Linear temporal attention gives agents memory across gaps

1Key Takeaways

• Persistent world models are what keep long‑horizon tasks coherent when the agent’s sensors go dark.
• Linear temporal attention and associative graph memories give embodied systems a way to write and read state across those dark intervals, eliminating the drift that has long plagued simulation‑to‑real pipelines.
• Before these advances, world models behaved like camera‑following renderers: they could generate plausible frames while observed, but they fell apart the moment the viewpoint changed.