Antiviral therapy design is myopic 🦠🙈 optimised only for the current strain. That's why you need a different Flu vaccine every year! Our #ICML2025 paper ADIOS proposes "shaper therapies" that steer viral evolution in our favour & remain effective. Work done Foerster Lab for AI Research 🧵👇

There is a lot of research alpha in looking at why things don’t work (when it’s plausible they should). Much better than looking at things that we know work and trying to figure out why.

Hyperparameters are the worst, but Hyperoptax makes dealing with them a little bit less bad

Very proud of this work! If you're interested in AI agents and their current challenges, give this a read. Thanks to my incredible collaborators and to Meta and UCL for enabling me to tackle something of this scale for my first PhD paper. Excited for what's ahead!

Interested in Long-Range Interaractions ? Come speak with us now (4:30pm-7pm) at our poster E-2802 @ #ICML2025 Ben Gutteridge Jacob Bamberger Michael Bronstein xiaowen dong

Gradual Disempowerment puts a name to one of the greatest and least controversial AI risks. How to maintain our current balance of power and human autonomy is one of the most pressing questions of our generation.

Unlock real diversity in your LLM! 🚀 LLM outputs can be boring and repetitive. Today, we release Intent Factored Generation (IFG) to: - Sample conceptually diverse outputs💡 - Improve performance on math and code reasoning tasks🤔 - Get more engaging conversational agents 🤖

Unlock the Hidden Diversity in Your Language Model. In our new paper, Intent Factored Generation (IFG), we propose an inference time method to increase the diversity of generations from LLMs. IFG leads to improvements in searching for solutions to maths and code problems. (1/6)

I think ARC is a great eval, but at this point we should just use nethack

I’m building a new team at Google DeepMind to work on Open-Ended Discovery! We’re looking for strong Research Scientists and Research Engineers to help us push the frontier of autonomously discovering novel artifacts such as new knowledge, capabilities, or algorithms, in an

Discovering new algorithms from data has the potential to transform research! But when should we meta-learn our algorithm as a neural network, and in what scenario do LLMs shine? Read to find out! 👇

🔍 Discovering algorithms lets us surpass human ingenuity… But what tools should we be using for discovery? LLMs? Evolution? Our new paper explores this question, check it out! 👇