We made traversle.io, a new daily word game! The goal is to traverse from a start word to a target word through a network of related words. (Our motivating question: is it possible to construct a network that allows human navigation?)

New paper! The Linear Representation Hypothesis is a powerful intuition for how language models work, but lacks formalization. We give a mathematical framework in which we can ask and answer a basic question: how many features can be stored under the hypothesis? 🧵 arxiv.org/abs/2602.11246

Excited to share a new working paper! What happened when Change.org integrated an AI writing tool into their platform? We provide causal evidence that petition text changed significantly while outcomes did not improve. 1/ arxiv.org/abs/2511.13949

Introducing AI to an Online Petition Platform Changed Outputs but not Outcomes

The rapid integration of AI writing tools into online platforms raises critical questions about their impact on content production and outcomes. We leverage a unique natural experiment on Change$.$org...

arxiv.org

another banger from @louisathomas.bsky.social www.newyorker.com/sports/sport...

Why Can’t the N.B.A. Move On from Its Old Stars?

Even as the league drastically evolves, the narratives around it are still orbiting its aging icons.

newyorker.com

New #NeurIPS2025 paper: how should we evaluate machine learning models without a large, labeled dataset? We introduce Semi-Supervised Model Evaluation (SSME), which uses labeled and unlabeled data to estimate performance! We find SSME is far more accurate than standard methods.

I am on the job market this year! My research advances methods for reliable machine learning from real-world data, with a focus on healthcare. Happy to chat if this is of interest to you or your department/team.

I've been working for many months on this article on Silicon Valley's under-the-radar role in bringing AI into schools across the US. I really hope you'll read it — here's a gift link — but I'll tell you some of the highlights in this thread. (1/x)

How Chatbots and AI Are Already Transforming Kids' Classrooms

Educators across the country are bringing chatbots into their lesson plans. Will it help kids learn or is it just another doomed ed-tech fad?

bloomberg.com

🚨 New postdoc position in our lab at Berkeley EECS! 🚨 (please reshare) We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences! More info in thread 1/3

This is great, & there's clear analogy to the burgeoning mechanism design community for AI alignment: who is providing RLHF votes? Do their preferences reflect yours? Discussions about social choice and collective constitutions are interesting, but "what and who is in the data" is just as important.

Nikhil Garg@nkgarg.bsky.social · 12mo ago

New piece, out in the Sigecom Exchanges! It's my first solo-author piece, and the closest thing I've written to being my "manifesto." #econsky #ecsky arxiv.org/abs/2507.03600

📢New POSITION PAPER: Use Sparse Autoencoders to Discover Unknown Concepts, Not to Act on Known Concepts Despite recent results, SAEs aren't dead! They can still be useful to mech interp, and also much more broadly: across FAccT, computational social science, and ML4H. 🧵

Are LLMs correlated when they make mistakes? In our new ICML paper, we answer this question using responses of >350 LLMs. We find substantial correlation. On one dataset, LLMs agree on the wrong answer ~2x more than they would at random. 🧵(1/7) arxiv.org/abs/2506.07962

@jessica.bsky.social on individual reporting as a means to build collective knowledge.

Individual experiences and collective evidence

Jessica Dai on theory for the world as it could be

argmin.net

ARR question: If I submit to a cycle, how long do those reviews "last"? e.g. if I submit to the July cycle but can't go to AACL, can I commit my July reviews to the conference associated with the next (October) cycle? @aclrollingreview.bsky.social

New work 🎉: conformal classifiers return sets of classes for each example, with a probabilistic guarantee the true class is included. But these sets can be too large to be useful. In our #CVPR2025 paper, we propose a method to make them more compact without sacrificing coverage.

I would like to spend up to 5-10 hours to learn about basic macroeconomics (I know it's maybe fake, but setting that aside for a moment...). Does anyone have any recommendations?

People love to hate on the transition 3-pointer as evidence of how the 3 has ruined basketball, but I think it's usually just the right play... if you have numbers in transition, your teammate can easily get a putback off a miss, so might as well try the 3

We'll present HypotheSAEs at ICML this summer! 🎉 Draft: arxiv.org/abs/2502.04382 We're continuing to cook up new updates for our Python package: github.com/rmovva/Hypot... (Recently, "Matryoshka SAEs", which help extract coarse and granular concepts without as much hyperparameter fiddling.)

GitHub - rmovva/HypotheSAEs: Hypothesizing interpretable relationships in text datasets using sparse autoencoders.

