2 citations · 3 across the 14 of their papers we have counts for
9 papers · 1 filter
Not All Variables Agree: Reliability-Aware Variable-Wise Gradient Surgery for Multivariate Time-Series Forecasting
Jinwoo Park, Hyeongwon Kang, Pilsung Kang
In data-driven training, multivariate time-series forecasting is usually optimized with a scalar loss averaged over samples, variables, and horizons. This averaging is convenient,…
FinLifeBench: Exhaustive Life-Event History and Financial-State Reconstruction from Longitudinal Banking Dialogue
Hangyeul Lee, Juyoung Oh, Jaeyong Ko +5
Repeated banking interactions require assistants to maintain complete, current, and traceable customer records as life changes emerge incidentally in routine requests. Existing ben…
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Seung Hun Han, Hyeongwon Kang, Jinwoo Park +1
Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresol…
Cliff Tokens: Analyzing Failure Trigger Tokens in LLM Mathematical Reasoning
Jaeyong Ko, Pilsung Kang, Jinu Lee +1
Large language models reach high accuracy in mathematical reasoning, but individual traces on the same problem diverge; some arrive at the correct answer while others fail. Prior w…
DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection
Hun Im, Jungi Lee, Subeen Cha +1
In industrial environments, new product categories arrive sequentially, requiring continual anomaly detection without access to past data. Normalizing Flows (NFs) provide exact den…
Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
Hyeongwon Kang, Jeongseob Kim, Jinwoo Park +1
Time-series anomaly detection often returns scores or intervals, while analysts need to understand the abnormal behavior and the evidence supporting it. We introduce SAGE (Speciali…
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Hyeongwon Kang, Jinwoo Park, Seunghun Han +1
Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essenti…
COMET: Codebook-based Online-adaptive Multi-scale Embedding for Time-series Anomaly Detection
Jinwoo Park, Hyeongwon Kang, Seung Hun Han +1
Time series anomaly detection is a critical task across various industrial domains. However, capturing temporal dependencies and multivariate correlations within patch-level repres…
AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs
Jaehee Kim, Pilsung Kang
Modern LLMs are increasingly accessed via black-box APIs, requiring users to transmit sensitive prompts, outputs, and fine-tuning data to external providers, creating a critical pr…