arXiv:2606.16533v1 Announce Type: new Abstract: World models are transitioning from passive visual generators to foundational, operational infrastructure for Physical AI: they must natively acquire world knowledge from heterogeneous experience, maintain persistent states over long horizons, and execute efficiently within real deployment constraints. We introduce Kairos, a native world model stack designed around these requirements. (1) Kairos learns the world by pioneering a Native Pre-training Paradigm governed by a Cross-Embodiment Data Curriculum, which organizes open-world videos, human be
The development of Kairos signifies a pivotal moment as world models transition from theoretical concepts to practical, robust infrastructure for Physical AI, driven by the increasing demand for autonomous systems.
A robust, native world model stack capable of acquiring and maintaining knowledge over long horizons is crucial for unlocking the full potential of physical AI, enabling more adaptive and efficient autonomous systems.
Current world model limitations in acquiring heterogeneous data and maintaining state over long periods are addressed, suggesting a future where physical AI can operate with greater autonomy and generalizability.
- · robotics companies
- · AI hardware manufacturers
- · logistics and manufacturing sectors
- · companies relying on narrow AI solutions
- · manual labor in repetitive physical tasks
Kairos will enable more sophisticated and reliable physical AI applications across various industries.
The proliferation of advanced physical AI driven by Kairos could accelerate automation, impacting labor markets and requiring new skill sets.
Improved world models could lead to the development of self-improving robotic systems, driving an exponential growth in autonomous capabilities.
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Read at arXiv cs.AI