arXiv:2510.16609v2 Announce Type: replace
Abstract: Test-time augmentation, such as Retrieval-Augmented Generation (RAG) or tool use, critically depends on an interplay between a model’s parametric knowledge and externally retrieved information. Howev…
arXiv:2503.19091v3 Announce Type: replace-cross
Abstract: In this paper, we consider nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We propose a Trust-Region Stochastic Sequential Quadratic Programm…
arXiv:2604.01197v1 Announce Type: cross
Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study the problem of learning to generate mixed states on a fini…
arXiv:2512.04705v2 Announce Type: replace-cross
Abstract: Deployment of dynamic neural networks on edge accelerators requires careful consideration of hardware constraints beyond conventional complexity metrics such as Multiply-Accumulate operations. …
arXiv:2603.04689v2 Announce Type: replace-cross
Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn sign…
arXiv:2511.20888v2 Announce Type: replace-cross
Abstract: This paper argues that DNNs implement a computational Occam’s razor — finding the `simplest’ algorithm that fits the data — and that this could explain their incredible and wide-ranging succe…
arXiv:2603.24545v1 Announce Type: cross
Abstract: We study the problem of detecting local geometry in random graphs. We introduce a model $\mathcal{G}(n, p, d, k)$, where a hidden community of average size $k$ has edges drawn as a random geometric gra…