FG as a Learnable Interface Reproduction, Interpretation, and Knowledge Transmission in the AI Age

Fiscal Geometry (FG) is presented here as a learnable interface for institutional knowledge: a representational layer that can be reproduced, interpreted, and transmitted across people, teams, and AI systems without relying on narrative explanation alone. The paper frames FG as an X–Y coordinate grammar that renders rule-based environments as observable structures—routing, gating, visibility, and change—so that complex institutional reasoning can be handed over, audited, compared, and re-used.

The central concern is knowledge transmission in the AI age: how expert judgment, regulatory logic, and workflow constraints can be encoded into forms that remain stable under delegation, scale, and model mediation. FG is treated as an intermediate representation between domain texts (policies, statutes, manuals, filings) and operational outputs (decisions, compliance actions, audits), enabling reproducible interpretation rather than one-off explanation. The paper outlines how FG supports two complementary uses: immediate structural visibility for decision-makers, and machine-ingestible structure for AI labs to build downstream computation, retrieval, and validation pipelines.

This document is intended as a methodological contribution to institutional analysis and applied AI: it focuses on reproducibility, interpretability, and structured handover—how complex rule-governed systems can be represented in a form that is both human-legible and model-readable.