arXiv:2602.18486v1 Announce Type: new Abstract: Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better modeled…
arXiv:2602.18473v1 Announce Type: new Abstract: Accurate analysis of medical time series (MedTS) data, such as electroencephalography (EEG) and electrocardiography (ECG), plays a pivotal role in healthcare applications, including the diagnosis of brain and heart diseases. MedTS data typically exhibit two…
arXiv:2602.19816v1 Announce Type: cross Abstract: Long-context modeling is essential for symbolic music generation, since motif repetition and developmental variation can span thousands of musical events. However, practical composition and performance workflows frequently rely on resource-limited devices (e.g., electronic instruments and…
arXiv:2602.18521v1 Announce Type: new Abstract: Continuous stress forecasting could potentially contribute to lifestyle interventions. This paper presents a novel, explainable, and individualized approach for stress prediction using physiological data from consumer-grade smartwatches. We develop a time series forecasting model that…
arXiv:2602.20117v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising approach for training reasoning language models (RLMs) by leveraging supervision from verifiers. Although verifier implementation is easier than solution annotation for many tasks, existing…
arXiv:2602.18523v1 Announce Type: new Abstract: Grokking — the abrupt transition from memorization to generalization long after near-zero training loss — has been studied mainly in single-task settings. We extend geometric analysis to multi-task modular arithmetic, training shared-trunk Transformers on dual-task…
arXiv:2505.05295v2 Announce Type: replace Abstract: Model monitoring is a critical component of the machine learning lifecycle, safeguarding against undetected drops in the model’s performance after deployment. Traditionally, performance monitoring has required access to ground truth labels, which are not always…
arXiv:2602.18528v1 Announce Type: new Abstract: Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to…
arXiv:2509.22295v5 Announce Type: replace Abstract: Cross-domain generalization is very important in Time Series Forecasting because similar historical information may lead to distinct future trends due to the domain-specific characteristics. Recent works focus on building unimodal time series foundation models and…
arXiv:2602.18531v1 Announce Type: new Abstract: The energy management problem in the context of smart grids is inherently complex due to the interdependencies among diverse system components. Although Reinforcement Learning (RL) has been proposed for solving Optimal Power Flow (OPF) problems,…
