
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.
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
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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