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Long-WAM 提出了一种在实时控制约束下扩展因果世界-动作模型上下文的模型-系统框架，发现更长的视觉历史仅在视频基础模型以自回归方式预训练时才显著有效，在 RoboCasa GR-1 上将成功率从 63.3% 提升至 78.7%，并在 Unitree G1 实机上实现 95% 的动态杯子堆叠成功率。
WorldPlay2 is an interactive world model that integrates a factorized hybrid control interface with compressed memory and stable distillation to enable real-time responsiveness and long-horizon consistency in control tasks.
This paper introduces a method to split action generation in vision-language-action models into slow and fast layers, enabling real-time reactive robot control and improving success rates from 42% to 97% with just 10 minutes of online data.
πR^2 introduces a reactive real-time flow policy for robot manipulation that splits conditioning into fast and slow channels and uses a latency-adaptive flow schedule, enabling closed-loop replanning ~4x faster than baseline policies and improving success rates by up to 30%.