LingBot-World: Open-Source World Simulator

LingBot-World is an open-source world simulator that evolves a video generation model into an interactive, real-time environment engine. Built on a 28B-parameter Mixture-of-Experts architecture, it achieves high-fidelity dynamics across diverse domains with sub-second latency at 16 fps, outperforming Genie 3 and Mirage 2 in dynamic degree while being fully open-source. - **Three-stage evolution pipeline:** A progressive training strategy transforms a pretrained video model into an interactive simulator: Stage I establishes a general video prior via the Wan2.2 14B model, Stage II injects world knowledge and action control through MoE middle-training on 60-second sequences, and Stage III adapts to causal attention with few-step distillation for real-time inference. - **Scalable data engine with hierarchical captioning:** A hybrid data engine ingests real-world footage, game engine recordings, and Unreal Engine synthetic data. A three-layer captioning strategy (narrative, scene-static, and dense temporal) disentangles motion control from scene generation, enabling precise action-contingent dynamics learning. - **Emergent spatial memory:** Without explicit 3D representations, the model maintains structural integrity of landmarks after 60 seconds out of view, reasons about unobserved state evolution (vehicles continuing trajectories off-screen), and supports coherent generation up to 10 minutes. VBench evaluation shows 0.8857 dynamic degree versus 0.76 for Yume-1.5 and 0.72 for HY-World 1.5. - **Versatile embodied AI applications:** Beyond visual synthesis, the framework supports promptable world events (global weather/style shifts and local object injection via text), an action agent trained on Qwen3-VL-2B for autonomous exploration, and 3D reconstruction from generated videos validating geometric consistency.