💡 The paper introduces Kairos, a native world model framework designed to support physical AI applications. The problem addressed is that current world models are limited in their ability to learn from diverse experiences, maintain persistent states over time, and deploy efficiently in real-world scenarios. To address this, Kairos pioneers a native pre-training paradigm that learns from open-world videos, human behavioral data, and robot interactions, organized into a progressive developmental pathway.

The method involves a native unified architecture equipped with hybrid linear temporal attention, which captures local dynamics, mid-range dependencies, and maintains persistent global memory. This architecture is designed to limit error accumulation and guarantee state propagation across extended horizons. Additionally, Kairos incorporates a deployment-aware system co-design to support low-latency rollout generation on various hardware platforms.

The results show that Kairos achieves top-level performance on embodied world-model, long-horizon, and action-policy benchmarks, while offering a strong efficiency-capability trade-off. The experiments demonstrate that Kairos can learn from diverse experiences, maintain persistent states, and deploy efficiently in real-world scenarios. Overall, the paper positions Kairos as a cohesive operational foundation for future self-evolving physical intelligence, providing a native world model stack that can support a wide range of physical AI applications.