Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority
Autonomous agents are moving from sandboxed text generators to operators of code, data, and physical infrastructure, and they increasingly learn while deployed. This reopens a question that alignment techniques answer only probabilistically: after an agent has adapted in the field, is the running system still confined to what its operator authorised? Here we show that confinement can be guaranteed as an invariant of the agent's execution architecture rather than a probabilistic outcome of its tr
Record details
Published: 6 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (6 July 2026), “Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority,” evidence record 214, https://ethics.ai/record/214 (originally published by arXiv).
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