Hypothesizing interpretable relationships in text datasets using sparse autoencoders. - rmovva/HypotheSAEs

github.com

Raj Movva@rajmovva.bsky.social · last yr.

💡New preprint & Python package: We use sparse autoencoders to generate hypotheses from large text datasets. Our method, HypotheSAEs, produces interpretable text features that predict a target variable, e.g. features in news headlines that predict engagement. 🧵1/

Yesterday's Game 6 was depressing, and this article precisely delineated the reasons why. And sometimes, a precise retelling of what you're feeling is all you need to feel better. www.nytimes.com/athletic/633... @thompsonscribe.bsky.social

These Warriors are old, tired and in trouble as Game 7 looms against Rockets

They're not done yet. Maybe a legendary performance awaits on Sunday. But the Warriors look like they're out of gas and out of answers.

nytimes.com

Check out Erica's nice work. They not only develop a well-grounded model for disparities in disease progression, but also conduct experiments with real NYP cardiology data! (Anyone who works in healthcare knows how much of a feat it is to use data other than MIMIC)

Erica Chiang@ericachiang.bsky.social · last yr.

I’m really excited to share the first paper of my PhD, “Learning Disease Progression Models That Capture Health Disparities” (accepted at #CHIL2025)! ✨ 1/ 📄: arxiv.org/abs/2412.16406

The US government recently flagged my scientific grant in its "woke DEI database". Many people have asked me what I will do. My answer today in Nature. We will not be cowed. We will keep using AI to build a fairer, healthier world. www.nature.com/articles/d41...

My ‘woke DEI’ grant has been flagged for scrutiny. Where do I go from here?

My work in making artificial intelligence fair has been noticed by US officials intent on ending ‘class warfare propaganda’.

nature.com

Good reading for PhD students on why meeting scheduling might be more important than you think: paulgraham.com/makersschedu...

1st post on bsky! What happens when a static benchmark comes to life? ✨ Introducing ChatBench, a large-scale user study where we *converted* MMLU questions into thousands of user-AI conversations. Then, we trained a user simulator on ChatBench to generate user-AI outcomes on unseen questions. 1/ 🧵

A weird (and seemingly fixable quirk) with ChatGPT Deep Research is hallucinations even when there is a linked citation with a highlighted passage: "X et al find Y (arXiv link)" and you click on the arXiv link, which is neither written by X nor do they find Y...

Stop everything you are doing and review the best Open Data chart you will see all year. Credit: @nkgarg.bsky.social's lab

Kenny Peng@kennypeng.bsky.social · last yr.

2) The dog name/baby name scatter plot: Max and Charlie are more dog name than human name. Dogs aren't called Ethan though.

My labmates really cooked with this one

Kenny Peng@kennypeng.bsky.social · last yr.

Our lab had a #dogathon 🐕 yesterday where we analyzed NYC Open Data on dog licenses. We learned a lot of dog facts, which I’ll share in this thread 🧵 1) Geospatial trends: Cavalier King Charles Spaniels are common in Manhattan; the opposite is true for Yorkshire Terriers.

Migration data lets us study responses to environmental disasters, social change patterns, policy impacts, etc. But public data is too coarse, obscuring these important phenomena! We build MIGRATE: a dataset of yearly flows between 47 billion pairs of US Census Block Groups. 1/5

I've learned MCMC 5+ different times in the last 10 years from courses, videos, blogposts, etc, and it never clicked. 30 minutes chatting with Claude this morning, and I finally feel like I've figured it out...

Really proud of @rajmovva.bsky.social and @kennypeng.bsky.social for this work! We hope that it's useful, and are already using it for many followup projects Preprint: arxiv.org/abs/2502.04382 Python package: github.com/rmovva/Hypot... Demo: hypothesaes.org

Raj Movva@rajmovva.bsky.social · last yr.

💡New preprint & Python package: We use sparse autoencoders to generate hypotheses from large text datasets. Our method, HypotheSAEs, produces interpretable text features that predict a target variable, e.g. features in news headlines that predict engagement. 🧵1/

(1/n) New paper/code! Sparse Autoencoders for Hypothesis Generation HypotheSAEs generates interpretable features of text data that predict a target variable: What features predict clicks from headlines / party from congressional speech / rating from Yelp review? arxiv.org/abs/2502.04382

💡New preprint & Python package: We use sparse autoencoders to generate hypotheses from large text datasets. Our method, HypotheSAEs, produces interpretable text features that predict a target variable, e.g. features in news headlines that predict engagement. 🧵1/