WRC 2026｜生数科技发布最新研究成果，提出通用世界模型五级发展路线

TL;DR - ShengShu Technology presented a five-level roadmap for general world models, progressing from world generation to autonomous agents and multi-agent orchestration. Its approach unifies multimodal understanding, future prediction, and physical action in a closed feedback loop.

• The roadmap spans world generation (L1), real-time interaction (L2), physical action (L3), autonomous world agents (L4), and world orchestration (L5).
• The MoT architecture uses modality-specific parameters and shared attention to jointly process images, video, language, and robot actions.
• ShengShu’s Motubrain reportedly delivers roughly 10× faster inference than Motus, adapts to new robot embodiments with 50–100 demonstrations, and scored 96.1 on RoboTwin 2.0.
• Advancing to L4–L5 requires capabilities including goal formation, active exploration, continual learning, long-term memory, and coordinated multi-agent planning.