#Singapore #ShengShu Technology #AI Roadmap #General World Models

ShengShu Technology's Five-Level Roadmap for General World Models

At the recent 2026 World Robot Conference (WRC) held in Singapore, ShengShu Technology made a significant announcement regarding its pioneering research on General World Models (GWMs). The company proposed an innovative five-level development roadmap designed to guide the evolution of these models from mere world generation to advanced autonomous actions and orchestration.

The Vision Behind GWMs

During his keynote address, Jun Zhu, the Founder and Chief Scientist at ShengShu Technology, emphasized the need for a new kind of General World Model. He stated, "Our aim is not merely to create a specialized model for a specific task, but to forge a foundational model capable of understanding the world, envisioning possible futures, and initiating actions."

Defining General World Models

Current research in world modeling encompasses various applications like video generation, environmental simulations, and robotic decision-making. However, a cohesive definition of a truly general model has proven elusive. Zhu elaborated that a General World Model must possess three core interdependent capabilities:

1. Understanding the World: Ability to integrate diverse sources of information to ascertain the current state.
2. Imagining Possible Futures: Capability of predicting potential outcomes of various actions.
3. Taking Action: Effecting change in the digital or physical environment to achieve specific goals while refining future actions based on feedback.

Zhu clarified that a General World Model is not a mere generator or collection of capabilities but a robust closed-loop system linking understanding, imagination, and action.

The Three Pillars of General World Models

To construct a General World Model, three foundational elements must be addressed:

• - Data: GWMs require a multi-layer data structure. This structure includes progressively detailed tiers ranging from raw observations to actionable insights. While the lower levels offer greater coverage and scale, higher tiers provide valuable, albeit scarce, data directly connected to tasks.
• - Architecture: GWMs must effectively process various modalities like images, video, and text within an integrated framework. The Mixture-of-Transformers (MoT) architecture is designed to achieve this, facilitating a cohesive interaction across different types of data.
• - Compute: Efficient computation is vital for both pre-training and real-time application of the models. Technologies such as model distillation and inference acceleration play critical roles in ensuring rapid decision-making in dynamic environments.

The Five-Level Roadmap Explained

The proposed five levels of GWMs are as follows:

• - Level 1 (World Generation): Initiates with generating coherent world trajectories, utilizing video data to help models learn spatial dynamics.
• - Level 2 (Interactive World): Advances to real-time input handling, allowing continuous interaction and modifications based on external stimuli, a capability exemplified by the release of Vidu S1.
• - Level 3 (Actionable World): Marks the transition from digital simulations to executing physical robot actions, as demonstrated in models like Motus and Motubrain. These models unify understanding, prediction, and action generation, achieving high levels of efficiency and speed.
• - Level 4 (Autonomous World Agent): Focuses on autonomous decision-making, where models begin to proactively engage with their environment, planning around long-term objectives.
• - Level 5 (World Orchestrator): Embodies a high level of autonomy by coordinating actions across multiple agents, humans, and tools for complex task execution.

Challenges Ahead for Advancing GWMs

While levels 1 to 3 have seen notable advancements, several challenges remain in reaching higher levels of sophistication. Six key areas need further development:
1. Integrated evaluation across understanding, imagination, and action.
2. Enhanced learning of physical dynamics and their ramifications.
3. Development of persistent and adaptable memory systems.
4. Online self-improvement through real-world interactions.
5. Efficient deployment under constraints of latency and resources.
6. Improvements in safety and controllability.

Looking to the Future

As ShengShu Technology continues to enhance its capabilities in World Generation, Interactive World, and Actionable World, it is also focused on developing autonomous agents. The ambition is to create a General Foundation Model that can continuously learn from real-world feedback, facilitating a seamless connection between digital and physical realms. The achievement of a true feedback loop involving understanding, imagination, and action is critical for advancing toward realizing general embodied intelligence. For more detailed information, visit ShengShu Technology.

This roadmap not only highlights the potential future of AI but also emphasizes the crucial steps necessary to bridge the gap between virtual models and real-world applications.