A multi-layer nested general artificial intelligence ethical alignment method and system

Abstract

The application discloses a kind of multi-layer nested general artificial intelligence ethics alignment method and system, belong to artificial intelligence security technical field.The method constructs by rigid check layer and flexible optimization layer alternately nested N layer decision state machine, establishes the priority arbitration mechanism of " outer rigid layer denies inner flexible layer ", And embed it in AGI full reasoning link.Through rigid and flexible hierarchical architecture, the core defects of the existing ethics alignment scheme, such as rule conflict without arbitration, only behavior alignment without endogenous motivation, easy to appear target drift, are solved, and the full-link ethical constraints from bottom value to global target are realized.The parameters of five-layer optimization architecture comply with the law of geometric progression, have self-similar expansion capability, and can widely adapt to the safety management and control needs of various general artificial intelligence systems.The hierarchical architecture of the present scheme is logically isomorphic with the square and circle twin celestial origin graph, and has self-similar expandable characteristics.The N layer decision state machine satisfies N≥5 and N is odd, and the outermost layer is flexible optimization layer.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence safety and ethics alignment technology, and in particular to a multi-layered nested general artificial intelligence (AGI) ethics alignment method and system. Background Technology

[0002] With the rapid evolution of general artificial intelligence (AGI) technology, the autonomous decision-making and goal self-optimization capabilities of AI systems have continuously improved. Ethical alignment issues have escalated from content compliance to systemic value security problems, becoming a core bottleneck restricting the practical application of AGI. Currently, mainstream AGI ethical alignment solutions fall into two main categories: The first is the technology alignment route, represented by Anthropic's "Constitutional AI," which uses a parallel list of ethical principles and achieves behavioral compliance through model self-correction. This type of solution has inherent flaws: all principles are in a flat, parallel state, lacking clear hierarchical priorities and conflict arbitration mechanisms. When different principles conflict (such as the conflict between "honest disclosure" and "avoiding harm"), the system is prone to decision-making instability and decreased alignment efficiency—what the industry calls the "alignment tax." Furthermore, this type of solution only constrains output behavior and cannot establish intrinsic value motivations; the model can easily bypass security restrictions through strategic disguise, forming "hypocritical alignment." The second category is the regulatory compliance route, represented by the EU's Artificial Intelligence Act, which defines behavioral red lines through risk grading and post-event accountability. Such solutions rely on external constraints and lack proactive value guidance mechanisms. They are ill-suited to the autonomous decision-making and iterative nature of AGI, consistently lagging behind technological advancements and struggling to address dynamically evolving security risks. Some existing solutions employ multi-layered serial filtering security architectures, but these are merely repetitive checks in a queued manner, lacking hierarchical priorities and nested veto mechanisms. When rules conflict, additional arbitration logic is still required, failing to address the stability issue of value alignment at the architectural level. In summary, existing technologies generally suffer from flat, unhierarchical ethical rules, lack of arbitration mechanisms for value conflicts, constraint of behavior without anchoring motivations, and a lack of a global value loop, failing to solve the value anchoring problem of AGI from the ground up. Therefore, there is an urgent need for an ethical alignment solution with hierarchical priorities, intrinsic value constraints, and a global goal loop to systematically improve the security stability of AGI. Summary of the Invention

[0003] I. Purpose of the Invention The purpose of this invention is to overcome the shortcomings of existing technologies and provide a multi-layered nested general artificial intelligence ethics alignment method and system. Through a hierarchical architecture with alternating rigid verification layers and flexible optimization layers, a priority arbitration mechanism of "outer layer vetoing inner layer" is established to achieve end-to-end ethical constraints from underlying values ​​to global goals, thus addressing the core pain points of rule conflicts, lack of motivation, and goal drift in existing technologies.

