Matrix Team
We build world models, video systems, agents, and robotic intelligence that learn from action, remember across time, and run efficiently enough to stay in the loop with real environments.
Vision
A world model is most useful when it is interactive: it receives actions, updates memory, predicts consequences, and returns feedback quickly enough for agents, robots, and humans to keep acting.
We aim for models that do more than generate plausible video. They should preserve structure, expose controllable state, support long-horizon reasoning, and become a substrate for perception, planning, and embodied learning.
Represent actions, constraints, rewards, controls, and environment responses as first-class signals.
Carry geometry, identity, temporal context, and learned experience across long rollouts.
Make inference and simulation fast enough for continuous feedback and scalable deployment.
Video models give us a visual substrate. World models add state and consequence. Agents close the loop by choosing actions. Robotics makes the loop physical.
Matrix Team connects these layers through research on controllable generation, interactive environments, memory, understanding, and efficient computation. The long-term target is an AI system that can perceive a situation, simulate alternatives, act, and learn from the outcome.
We model the loop between agents, controls, environments, and visual change so intelligence can be grounded in what a system can do.
+Persistent memory keeps identity, geometry, intent, and causal traces available across long interactions instead of resetting every frame.
+Interactive intelligence needs fast inference, stable rollout, and careful compute allocation so models can remain responsive while scaling.
+We connect video, robotics, games, and physical environments so learned models can transfer across scenes, tasks, and control modes.
+Research systems
Selected papers, datasets, and systems ordered by public release date, with lightweight previews for fast browsing.
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A compact group of researchers building interactive world models and agent systems. Interests are shown instead of hierarchy. View full list. Listed alphabetically.
About
Matrix Team is an independent research interest group bringing together young researchers across world models, video systems, agents, robotics, and interactive simulation.
We stay small, practical, and research-driven: build systems, test ideas in public projects, and collaborate with people who care about long-horizon memory, embodied interaction, and efficient computation.
Call for Industrial Cooperation & Support As an independent research interest group, we are actively seeking support and resources from industry organizations and other partners to further our mission. We believe that collaboration with industry leaders and stakeholders can significantly accelerate the development of advanced simulation technologies.
If you are interested in our work and see potential for cooperation or support, we would be thrilled to discuss how we can collaborate. Whether it's through funding, expertise, or other forms of assistance, your contribution is essential to our continued growth and impact.
Please don’t hesitate to reach out to us at fengruili.frl@gmail.com to explore opportunities for collaboration.
