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 relationlevel 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.

Recent advances in large video-language models have revolutionized video understanding tasks.However, their efficiency is greatly constrained by processing high volumes of visual tokens.Existing token compression strategies apply a fixed compression ratio, ignoring varying semantic density across video clips.Consequently, this leads to inadequate representation of information-rich clips due to insufficient tokens and unnecessary computation on static or content-poor ones.To address this, we propose LangDC, a Language-aware Dynamic Token Compressor.LangDC leverages a lightweight language model to describe video clips, converting them into soft caption tokens as visual representations.Trained with our proposed semantic density-aware supervision, LangDC aims to 1) cover key visual cues necessary for downstream task reasoning and 2) dynamically adjust compression ratios based on scene richness, reflected by description length.Our design mimics how humans dynamically express what they see: complex scenes (seeing more) elicit more detailed language to convey nuances (saying more), whereas simpler scenes are described with fewer words.Experimental results show that our method reduces FLOPs by 49% compared to VideoGPT+ while maintaining competitive performance.Furthermore, qualitative results demonstrate our approach adaptively adjusts the token compression ratio based on video segment richness.Codes are available at https://github.com/NIneeeeeem/LangDC.