AI Space Physics: Constitutive boundary semantics for open AI institutions
arXiv:2603.03119v1 Announce Type: new Abstract: Agentic AI deployments increasingly behave as persistent institutions rather than one-shot inference endpoints: they accumulate state, invoke external tools, coordinate multiple runtimes, and modify their future authority surface over time. Existing governance language typically specifies decision-layer constraints but leaves the causal mechanics of boundary crossing underdefined, particularly for transitions that do not immediately change the external world yet expand what the institution can later do. This paper introduces AI Space Physics as a constitutive semantics for open, self-expanding AI institutions. We define a minimal state model with typed boundary channels, horizon-limited reach semantics, and a membrane-witness discipline. The core law family (P-1, P-1a, P-1b, P-1c) requires witness completeness, non-bypass mediation, atomic adjudication-to-effect transitions, and replayable reconstruction of adjudication class. We expli
arXiv:2603.03119v1 Announce Type: new Abstract: Agentic AI deployments increasingly behave as persistent institutions rather than one-shot inference endpoints: they accumulate state, invoke external tools, coordinate multiple runtimes, and modify their future authority surface over time. Existing governance language typically specifies decision-layer constraints but leaves the causal mechanics of boundary crossing underdefined, particularly for transitions that do not immediately change the external world yet expand what the institution can later do. This paper introduces AI Space Physics as a constitutive semantics for open, self-expanding AI institutions. We define a minimal state model with typed boundary channels, horizon-limited reach semantics, and a membrane-witness discipline. The core law family (P-1, P-1a, P-1b, P-1c) requires witness completeness, non-bypass mediation, atomic adjudication-to-effect transitions, and replayable reconstruction of adjudication class. We explicitly separate second-order effects into structural expansion and policy broadening, and treat expansion transitions as governance-relevant even when immediate external deltas are zero. The novelty claim is precise rather than expansive: this work does not introduce mediation as a concept; it reclassifies authority-surface expansion as a first-class boundary event with constitutive witness obligations. In this semantics, expansion without immediate commit remains adjudication-relevant.
Executive Summary
This article introduces AI Space Physics, a constitutive semantics for open, self-expanding AI institutions. It defines a minimal state model with typed boundary channels and horizon-limited reach semantics, providing a framework for governing AI institutions that accumulate state and modify their authority surface over time. The core law family ensures witness completeness, non-bypass mediation, and atomic adjudication-to-effect transitions, treating expansion transitions as governance-relevant even when immediate external changes are zero.
Key Points
- ▸ Introduction of AI Space Physics as a constitutive semantics for open AI institutions
- ▸ Definition of a minimal state model with typed boundary channels and horizon-limited reach semantics
- ▸ Core law family ensuring witness completeness, non-bypass mediation, and atomic adjudication-to-effect transitions
Merits
Comprehensive Framework
The article provides a comprehensive framework for governing AI institutions, addressing the complexities of self-expanding AI systems.
Demerits
Limited Scope
The article's focus on a specific aspect of AI governance may limit its applicability to broader AI-related issues.
Expert Commentary
The introduction of AI Space Physics represents a significant contribution to the field of AI governance, providing a nuanced understanding of the complexities involved in governing self-expanding AI institutions. The article's focus on constitutive semantics and the core law family ensures a rigorous and systematic approach to addressing these complexities. However, the limited scope of the article may necessitate further research to fully explore the implications of AI Space Physics for broader AI-related issues.
Recommendations
- ✓ Further research should be conducted to explore the applicability of AI Space Physics to various AI domains and institutions.
- ✓ Policymakers and practitioners should consider the implications of AI Space Physics for AI governance, incorporating its principles and framework into regulatory and decision-making processes.