🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

📢(1/11)Diffusion LMs are fast and controllable at inference time! But why restrict such benefits for processing text data? We are excited to announce LaViDa, one of the first and fastest large diffusion LM for vision-language understanding!!

I am excited to share my new paper "Streaming Sliced Optimal Transport". I introduce the first algorithm for computing sliced Wasserstein distance from sample streams to make sliced optimal transport even faster and more scalable. (1/10)

Announcing the 2025 IJCAI Computers and Thought Award winner ✨Aditya Grover Aditya Grover, Inception UCLA. Dr. Grover is honored for uniting deep generative models, representation learning & RL to advance scientific reasoning. Congratulations! ijcai.org/awards

🎉 Our paper “Representations Shape Weak-to-Strong Generalization” is accepted at #ICML2025! We study weak-to-strong generalization (W2SG)—a core problem in superalignment—and offer new insights into the role of models' internal representations in W2SG. 1/

CVPR-bound! ✈️ I'll be presenting CoLLM on Friday, 6/13 (Morning, #364) and looking for my next challenge as a full-time Scientist/Engineer. If you're hiring or just want to chat about exciting research, find me there! My work: hmchuong.github.io #CVPR2025 #JobHunt

Since our launch earlier this year, we are thrilled to witness the growing community around dLLMs. The Mercury tech report from Inception is now on arXiv.org with more extensive evaluations: arxiv.org/abs/2506.17298 New model updates dropping later this week!

Seeing text-to-text regression work for Google’s massive compute cluster (billion $$ problem!) was the final result to convince us we can reward model literally any world feedback. Paper: arxiv.org/abs/2506.21718 Code: github.com/google-deepmin… Just train a simple encoder-decoder

(1/6)Our work Reflect-DiT was accepted to #ICCV2025 ! Reflect-DiT allows the model to reflect on its past generations and textual feedback to self-correct and improve, extending reasoning to text-to-image generation.