arXiv:2603.24475v1 Announce Type: new
Abstract: Accurate forecasting of state-of-health (SOH) is essential for ensuring safe and reliable operation of lithium-ion cells. However, existing models calibrated on laboratory tests at specific conditions of…
arXiv:2603.24567v1 Announce Type: cross
Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions. In this work, we propose a …
arXiv:2603.24440v1 Announce Type: new
Abstract: Computer-use agents (CUAs) hold great promise for automating complex desktop workflows, yet progress toward general-purpose agents is bottlenecked by the scarcity of continuous, high-quality human demons…
arXiv:2603.24477v1 Announce Type: cross
Abstract: Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability to efficiently solve …
arXiv:2603.24431v1 Announce Type: new
Abstract: Parametric roll is a rare but high-consequence instability that can trigger abrupt regime changes in ship response, including pronounced shifts in roll statistics and tail risk. This paper develops a dat…
arXiv:2603.24436v1 Announce Type: cross
Abstract: Enes The proposed architecture is a mixture of experts, which allows for the model entities, such as the causal relationships, to be further parameterized. More specifically, an attempt is made to expl…
arXiv:2603.24428v1 Announce Type: new
Abstract: Accurate subseasonal weather forecasting remains a major challenge due to the inherently chaotic nature of the atmosphere, which limits the predictive skill of conventional models beyond the mid-range ho…
arXiv:2603.24400v1 Announce Type: cross
Abstract: We propose a neural network model for contextual regression in which the regression model depends on contextual features that determine the active submodel and an algorithm to fit the model. The propos…
arXiv:2603.24384v1 Announce Type: new
Abstract: The theory of Local Intrinsic Dimensionality (LID) has become a valuable tool for characterizing local complexity within and across data manifolds, supporting a range of data mining and machine learning …
arXiv:2603.24392v1 Announce Type: cross
Abstract: Privacy and algorithmic fairness have become two central issues in modern machine learning. Although each has separately emerged as a rapidly growing research area, their joint effect remains comparati…