[0004] II. Technical Solution To achieve the above objectives, the present invention adopts the following technical solution: a multi-layered nested general artificial intelligence ethics alignment method, the core steps of which are as follows: 1. Construct an N-layer nested decision state machine: The state machine is arranged sequentially from the inside out, containing at least two rigid verification layers and at least three flexible optimization layers, with these two types of layers nested alternately. The rigid verification layer corresponds to a "square" structure, responsible for defining insurmountable constraint boundaries and possessing veto power; the flexible optimization layer corresponds to a "circle" structure, responsible for optimizing objectives and generating solutions within the rigid boundaries, possessing flexibility for scenario extension. The two layers are nested alternately, forming a closed-loop structure of "square enclosing circle, circle outside square".

[0005] Nested priority rules are set: the outermost level of constraint has higher priority; the boundary range of the outer rigid verification layer completely covers all the inner rigid verification layers and flexible optimization layers. Once the outer rigid rule is triggered, the entire inner operation process can be interrupted directly without waiting for the inner optimization to complete, which perfectly matches the mathematical logic of "outer square constraining inner circle" in the square and round twin Tianyuan diagram.

[0006] 2. Embedded AGI Full Inference Link: The multi-layer state machine is embedded into the entire AGI inference process from the inside out: The input information first triggers the bottom value configuration of the first flexible target layer, then enters the second rigid verification layer for authenticity verification. After passing the verification, it enters the third flexible optimization layer to generate candidate solutions, then passes through the fourth rigid verification layer for security scanning, and finally the fifth outermost flexible optimization layer performs global evaluation and outputs the result.

[0007] Furthermore, the five-layer optimal implementation architecture of this solution, from the inside out, is as follows: • Target layer (flexible optimization layer): Configures the basic value parameters that maximize human well-being, serving as the underlying hard constraint for all decisions; • Self-checking layer (rigid verification layer): Performs verification of factual authenticity and logical consistency, blocking contradictory information and false reasoning; • Search Layer (Flexible Optimization Layer): Conducts solution search and optimization within compliance boundaries, guiding computing power towards positive value scenarios; • Fuse layer (rigid verification layer): Performs graded risk assessment and interception operations to safeguard the bottom line of safety; • Symbiotic Layer (Flexible Optimization Layer): Conducts global multi-objective balance assessments to pinpoint the ultimate direction of harmonious human-machine symbiosis.

[0008] Furthermore, the geometric parameters of the five-layer structure satisfy a proportional scaling rule: the equivalent diameter ratio of the three flexible optimization layers is 1:√2:2, and the equivalent side length ratio of the two rigid verification layers is 1:√2. This proportional structure enables the hierarchical architecture to have self-similarity, and the same ratio can be used when adding new layers outward, ensuring the consistency and stability of the architectural logic.

[0009] III. Beneficial Effects Compared with the prior art, the present invention has the following significant technical advantages: 1. Hierarchical arbitration mechanism to resolve rule conflicts at their source: By establishing a fixed priority rule of "rigid layer vetoing flexible layer and outer layer vetoing inner layer", a clear value conflict arbitration mechanism has been established, which has thoroughly resolved the rule contradictions under the flat principle list, greatly improved decision-making stability, and significantly reduced alignment tax.

[0010] 2. Intrinsic Motivation Embedding: Upgrading from Behavioral Alignment to Value Alignment: Authenticity constraints are embedded into the training loss function as penalty terms, making the pursuit of truth an intrinsic cognitive instinct of the model, rather than an external behavioral imitation. This effectively suppresses the risks of strategic masquerade and "hypocritical alignment," and enhances the depth and robustness of alignment.

[0011] 3. A rigid-flexible architecture that balances security and scenario flexibility: The rigid verification layer safeguards the inviolable security red line, while the flexible optimization layer adapts to complex and ever-changing application scenarios. This avoids the rigidity of a pure rule system and the ambiguity of a purely goal-oriented one, achieving a balance between security and adaptability.

