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

New paper with @rjantonello.bsky.social @csinva.bsky.social, Suna Guo, Gavin Mischler, Jianfeng Gao, & Nima Mesgarani: We use LLMs to generate VERY interpretable embeddings where each dimension corresponds to a scientific theory, & then use these embeddings to predict fMRI and ECoG. It WORKS!

In our new paper, we explore how we can build encoding models that are both powerful and understandable. Our model uses an LLM to answer 35 questions about a sentence's content. The answers linearly contribute to our prediction of how the brain will respond to that sentence. 1/6

New paper: Ask 35 simple questions about sentences in a story and use the answers to predict brain responses. Interpretable, compact, & surprisingly high performance in both fMRI and ECoG. 🧵 biorxiv.org/content/10.1...

We’ve discovered a literal miracle with almost unlimited potential and it’s being scrapped for *no reason whatsoever*. This isn’t even nihilism, it’s outright worship of death and human suffering.

Binz et al. (in press, Nature) developed an LLM called Centaur that better predicts human responses in 159 of 160 behavioural experiments compared to existing cognitive models. See: arxiv.org/abs/2410.20268

We're excited about self-play unlocking continuously improving agents. RL selects CoT patterns from LLMs. Games=perfect testing grounds. SPIRAL: models learn via self-competition. Kuhn Poker → +8.7% math, +18.1 Minerva Math! 🃏 Paper: huggingface.co/papers/2506.... Code: github.com/spiral-rl/spiral

🚨 New preprint 🚨 Prior work has mapped how the brain encodes concepts: If you see fire and smoke, your brain will represent the fire (hot, bright) and smoke (gray, airy). But how do you encode features of the fire-smoke relation? We analyzed fMRI with embeddings extracted from LLMs to find out 🧵

What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results. www.nature.com/articles/s42...

🚨Paper alert!🚨 TL;DR first: We used a pre-trained deep neural network to model fMRI data and to generate images predicted to elicit a large response for each many different parts of the brain. We aggregate these into an awesome interactive brain viewer: piecesofmind.psyc.unr.edu/activation_m...

What are the organizing dimensions of language processing? We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals

When it comes to text prediction, where does one LM outperform another? If you've ever worked on LM evals, you know this question is a lot more complex than it seems. In our new #acl2025 paper, we developed a method to find fine-grained differences between LMs: 🧵1/9

Five people have seen a color never before visible to the naked human eye, thanks to a new retinal stimulation technique called Oz. Learn more in #ScienceAdvances: scim.ag/442Hjn6

Two clinical trials reported in Nature demonstrate the safety of stem cell therapies for Parkinson’s disease. The papers investigate the use of cells derived from human induced pluripotent stem cells and human embryonic stem cells. go.nature.com/4ikcJc2 go.nature.com/4jfSRYX 🧪

New preprint! Excited to share our latest work “Accelerated learning of a noninvasive human brain-computer interface via manifold geometry” ft. outstanding former undergraduate Chandra Fincke, @glajoie.bsky.social, @krishnaswamylab.bsky.social, and @wutsaiyale.bsky.social's Nick Turk-Browne 1/8

New preprint “Monkey See, Model Knew: LLMs accurately predict visual responses in humans AND NHPs” Led by Colin Conwell with @emaliemcmahon.bsky.social Akshay Jagadeesh, Kasper Vinken @amrahs-inolas.bsky.social @jacob-prince.bsky.social George Alvarez @taliakonkle.bsky.social & Marge Livingstone 1/n

🚨 New lab paper!🚨 A dream study of mine for nearly 20 yrs not possible until now thanks to NIH 🧠 funding & 1st-author lead @seeber.bsky.social We tracked hippocampal activity as people walked memory-guided paths & imagined them again. Did brain patterns reappear?🧵👇 www.nature.com/articles/s41...

2018: Saliency maps give plausible interpretations of random weights, triggering skepticism and catalyzing the mechinterp cultural movement, which now advocates for SAEs. 2025: SAEs give plausible interpretations of random weights, triggering skepticism and ...

Yi Ma & colleagues managed to simplify DINO & DINOv2 by removing many ingredients and adding a robust regularization term from information theory (coding rate) that learn informative decorrelated features. Happy to see principled approaches advance deep representation learning!

Can LLMs be used to discover interpretable models of human and animal behavior?🤔 Turns out: yes! Thrilled to share our latest preprint where we used FunSearch to automatically discover symbolic cognitive models of behavior. 1/12

"...responses of V4 neurons under naturalistic conditions can be explained by a hierarchical three-stage model where each stage consists entirely of units like those found in area V1" #NeuroAI www.biorxiv.org/content/10.1...

At NeurIPS this week, presenting our work on crafting *interpretable embeddings* by asking yes/no questions to black-box LLMs. Drop me a message if you want to chat about interpretability/language neuroscience!

I tried to find everyone who works in the area but I certainly missed some folks so please lmk... go.bsky.app/BYkRryU

Science faces an explainability crisis: ML models can predict many natural phenomena but can't explain them We tackle this issue in language neuroscience by using LLMs to generate *and validate* explanations with targeted follow-up experiments

Mechanistic interp has made cool findings but struggled to make them useful We show that "induction heads" found in LLMs can be reverse-engineered to yield accurate & interpretable next-word prediction models