Kairos 3.0 - A commercial application world model open-sourced by DaXiao Robotics

What is Kairos 3.0?

Kairos 3.0 (Kaisheng World Model 3.0) is an industry-first ACE embodied development paradigm and the first open-source and commercially applied world model launched by DaXiao Robotics. Kairos 3.0 is an open-source, high-efficiency world model focused on learning the dynamics, causal relationships, and physical laws of the real world. It achieves understanding and prediction of the world through long-term video generation. The model employs a linear-time complexity DiT architecture, combined with sliding window, expanding sliding window, and gated linear attention mechanisms, enabling efficient processing of long video sequences to generate complex and physically consistent dynamic interactive scenes. Kairos 3.0 embodied intelligence provides a high-fidelity virtual training environment, helping robots better understand the world and achieve autonomous interaction.

Key features of Kairos 3.0

• Long-series video generationThe model can generate complex, multi-stage dynamic interactive scenes, support long-term video output, and maintain temporal continuity and physical consistency.
• Modeling physical lawsBy deeply learning the underlying logic of physical laws and human behavior, dynamic events that conform to common sense in physics can be generated, such as the movement and collision of objects.
• Multimodal input supportIt supports multiple modal inputs such as text and images, and can generate corresponding video content based on the input, such as text-to-video (T2V) and image-to-video (I2V).
• Cross-scenario generalization capabilityIt has strong generalization capabilities and can be adapted to a variety of application scenarios, such as warehousing and logistics, security monitoring, and smart home.

Technical principles of Kairos 3.0

• Video VAE (Variational Autoencoder)Using WAN2.1 VAE, the video is compressed into a low-dimensional latent representation while maintaining high reconstruction fidelity. For example, a video of shape... 3×T×H×W The video encoding is 16×T/4×H/8×W/8 The potential representation is that the compression ratio reaches 48 times.
• Multimodal condition encoderUsing a conditional encoder based on a vision-language model (VLM), text cues are embedded into the model to provide semantically rich conditional information for video generation.
• DiT architecture with linear time complexityIt replaces the traditional attention mechanism with quadratic time complexity, and adopts a design that combines linear attention and local attention to support efficient modeling of long video sequences.
  • Sliding Window Attention (SWA)It focuses on local time dynamics and is suitable for short-term motion continuity and local physical interactions.
  • Expanded Sliding Window Attention (DSWA)By expanding the temporal receptive field through an expansion factor, dependencies can be captured over a longer time span.
  • Gated Linear Attention (GLA)It supports modeling global temporal causality, enabling long-term inference and physically consistent event evolution.

Kairos 3.0 project address

• GitHub repository: https://github.com/kairos-agi/kairos-sensenova-robot

Application scenarios of Kairos 3.0

• Warehousing and LogisticsKairos 3.0 can simulate the sorting and handling processes in a warehouse environment, helping to optimize robot path planning and improve warehouse automation efficiency.
• Smart HomeThe model trains home service robots to better understand user needs and provide personalized services by simulating human behavior and object interactions in a home setting.
• Security monitoringKairos 3.0 can generate videos of abnormal behavior in monitored scenarios, improving the security system's ability to warn of potential threats and enhancing public safety.
• HealthcareThe model simulates surgical procedures and rehabilitation training in medical scenarios, assisting medical robots in precise training and improving the quality and efficiency of medical services.
• Energy ManagementKairos 3.0 can generate energy facility inspection and maintenance scenarios, helping inspection robots quickly identify equipment faults and improve the efficiency of energy facility operation and maintenance.