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,…
arXiv:2511.17628v3 Announce Type: replace Abstract: Precipitation nowcasting, which aims to provide high spatio-temporal resolution precipitation forecasts by leveraging current radar observations, is a core task in regional weather forecasting. Recently, the cascaded architecture has emerged as the mainstream paradigm for…
arXiv:2602.18572v1 Announce Type: new Abstract: Reliable real estate price indicators are typically published at city level and low frequency, limiting their use for neighborhood-scale monitoring and long-horizon planning. We study whether sub-city price indices can be forecasted at weekly frequency…
arXiv:2602.14322v2 Announce Type: replace Abstract: We investigate how formal temporal logic specifications can enhance the safety and robustness of reinforcement learning (RL) control in aerospace applications. Using the open source AeroBench F-16 simulation benchmark, we train a Proximal Policy Optimization…
