Author

Lingting Zhu

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Preprint Aug 2026

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

CoverPrune is introduced, a training-free framework that formulates inference-time token pruning as an Optimal Transport (OT) problem, and the Feature-Spatial-Temporal (FST) transport cost and target capacity, along with an efficient Spatial-Guided Greedy Selection (SGS) algorithm to approximate the OT objective.

Peng Ling, Ying-Da Yin, Ling-Ting Zhu et al. · 0 citations

#artificial intelligence Jun 2026

Policy and World Modeling Co-Training for Language Agents

PaW is a Policy and World modeling co-training framework that adds auxiliary WM supervision to the same policy during RL, without changing the inference paradigm, and shows consistent improvements over strong RL baselines across models and RL algorithms.

Ning Lu, Baijiong Lin, Sheng-Cai Liu et al. · arXiv.org · 12 citations · ⚡2

Jul 2026

MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction

The key idea is to progressively densify anchored VecSet latents via hierarchical point-shuffle upsampling, increasing spatial capacity for fine-grained geometry modeling and replacing global cross-attention with AVS-Conv, a geometry-aware local aggregation operator operating within local neighborhoods rather than the...

Dehao Hao, Kaiyi Zhang, Tanghui Jia et al. · arXiv.org · 0 citations