arXiv:2506.14787v2 Announce Type: replace Abstract: In modern industrial and logistics environments, the rapid expansion of fast delivery services has heightened the demand for storage systems that combine high efficiency with increased density. Multi-deep autonomous vehicle storage and retrieval systems (AVS/RS)…
arXiv:2509.10521v1 Announce Type: new Abstract: Personalized federated learning (PFL) often fails under label skew and non-stationarity because a single global parameterization ignores client-specific geometry. We introduce VGM$^2$ (Variational Gaussian Mixture Manifold), a geometry-centric PFL framework that (i) learns client-specific parametric…
arXiv:2502.01042v5 Announce Type: replace Abstract: Large language models (LLMs) exhibit exceptional capabilities across various tasks but also pose risks by generating harmful content. Existing safety mechanisms, while improving model safety, often lead to overly cautious behavior and fail to fully…
arXiv:2509.10520v1 Announce Type: new Abstract: In production systems, contextual bandit approaches often rely on direct reward models that take both action and context as input. However, these models can suffer from confounding, making it difficult to isolate the effect of…
arXiv:2509.12187v1 Announce Type: cross Abstract: Novel view synthesis (NVS) of in-the-wild garments is a challenging task due significant occlusions, complex human poses, and cloth deformations. Prior methods rely on synthetic 3D training data consisting of mostly unoccluded and static objects,…
arXiv:2509.10519v1 Announce Type: new Abstract: Approximate multipliers (AppMults) are widely used in deep learning accelerators to reduce their area, delay, and power consumption. However, AppMults introduce arithmetic errors into deep learning models, necessitating a retraining process to recover accuracy. A…
arXiv:2509.11824v1 Announce Type: cross Abstract: Online AI platforms for creating music from text prompts (AI music), such as Suno and Udio, are now being used by hundreds of thousands of users. Some AI music is appearing in advertising, and even…
arXiv:2509.10518v1 Announce Type: new Abstract: We introduce the Holographic Knowledge Manifold (HKM), a four-phase pipeline that achieves zero catastrophic forgetting in AI knowledge representation while maintaining minimal memory growth and high efficiency. Leveraging fractal quantization, probabilistic entanglement, and dynamic diffraction…
arXiv:2509.11511v1 Announce Type: cross Abstract: Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medical diagnosis and fraud detection. Traditional oversampling techniques, including SMOTE and its variants,…
arXiv:2509.10517v1 Announce Type: new Abstract: Machine learning models hold significant potential for predicting in-hospital mortality, yet data privacy constraints and the statistical heterogeneity of real-world clinical data often hamper their development. Federated Learning (FL) offers a privacy-preserving solution, but its…
