15 papers
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…
XForecast: The KDD 2026 Finance Day Challenge on News-Grounded Financial Forecasting
Jaehoon Lee, Jun Seo, Seunghan Lee +10
Financial forecasting has traditionally relied on numerical time-series (TS) signals, while textual information such as news remains largely underutilized. We introduce XForecast,…
Explaining Time Series Forecasting with Horizon-Resolved Attribution
Seunghan Lee, Jun Seo, Jaehoon Lee +9
Recent advances in explaining time series (TS) models have produced methods that identify which past values a prediction depends on. However, most existing methods return a single…
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…
FinVerse: Toward More Realistic Evaluation for Financial Time-Series Forecasting
Jaehoon Lee, Jun Seo, Seunghan Lee +17
Time-series foundation models (TSFMs) are increasingly evaluated on broad benchmarks that apply uniform conventional forecast evaluation metrics across heterogeneous time-series. H…
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…
Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting
Seunghan Lee, Jaehoon Lee, Jun Seo +9
The direction of change --- whether a series will move up or down --- is often as important as its exact value in decisiondriven applications such as risk management and financial…
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…
Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing metho…
Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
Seunghan Lee, Jaehoon Lee, Jun Seo +7
Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches overlook input-level relevance…
ReSpec: Relevance and Specificity Grounded Online Filtering for Learning on Video-Text Data Streams
Chris Dongjoo Kim, Jihwan Moon, Sangwoo Moon +7
The rapid growth of video-text data presents challenges in storage and computation during training. Online learning, which processes streaming data in real-time, offers a promising…
MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations
Kyungho Bae, Jinhyung Kim, Sihaeng Lee +3
In this work, we tackle action-scene hallucination in Video Large Language Models (Video-LLMs), where models incorrectly predict actions based on the scene context or scenes based…
See It All: Contextualized Late Aggregation for 3D Dense Captioning
Minjung Kim, Hyung Suk Lim, Seung Hwan Kim +3
3D dense captioning is a task to localize objects in a 3D scene and generate descriptive sentences for each object. Recent approaches in 3D dense captioning have adopted transforme…
Bi-directional Contextual Attention for 3D Dense Captioning
Minjung Kim, Hyung Suk Lim, Soonyoung Lee +2
3D dense captioning is a task involving the localization of objects and the generation of descriptions for each object in a 3D scene. Recent approaches have attempted to incorporat…