mlsanityData to 7 Oct 2026

AI Space Physics: Constitutive boundary semantics for open AI institutions

arXiv 2603.03119 ↗First posted 3 Mar 2026cs.AI cs.LOcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 9 of its 27 references that are in the map. First posted on arXiv 3 Mar 2026.

Builds on

9 of 27 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Semantic Laundering in AI Agent Architectures: Why Tool Boundaries Do Not Confer Epistemic Warrant13 Jan
  2. 2025Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition28 Jul
  3. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct

Other references · 6

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  2. 2019Optimal Policies Tend to Seek Powercited by 115
  3. 2019Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspectivecited by 107
  4. 2018Penalizing side effects using stepwise relative reachabilitycited by 38
  5. 2025Inference-Time Reward Hacking in Large Language Modelscited by 32
  6. 2026The Responsibility Vacuum: Organizational Failure in Scaled Agent Systemscited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Runtime Governance for AI Agents: Policies on Paths
  2. 2026One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI
  3. 2026Active Inference: A method for Phenotyping Agency in AI systems?
  4. 2026Mind The Gap: How The Technical Mechanism Of Agentic AI Outpace Global Legal Frameworks
  5. 2026Behavioral Governance for Autonomous AI Agents: The AgentBound Framework
  6. 2026Sovereign Agents: Towards Infrastructural Sovereignty and Diffused Accountability in Decentralized AI
  7. 2026A pragmatic approach to regulating AI agents
  8. 2026Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems