Axiomatic Convergence in Constraint-Governed Generative Systems: Definition, Hypothesis, Taxonomy, and Experimental Protocol
Published:Dec 29, 2025 09:12
•1 min read
•r/ArtificialInteligence
Analysis
This preprint introduces the Axiomatic Convergence Hypothesis (ACH), focusing on the observable convergence behavior of generative systems under fixed constraints. The paper's strength lies in its rigorous definition of "axiomatic convergence" and the provision of a replication-ready experimental protocol. By intentionally omitting proprietary details, the authors encourage independent validation across various models and tasks. The identification of falsifiable predictions, such as variance decay and threshold effects, enhances the scientific rigor. However, the lack of specific implementation details might make initial replication challenging for researchers unfamiliar with constraint-governed generative systems. The introduction of completeness indices (Ċ_cat, Ċ_mass, Ċ_abs) in version v1.2.1 further refines the constraint-regime formalism.
Key Takeaways
Reference
“The paper defines “axiomatic convergence” as a measurable reduction in inter-run and inter-model variability when generation is repeatedly performed under stable invariants and evaluation rules applied consistently across repeated trials.”