"Visual General Intelligence: A White Paper" is now on arXiv! arxiv.org/abs/2608.25924 We've discussed: - What is Visual Intelligence? - Can we develop it toward General Intelligence (VGI)? In the VGI white paper, we bring together diverse perspectives on Visual Intelligence and discuss how intelligence grounded in visual modalities and experiences could develop toward General Intelligence. Beyond proposing a single definition of VGI, we introduce, propose, and discuss different perspectives on what Visual Intelligence could be and how we could develop it toward General Intelligence. This paper is a starting point for a broader discussion on the future of Visual Intelligence. Many thanks to all the great co-authors!

How can we log, audit, and improve deployed AI models and agents for medicine? 🏥 AI is rapidly entering the clinic. Although models are already deployed across millions of patients, there is no consistent approach to monitor model use, assess performance, detect failure modes, or measure clinical impact. Model cards and data sheets describe a model before deployment. After deployment, there is no standard to record how, when, by whom, and for whom health AI models are used. We need a “black box” flight recorder for medical AI. ✈️ Co-supervised by @zakkohane and @marinkazitnik, I led a team across 53 institutions and 11 countries to develop MedLog, a protocol for event-level logging of medical AI. Each time an AI model makes a prediction, MedLog creates a record with nine core fields: header, model, user, target, inputs, artifacts, outputs, outcomes, and feedback. In our updated pre-print, we now report four real-world pilot implementations of MedLog across the US, Switzerland, and Vietnam. Predictive and generative AI, with 3M+ logged model invocations across up to 791K patients. 🇨🇭ICU deterioration monitoring in Switzerland: 223,840 records, 114 days, 212 patients 🇻🇳Wearable tetanus progression monitoring in Vietnam: 3,406 records, 289 days, 15 patients 🇺🇸LLM-based sepsis quality reporting in San Diego: 3,766 records, 89 days, 60 patients 🇺🇸Patient non-attendance prediction in New York: 2,914,264 records, 244 days, 791,319 patients A thread on what MedLog revealed and how to get involved 🧵👇 1/n

Introducing Real-time RL. In the real world, time isn't free. The environment keeps "moving" even when you're computing your next action. We show how RL agents can learn to adaptively think in real-time games. 1/🧵

Excited to share our #SIGGRAPH2026 paper: Go-with-the-Track! We utilize point-tracks as spatiotemporal conditioning to precisely insert multiple reference images into generated videos. Paper: arxiv.org/pdf/2606.20891 Project page: eyeline-labs.github.io/Go-wi…