{
  "title": "World-model planning in a timed control loop",
  "caption": "In this planner example, a world model receives current state and candidate actions, and predicted results enter planning. Feasibility checks occur before timed controller execution. New observations supply feedback. Monitoring and measured evaluation are independent requirements. Author-created explanatory diagram. Playback time is illustrative and is not measured runtime.",
  "alt": "Planner flow: action predictions enter planning, feasibility filtering occurs before timed execution, and observations supply feedback.",
  "sources": [
    {
      "path": "content/blog/2026-06-09_world-models-for-embodied-ai/post.md",
      "description": "Article mechanism and retained citations"
    },
    "https://liyang.page/wm-tutorial/",
    "https://arxiv.org/abs/2607.00836",
    "https://web.stanford.edu/class/ee364b/lectures/mpc_slides.pdf"
  ],
  "limitations": [
    "This playback is an example. Playback duration is not measured runtime.",
    "This is a planner-use example, not a mandatory deployment architecture for every world model.",
    "The filter does not certify safety. Monitoring and measured outcome evaluation are independent requirements."
  ]
}