arXiv Machine Learning By Takayuki Kuriyama

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Aug 14

Algebraic Decomposition Theory for Transformer Length Generalization

arXiv:2608. 13433v1 Announce Type: cross Abstract: Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterization of which tasks admit length generalization.

By Andy Yang, Blerta Veseli, Corentin Barloy, Micha\"el Cadilhac, Andreas Krebs, Charles Paperman, Howard Straubing, Michael Hahn