What is Kairos 3.0
Kairos 3.0 (Enlightenment World Model 3.0) is an industry-first ACE embodied R&D paradigm launched by Daxiao Robotics, and the first open-source world model with commercial applications. Kairos 3.0 is an open-source, efficient world foundation model focused on learning the dynamics, causal relationships, and physical laws of the real world, achieving understanding and prediction of the world through long-horizon video generation. The model adopts a DiT architecture with linear time complexity, combined with sliding window, dilated sliding window, and gated linear attention mechanisms, enabling efficient processing of long video sequences and generation of complex dynamic interaction scenes that conform to physical laws. Kairos 3.0 embodied intelligence provides a high-fidelity virtual training environment, helping robots better understand the world and achieve autonomous interaction.
Main Features of Kairos 3.0
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Long-Horizon Video Generation: The model can generate complex, multi-stage dynamic interaction scenes, support long-horizon video output, and maintain temporal coherence and physical consistency.
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Physical Law Modeling: Through deep learning of physical laws and the underlying logic of human behavior, it generates dynamic events that conform to physical common sense, such as object motion and collisions.
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Multimodal Input Support: 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), image-to-video (I2V), etc.
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Cross-Scenario Generalization Capability: Possesses strong generalization capability, adapting to various application scenarios such as warehouse logistics, security monitoring, smart home, etc.
Technical Principles of Kairos 3.0
- Video VAE (Variational Autoencoder): Adopts WAN2.1 VAE to compress video into low-dimensional latent representations while maintaining high reconstruction fidelity. For example, encoding a video of shape 3×T×H×W into a latent representation of 16×T/4×H/8×W/8 , achieving a compression ratio of 48x.
- Multimodal Condition Encoder: Uses a condition encoder based on a vision-language model (VLM) to embed text prompts into the model, providing semantically rich condition information for video generation.
- DiT Architecture with Linear Time Complexity: Replaces the traditional attention mechanism with quadratic time complexity, adopting a design that combines linear attention with local attention, supporting efficient modeling of long video sequences.
- Sliding Window Attention (SWA): Focuses on local temporal dynamics, suitable for short-term motion continuity and local physical interactions.
- Dilated Sliding Window Attention (DSWA): Expands the temporal receptive field through dilation factors, capturing dependencies over longer time ranges.
- Gated Linear Attention (GLA): Supports modeling of global temporal causal relationships, enabling long-horizon reasoning and physically consistent event evolution.
Project Address of Kairos 3.0
- GitHub Repository：https://github.com/kairos-agi/kairos-sensenova-robot
Application Scenarios of Kairos 3.0
- Warehouse Logistics: Kairos 3.0 can simulate the cargo sorting and handling process in warehouse environments, helping optimize robot path planning and improve warehouse automation efficiency.
- Smart Home: By simulating human behavior and object interactions in home scenarios, the model trains home service robots to better understand user needs and provide personalized services.
- Security Monitoring: Kairos 3.0 can generate videos of abnormal behavior in surveillance scenarios, improving the security system's ability to provide early warnings of potential threats and enhancing public safety.
- Healthcare: The model simulates surgical operations and rehabilitation training in medical scenarios, assisting medical robots in precise training and improving the quality and efficiency of healthcare services.
- Energy Management: Kairos 3.0 can generate inspection and maintenance scenarios for energy facilities, helping inspection robots quickly identify equipment faults and improving the efficiency of energy facility operations and maintenance.