Tag
world models
World models learn environment dynamics from observations so agents can predict outcomes and plan ahead. They matter in reinforcement learning, robotics, and video understanding, where latent planning, long-horizon control, and multi-agent action binding can reduce compute and improve decision quality.
3 articles
Research/Apr 6
Hierarchical Planning Cuts World-Model Search Cost
A hierarchical latent world-model planner improves long-horizon control and cuts planning compute, with zero-shot gains on real robots.
Research/Apr 3
ActionParty binds actions to multiple agents
ActionParty tackles multi-agent control in video world models by binding actions to subjects, with reported gains in action-following and identity consistency.
Industry News/Apr 3
Five AI Infra Frontiers Bessemer Expects for 2026
Bessemer’s 2026 AI infra roadmap points to memory, continual learning, RL, inference, and world models as the next big build areas.