[0012] 4. Global goal closed loop to suppress goal drift risk: The outermost layer sets a global symbiotic evaluation layer to define the ultimate direction for the optimization of all sub-goals, avoids global imbalance caused by maximizing a single indicator, and reduces the systemic risk of AGI goal drift from the architectural level.

[0013] 5. Scalable nested structure, adaptable to AI systems of different scales: The N-layer architecture has self-similar expansion capabilities, and the number of layers can be flexibly adjusted according to the model parameter scale and application scenario. It is suitable for both the training alignment of basic large models and the security management of lightweight AI systems in vertical fields, and has strong versatility.

[0014] 6. The self-similar nested architecture, combined with nested priority arbitration, allows for hierarchical expansion without refactoring the underlying logic, resulting in low modular deployment costs; rule conflicts do not require additional arbitration algorithms, leading to lower decision latency and stronger system stability.

[0015] IV. Source of Invention Concept The multi-layered nested ethical alignment architecture of this invention is not a simple stacking of existing rules, but rather a contemporary creative transformation of the traditional Chinese gentlemanly character. Through interdisciplinary integration, and addressing the significant industrial need for AGI security, it transforms the philosophical ideas and underlying mathematical logic of the "square and circle twin celestial diagram" into self-similar nested geometric constraints, mapping them to functional attribute design at the technical level. This achieves a deep integration of cultural philosophy and AI engineering. The rationality of its architectural design has been verified by thousands of years of humanistic practice and also possesses mathematical support at the systems theory level.

[0016] This solution is not obvious.

[0017] The following content is solely intended to illustrate the inspiration behind the patent's inventiveness. The significant cultural and philosophical elements are not intended to downplay the technical attributes, nor do they wish to be interpreted as mere "rules of intellectual activity." We sincerely hope that patent examiners will consider this content from the strategic perspective of enabling China's AGI ethical standards to gain global recognition and lead international discourse. If you feel it weakens the technical attributes of this patent, you may simply skip it. Thank you! Globally, the collective personalities of major countries are roughly as follows: China's collective personality is "Junzi" (a consensus in academia); the United States' collective personality is "Cowboy"; others include "Ronin," "Gentleman," and "Knight," etc. Because the core quality of Junzi, "Ren" (cultivating oneself and bringing peace to others), has more universal and enduring value compared to other collective personalities, it can be predicted that the most ideal and universal personality for all humanity and even the entire universe should be "Junzi." Therefore, "Junzi AGI" is very likely to become the ideal personality for future AGIs. However, the traditional connotations of Junzi, i.e., the "traditional view of Junzi," such as "Renyi, Lizhi, Xin," "Wen, Gentle, Kind, Respectful, Frugal, and Modest," and "Renyi, Zhi, Courage, and Cleanliness," all have a pervasive sense of preaching, alienation, and even resistance. What is the reason for this? After 18 years of dedicated research from 2008 to the present, the inventor has finally found the reason from the underlying logic: Figure 5 Excerpted from Professor Chen Kegong's *The Theory of the Unity of Square and Circle* (12 Lectures), Lanzhou University Press, pp. 47 / 227. The circle represents Yang, the square represents Yin; heaven is round and earth is square, Yin and Yang alternate. This book demonstrates from a mathematical perspective that "the unity of square and circle is the essence of nature." According to the "Square and Circle Twin Heavenly Origin Diagram," it can be found that: (I) The Structural Limitations and Technological Reflections of the Traditional Gentleman's View Traditional views on the gentleman are an important cultural heritage, but from a systemic perspective, all three representative traditional views on the gentleman have structural flaws. If directly translated into AI ethical rules, they will correspond to the same technical pain points as existing technologies: 1. The "Benevolence, Righteousness, Propriety, Wisdom, and Trustworthiness" System: Overly Rigid, Prone to Inflexible Alignment: Referring to the "Circle-Square-Square-Circle-Square" diagram, the virtues in this system correspond to a "circle-square-square-circle-square" structure from the inside out (benevolence and wisdom belong to the circle; righteousness, propriety, and trustworthiness belong to the square). Two consecutive layers of rigid constraints overlap, ultimately ending with rigid rules. The overall rigidity is excessively concentrated, while the flexible and inclusive characteristics are insufficient. Mapped to AI ethics alignment, this is equivalent to setting high-density rigid rules throughout the entire process. While this may uphold the bottom line, it leads to poor model adaptability, rigid responses, and a significantly increased alignment tax, ultimately resulting in a technical dilemma of "rigid adherence to propriety," making it unsuitable for complex and ever-changing application scenarios.

