arXiv:2603.02697v2 Announce Type: replace-cross Abstract: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction. ShareVerse leverages the generation capability of large video models and integrates three key innovations: 1) A dataset for large-scale multi-agent interactive world modeling is built on the CARLA simulation platform, featuring diverse scenes, weather conditions, and interactive trajectories with paired multi-vie
The accelerating capabilities of large video models and the demand for more sophisticated multi-agent simulation environments are converging to make such frameworks feasible and necessary.
This framework represents a significant step towards enabling more realistic and complex AI agent interactions in simulated environments, which is crucial for advancing AI research and development across various domains.
The ability to generate consistent multi-agent videos for shared world modeling provides improved tooling for training and testing complex AI systems, offering a more dynamic and interactive simulation platform than previously available.
- · AI research labs
- · Autonomous driving companies
- · Simulation platform developers
- · Game development studios
- · Companies relying on static or single-agent simulation tools
Improved training data and testing environments for multi-agent AI systems, particularly in robotics and autonomous systems.
Faster development and deployment of more robust and intelligent AI agents capable of complex decision-making in interactive environments.
Accelerated progress in areas like humanoid robotics and general AI, potentially leading to agentic systems that can operate with high fidelity in real-world scenarios.
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Read at arXiv cs.AI