1 citations · 1 across the 3 of their papers we have counts for
5 papers
KVCMAS: Efficient KV cache Correction for Shared Context in Multi-Agent Systems
Hyesung Jeon, Hyeongju Ha, Seoyoung Lee +2
Prompt-specialized multi-agent systems enable multiple agents to share a model while performing complementary roles to solve complex tasks. However, agent-specific prefixes change…
PReCache: Efficient KV Cache Sharing for Multi-LoRA Agents via Low-Rank Precomputation and Neutral Reconstruction
Hyesung Jeon, Hyeongju Ha, Jae-Joon Kim
Multi-LoRA agent systems enable efficient role specialization by sharing a common backbone model. However, each agent repeatedly processes the growing shared trajectory and constru…
LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents
Hyesung Jeon, Hyeongju Ha, Jae-Joon Kim
Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightweight adapters. Despite sharing b…
QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
Hyesung Jeon, Seojune Lee, Beomseok Kang +2
The demand for efficient deployment of large language models (LLMs) has driven interest in quantization, which reduces inference cost, and parameter-efficient fine-tuning (PEFT), w…
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
Hyesung Jeon, Yulhwa Kim, Jae-joon Kim
Due to the high memory and computational costs associated with large language models (LLMs), model compression techniques such as quantization, which reduces inference costs, and p…