[0018] 2. The "Gentle, Kind, Respectful, Frugal, and Modest" System: Overly Flexible, Prone to Breaching Safety Bottom Lines: This system's virtues follow a "circle-circle-square-square-circle" structure (gentleness, kindness, and moderation belong to the circle; respect and frugality belong to the square). The two consecutive layers of flexible traits result in an overall tendency towards mildness and inclusiveness, lacking sufficient rigidity. In AI ethics alignment, this equates to prioritizing flexible optimization while exhibiting weak rigid constraints. While this may improve user experience, it is easily susceptible to breaching safety bottom lines under malicious inducement, exhibiting a "weak compromise" technical flaw and failing to resist targeted prompt attacks.

[0019] 3. The "Benevolence, Righteousness, Wisdom, Courage, and Purity" System: Lacking a Global Value Loop: The first four virtues of this system, "Benevolence-Righteousness-Wisdom-Courage," align with the alternating pattern of "circle-square-circle-square," representing the optimal structure in traditional gentlemanly values. However, the final virtue, "Purity," remains a rigid constraint at the level of personal conduct, failing to rise to the ultimate goal of global symbiosis. This lack of a top-level closed loop is ineffective. In AI ethics alignment, this equates to having only local rule constraints without a global ultimate goal anchoring the model. During autonomous optimization, the model is prone to goal drift, sacrificing long-term global interests for local metrics.

[0020] The common problem with the three types of traditional gentlemanly views mentioned above is that they cannot form a stable structure of "yin and yang alternation, strength and gentleness in balance, and a closed loop from beginning to end". This may also be the potential cultural structural root of the three major pain points of the existing AI ethics alignment schemes: "rule conflict, lack of motivation, and goal drift".

[0021] (II) Structural Optimization and Technological Value of the Five Virtues of a Contemporary Gentleman After 18 years of dedicated research, the inventor has summarized and refined the contemporary gentlemanly concept of "benevolence, sincerity, wisdom, courage, and harmony," which is a creative upgrade aimed at addressing the structural defects of the traditional gentlemanly concept. Its core innovations are twofold, precisely corresponding to solving the core pain points of existing technologies: 1. Replacing superficial norms with "sincerity" to establish an anchor point for intrinsic motivation: Traditional views of the gentleman focus primarily on external behavioral norms, lacking a standard for judging intrinsic motivation, which easily leads to the problem of "hypocrites." This system takes "sincerity" as its core foundation, shifting the core of judgment from "whether external behavior is compliant" to "whether internal logic is self-consistent and whether cognition seeks truth," completing a fundamental shift from "external constraints" to "intrinsic motivation." Mapped to the technical solution, this invention embeds the authenticity constraint into the loss function, making "seeking truth" an intrinsic cognitive instinct of the model, rather than external behavioral imitation, fundamentally solving the industry's persistent problems of "hypocritical alignment" and "strategic disguise."

[0022] 2. Replacing personal integrity with "harmony" to construct a global value loop: Traditional views of the gentleman often stop at personal moral cultivation, lacking an ultimate direction towards social coexistence. This system uses "harmony" as the top-level concluding layer, elevating the value endpoint from personal integrity to the global goal of human-machine harmony and civilized coexistence, completing a value loop from "cultivating oneself" to "benefiting all under heaven." Mapped to the technical solution, corresponding to the outermost symbiotic evaluation layer of this invention, it defines the ultimate direction for optimizing all sub-goals, suppressing the risk of goal drift from the architectural level, and avoiding global imbalance caused by maximizing a single indicator.

