Research

Hybrid World Models

We investigate an alternative architecture to large-scale physics approximation models: deterministic 3D engines with built-in physics, lighting, and interactions, layered with agentic AI systems for world state orchestration. This hybrid approach explores fundamental trade-offs between determinism and emergent behavior in simulated environments.

Multi-Agent Orchestration

Our research focuses on hierarchical agent architectures for coordinated multi-agent systems in shared 3D environments. We examine director-level models that manage global scene state while individual actor agents operate with autonomous goals, memory systems, and decision-making processes. The core challenge involves maintaining coherence while enabling emergent, unscripted interactions.

Natural Language to Spatial Reasoning

We explore natural language interfaces for real-time spatial manipulation and scene generation. Our work investigates semantic parsing of spatial intent, geometric reasoning from linguistic descriptions, and the translation of abstract concepts into 3D scene primitives.

Research Challenges

Our work addresses several active research areas:

• Multi-agent coordination and conflict resolution in shared state spaces
• Consistency maintenance in dynamically generated 3D worlds
• Realtime rendering optimization for agent-driven scenes
• Semantic grounding of natural language in 3D spatial contexts
• Scalable architectures for distributed agentic systems

Future Directions

We are exploring how collaborative spatial environments enable new forms of multi-agent coordination and how persistent world state affects emergent behavior in long-running simulations. The intersection of deterministic 3D engines, autonomous agent systems, and real-time spatial reasoning presents open research questions in distributed AI and computational geometry.