Other possible explanations
Formal assurance, treating AI as a tool, strict separation of permissions and mature deployment controls may substantially reduce the jump in risk from greater capability to loss of control in practice.
Professional inquiry into model capability, system assurance and authority to act in the world.
Author-authorized full-text publication. Downloads below are separately verified files; website summaries are not manuscript translations. Publication restrictions printed in older source manuscripts are superseded by the author’s release decision of 16 October 2026; the academic text itself is unchanged.
The available Chinese independent-publication edition is V1.3. The earlier registered Canonical version V1.1 remains a historical identity, not the version of this new PDF. The professional web explanation retains its own source version.
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All disciplines · Cross-disciplinary scrutiny
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Model capability → a system formed by configurations, tools, permissions and organizations → open-world evaluation and property-specific assurance → effective human control → real-world action chains → layered reversibility and recovery → lifecycle permissions, reduced intervention, retirement and low-technology backups.
Distinguish model and system as objects of assessment; examine open-world evaluation, limits of formal methods, the seven effective-control requirements and lifecycle engineering.
Technical properties can be established within their own scope. Rights, authorization, legitimacy, subjecthood and civilizational purposes require a new handover of responsibility.
Model scores, formal proofs and a human in the loop do not automatically establish system safety, effective control, institutional authorization or sovereignty over action.
Systems science, political and legal inquiry, epistemology, Human-COS/AGI-COS, and applied fields such as medicine and education must take up their own factual responsibilities.
Computation can extend civilization’s capacity to act. Capability, permissions, responsibility, exit and real-world repair must nevertheless be assessable together.
S = professional support; L = limit; I = interface; R = refutation or revision trigger. This is not a list of proofs of the theory. The table links to English node summaries. The individual English public node explanations are also available: M1 · M2 · M3 · M4 · M5 · M6 · M7 · M8.
| Node | Relation | Professional responsibility |
|---|---|---|
| M1 summary | I | Interface only; this page does not settle M1. |
| M2 summary | S | The AGI turning point first concerns changes in capability and action chains. |
| M3 summary | S/L | Speed, autonomy and high-consequence execution must be distinguished from real-world irreversibility. |
| M4 summary | L | Greater capability cannot automatically determine civilizational direction. |
| M5 summary | S | Permissions, effective control, reduced intervention, retirement and low-technology backups. |
| M6 summary | S/R | Evaluation, testing, verification and validation, reversibility, recovery and exit directly belong to validation. |
| M7 summary | S/L | Applied disciplines must demonstrate the consequences of technology entering everyday life. |
| M8 summary | L | Computation cannot determine civilization’s purposes or meaning. |
Academic work does not serve as an endorsement. Evidence can support, limit, refute or require the upstream theory to be rewritten.
Formal assurance, treating AI as a tool, strict separation of permissions and mature deployment controls may substantially reduce the jump in risk from greater capability to loss of control in practice.
If formal assurance and deployment controls reliably cover major failure modes across broad environments, strengthen the account of their safety contribution. If out-of-distribution, interaction or responsibility-chain failures recur, reduce it.
Model capability and assurance do not constitute real-world authorization. Action also requires the applicable jurisdiction, responsible actors, stopping conditions and audit boundaries.
Redesign the framework if assurance or control indicators fail to predict real consequences, or if new architectures change the capability-to-action chain.
A shared language, not a superior discipline.
Bring findings back into real organizations, institutions and chains of consequence.
State a direction openly without rewriting the evidence to fit it.
Bring evidence, a counterexample, a replication or a better path
| Field | Recorded source information |
|---|---|
| Current public-page status | SUBMISSION_STATUS_UNVERIFIED |
| Portal action | CURRENT_PORTAL_STATUS_UNVERIFIED |
| Web publication mode | AUTHOR_AUTHORIZED_FULLTEXT + HISTORICAL_PROVENANCE |
| Publication boundary | The source requires checking public history, first-publication, preprint and AI policies for each target journal. Do not newly publish submission-derived full text. |
| Evidence date | 27 August 2026 governance baseline |
| Confidence in publication-state evidence | MEDIUM |
| Source version | V1.1 |
| Exact source-basis record | DIRECT CURRENT · V1.1 |
These are the existing public records, not a new verification of submission status. View manuscript identity and full texts.
This page translates the existing public web explanation, not an unpublished manuscript. Its source is the Chinese public page in the saved main baseline 43ef8de. Linked works retain their own publication boundaries. Chinese source page.