arXiv AI

Extremal Chowla sets and their linear analogues: A human-AI mathematical investigation using Co-Scientist

arXiv:2607. 24847v1 Announce Type: cross Abstract: We introduce an extremal invariant associated with Chowla-type order conditions in finite groups.

arXiv Machine Learning
Sep 4

What is Smoothness?

arXiv:2609. 03246v1 Announce Type: cross Abstract: Smoothness of a function on the real line is reflected in the decay of its Fourier transform, which suggests that smoothness of a function in $L^2(G)$ for a group $G$ should mean concentration of the Fourier coefficients at low frequency.

By Zachary P Bradshaw
arXiv Machine Learning
Sep 4

Relative Prime Factorization and Finite-State Presentations under Fixed Finite-Monoid Observation

The paper investigates exact factorization and canonical presentations of languages relative to a fixed finite‑monoid observation. It shows that unique factorization does not guarantee a finite relative presentation property (FRP) by presenting a 36‑element quotient with infinite valid prime‑return rules, and introduces the stronger finite‑state relative presentation property (FSRP). The authors further define prime‑target left‑division determinism (PTLD), prove its implications for factorization and rule bounds, and provide efficient learning algorithms for the canonical PTLD presentation and FSRP controller.

By Takayuki Kuriyama
arXiv Machine Learning
Jul 14

The VC dimension of partial concept classes via Radon's theorem

arXiv:2607. 10751v1 Announce Type: new Abstract: Following Alon, Hanneke, Holzman, and Moran (FOCS 2021), we define a partial concept class (PCC) as a family of partial functions \(f: V\to\{0,1,\ast\}\); equivalently, its concepts partition the ground set into black ($f^{-1}(1)$), grey ($f^{-1}(\ast)$), and white parts ($f^{-1}(0)$).

By Grigory Ivanov, Attila Jung, M\'arton Nasz\'odi