🚀Excited to announce Dream 7B (Diffusion reasoning model): the most powerful open diffusion large language model to date.

Extremely happy to share that our PhD student Tiancheng Hu received the Apple Scholars in AI/ML PhD Fellowship! 🎉 The fellowship will support his research on LLM-based simulation and LLM personalisation. Congratulations again, Tiancheng Hu! 🥳 machinelearning.apple.com/updates/apple-…

🎮 Computer Use Agent Arena is LIVE! 🚀 🔥 Easiest way to test computer-use agents in the wild without any setup 🌟 Compare top VLMs: OpenAI Operator, Claude 3.7, Gemini 2.5 Pro, Qwen 2.5 vl and more 🕹️ Test agents on 100+ real apps & webs with one-click config 🔒 Safe & free

Happy to share that MVoT got accepted to ICML 2025 ICML Conference 🎉🎉#ICML If you are interested, do check out our paper and here are some other materials: 📰Report on IEEE Spectrum: spectrum.ieee.org/visual-reasoni… 🎤TWIML Podcast with Sam: twimlai.com/podcast/twimla…

We propose Neurosymbolic Diffusion Models! We find diffusion is especially compelling for neurosymbolic approaches, combining powerful multimodal understanding with symbolic reasoning 🚀 Read more 👇

We achieved the first instance of successful subword-to-byte distillation in our (just updated) paper. This enables creating byte-level models at a fraction of the cost of what was needed previously. As a proof-of-concept, we created byte-level Gemma2 and Llama3 models. 🧵

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement

🚀New Preprint Alert 🚀 Can Multimodal Retrieval Enhance Cultural Awareness in Vision-Language Models? Excited to introduce RAVENEA, a new benchmark aimed at evaluating cultural understanding in VLMs through RAG.

🤖⚛️Can AI truly see Physics? Test your model with the newly released SeePhys Benchmark! 🚀 🖼️Covering 2,000 vision-text multimodal physics problems spanning from middle school to doctoral qualification exams, the SeePhys benchmark systematically evaluates LLMs/MLLMs on tasks

🔥 We teach LLMs to say how confident they are on-the-fly during long-form generation. 🤩No sampling. No slow post-hoc methods. Not limited to short-form QA! ‼️Just output confidence in a single decoding pass. ✅Better calibration! 🚀 20× faster runtime. arXiv:2505.23912 👇

Automating Multi-Agent Design: 🧩Multi-agent systems aren’t just about throwing more LLM agents together. 🛠️They require mastering the subtle art of prompting and agent orchestration. Introducing MASS🚀- Our new agent optimization framework for better prompts and topologies!

🔁 What if you could bootstrap a world model (state1 × action → state2) using a much easier-to-train dynamics model (state1 × state2 → action) in a generalist VLM? 💡 We show how a dynamics model can generate synthetic trajectories & serve for inference-time verification 🧵👇

🤯What we know about RL for reasoning might not hold outside math and code? We revisit established findings on RL for LLM reasoning on six domains (Math, Code, Science, Logic, Simulation, Tabular) and found that previous conclusions drawn on math and code are surprisingly

Excited to share our new survey on the reasoning paradigm shift from "Think with Text" to "Think with Image"! 🧠🖼️ Our work offers a roadmap for more powerful & aligned AI. 🚀 📜 Paper: arxiv.org/pdf/2506.23918 ⭐ GitHub (400+🌟): github.com/zhaochen0110/A…

Working on LLM social simulation and need data? Excited to announce our iNews paper is accepted to #ACL2025! 🥳 It's a large-scale dataset for predicting individualized affective responses to real-world, multimodal news. arxiv.org/abs/2503.03335 🤗 Data: huggingface.co/datasets/piteh…

🚨Announcing Zebra-CoT, a large-scale dataset of high quality interleaved image-text reasoning traces 📜. Humans often draw visual aids like diagrams when solving problems, but existing VLMs reason mostly in pure text. 1/n