arXiv:2602.20844v2 Announce Type: replace-cross
Abstract: Stochastic network models play a central role across a wide range of scientific disciplines, and questions of statistical inference arise naturally in this context. In this paper we investigate…
arXiv:2602.15091v2 Announce Type: replace
Abstract: Mixture-of-Experts (MoE) architectures decompose prediction tasks into specialized expert sub-networks selected by a gating mechanism. This letter adopts a communication-theoretic view of MoE gating,…
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 many algorithms suffering f…
arXiv:2505.16950v4 Announce Type: replace-cross
Abstract: Transformer LLMs have been shown to exhibit strong reasoning ability that scales with inference-time compute, most prominently through token-space “thinking” chains of thought. A growing line o…
arXiv:2406.00300v3 Announce Type: replace
Abstract: Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this …
arXiv:2603.23544v1 Announce Type: cross
Abstract: Peak-to-average power ratio (PAPR) remains a major limitation of multicarrier modulation schemes such as orthogonal frequency-division multiplexing (OFDM), reducing power amplifier efficiency and limit…
arXiv:2603.23561v1 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:2603.23787v1 Announce Type: cross
Abstract: Prediction of wireless channels and their statistics is a fundamental procedure for ensuring performance guarantees in wireless systems. Statistical radio maps powered by Gaussian processes (GPs) offer…