Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task-relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we derive a key requirement for scene graph pruning: preserving spatial relations that are most pertinent to the specific 3D-VL task. Guided by this insight, we propose the Conceptual-Adjacent Scene Graph Pruner (CAPruner). CAPruner integrates fuzzy semantic relevance with spatial proximity to estimate the importance of relations, enabling the selection of critical relations in a task-specific context. Moreover, to avoid costly relation-level annotations, CAPruner is trained by supervising the aggregated scores of each node's incident edges. Extensive experiments demonstrate that CAPruner effectively preserves relations essential for spatial reasoning, leading to substantial performance improvements of LLMs on 3D-VL tasks. Code is available at https://github.com/fz-zsl/CAPruner.

翻译：近年来，大语言模型（LLM）已被应用于3D视觉语言（3D-VL）任务，这类任务需要空间推理能力，以相对于锚点识别目标对象。场景图常被用于表示此类关系，但在完整图上进行推理会带来高昂的token成本和计算效率低下问题，因此亟需进行剪枝。现有剪枝方法主要依赖空间邻近性，且常移除了与任务相关的边关系，从而损害了可靠的空间推理能力。为解决这些局限，我们推导出场景图剪枝的关键需求：保留与特定3D-VL任务最相关的空间关系。基于此见解，我们提出概念临近场景图剪枝器（CAPruner）。CAPruner将模糊语义相关性与空间邻近性相结合来估计关系的重要性，从而能在特定任务上下文中选择关键关系。此外，为避免昂贵的关系级标注，CAPruner通过监督每个节点关联边的聚合得分进行训练。大量实验表明，CAPruner能有效保留对空间推理至关重要的关系，从而显著提升LLM在3D-VL任务上的性能。代码开源地址：https://github.com/fz-zsl/CAPruner。