Axiomatic Convergence in Constraint-Governed Generative Systems: Definition, Hypothesis, Taxonomy, and Experimental Protocol
Published:Dec 29, 2025 09:14
•1 min read
•r/artificial
Analysis
This preprint introduces a significant hypothesis regarding the convergence behavior of generative systems under fixed constraints. The focus on observable phenomena and a replication-ready experimental protocol is commendable, promoting transparency and independent verification. By intentionally omitting proprietary implementation details, the authors encourage broad adoption and validation of the Axiomatic Convergence Hypothesis (ACH) across diverse models and tasks. The paper's contribution lies in its rigorous definition of axiomatic convergence, its taxonomy distinguishing output and structural convergence, and its provision of falsifiable predictions. The introduction of completeness indices further strengthens the formalism. This work has the potential to advance our understanding of generative AI systems and their behavior under controlled conditions.
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.”