Glad to share our work at #ACL2023, "MPChat: Towards Multimodal Persona-Grounded Conversation" arxiv.org/abs/2305.17388 ! #multimodal #persona_chat authors: Jaewoo Ahn, Yeda Song, Gunhee Kim

I love our Michigan AI Lab MichiganAI! A group of people who not only does some of the coolest research in AI, but also care for and of each other, and enjoy each other’s company. A picture from this week’s fun picnic. ❤️

LLM chatbots are moving fast, but how do we make them better? In my new blog at The Gradient, I argue that an important next step is giving them a sense of "purpose."

I finally wrote another blogpost: ysymyth.github.io/The-Second-Hal… AI just keeps getting better over time, but NOW is a special moment that i call “the halftime”. Before it, training > eval. After it, eval > training. The reason: RL finally works. Lmk ur feedback so I’ll polish it.

🚀 Excited to announce our 4th Workshop on Computer Vision in the Wild (CVinW) at #CVPR2025 2025! 🔗 computer-vision-in-the-wild.github.io/cvpr-2025/ ⭐We have invinted a great lineup of speakers: Prof. Kaiming He, Prof. Boqing Gong, Prof. Cordelia Schmid, Prof. Ranjay Krishna, Prof. Saining Xie, Prof.

Excited to speak at the Workshop on Computer Vision in the Wild #CVPR2025 2025! 🎥🌍 🗓️ June 11 | 📍 Room 101 B, Music City Center, Nashville, TN 🎸 🧠 Talk: From Perception to Action: Building World Models for Generalist Agents Let’s connect if you're around! #CVPR2025 #robotics

We're heading to #CVPR2025! 📰Curious about what’s coming? Take a look at our list of accepted papers and come to meet the authors! Get ready for innovative #AI research and fresh insights!

Arrived in Nashville for #CVPR 🤠 Excited to present MONDAY, a collaboration with LG AI Research! 📍 MMFM Workshop - Thu, 9:40 AM 📍 Main Conference - Fri, 4:00 PM Let’s connect and chat!🤝 Also exploring Summer 2026 internships 🔍 MONDAY website: monday-dataset.github.io

Can scaling data and models alone solve computer vision? 🤔 Join us at the SP4V Workshop at #ICCV2025 in Hawaii to explore this question! 🎤 Speakers: Danfei Xu, joao carreira, Jiajun Wu, Kristen Grauman, Saining Xie, Vincent Sitzmann 🔗 sp4v.github.io

The race for LLM "cognitive core" - a few billion param model that maximally sacrifices encyclopedic knowledge for capability. It lives always-on and by default on every computer as the kernel of LLM personal computing. Its features are slowly crystalizing: - Natively multimodal

✨Two life updates✨ 1. Started my internship at LG AI Research in Ann Arbor, Michigan — Advancing AI for a better life! 🔮 2. Advanced to PhD candidacy at UMich CSE. This means I’ve completed my coursework and passed the qualification process. 🙌

Flow Q-learning (FQL) is a simple method to train/fine-tune an expressive flow policy with RL. Come visit our poster at 4:30p-7p this Wed (evening session, 2nd day)!

🚨🚨New paper on core RL: a way to train value-functions via flow-matching for scaling compute! No text/images, but a flow directly on a scalar Q-value. This unlocks benefits of iterative compute, test-time scaling for value prediction & SOTA results on whatever we tried. 🧵⬇️