{"ID":6023687,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-09T03:01:22.582788088Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05463","arxiv_id":"2607.05463","title":"Governable Individuals: An Identity Layer for Embodied Agents That Keep Learning","abstract":"Embodied artificial intelligence is moving from deployable models to persistent agents that learn in the field, acquire skills and migrate across bodies. Governing such a system means governing an individual, not a model, and existing proposals (agent identifiers, activity logs, guardrails) do not survive an agent that keeps rewriting itself. We propose the governable individual: an agent whose competence may change without bound, but whose authority, memory schema, embodiment rights and capability roster can widen only through signed lifecycle transitions that update a public identity commitment. In our tests, neither learned judgement nor behavioural testing was sufficient to carry this on its own; the load-bearing layer must be architectural. We describe the abstraction, a runtime mechanism that realizes it, and the open problems in between.","short_abstract":"Embodied artificial intelligence is moving from deployable models to persistent agents that learn in the field, acquire skills and migrate across bodies. Governing such a system means governing an individual, not a model, and existing proposals (agent identifiers, activity logs, guardrails) do not survive an agent that...","url_abs":"https://arxiv.org/abs/2607.05463","url_pdf":"https://arxiv.org/pdf/2607.05463v1","authors":"[\"Xue Qin\",\"Simin Luan\",\"Cong Yang\",\"Zhijun Li\"]","published":"2026-07-06T03:04:27Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\"]","methods":"[]","has_code":false}