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cs.AI2026
OmniDrop: Layer-wise Token Pruning for Omni-modal LLMs via Query-Guidance
Yeo Jeong Park, Hyemi Jang, Minseo Choi +3
Omni-modal large language models have demonstrated remarkable potential in holistic multimodal understanding; however, the token explosion caused by high-resolution audio and video…
cs.LG2026
LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
Jinwoo Ahn, Ingyu Seong, Akhil Kedia +4
Transformer-based large language models (LLMs) rely on key-value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves…
cs.LG2026
On the Importance of a Multi-Scale Calibration for Quantization
Seungwoo Son, Ingyu Seong, Junhan Kim +2
Post-training quantization (PTQ) is a cornerstone for efficiently deploying large language models (LLMs), where a small calibration set critically affects quantization performance.…