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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Fragmented Intention in Autonomous Agent Ecosystems: Structural Limits of Model-Level Safety and a Coordination-Layer Hypothesis

Authors: Bartels, Heiko Alexander;

Fragmented Intention in Autonomous Agent Ecosystems: Structural Limits of Model-Level Safety and a Coordination-Layer Hypothesis

Abstract

This paper introduces fragmented intention - a structural condition in distributed AI agent ecosystems where no single model invocation contains sufficient information to infer the global objective of a coordinated action sequence. We prove that under such conditions, model-level safety mechanisms become structurally insufficient (Detection Collapse Theorem), and propose the Autonomous Systems Coordination Layer (ASCL) as a coordination framework binding persistent identity, behavioral trust, economic accountability, and capability-scoped interaction. Includes formal proofs, Monte Carlo simulation (10,000 trials), and a normative specification.

Keywords

Machine Learning, Machine Learning/ethics, distributed systems, Artificial intelligence, Game Theory, coordination layer, Cryptography, autonomous agents, multi agent systems, Machine Learning/standards, Computer Security, Machine Learning/economics, fragmented intention

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average