CoverPrune: Coverage-Driven Token Pruning for 3D VLMs via Optimal Transport

While 3D Vision-Language Models (3D VLMs) have demon-strated remarkable spatial reasoning capabilities, they suffer from mas-sive visual token counts that create severe computational bottlenecksduring inference. Existing token pruning methods primarily rely on di-versity-based selection, discarding similar tokens to maximize dispersion.However, in 3D environments, this approach frequently drops represen-tative prototype tokens in favor of outliers, breaking the multi-view con-sistencies and geometric structures essential for spatial reasoning. In thispaper, we propose a paradigm shift for 3D VLM token pruning: frommaximizing diversity to preserving visual evidence coverage. We intro-duce CoverPrune, a training-free framework that formulates inference-time token pruning as an Optimal Transport (OT) problem. To over-come the intractable combinatorial subset selection inherent in this for-mulation, we design the Feature-Spatial-Temporal (FST) transport costand target capacity, along with an efficient Spatial-Guided Greedy Selec-tion (SGS) algorithm to approximate the OT objective. Furthermore, wepropose CoverPrune-Lite, an accelerated variant utilizing spatially struc-tured local matching for minimal overhead. Extensive experiments acrossmultiple 3D visual-spatial reasoning benchmarks demonstrate that ourmethods achieve state-of-the-art token efficiency, maintaining robust rea-soning performance even under highly aggressive pruning budgets. Visitour project website at https://github.com/Brucess/CoverPrune.