A new world wodel from Meta FAIR after all the chaos. 🌍 Meet DINO-world: a generalist video world model that predicts the future—in latent space. Trained on uncurated videos with DINOv2, it learns diverse temporal dynamics (driving, indoors, sims), beats prior models on segmentation & depth, and even grasps intuitive physics. Bonus: it can be fine-tuned for action-conditioned planning.

ICML’s Statement about subversive hidden LLM prompts We live in a weird timeline…

Not Even Bronze: Evaluating LLMs on 2025 International Math Olympiad 🥉 Nice blog post from the team behind MathArena: Evaluating LLMs on Uncontaminated Math Competitions () providing independent analysis of LLM performance on IMO.

Not Even Bronze: Evaluating LLMs on 2025 International Math Olympiad 🥉 Nice blog post from the team behind MathArena: Evaluating LLMs on Uncontaminated Math Competitions () providing independent analysis of LLM performance on IMO.

Engineers spend 70% of their time understanding code, not writing it. That’s why we built Asimov at @reflection_ai. The best-in-class code research agent, built for teams and organizations.

Humanity has prevailed (for now!) I'm completely exhausted. I figured, I had 10h of sleep in the last 3 days and I'm barely alive. I'll post more about the contest when I get some rest. (To be clear, those are provisional results, but my lead should be big enough)

Some updates 🚨 I finished my Ph.D at @uwcse in June 2025! After a year at AI2 as a Research Scientist, I am joining CMU @LTIatCMU & @mldcmu (courtesy) as an Assistant Professor in Fall 2026. The journey, acknowledgments & recruiting in 🧵

Hikaru Utada and Yuval Noah Harari talk about The Evolution of AI and Creativity Gotta admit, this was not on my bingo card!

Excited to take part in @TEDAISF this fall!

1 decade ago: Reinforcement Learning Prompt Engineer in Sec. 5.3 of «Learning to Think …» [2]. Adaptive Chain of Thought! An RL net learns to query another net for abstract reasoning & decision making. Going beyond the 1990 World Model for millisecond-by-millisecond planning [1].  [2] J. Schmidhuber (JS, 2015). «On Learning to Think: Algorithmic Information Theory for Novel Combinations of RL Controllers and Recurrent Neural World Models.» ArXiv 1210.0118  [1] JS (1990). “Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments.» TR FKI-126-90, TUM. (This report also introduced artificial curiosity and intrinsic motivation through generative adversarial networks.)