Are you at #NeurIPS2025? Check out the #KempnerInstitute’s Day 2 presentations! 💡 #AI #NeuroAI @cpehlevan.bsky.social @kanakarajanphd.bsky.social @thomasfel.bsky.social @andykeller.bsky.social @binxuwang.bsky.social @njw.fish @yilundu.bsky.social

🐇Into the Rabbit Hull — Part 1: A Deep Dive into DINOv2🧠 Our latest Deeper Learning blog post is an #interpretability deep dive into one of today’s leading vision foundation models: DINOv2. 📖Read now: bit.ly/4nNfq8D Stay tuned — Part 2 coming soon. #AI #VLMs #DINOv2

Into the Rabbit Hull – Part I - Kempner Institute

This blog post offers an interpretability deep dive, examining the most important concepts emerging in one of today’s central vision foundation models, DINOv2. This blogpost is the first of a […]

bit.ly

The Bau lab is on fire ! 😍

The secret life of an LM is defined by its internal data types. Inner layers transport abstractions that are more robust than words, like concepts, functions, or pointers. In new work yesterday, @arnabsensharma.bsky.social et al identify a data type for *predicates*. bsky.app/profile/arn...

Interested in doing a PhD at the intersection of human and machine cognition? ✨ I'm recruiting students for Fall 2026! ✨ Topics of interest include pragmatics, metacognition, reasoning, & interpretability (in humans and AI). Check out JHU's mentoring program (due 11/15) for help with your SoP 👇

JHU Cognitive Science@jhucogsci.bsky.social · 10mo ago

The department of Cognitive Science @jhu.edu is seeking motivated students interested in joining our interdisciplinary PhD program! Applications due 1 Dec Our PhD students also run an application mentoring program for prospective students. Mentoring requests due November 15. tinyurl.com/2nrn4jf9

Pleased to share new work with @sflippl.bsky.social @eberleoliver.bsky.social @thomasmcgee.bsky.social & undergrad interns at Institute for Pure and Applied Mathematics, UCLA. Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models www.arxiv.org/pdf/2510.15987 🧵1/n

🧠 Thrilled to share our NeuroView with Ellie Pavlick! "From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?" AI foundation models are coming to neuroscience—if scaling laws hold, predictive power will be unprecedented. But is that enough? Thread 🧵👇

Neuron@cp-neuron.bsky.social · 10mo ago

This is so cool. When you look at representational geometry, it seems intuitive that models are combining convex regions of "concepts", but I wouldn't have expected that this is PROVABLY true for attention or that there was such a rich theory for this kind of geometry.

Thomas Fel@thomasfel.bsky.social · 10mo ago

🕳️🐇Into the Rabbit Hull – Part II Continuing our interpretation of DINOv2, the second part of our study concerns the *geometry of concepts* and the synthesis of our findings toward a new representational *phenomenology*: the Minkowski Representation Hypothesis

🕳️🐇Into the Rabbit Hull – Part II Continuing our interpretation of DINOv2, the second part of our study concerns the *geometry of concepts* and the synthesis of our findings toward a new representational *phenomenology*: the Minkowski Representation Hypothesis

🕳️🐇 𝙄𝙣𝙩𝙤 𝙩𝙝𝙚 𝙍𝙖𝙗𝙗𝙞𝙩 𝙃𝙪𝙡𝙡 – 𝙋𝙖𝙧𝙩 𝙄 (𝑃𝑎𝑟𝑡 𝐼𝐼 𝑡𝑜𝑚𝑜𝑟𝑟𝑜𝑤) 𝗔𝗻 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗗𝗜𝗡𝗢𝘃𝟮, one of vision’s most important foundation models. And today is Part I, buckle up, we're exploring some of its most charming features. :)

Superposition has reshaped interpretability research. In our @unireps.bsky.social paper led by @andre-longon.bsky.social we show it also matters for measuring alignment! Two systems can represent the same features yet appear misaligned if those features are mixed differently across neurons.

Superposition disentanglement of neural representations reveals hidden alignment

The superposition hypothesis states that a single neuron within a population may participate in the representation of multiple features in order for the population to represent more features than the ...

arxiv.org

For XAI it’s often thought explanations help (boundedly rational) user “unlock” info in features for some decision. But no one says this, they say vaguer things like “supporting trust”. We lay out some implicit assumptions that become clearer when you take a formal view here arxiv.org/abs/2506.22740

Explanations are a means to an end

Modern methods for explainable machine learning are designed to describe how models map inputs to outputs--without deep consideration of how these explanations will be used in practice. This paper arg...

arxiv.org

🚨Updated: "How far can we go with ImageNet for Text-to-Image generation?" TL;DR: train a text2image model from scratch on ImageNet only and beat SDXL. Paper, code, data available! Reproducible science FTW! 🧵👇 📜 arxiv.org/abs/2502.21318 💻 github.com/lucasdegeorg... 💽 huggingface.co/arijitghosh/...

Check out @mryskina.bsky.social's talk and poster at COLM on Tuesday—we present a method to identify 'semantically consistent' brain regions (responding to concepts across modalities) and show that more semantically consistent brain regions are better predicted by LLMs.

Maria Ryskina@mryskina.bsky.social · 11mo ago

Interested in language models, brains, and concepts? Check out our COLM 2025 🔦 Spotlight paper! (And if you’re at COLM, come hear about it on Tuesday – sessions Spotlight 2 & Poster 2)!

