Lei Feng Net Publishes Final Three Layers of Cross-Domain Migration Governance Code

The fifth article, Cross-Domain Top-Level Regulation Layer: Cross-Domain Audit Rules for Regulators Themselves, says global AI governance has a blind spot: it constrains business AI but not regulatory AI, and regulates industry models but does not audit governance models. According to the article, regulatory tools carry negative migration risks, and old rules can trap new technologies and paradigms. It applies the code's NT1-NT4 negative migration categories to regulators, citing NT2 semantic drift when old industry rules are forced into AGI scenarios and NT4 power smuggling when industry governance paradigms or leading institutions' routines become national standard-drafting paradigms.

The fifth layer sets a mandatory process: all regulatory tools, governance models and institutional rules must first pass a knowledge lineage validator, then a copper-line interface simulation tester, and finally a recursive fraud-detection audit engine before they can become formal policy text. It also proposes a closed-loop meta-regulation framework covering cross-domain filing of knowledge packages, mandatory accident reporting, risk-graded handling, joint enforcement coordination, high-risk circuit breaking, version lineage tracing and rule iteration.

The article further describes a judicial grading system that divides illegal migration into four levels: warning and rectification, forced withdrawal, industry ban, and criminal accountability. It also includes civil liability boundaries, criminal liability determination and cross-border risk accountability, and introduces a sovereign AI knowledge border theory that distinguishes domestic compliant migration, cross-border risk migration and international paradigm mutual recognition. To prevent regulatory capture, the framework calls for isolation of industry interests from rule-making, audits of industry self-governance loopholes, iteration for lagging rules, whistleblower protection and a transparent audit baseline.

The sixth article, Civilization-Level Migration Risk Control Layer: Long-Cycle Irreversible Civilization Disturbance Identification, extends the framework from national meta-regulation to civilization survival. It says current risk-control systems focus on short-term, local and reversible technical or economic losses and ignore slow, diffuse, intergenerational and potentially irreversible disturbances from AI cross-domain migration. The article identifies three transmission paths: NT2 semantic cross-generational drift, in which AI-generated narrative paradigms slowly rewrite basic cognitive definitions and shift consensus over generations; NT4 paradigm power smuggling, in which a technical governance paradigm from one field enters education, culture or ethics without verification; and NT1 structural mismatch, in which silicon-based high-speed iteration is forced onto the slow evolution of carbon-based civilization, creating a time-scale mismatch that increases entropy.

The sixth layer proposes a set of civilization steady-state indicators, including basic consensus retention rate, intergenerational cognitive deviation increment, social narrative diversity, civilization self-repair response time, and the ratio between silicon-based iteration cycles and carbon-based cultural evolution cycles. It sets three disturbance thresholds: Level 1 local short-term fluctuation that the system can repair itself; Level 2 regional cognitive shift requiring active intervention; and Level 3 cross-generational irreversible damage that permanently lowers civilization steady state. The article says large-scale AI models intended for long-term public deployment must add civilization disturbance pre-simulation and produce century-scale consensus drift projections. A Level 2 warning would trigger restricted release, and Level 3 would trigger a direct circuit breaker. It also states that silicon should handle high-speed computing, simulation and optimization, while carbon-based entities hold value definition, civilization goals and final adjudication, and it prohibits unlimited cross-domain intrusion by silicon paradigms into the bottom value domain of carbon-based civilization.

The seventh article, Code Final Lock Layer: Closed-Loop Verification of System Self-Consistency and Paradigm Boundaries, closes the seven-layer chain. It says most theoretical systems explain the world outward but lack a rigid mechanism for self-audit; if a system itself undergoes NT negative migration, it can become self-reinforcing, reject counterexamples and lock thought. The article states that any governance system carries NT1-NT4 risks and must include a falsification loop aimed at itself.

The seventh layer establishes three mechanisms. The first is system self-consistency verification, which traces back from the seventh layer to the first, compares input-output definitions and checks for logical gaps, semantic drift or paradigm smuggling; any mismatch triggers a system warning and version iteration. The second is an open falsification clause: anyone who constructs a valid counterexample showing that the NT1-NT4 framework fails or that the seven-layer chain has an irreparable logical loophole can submit material and start a baseline review. Valid counterexamples must be reproducible, traceable and point to a specific NT category. The third is paradigm boundary locking, which defines the code's applicable and invalid domains. The article says the framework is for risk audits of cross-domain migration in artificially constructed information systems and should not be generalized to all natural or biological evolution; beyond that boundary, the framework automatically becomes invalid.

The report says the full code text, SVA assertions, simulation cases and audit lists are archived with hash baselines, with permanent version lineage and change tracing to prevent hidden paradigm smuggling. Storage nodes are fixed at Jinri Toutiao, Douyin and GitHub for three-way cross-verification against single-point tampering. Iteration is allowed, but the process must be fully auditable; new versions cannot directly erase old versions and must be cross-checked against them for introduced NT negative migration. The articles also repeat a silicon-based constraint: the code is described as a carbon-based original system, and silicon models may only perform logic verification, truth matching and rule checking, not replace original interpretation, generate independent conclusions or overstep system definitions. Final judgment is described as carbon-based awareness source judgment plus silicon-based rule objective verification, forming a two-way mutual-check truth.