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arXiv AI By Genliang Zhu, Chu Wang

Authorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback

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The paper introduces "authorization succession," a framework that preserves authority across self‑modifying AI agent populations that can replace, fork, or roll back. It defines a protocol binding each generation to a manifest, root, unique parent, lineage, and population sequence, and establishes invariants that control root‑lifetime consumption and population exposure. The authors prove properties such as population‑safe succession, fork conservation, and rollback non‑reminting, and validate the approach with an executable evaluation covering 32 decisions and external adapters for two mutation systems.

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