As world models transition from passive visual generators to foundational infrastructure for Physical AI, Kairos emerges to meet these requirements.\n\n1. Native Pre-training Paradigm: Kairos learns the world through a Cross-Embodiment Data Curriculum that organizes open-world videos, human behavioral data, and robot interactions into a progressive developmental pathway.\n\n2. Unified World Understanding: Kairos maintains the world with a Native Unified Architecture equipped with Hybrid Linear Temporal Attention, where sliding-window attention captures local dynamics, dilated sliding windows capture mid-range dependencies, and gated linear attention maintains persistent global memory. We establish formal theoretical bounds that strictly limit error accumulation, mathematically guaranteeing state propagation across extended horizons.\n\n3. Low-latency Operation: Kairos incorporates a Deployment-Aware System Co-Design to support low-latency rollout generation on server and consumer-grade hardware for real-world observation-action-feedback loops.\n\nExperiments show that Kairos achieves top-level performance in embodied world-model, long-horizon, and action-policy benchmarks, demonstrating a strong efficiency-capability trade-off. Together, these results position Kairos as a cohesive operational foundation for future self-evolving physical intelligence.\n\nBlogger's Review: The design philosophy of Kairos not only disrupts traditional world models but also lays a solid foundation for the self-evolution of Physical AI. Its innovative temporal attention mechanism and deployment-aware design showcase a perfect blend of efficiency and intelligence, making it a noteworthy development.