7 accepted papers
Anomaly detection platforms in real-world environments require continuous interaction between automated systems and domain experts, as anomalies evolve dynamically and their definitions vary across contexts. Therefore, an effective platform must collaborate with experts and incorporate their feedbac
Multi-agent debate has shown promise for improving the reasoning of large language models, yet recent theory suggests its benefits are highly regime-dependent. While interaction can amplify informative signals under corrective conditions, symmetric debate dynamics are neutral in expectation, often m…
The evaluation of large language models (LLMs) has predominantly relied on static datasets, which offer limited scalability and fail to capture the evolving reasoning capabilities of recent models. To overcome these limitations, we propose an agent-centric benchmarking paradigm that moves beyond sta…
In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points without semantic context, overlooking rich textual metadata such as feature descrip…
Tabular data, widely used across industries, remains underexplored in deep learning. Self-supervised learning (SSL) shows promise for pre-training deep neural networks (DNNs) on tabular data, but its potential is hindered by challenges in designing suitable augmentations. Unlike image and text data,…
Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evolves over time, leading to a "new normal problem", where the distribution of normality can be changed due to the distrib…
Time series forecasting has become a critical task due to its high practicality in real-world applications such as traffic, energy consumption, economics and finance, and disease analysis. Recent deep-learning-based approaches have shown remarkable success in time series forecasting. Nonetheless, du…