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arXiv:2603.23889v1 Announce Type: new Abstract: When safety is formulated as a limit of cumulative cost, safe reinforcement learning (RL) aims to learn policies that maximize …
arXiv:2603.23926v1 Announce Type: new Abstract: Online reinforcement learning in infinite-horizon Markov decision processes (MDPs) remains less theoretically and algorithmically developed than its episodic counterpart, with …
arXiv:2603.23961v1 Announce Type: new Abstract: Deep-sea cold seep stage assessment has traditionally relied on costly, high-risk manned submersible operations and visual surveys of macrofauna. Although …
arXiv:2603.23967v1 Announce Type: new Abstract: Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free …
arXiv:2603.23977v1 Announce Type: new Abstract: Deep learning architectures are fundamentally inspired by neuroscience, particularly the structure of the brain's sensory pathways, and have achieved remarkable …
arXiv:2603.23984v1 Announce Type: new Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, …
arXiv:2603.23985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning …
arXiv:2603.23987v1 Announce Type: new Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the …
arXiv:2603.23994v1 Announce Type: new Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. …
arXiv:2603.24002v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) for high-dimensional and high-order partial differential equations (PDEs) are primarily constrained by the $\mathcal{O}(d^k)$ spatial derivative …
arXiv:2603.24025v1 Announce Type: new Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It's common that only …
arXiv:2603.24033v1 Announce Type: new Abstract: Predict-and-search (PaS) methods have shown promise for accelerating mixed-integer linear programming (MILP) solving. However, existing approaches typically assume variable independence …
