9 papers · 1 filter
Not All Information Is Worth Asking For: Decision-Informed Information Seeking with LLMs
Seunghan Lee, Jun Seo, Junhyeok Kang +9
Large language models (LLMs) are increasingly used to support decisions under incomplete information, where deciding what to look up and when to stop matters as much as the final a…
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection
Seunghan Lee, Jun Seo, Jaehoon Lee +9
Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existing methods therefore either se…
EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting
Seunghan Lee, Sangjun Han, Jun Seo +9
Time series foundation models (TSFMs) are pretrained on series from diverse domains, where demand series make up only a small fraction. Demand data has properties that such corpora…
EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series
Seunghan Lee, Jaehoon Lee, Jun Seo +9
This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial forecasting. While recent TSFMs a…
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
Seunghan Lee, Jun Seo, Jaehoon Lee +9
Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., returning a numeric forecast). O…
When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
Hoyoung Lee, Suhwan Park, Seunghan Lee +15
Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial sourc…
AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, existing methods produce entity…
FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which exhibit unique characteristics…
FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing
Jaehoon Lee, Suhwan Park, Taeyoon Lim +9
The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and numerical information have gaine…