ErisML: A Unified Modeling Language for Pervasive AI Governance

Foundation models are migrating from data centers to pervasive environments---homes, hospitals, factories, and cities---where they encounter heterogeneous sensors, conflicting objectives, ambiguous regulations, and multi-agent dynamics. Current approaches fragment goals, code, norms, and planning into separate representations and reduce moral evaluation to scalar scores, discarding the multi-dimensional structure that principled ethical reasoning requires. This paper presents ErisML, a unified modeling language and runtime library (v3.0, open-source on PyPI) integrating environment dynamics, agency, multi-objective intents, normative structures, and strategic interaction. At its core, ErisML replaces scalar moral scores with a nine-dimensional whose axes correspond to distinct ethical dimensions---consequences, rights, fairness, autonomy, privacy, societal impact, virtue, legitimacy, and epistemic quality. The DEME~2.0 (Democratically-governed Ethical Modules) subsystem provides a three-layer enforcement architecture (Reflex, Tactical, Strategic) with tiered Ethics Modules, Pareto-optimal reasoning, and cryptographic audit trails. The mathematical foundations draw on the Geometric Ethics framework: moral evaluation as tensorial objects on a nine-dimensional moral manifold, gauge invariance under the D_4 dihedral group encoding Hohfeldian normative symmetries, and a Noether-style conservation law ensuring harm cannot appear or disappear through re-description. The Bond Invariance Principle (BIP) provides a falsifiable coherence criterion, empirically validated across 20,030 advice-column letters and 109,294 cross-lingual passages spanning 3,000 years and 11 languages, with a preliminary, unpublished analysis reporting all nine dimensions are linearly decodable and cross-lingually transferable (the cross-lingual transfer being carried by the multilingual LaBSE encoder's language-agnostic representation); this analysis is an unpublished single-author probe study and does not constitute peer-reviewed independent verification. ErisML compiles to planners (PDDL), verifiers (PRISM), and reinforcement-learning backends, and enforces norm-gated action selection at sub-microsecond latency on embedded hardware. Author preprint deposited for archival and citation. Draft — pending author review.