让AI通过自我进化掌握空间推理技能，精准使用工具完成几何任务。
Self-Evolving Neuro-Symbolic Skills for Tool-Augmented Spatial Reasoning
- 将工具操作与几何计算抽象为可复用的原子指令，分两类技能组合使用。
- 在三个基准上准确率提升，工具调用更精确，失败轨迹被用来优化技能。
- 适合需要复杂空间推理和工具协同的任务，如机器人规划、视觉问答。
大型视觉语言模型在多模态推理中表现强劲，但在需要精细空间感知和几何计算的任务上仍不可靠。工具增强提供自然解决方案，但现有方法要么从零规划工具调用而无依赖约束，要么依赖固定流程，冗余且泛化能力差。有效的空间推理智能体应积累可复用经验并自适应组合。为此，我们提出NeSy-Spatial，一种神经符号框架，实现空间技能的自我演化。该框架将工具交互与几何操作抽象为带类型的可执行原子指令，并组合成两类互补技能：工具使用技能用于组织工具执行，几何技能用于结构化几何推理。推理时，系统闭环检索并执行相关技能；演化时，分析缓冲的成功与失败轨迹，优化技能结构并剔除不可靠或无效条目。在三个空间推理基准上的实验表明，NeSy-Spatial持续提升推理准确率，工具使用更精准。
原文摘要 · Abstract (English)
Large vision-language models have achieved strong performance in multimodal reasoning, but they remain unreliable on fine-grained spatial tasks that demand both precise spatial perception and fine-grained geometric computation beyond end-to-end generation. Tool augmentation offers a natural solution, while existing methods either plan tool calls from scratch without explicit dependency constraints or rely on fixed pipelines that are redundant and generalize poorly across spatial tasks. An effective spatial reasoning agent should instead accumulate reusable experience and adaptively compose it for new problems. To this end, we propose NeSy-Spatial, a neuro-symbolic framework for self-evolving spatial skills. NeSy-Spatial abstracts tool interactions and geometric operations into typed executable atomic instructions and composes them into two complementary skill types: Tool-Use Skills for organizing tool execution and Geometry Skills for structured geometric reasoning. During inference, NeSy-Spatial retrieves and executes relevant skills in a closed-loop process. During evolution, it analyzes buffered successful and failed trajectories to refine skill structures and prune unreliable or inactive entries. Experiments on three spatial reasoning benchmarks show that NeSy-Spatial consistently improves reasoning accuracy with more precise tool utilization.
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