arXiv:2410.05493v2 Announce Type: replace
Abstract: We study transformers’ in-context learning of variable-length Markov chains (VOMCs), focusing on the finite-sample accuracy as the number of in-context examples increases. Compared to fixed-order Mar…
arXiv:2603.26998v1 Announce Type: cross
Abstract: For status update systems operating over unreliable energy-constrained wireless channels, we address Weaver’s long-standing Level-C question: do my packets actually improve the plant’s behavior? Each f…
arXiv:2603.27074v1 Announce Type: cross
Abstract: Forecasting is usually framed as a problem of model choice. This paper starts earlier, asking how much predictive information is available at each horizon. Under logarithmic loss, the answer is exact: …
arXiv:2411.15702v5 Announce Type: replace-cross
Abstract: Interactive computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-oriented charact…
arXiv:2603.26197v1 Announce Type: cross
Abstract: Reliable transmission of 3D point clouds over wireless channels is challenging due to time-varying signal-to-noise ratio (SNR) and limited bandwidth. This paper introduces sensitivity-aware filtering a…
arXiv:2511.06494v2 Announce Type: replace-cross
Abstract: Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed number of expert…
arXiv:2504.05654v5 Announce Type: replace-cross
Abstract: By analogy to the terminology of curved exponential families in statistics, we define curved Bregman divergences as Bregman divergences restricted to non-affine parameter subspaces and sub-dime…
arXiv:2603.19562v3 Announce Type: replace
Abstract: Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with modality-specific patches. This study first reveals …
arXiv:2603.23561v2 Announce Type: cross
Abstract: The sample compression conjecture is: Each concept class of VC dimension d has a compression scheme of size d.In this paper, for any concept class of finite functions, we present a labeled sample compr…
arXiv:2603.24929v1 Announce Type: cross
Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deployment. However, traditional evaluation approaches provide limited insight into model confid…