful person — definitely apply if you're interested in memory & perception at the intersection of AI & cognitive (neuro)science!

Very important point! We've made arguments from a computational perspective that low-variance features can be computationally relevant (bsky.app/profile/lamp...), but it's much cooler to see it demonstrated on a model of real neural dynamics

Great work by Andrea & co, now out with more datasets, models, and analyses!

I was honored to speak at Princeton’s symposium on The Physics of John Hopfield: Learning & Intelligence this week. I sketched out a perspective that ties together some of our recent work on ICL vs. parametric learning, and some possible links to hippocampal replay: 1/

Can't tell you how great it is to finally be able to release and talk about this work, SIMA 2, the next step toward embodied intelligence in rich, interactive 3D worlds! deepmind.google/sima

Today in Nature Machine Intelligence, Kazuki Irie & I discuss 4 classic challenges for neural nets — systematic generalization, catastrophic forgetting, few-shot learning, & reasoning. We argue there is a unifying fix: the right incentives & practice. rdcu.be/eLRmg

What aspects of human knowledge do vision models like CLIP fail to capture, and how can we improve them? We suggest models miss key global organization; aligning them makes them more robust. Check out LukasMuttenthaler's work, finally out (in Nature!?) www.nature.com/articles/s41... + our blog! 1/3

New work to appear @ TACL! Language models (LMs) are remarkably good at generating novel well-formed sentences, leading to claims that they have mastered grammar. Yet they often assign higher probability to ungrammatical strings than to grammatical strings. How can both things be true? 🧵👇

Apologies for being quiet on here lately — been focusing on the more important things in life :)

Pleased to share that our survey "Getting aligned on representational alignment" — on representational alignment across cognitive (neuro)science and machine learning — is now published in TMLR! openreview.net/forum?id=Hiq... Kudos to @sucholutsky.bsky.social @lukasmut.bsky.social for leading this!

🚨 New preprint alert! 🧠🤖 We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up. 🧠📈 A 🧵: tinyurl.com/yr8tawj3

Why does AI sometimes fail to generalize, and what might help? In a new paper (arxiv.org/abs/2509.16189), we highlight the latent learning gap — which unifies findings from language modeling to agent navigation — and suggest that episodic memory complements parametric learning to bridge it. Thread:

How can an imitative model like an LLM outperform the experts it is trained on? Our new COLM paper outlines three types of transcendence and shows that each one relies on a different aspect of data diversity. arxiv.org/abs/2508.17669