We are launching a new blog; Reliable-AI.Review. First post is up: On the Impossibility of Mitigating AI Jailbreaks.

LLMs are largely memory bound! Tokenizers can eat >30% of on‑device memory for small (<1B) models, because we reuse the huge vocabularies from big models. Our method lets you distill large models into small ones with much smaller vocabulary, reducing overall memory footprint.

If “getting started with agents” feels like setup hell — same. So we made a starter tutorial: First agent running in <14 minutes, no Docker/AWS. Laptop + API key only. 👇 www.youtube.com/watch?v=gzNW...

Want to teach AI agents to use apps like humans? Get started with digital agents research using OpenApps, our new Python-based environment.

Release Day 🎉 Meet OpenApps — a pure-Python, open-source ecosystem for stress-testing UI agents at scale. Runs on a single CPU. Generates thousands of unique UI variations. And it reveals just how fragile today’s SOTA agents are. (Yes, even GPT-4 and Claude struggle.)

Excited to be attending NeurIPS 2025 — my 10-year anniversary since I first walked into NeurIPS back in 2015, just before starting my PhD in Amsterdam. Grateful for a decade of learning, growing, and being part of this incredible community. If you’re around this year, would love to meet.

One can manipulate LLM rankings to put any model in the lead—only by modifying the single character separating demonstration examples. Learn more in our new paper arxiv.org/abs/2510.05152 w/ Jingtong Su, Jianyu Zhang, @karen-ullrich.bsky.social , and Léon Bottou. 🧵

Y’all, I am at #COLM this week, very excited to learn, and meet old and new friends. Please reach out on Whova!

How would you make an LLM "forget" the concept of dog — or any other arbitrary concept? 🐶❓ We introduce SAMD & SAMI — a novel, concept-agnostic approach to identify and manipulate attention modules in transformers.

Aligned Multi-Objective Optimization (A-🐮) has been accepted at #ICML2025! 🎉 We explore optimization scenarios where objectives align rather than conflict, introducing new scalable algorithms with theoretical guarantees. #MachineLearning #AI #Optimization

🎉🎉 Our paper just got accepted to #ICLR2025! 🎉🎉 Byte-level LLMs without training and guaranteed performance? Curious how? Dive into our work! 📚✨ Paper: arxiv.org/abs/2410.09303 Github: github.com/facebookrese...

Thursday is busy: 9-11am I will be at the Meta AI Booth 12.30-2pm Mission Impossible: A Statistical Perspective on Jailbreaking LLMs (neurips.cc/virtual/2024...) OR End-To-End Causal Effect Estimation from Unstructured Natural Language Data (neurips.cc/virtual/2024...)

Starting with Fei-Fei Li’s talk 2.30, after that I will mostly be meeting people and wonder the poster sessions.

Folks, I am posting my NeurIPS schedule daily in hopes to see folks, thanks @tkipf.bsky.social for the idea ;) 11-12.30 WiML round tables 1.30-4 Beyond Decoding, Tutorial

I will be at #Neurips2024 next week to talk about these two papers and host a workshop on #NeuralCompression.

Pro-tip: Use massive black Friday deals at scientific publishing houses to for example buy a copy of @jmtomczak.bsky.social book on generative modeling (long overdue)

#Tokenization is undeniably a key player in the success story of #LLMs but we poorly understand why. I want to highlight progress we made in understanding the role of tokenization, developing the core incidents and mitigating its problems. 🧵👇

🚨 Internship Opportunity at FAIR NY 🚨 I got one PhD internship position available for 2025! Interested in exploring the intersection of information theory, probabilistic reasoning, and LLMs? 📩 Send me a DM with your CV, website, and GScholar profile by October 14th.

🎉 Exciting News! 🎉 Two papers have been accepted at #NeurIPS2024 ! 🙌🏼 These papers are the first outcomes of my growing focus on LLMs. 🍾 Cheers to Nikita Dhawan and Jingtong Su + all involved collaborators: @cmaddis.bsky.social Leo Cotta, Rahul Krishnan, Julia Kempe

Deadline for the #neurips2024 workshop on #InformationTheory and #Compression approaching soon! Submit by September 30th. More info neuralcompression.github.io/workshop24