3 papers
cs.CV2026
On the Efficacy of Self-Supervised Point Cloud Encoders for Efficient 3D Large Language Models
Yao Zheng, Tian Zhang
3D point cloud-language models (3D-LLMs) enable 3D understanding by pairing point cloud encoders with large language models, but existing methods rely on costly multi-modal encoder…
cs.AI2026
Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction
Yujia Tong, Yuxi Wang, Yunyang Wan +3
Model compression techniques such as quantization and pruning are widely used to reduce the deployment cost of large language models (LLMs), with existing evaluations focusing almo…
cs.LG2026
Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
Tian Zhang, Yujia Tong, Junhao Dong +3
The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearning in quantized models. However…