arXiv:2512.18275v1 Announce Type: new Abstract: Federated Learning (FL) methods are often designed for specific client participation patterns, limiting their applicability in practical deployments. We introduce the FedSUM family of algorithms, which supports arbitrary client participation without additional assumptions on data…
arXiv:2506.16289v2 Announce Type: replace-cross Abstract: This paper explores the relationship between the condition number of a neural network’s weight tensor and the extent of information encoded by the associated processing unit, viewed through the lens of information theory. It argues…
arXiv:2512.18295v1 Announce Type: new Abstract: Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge. Existing methods particularly those based on experience replay typically store…
arXiv:2510.17638v2 Announce Type: replace-cross Abstract: Forecasting is not only a fundamental intellectual pursuit but also is of significant importance to societal systems such as finance and economics. With the rapid advances of large language models (LLMs) trained on Internet-scale data,…
arXiv:2512.18309v1 Announce Type: new Abstract: We introduce Embedded Safety-Aligned Intelligence (ESAI), a theoretical framework for multi-agent reinforcement learning that embeds alignment constraints directly into agents internal representations using differentiable internal alignment embeddings. Unlike external reward shaping or post-hoc safety constraints,…
arXiv:2512.13634v2 Announce Type: replace-cross Abstract: We consider statistical tasks in high dimensions whose loss depends on the data only through its projection into a fixed-dimensional subspace spanned by the parameter vectors and certain ground truth vectors. This includes classifying mixture…
arXiv:2512.18317v1 Announce Type: new Abstract: This paper presents a trustworthy reinforcement learning approach for the control of industrial compressed air systems. We develop a framework that enables safe and energy-efficient operation under realistic boundary conditions and introduce a multi-level explainability…
arXiv:2512.19004v1 Announce Type: cross Abstract: Diffusion Large Language Models (DLLMs) enable fully parallel token decoding but often remain impractical at inference time due to the many denoising iterations required to refine an information-free, fully masked initialization into coherent text. Most…
A new method could enable users to design portable medical devices, like a splint, that can be rapidly converted from flat panels to a 3D object without any tools.
arXiv:2512.19190v1 Announce Type: cross Abstract: Walking has always been a primary mode of transportation and is recognized as an essential activity for maintaining good health. Despite the need for safe walking conditions in urban environments, sidewalks are frequently obstructed by…
