Universal embodied intelligence demands robust generalization across heterogeneous embodiments, such as autonomous driving, robotics, and unmanned aerial vehicles (UAVs). However, existing embodied brain in training a unified model over diverse embodiments frequently triggers long-tail data, gradient interference, and catastrophic forgetting, making it notoriously difficult to balance universal generalization with domain-specific proficiency. In this report, we introduce ACE-Brain-0, a generalist foundation brain that unifies spatial reasoning, autonomous driving, and embodied manipulation within a single multimodal large language model~(MLLM). Our key insight is that spatial intelligence serves as a universal scaffold across diverse physical embodiments: although vehicles, robots, and UAVs differ drastically in morphology, they share a common need for modeling 3D mental space, making spatial cognition a natural, domain-agnostic foundation for cross-embodiment transfer. Building on this insight, we propose the Scaffold-Specialize-Reconcile~(SSR) paradigm, which first establishes a shared spatial foundation, then cultivates domain-specialized experts, and finally harmonizes them through data-free model merging. Furthermore, we adopt Group Relative Policy Optimization~(GRPO) to strengthen the model's comprehensive capability. Extensive experiments demonstrate that ACE-Brain-0 achieves competitive and even state-of-the-art performance across 24 spatial and embodiment-related benchmarks.

翻译：通用具身智能需要在自动驾驶、机器人学和无人机等异构具身体系中实现稳健的泛化。然而，现有方法在多样化具身体系上训练统一模型时，常引发长尾数据、梯度干扰与灾难性遗忘问题，导致在通用泛化与领域专精能力间取得平衡极为困难。本报告介绍了ACE-Brain-0——一个将空间推理、自动驾驶与具身操作统一于单一多模态大语言模型（MLLM）的通用基础大脑。我们的核心洞见在于：空间智能可作为跨异质物理具身的通用支架。尽管车辆、机器人和无人机在形态上差异显著，但它们共同需要建模三维心智空间，这使得空间认知成为跨具身迁移的自然且领域无关的基础。基于此洞见，我们提出“支架-专精-调和”（SSR）范式：首先建立共享空间基础，继而培养领域专精专家，最终通过无数据模型融合实现协同。此外，我们采用分组相对策略优化（GRPO）以增强模型的综合能力。大量实验表明，ACE-Brain-0在24个空间与具身相关基准测试中取得了具有竞争力乃至最先进的性能。