Independent Convergence: The POLIFONS Method and the Cross-Disciplinary Discovery of an Accountable Human–Machine Collaboration Structure

This note maps an independent convergence between POLIFONS — a method within AI-Based Jurimetrics, born from the demands of legal practice and professional accountability — and findings that emerged separately across three areas of artificial-intelligence engineering: natural language processing, compiler optimization, and agent engineering. Without mutual reference, these areas arrive at one shared structure: the machine proposes, an external authority verifies, and a human judges; generation becomes cheap while judgment becomes the scarce resource. POLIFONS instantiates this structure for law, contributing three elements the others lack: nexus verification (norm–fact subsumption), a verification gate anchored to primary legal sources, and a non-delegable professional accountability. This note does not claim POLIFONS as an empirically validated method; it asserts two verifiable facts: the dated priority of its formulation, and its distinctive origin in the discipline of law. Canonical method referenced: DOI 10.5281/zenodo.20838632.