This paper explores agentic 3D spatial understanding, i.e., MLLM agents performing 3D reasoning through tool use. Existing methods often misuse tools and exhibit biased tool preferences under 3D scenarios, leaving the agentic paradigm with only marginal gains over non-agentic strategies. We reveal that 3D spatial reasoning tasks are heterogeneous across scenes, while these agents apply a uniform tool-use strategy to all scenes rather than selecting tools according to the specific scene and task. To address this, we propose Skill-3D, a framework that learns self-evolving scene-aware skills. Specifically, Skill-3D identifies the task scene and records the agent's tool-use trajectory into a Scene Memory, where successful trajectories from similar scenes are aggregated and distilled into a reusable scene-aware skill, with failed ones attached to the skill as lessons. During training, once a similar scene recurs, the corresponding skill is injected to guide the agent, producing new trajectories whose successes and failures further refine the skill, forming a loop in which the memory and the skill library co-evolve. Experiments show that Skill-3D substantially improves tool utilization in 3D spatial reasoning (from 39% to 78% on VSI-Bench), driving the agent toward correct and sufficient tool use. For instance, it improves Gemini-3-Flash by 67% on MMSI-Bench. Furthermore, we conduct agentic post-training over skill-guided trajectories, which boosts Qwen3-VL-8B by 43% on VSI-Bench.

翻译：本文探索智能体三维空间理解，即多模态大语言模型代理通过工具使用进行三维推理。现有方法在三维场景下常误用工具并表现出偏颇的工具偏好，导致智能体范式相比非智能体策略仅有微弱提升。我们发现三维空间推理任务在不同场景间存在异构性，而现有代理对所有场景采用统一的工具使用策略，而非根据具体场景和任务选择工具。为此，我们提出Skill-3D框架，该框架学习自我演化的场景感知技能。具体而言，Skill-3D识别任务场景并将代理的工具使用轨迹记录至场景记忆库中，其中相似场景的成功轨迹被聚合蒸馏为可复用的场景感知技能，失败轨迹则作为经验教训附加于技能之上。训练时，一旦相似场景重现，相应技能即被注入以引导代理，产生新轨迹并利用其成功/失败经验进一步优化该技能，形成记忆库与技能库协同演化的闭环。实验表明，Skill-3D显著提升了三维空间推理中的工具利用率（VSI-Bench上从39%提升至78%），驱动代理实现正确且充分的工具使用。例如，该方法使Gemini-3-Flash在MMSI-Bench上的性能提升67%。此外，我们在技能引导轨迹上对代理进行后训练，使Qwen3-VL-8B在VSI-Bench上的性能提升43%。