AI
What if you could guide a robot by drawing arrows that show pushes and pulls? Goal Force is a new way to steer video world models with physics, not vague text prompts. Instead of describing goals in words or target images, users sketch force vectors and intermediate dynamics. The model
AI
AI agents usually follow a slow cycle: generate an idea, execute it, then check the results. This study shows a faster path: teach agents to predict outcomes first, then verify only the most promising options. * They build “execution priors” (inspired by world models) so runtime checks can be replaced by
AI
TL;DR: Giving AI agents a "what happens next?" simulator doesn’t automatically make them smarter. Researchers tested whether agents built on vision–language models can use a generative world model—a tool that predicts future states—to preview outcomes before acting. * Agents rarely choose to simulate: in
EmbodiedAI
What’s new As AI video models are used as “world models” for robots, we need to know if their imagined futures match reality. WoW-World-Eval (Wow, wo, val) is a public benchmark that stress-tests these models before we trust them on real machines. * Five skills: perception, planning, prediction, generalization, execution.
AI
Can video AIs guide real robots? Meet the WoW-World-Eval Embodied Turing Test Robots need 'world models' that predict what happens next. A new benchmark, WoW-World-Eval (Wow, wo, val), tests video foundation models on 609 robot manipulation samples across five skills: perception, planning, prediction, generalization, and execution. * 22-metric score
AI
What if the brain builds and tests ideas the way modern generative AI produces images and text? A new paper by Claudius Gros argues that clear, testable generative principles—not obscure tricks—drove AI's leap, and neuroscience can probe whether similar rules guide the brain. Five takeaways * World
Robotics
What's new PhysWorld lets robots learn skills from AI-generated videos—without collecting any real robot data. How it works * Given a task and one photo of the scene, a video model "imagines" a demonstration. * PhysWorld reconstructs a physics-based world from that video. * An object-centric learner turns
LLM
Can an LLM judge the best playlist, not just the next song? Recommender systems often serve slates—ordered lists like your home feed or a playlist. Modeling what a person prefers across domains is hard. This study tests Large Language Models as a 'world model' of user preferences: