When I first saw diffusion models, I was blown away by how naturally they scale during inference: you train them with fixed flops, but during test time, you can ramp it up by like 1,000x. This was way before it became a big deal with o1. But honestly, the scaling isn’t that

🚀 DeepSeek-R1 is here! ⚡ Performance on par with OpenAI-o1 📖 Fully open-source model & technical report 🏆 MIT licensed: Distill & commercialize freely! 🌐 Website & API are live now! Try DeepThink at chat.deepseek.com today! 🐋 1/n

I’ve changed so little. From my 1978 Bachelor’s thesis: “The adult human mind is very complex, but the question remains open whether the learning processes that constructed it in interaction with the environment are similarly complex. Much evidence and many peoples’ intuitions

Our computer vision textbook is now available for free online here: visionbook.mit.edu We are working on adding some interactive components like search and (beta) integration with LLMs. Hope this is useful and feel free to submit Github issues to help us improve the text!

Thanks for sharing! Rohan Paul We hope our paper provides insights into a new direction for LLM fine-tuning

LLMs, trained only on text, might already know more about other modalities than we realized; we just need to find ways elicit it. project page: sophielwang.com/sensory w/ Phillip Isola and Brian Cheung

We’ve opened a Discussion Forum on GitHub for our paper on using evolution strategies to fine-tune LLMs. If you have any questions, suggestions, or thoughts about this research direction, feel free to join the discussion — there are already a few discussion threads live:

[1/7] Paired multimodal learning shows that training with text can help vision models learn better image representations. But can unpaired data do the same? Our new work shows that the answer is yes! w/ Shobhita Sundaram Chenyu (Monica) Wang, Stefanie Jegelka and Phillip Isola

Over the past year, my lab has been working on fleshing out theory/applications of the Platonic Representation Hypothesis. Today I want to share two new works on this topic: Eliciting higher alignment: arxiv.org/abs/2510.02425 Unpaired rep learning: arxiv.org/abs/2510.08492 1/9

(1/5) Beyond Next-Token Prediction, introducing Next Semantic Scale Prediction! Our NeurIPS Conference NeurIPS 2025 paper HDLM is out! Check out the new language modeling paradigm: Next Semantic Scale Prediction via Hierarchical Diffusion Language Models. It largely generalizes

✨Introducing ECHO, the newest in-the-wild image generation benchmark! You’ve seen new image models and new use cases discussed on social media, but old benchmarks don’t test them! We distilled this qualitative discussion into a structured benchmark. 🔗 echo-bench.github.io

(1/6) Check out our new paper: Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model A Latent Reasoner! arxiv: arxiv.org/abs/2510.03206 Do diffusion language models (DLMs) need to be discrete? No! We show that continuous diffusion models are more