1/🚨 New preprint How do #LLMs’ inner features change as they train? Using #crosscoders + a new causal metric, we map when features appear, strengthen, or fade across checkpoints—opening a new lens on training dynamics beyond loss curves & benchmarks. #interpretability

Employing mechanistic interpretability to study how models learn, not just where they end up 2 papers find: There are phase transitions where features emerge and stay throughout learning 🤖📈🧠 alphaxiv.org/pdf/2509.17196 @amuuueller.bsky.social @abosselut.bsky.social alphaxiv.org/abs/2509.05291

Mechanistic interpretability often relies on *interventions* to study how DNNs work. Are these interventions enough to guarantee the features we find are not spurious? No!⚠️ In our new paper, we show many mech int methods implicitly rely on the linear representation hypothesis🧵

I was part of an interesting panel discussion yesterday at an ARC event. Maybe everybody knows this already, but I was quite surprised by how "general" intelligence was conceptualized in relation to human intelligence and the ARC benchmarks.

Phenomenology → principle → method. From observed phenomena in representations (conditional orthogonality) we derive a natural instantiation. And it turns out to be an old friend: Matching Pursuit! 📄 arxiv.org/abs/2506.03093 See you in San Diego, @neuripsconf.bsky.social 🎉 #interpretability

From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit

Motivated by the hypothesis that neural network representations encode abstract, interpretable features as linearly accessible, approximately orthogonal directions, sparse autoencoders (SAEs) have bec...

arxiv.org

🚨Our preprint is online!🚨 www.biorxiv.org/content/10.1... How do #dopamine neurons perform the key calculations in reinforcement #learning? Read on to find out more! 🧵

Are there conceptual directions in VLMs that transcend modality? Check out our COLM oral spotlight 🔦 paper! We use SAEs to analyze the multimodality of linear concepts in VLMs with @chloesu07.bsky.social, @thomasfel.bsky.social, @shamkakade.bsky.social and Stephanie Gil arxiv.org/abs/2504.11695

Check out our COLM 2025 (oral) 🎤 SAEs reveal that VLM embedding spaces aren’t just "image vs. text" cones. They contain stable conceptual directions, some forming surprising bridges across modalities. arxiv.org/abs/2504.11695 Demo 👉 vlm-concept-visualization.com

Isabel Papadimitriou@isabelpapad.bsky.social · 11mo ago

Are there conceptual directions in VLMs that transcend modality? Check out our COLM oral spotlight 🔦 paper! We use SAEs to analyze the multimodality of linear concepts in VLMs with @chloesu07.bsky.social, @thomasfel.bsky.social, @shamkakade.bsky.social and Stephanie Gil arxiv.org/abs/2504.11695

Excited to announce the first workshop on CogInterp: Interpreting Cognition in Deep Learning Models @ NeurIPS 2025! 📣 How can we interpret the algorithms and representations underlying complex behavior in deep learning models? 🌐 coginterp.github.io/neurips2025/ 1/4

How do language models generalize from information they learn in-context vs. via finetuning? In arxiv.org/abs/2505.00661 we show that in-context learning can generalize more flexibly, illustrating key differences in the inductive biases of these modes of learning — and ways to improve finetuning. 1/

Our work finding universal concepts in vision models is accepted at #ICML2025!!! My first major conference paper with my wonderful collaborators and friends @matthewkowal.bsky.social @thomasfel.bsky.social @Julian_Forsyth @csprofkgd.bsky.social Working with y'all is the best 🥹 Preprint ⬇️!!

Harry Thasarathan@hthasarathan.bsky.social · 2y ago

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"! arxiv.org/abs/2502.03714 (1/9)

Accepted at #ICML2025! Check out the preprint. HUGE shoutout to Harry (1st PhD paper, in 1st year), Julian (1st ever, done as an undergrad), Thomas and Matt! @hthasarathan.bsky.social @thomasfel.bsky.social @matthewkowal.bsky.social

Harry Thasarathan@hthasarathan.bsky.social · 2y ago

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"! arxiv.org/abs/2502.03714 (1/9)

<proud advisor> Hot off the arXiv! 🦬 "Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation" 🌍 Appa is our novel 1.5B-parameter probabilistic weather model that unifies reanalysis, filtering, and forecasting in a single framework. A thread 🧵

Have you thought that in computer memory model weights are given in terms of discrete values in any case. Thus, why not do probabilistic inference on the discrete (quantized) parameters. @trappmartin.bsky.social is presenting our work at #AABI2025 today. [1/3]

New in the Deeper Learning blog: Kempner researchers show how VLMs speak the same semantic language across images and text. bit.ly/KempnerVLM by @isabelpapad.bsky.social ,Chloe Huangyuan Su, @thomasfel.bsky.social, Stephanie Gil, and @shamkakade.bsky.social #AI #ML #VLMs #SAEs

Interpreting the Linear Structure of Vision-Language Model Embedding Spaces - Kempner Institute

Using sparse autoencoders, the authors show that vision-language embeddings boil down to a small, stable dictionary of single-modality concepts that snap together into cross-modal bridges. This resear...

bit.ly

Firing rates in visual cortex show representational drift, while temporal spike sequences remain stable www.sciencedirect.com/science/arti... Great work by Boris Sotomayor and with @battaglialab.bsky.social

Firing rates in visual cortex show representational drift, while temporal spike sequences remain stable

Neural firing-rate responses to sensory stimuli show progressive changes both within and across sessions, raising the question of how the brain mainta…

sciencedirect.com

PINEAPPLE, LIGHT, HAPPY, AVALANCHE, BURDEN Some of these words are consistently remembered better than others. Why is that? In our paper, just published in J. Exp. Psychol., we provide a simple Bayesian account and show that it explains >80% of variance in word memorability: tinyurl.com/yf3md5aj