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arXiv:2603.09032v1 Announce Type: new Abstract: Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict …
arXiv:2603.09036v1 Announce Type: new Abstract: LM-based agents excel when given high-level action APIs but struggle to ground language into low-level control. Prior work has LLMs …
arXiv:2603.09053v1 Announce Type: new Abstract: Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains …
arXiv:2603.09062v1 Announce Type: new Abstract: Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift …
arXiv:2603.09065v1 Announce Type: new Abstract: Decoding from large language models (LLMs) typically relies on fixed sampling hyperparameters (e.g., temperature, top-p), despite substantial variation in task …
arXiv:2603.09078v1 Announce Type: new Abstract: We introduce exclusive self attention (XSA), a simple modification of self attention (SA) that improves Transformer's sequence modeling performance. The …
arXiv:2603.09082v1 Announce Type: new Abstract: To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent …
arXiv:2603.09085v1 Announce Type: new Abstract: By capturing the prevailing sentiment and market mood, textual data has become increasingly vital for forecasting commodity prices, particularly in …
arXiv:2603.09090v1 Announce Type: new Abstract: In reinforcement learning environments with state-dependent action validity, action masking consistently outperforms penalty-based handling of invalid actions, yet existing theory …
arXiv:2603.09103v1 Announce Type: new Abstract: Batteries with silicon-graphite-based anodes, which offer higher energy density and improved charging performance, introduce pronounced voltage hysteresis, making state-of-charge (SoC) …
arXiv:2603.09117v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where …
arXiv:2603.09145v1 Announce Type: new Abstract: Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features …