Coordinated, heterogeneous model composition under a shared governance and audit layer

Inventor(s)

Joseph JM Walker, Pair A Dimes, IncFollow

Abstract

This paper introduces the Modular Artificial Specialized Intelligence (MASI) framework, a distributed cognitive architecture designed to address the safety, transparency, and governability limitations of monolithic foundation models. MASI decouples reasoning into four canonical, independently operational roles—Wisdom, Foresight, Empathy, and Precision—coordinated through a standardized communication layer known as the MASI Bus. The framework enforces a Consult-Before-Execute (CBE) protocol, requiring multi-perspective verification, drift detection, and ethical arbitration prior to any system output or tool execution. This deliberative process is quantified via the MASI Trust Score ($T_{masi}$), a dynamic metric integrating fairness, logic, and uncertainty into a verifiable audit artifact. Additionally, the paper defines a decentralized governance structure (MASI DAO) and an economic incentive model (Modular Contribution Credits) to foster an interoperable, safety-aligned ecosystem. We present the technical specifications, schema definitions, and failure-containment logic required to implement MASI as a robust standard for institutional intelligence.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Walker, Joseph JM, "Coordinated, heterogeneous model composition under a shared governance and audit layer", Technical Disclosure Commons, (December 15, 2025)
https://www.tdcommons.org/dpubs_series/9043