The ACR Model for Governed Intent–Effect State Transitions

Modern computational systems increasingly operate in environments where it is operationally important to distinguish between what a system intended to do, what actions it attempted, what it observed from the environment, and what state it ultimately committed. Conventional execution models often collapse these stages into a single opaque transition, making it difficult to guarantee governance compliance, reconstruct authoritative system state, or explain execution after the fact. This paper introduces ACR Runtime (Autonomous Control Runtime), a governance-committed runtime semantics for systems operating in partially observable and externally governed environments. ACR Runtime defines a runtime model in which each execution step records intent, issued effects, observed evidence, and interpreted outcome in a transition record. These records contain sufficient information to deterministically reconstruct authoritative operational state through replay. Execution is structured into three explicitly governed runtime stages: intent admission, effect execution, and effect interpretation, ensuring that externally visible actions are mediated by governance both before and after interaction with the environment. Committed history therefore forms a transition-sufficient semantic trace. Rather than relying on latent environment state or re-executing runtime procedures, replay reconstructs authoritative operational state from governed transition records whose interpretation is versioned and constitution-constrained. The model thereby achieves deterministic authoritative state evolution under externally nondeterministic interaction. The purpose of this paper is not to provide a complete theory of distributed or autonomous systems. Instead, it defines a minimal root formalism and stable terminology for governance-constrained runtimes interacting with open environments. By publishing the architectural core of the model, the work establishes a durable and citable specification that can serve as a reference point for future implementations and extensions.