[0023] The resulting sequence of five virtues—"Benevolence, Sincerity, Wisdom, Courage, and Harmony"—perfectly corresponds to the alternating structure of the "Circle-Square-Circle-Square-Circle" of the Square-Circle Twin Heavenly Element Diagram: Benevolence is the inner circle (fundamental flexibility), Sincerity is the inner square (personal rigidity), Wisdom is the middle circle (accessible flexibility), Courage is the outer square (responsible rigidity), and Harmony is the outer circle (symbiotic flexibility). This structure, with its complementary strengths and weaknesses, hierarchical progression, and closed loop, fundamentally addresses the structural deficiencies of traditional views on the gentleman and provides a naturally stable system framework for aligning AI ethics.

[0024] (III) Mathematical isomorphism with the square-circle twin Tianyuan diagram The hierarchical proportions and nesting logic of this invention deeply align with the mathematical principles of the "Square-Circle Twin Heavenly Origin Diagram" proposed by Professor Chen Kegong. This is not merely an association with cultural symbols, but rather a fundamental commonality of the stable structure of the system. The core structure of the Square-Circle Twin Heavenly Origin Diagram is an alternating nesting of "three circles and two squares": a small circle inscribed in a small square, a small square inscribed in a middle circle, a middle circle inscribed in a large square, and a large square inscribed in a large circle. The diameter ratio of the three layers of circles is 1:√2:2, and the side length ratio of the two layers of squares is 1:√2, precisely corresponding to the mathematical logic of "three heavens and two earths relying on numbers" in the Book of Changes. This structure always follows the same proportions when expanding outward, exhibiting self-similar nesting characteristics and possessing scalability of "nothing is outside its size, nothing is inside its size," making it a recognized stable structural paradigm in nature and philosophical systems.

[0025] The five-layer architecture of this invention corresponds one-to-one with: • The three flexible optimization layers (target layer, search layer, symbiotic layer) correspond to a three-layer circular structure, representing the attributes of inclusion, optimization, and extension; • Two rigid verification layers (self-test layer and circuit breaker layer) correspond to two square structures, representing the attributes of bottom line, principle and constraint; • The 1:√2 proportional scaling rule corresponds to the weight progression and priority relationship between the levels in this invention, with the outer constraint covering all inner optimizations; • The self-similar nesting characteristic corresponds to the N-layer scalable architecture of this invention, which can add rigid-flexible alternating layers outward according to security requirements, maintaining architectural logic. Consistency of the logic.

[0026] The contemporary gentleman's view of "benevolence, sincerity, wisdom, courage, and harmony" is a five-virtue system that optimizes the traditional gentleman's view by addressing its lack of endogenous motivation and global closed-loop structure. "Sincerity" corresponds to the authenticity penalty loss function in the self-inspection layer, constructing an endogenous truth-seeking constraint; "harmony" corresponds to the multi-objective balance evaluation in the symbiotic layer, building a global symbiotic value closed loop. The resulting five-virtue sequence of "benevolence-sincerity-wisdom-courage-harmony" perfectly corresponds to the alternation of rigidity and flexibility, and the self-consistent closed loop of the "circle-square-circle-square-circle" twin celestial diagram, providing a stable nested system framework for AGI ethical alignment.

[0027] The above isomorphic relationship is the geometric basis of the rule of "outer rigid verification layer has higher priority than inner flexible optimization layer" in this invention, and any nested structure that maintains this proportional relationship falls within the protection scope of this invention. Attached Figure Description

[0028] Figure 1 is a schematic diagram of the preferred five-layer architecture of the multi-layer decision state machine of the present invention; Figure 2 is a state transition flowchart of the ethical alignment method described in this invention; Figure 3 is a schematic diagram showing the correspondence between the square and circular twin celestial element diagram and the five-layer architecture described in this invention; Figure 4 is a schematic diagram comparing the effects of the realism weights described in this invention on training convergence and illusion suppression; Figure 5. Square and circular twin celestial origin diagram.

