3 papers
cs.AI2026
CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
Trang Nguyen, Eulrang Cho, Bingqing Chen +1
Agents often work on complex problems that require millions of tokens of context, which necessitates compacting across sessions due to limited context windows. We develop CliffComp…
cs.LG2024
BoA: Attention-aware Post-training Quantization without Backpropagation
Junhan Kim, Ho-young Kim, Eulrang Cho +3
Post-training quantization (PTQ) is a promising solution for deploying large language models (LLMs) on resource-constrained devices. Early methods developed for small-scale network…
cs.LG2024
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
Junhan Kim, Chungman Lee, Eulrang Cho +4
With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such a…