LERA: Reinstating Judgment as a Structural Precondition for Execution in Automated Systems
Jing, Liu
TL;DR
The paper addresses the problem that as automated systems move toward direct execution, accountability hinges on a structural place for judgment within the architecture. It proposes LERA, a Judgment–Governance Architecture, consisting of a Judgment Formation Layer (LERA-J) and a Governance Gate (LERA-G), to render judgment a non-bypassable precondition for execution via a Judgment Root Node. The key contributions are the formal identification of the Judgment Root Node as a root-level governance condition, the separation of judgment from execution, and the enforcement of an irreducible execution boundary that preserves accountability even as systems gain autonomy. The approach flips the default from execution-permitted to execution-permitted only after governance, thereby constraining irreversible actions to be under explicit human governance and strengthening structural accountability in future AI systems.
Abstract
As automated systems increasingly transition from decision support to direct execution, the problem of accountability shifts from decision quality to execution legitimacy. While optimization, execution, and feedback mechanisms are extensively modeled in contemporary AI and control architectures, the structural role of judgment remains undefined. Judgment is typically introduced as an external intervention rather than a native precondition to execution. This work does not propose a new decision-making algorithm or safety heuristic, but identifies a missing structural role in contemporary AI and control architectures. This paper identifies this absence as a missing Judgment Root Node and proposes LERA (Judgment-Governance Architecture) , a structural framework that enforces judgment as a mandatory, non-bypassable prerequisite for execution. LERA is founded on two axioms: (1) execution is not a matter of system capability, but of structural permission, and (2) execution is not the chronological successor of judgment, but its structural consequence. Together, these axioms decouple execution legitimacy from computational capacity and bind it to judgment completion through a governance gate. LERA does not aim to optimize decisions or automate judgment. Instead, it institutionalizes judgment as a first-class architectural component, ensuring that execution authority remains accountable. By reinstating judgment at the execution boundary, LERA establishes a foundational architecture for judgment-governed automation.