🔥 ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU

💡 The paper presents ABot-World-0, a system for real-time, long-horizon, closed-loop interaction in a virtual world. The system is trained on a large dataset of videos, games, and simulation engines to learn controllable world dynamics. The authors propose a multi-source data infrastructure to collect and process data, and a unified pipeline to apply quality checks, assessment, and synchronization of actions and text annotations.

The system uses a teacher-forcing approach to train an action-conditioned video world model, which is then distilled into a causal student model through a process of teacher forcing and ODE distillation. The authors also introduce Long Forcing, a method to align long student self-rollouts with an extended-horizon teacher, mitigating accumulated distribution shift and autoregressive drift.

The system provides a unified control interface for scene roaming and third-person character interaction, and uses reference-character memory to provide persistent appearance cues for identity consistency during third-person rollouts. The authors also co-design a streaming inference stack with a lightweight VAE decoder, efficient attention, memory-aware scheduling, and low-bit DIT inference.

The results show that ABot-World-0 can stream 720p video at up to 16 frames per second on a single NVIDIA RTX 5090 desktop GPU, with 1.2 seconds action-to-first-frame latency and approximately 19 GB peak VRAM. Experiments on World Roam Benchmark and extended interactive rollouts demonstrate competitive controllability and coherent long-horizon world evolution. Overall, the paper presents a novel approach to real-time, long-horizon, closed-loop interaction in virtual worlds, with potential applications in fields such as robotics, gaming, and simulation.

📅 Published on Jul 21

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.19191
• PDF: https://arxiv.org/pdf/2607.19191
• Project Page: https://abot-world.amap.com/

🤖 Models citing this paper:
• https://huggingface.co/acvlab/ABot-World-0-5B-LF

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/acvlab/abot-world-interactive

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📢 By: https://xn--r1a.website/PaperNexus

#VirtualWorldSimulation #InteractiveWorldModels #RealTimeWorldDynamics #ClosedLoopInteraction #ArtificialIntelligenceForGames