Awa Dieng

The UN General Assembly has appointed 40 experts of the Independent International Scientific Panel on AI. 🏛️ This body begins its work as a scientifically-grounded foundation, ensuring global understanding is driven by evidence-based scientific assessments. #DigitalCooperation

🚨 CFP OPEN! We’re launching the #AFAA2026 Workshop at @iclr_conf on fairness across alignment and agentic AI systems. Submit full or tiny papers! Interdisciplinary work especially welcome :D 🗓 Deadline: Jan 31 (AoE) | 🔗 afciworkshop.org #AFAA2026 #ICLR2026

🚨 CFP OPEN! We’re launching the #AFAA2026 Workshop at @iclr_conf on fairness across alignment and agentic AI systems. Submit full or tiny papers! Interdisciplinary work especially welcome :D 🗓 Deadline: Jan 31 (AoE) | 🔗 afciworkshop.org #AFAA2026 #ICLR2026

LLM memory is considered one of the hardest problems in AI. All we have today are endless hacks and workarounds. But the root solution has always been right in front of us. Next-token prediction is already an effective compressor. We don’t need a radical new architecture. The missing piece is to continue training the model at test-time, using context as training data. Our full release of End-to-End Test-Time Training (TTT-E2E) with @NVIDIAAI, @AsteraInstitute, and @StanfordAILab is now available. Blog: nvda.ws/4syfyMN Arxiv: arxiv.org/abs/2512.23675 This has been over a year in the making with @arnuvtandon and an incredible team.

🚨 CFP OPEN! We’re launching the #AFAA2026 Workshop at @iclr_conf on fairness across alignment and agentic AI systems. Submit full or tiny papers! Interdisciplinary work especially welcome :D 🗓 Deadline: Jan 31 (AoE) | 🔗 afciworkshop.org #AFAA2026 #ICLR2026

#NewPaper Have you been wondering how your favorite LLM, e.g. Llama, Mistral, or Gemma performs on materials property prediction? We have just released LLM4Mat-Bench, an extensive benchmark for materials property prediction with LLMs! LLM4Mat-Bench has unique features: ☀️It spans 10 data collections, containing more than 2.6 Million data points. ☀️It covers 45 distinct material properties. ☀️It covers three different material representations: CIF, text description, and composition. ☀️It provides baseline results from different types and sizes of LLMs, e.g. Llama, Mistral, Gemma, MatBERT, and LLM-Prop. With materials data scattered everywhere, we believe LLM4Mat-Bench represents a unified data source for driving research on leveraging LLMs for materials science. The benchmark will be maintained and we look forward to your task and data contributions. Our @andre_niyongabo will present the paper at the AI4Mat #NeurIPS2024 workshop this December. Paper: arxiv.org/abs/2411.00177 Code: github.com/vertaix/LLM4Ma… Authors: Andre Niyongabo Rubungo (@andre_niyongabo), Kangming Li (@KangmingLi_), Jason Hattrick-Simpers, and Adji Bousso Dieng (@adjiboussodieng) #AI4Materials #MatSci #NLP4Science #Benchmarks #LLMs #Vertaix

Today we announce TRINDs, a dataset and benchmarking pipeline that uses synthetic personas to train and optimize performance of LLMs for tropical and infectious diseases, which are out-of-distribution for most models. Learn more →goo.gle/4cTAZR4

Today is my last day at Google. I started over 8 years ago, with a mandate to build a team doing bleeding edge AI research from Montreal, in what would be the first big tech AI research lab in the city. These years led to countless amazing scientific contributions from my team, to several initiatives nurturing the Montreal AI ecosystem, and to many new invaluable friendships across the globe at Google. It is with a heavy heart that I say goodbye, but I know I’m leaving behind an exceptionally strong Google DeepMind group in Montreal for which its best accomplishments are still ahead. There are too many people to thank, but I can’t pass on thanking Samy Bengio and @JeffDean who first believed in me and the opportunity of building a research lab in Montreal. I’m still working on determining the details of my next chapter, but certainly it will be grounded in my continuing motivation to leverage and make the most out of our enormous and talented local AI ecosystem.