Author

Daeshik Kim

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#machine learning Preprint Sep 2026

When Text Matters: Design Principles for Visual Token Pruning in Vision-Language Model

Visual token pruning has been widely studied as a practical approach to reducing the computational cost of large vision-language models. However, it struggles to preserve essential visual information, which can lead to substantial performance degradation. In particular, image-based token selection can overlook task-rel...

Min-Chan Kang, Kyeonghye Park, Seoyoung Cho et al. · 0 citations

#machine learning Preprint Sep 2026

Beyond Reconstruction Loss in Post-Training Quantization: Balanced Fitting for Large Vision-Language Models

Post-training quantization (PTQ) enables efficient deployment of large vision-language models (LVLMs), but is typically calibrated on a small set while expected to generalize across diverse downstream tasks. Although recent PTQ methods for LVLMs incorporate sensitivity signals, they still minimize reconstruction loss w...

Min-Chan Kang, Kyeonghye Park, Seungyeon Sa et al. · 0 citations