3 papers
cs.LG2025
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
Dongchan Cho, Jiho Han, Keumyeong Kang +3
Real-world multivariate time series anomalies are rare and often unlabeled. Additionally, prevailing methods rely on increasingly complex architectures tuned to benchmarks, detecti…
cs.DB2025
Rethinking Caching for LLM Serving Systems: Beyond Traditional Heuristics
Jungwoo Kim, Minsang Kim, Jaeheon Lee +6
Serving Large Language Models (LLMs) at scale requires meeting strict Service Level Objectives (SLOs) under severe computational and memory constraints. Nevertheless, traditional c…
cs.CL2025
SeDi-Instruct: Enhancing Alignment of Language Models through Self-Directed Instruction Generation
Jungwoo Kim, Minsang Kim, Sungjin Lee
The rapid evolution of Large Language Models (LLMs) has enabled the industry to develop various AI-based services. Instruction tuning is considered essential in adapting foundation…