Introducing BenchAgents: a framework for automated benchmark creation, using multiple LLM agents that interact with each other and with developers to generate diverse, high-quality, and challenging benchmarks w/ Varun Chandrasekaran Neel Joshi Besmira Nushi 💙💛 Vidhisha Balachandran Microsoft Research 🧵1/8

Paper submissions (due today by 11:59pm UTC) are flowing in! We're excited to share these intriguing approaches from top universities, AI labs, and Fortune 500 companies soon. Review paper + winners announcement coming Dec. 6.

Introducing 🧞Genie 2 🧞 - our most capable large-scale foundation world model, which can generate a diverse array of consistent worlds, playable for up to a minute. We believe Genie 2 could unlock the next wave of capabilities for embodied agents 🧠.

Great to have Searching Latent Program Spaces (arxiv.org/abs/2411.08706) recognised with a 🥉3rd place paper award in the ARC Prize! It was a pleasure working on this with Clem Bonnet . Looking forward to continuing to develop these ideas even further.

If you are interested in scalable search based 🔎 policy improvement operators come chat with me at our poster on SPO tommorow!

Are you interested in policy + people + AI? Interested in program synthesis? How about reinforcement learning?🧐 December 17th, tune in with our Research Connections group to chat about these topics with Levi Lelis Anna Kawakami and Dennis Soemers!

Epic Xmas haul ⛄ looking forward to getting stuck in #activeinference Nick Chater CoCoSci MIT Clem Bonnet

Whatever you think, you’re underestimating AI.

Checkout Clem Bonnet discussing searching latent program spaces tommorow on Machine Learning Street Talk !

Thrilled to see our NeurIPS 2024 paper, Sequential Monte Carlo Policy Optimisation (arxiv.org/abs/2402.07963), featured in Kevin's Reinforcement Learning: A Comprehensive Overview, which additionally recognises SMC as a competitive, scalable online planner. A fantastic modern