arXiv Machine Learning

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.

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
arXiv Machine Learning
Aug 12

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference

arXiv:2608. 10288v1 Announce Type: new Abstract: The Large Language Model from Power Law Decoder Representations (PLDR-LLM) and its attention, Power Law Graph Attention (PLGA), replace the fixed bilinear form of scaled dot-product attention (SDPA) with a learned, input-generated bilinear operator $G_{LM}$, built from a positive tensor $A_{LM}$ by elementwise power laws.

By Burc Gokden
arXiv AI
Jun 16

The Faithfulness Gap: Certifying Semantic Equivalence Between Natural-Language and Formal Mathematical Statements

arXiv:2606. 16541v1 Announce Type: new Abstract: Autoformalization, translating natural-language mathematics into formal proof assistants, is bottlenecked not by translation fluency but by \emph{faithfulness}: a formal statement can typecheck and be provable, yet still encode a different theorem than the source intended.

By Noor Islam S. Mohammad, Tamim Sheikh