arXiv:2512.22186v1 Announce Type: new Abstract: Tennis strategy optimization is a challenging sequential decision-making problem involving hierarchical scoring, stochastic outcomes, long-horizon credit assignment, physical fatigue, and adaptation to opponent skill. I present a reinforcement learning framework that integrates a custom tennis…
arXiv:2512.22189v1 Announce Type: new Abstract: The integration of physics-based knowledge with machine learning models is increasingly shaping the monitoring, diagnostics, and prognostics of electrical transformers. In this two-part series, the first paper introduced the foundations of Neural Networks (NNs) and…
arXiv:2512.22179v1 Announce Type: new Abstract: A fundamental limitation of supervised deep learning in high-dimensional tabular domains is “Generalization Collapse”: models learn precise decision boundaries for known distributions but fail catastrophically when facing Out-of-Distribution (OOD) data. We hypothesize that this failure…
arXiv:2512.22178v1 Announce Type: new Abstract: The growing demand for intelligent, adaptive resource management in next-generation wireless networks has underscored the importance of accurate and scalable wireless traffic prediction. While recent advancements in deep learning and foundation models such as large…
arXiv:2512.22170v1 Announce Type: new Abstract: Post-training alignment of video generation models with human preferences is a critical goal. Developing effective Reward Models (RMs) for this process faces significant methodological hurdles. Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer…
arXiv:2512.23626v1 Announce Type: cross Abstract: Most causal discovery methods recover a completed partially directed acyclic graph representing a Markov equivalence class from observational data. Recent work has extended these methods to federated settings to address data decentralization and privacy constraints,…
arXiv:2512.22200v1 Announce Type: new Abstract: The ruling method in modern Artificial Intelligence spanning from Deep Reinforcement Learning (DRL) to Large Language Models (LLMs) relies on a surge of static, externally defined reward functions. While this “extrinsic maximization” approach has rendered…
arXiv:2406.13376v2 Announce Type: replace Abstract: Supervised imitation-based approaches are often favored over off-policy reinforcement learning approaches for learning policies offline, since their straightforward optimization objective makes them computationally efficient and stable to train. However, their performance is fundamentally limited by…
arXiv:2512.22212v1 Announce Type: new Abstract: Since transformer was firstly published in 2017, several works have been proposed to optimize it. However, the major structure of transformer remains unchanged, ignoring one of its main intrinsic limitations, which is the same static…
arXiv:2508.12569v3 Announce Type: replace Abstract: Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional…
