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  "text": "MolmoAct: Action Reasoning Models that can Reason in Space\n\n\"Reasoning is central to purposeful action, yet most robotic foundation  models map perception and instructions directly to control, which limits  adaptability, generalization, and semantic grounding. We introduce  Action Reasoning Models (ARMs), a class of vision-language-action models  that integrate perception, planning, and control through a structured  three-stage pipeline. Our model, MolmoAct, encodes observations and  instructions into depth-aware perception tokens, generates mid-level  spatial plans as editable trajectory traces, and predicts precise  low-level actions, enabling explainable and steerable behavior.  MolmoAct-7B-D achieves strong performance across simulation and  real-world settings: 70.5% zero-shot accuracy on SimplerEnv Visual  Matching tasks, surpassing closed-source Pi-0 and GR00T N1\"",
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