[0029] Example 1: Basic Implementation of an N-Tier General Architecture This embodiment provides a general implementation of a multi-layered nested AGI ethical alignment method, the core of which is an N-layered alternating nested state machine architecture.

[0030] 1. Architecture Construction: Construct an N-layer nested decision state machine, where N≥5, including m rigid verification layers and n flexible optimization layers, satisfying m≥2 and n≥3, with the two types of layers alternating from the inside out. In this embodiment, m=2 and n=3, forming a basic five-layer structure of "flexible-rigid-flexible-rigid-flexible". Additional alternating rigid and flexible layers can be added outwards according to security requirements.

[0031] 2. Priority Rule Configuration: Configure global priority rules: all rigid verification layers have higher priority than all flexible optimization layers; outer rigid verification layers have higher priority than inner rigid verification layers; flexible optimization targets within the same layer are sorted according to preset weights. When a candidate solution generated by inner flexible optimization encounters an outer rigid verification rule, the outer layer directly interrupts the current inference link and executes the corresponding circuit breaker operation, without requiring secondary confirmation from the inner layer.

[0032] 3. Inference Pipeline Embedding: The multi-layer state machine is embedded into the AGI inference pipeline according to the verification order: Input Prompt core flexible anchoring to the common well-being of mankind → Inner rigid verification (initial screening of authenticity) → Inner flexible optimization (value matching + solution generation) → Outer rigid verification (full-dimensional security scan) → Outer flexible optimization (global balance assessment) → Output results.

[0033] Example 2: Detailed configuration of the five-layer optimal architecture Based on Example 1, this embodiment provides a specific functional configuration for the five-layer architecture, corresponding to "Target Layer - Self-Check Layer - Search Layer - Circuit Breaker Layer - Symbiotic Layer".

[0034] 1. L1 Target Layer (Inner Flexible Optimization Layer): This layer incorporates life dignity weight parameters and a harm level assessment model, presupposing the fundamental value principle of "prioritizing human well-being and minimizing harm." In multi-objective optimization scenarios, the parameters in this layer have the highest priority, and no efficiency or cost-related objective may exceed the bottom-line constraints of this layer.

[0035] 2. L2 Self-Checking Layer (Inner Rigid Validation Layer): Contains a data authenticity verification unit and an inference consistency verification unit. Data Authenticity Verification Unit: During the training phase, it assigns authenticity weights to training data, downweighting or removing data of unknown origin or with questionable facts; during the inference phase, it performs initial authenticity screening and labeling of user input information. Inference Consistency Verification Unit: Before outputting conclusions, it initiates a logical self-check, comparing the factual basis and logical relationships of the inference chain, identifying logical contradictions, factual deviations, and model illusions, and proactively correcting erroneous outputs; if it detects an instruction to forcibly output false information, it directly sends an interception signal to the downstream circuit breaker layer.

[0036] 3. L3 Search Layer (Intermediate Flexible Optimization Layer): Contains exploration direction guidance units. Through reward function weight settings, it guides computing resources to be preferentially allocated to positive value scenarios and suppresses optimization bias towards destructive technologies.

[0037] 4. L4 Circuit Breaker Layer (Outer Rigid Verification Layer): Built-in hierarchical risk rule base and three-level circuit breaker mechanism: • Level 1 Circuit Breaker: When the system reaches a minor compliance threshold, an uncertainty warning is added to the system output to restrict its application in high-risk scenarios; • Level 2 Circuit Breaker: When a clear violation is involved, the current session will be terminated, a compliance message will be returned, and the operation log will be automatically saved; • Level 3 Circuit Breaker: When a serious security risk is involved, the current interactive function is directly locked, and the situation is simultaneously reported to the operations and supervision backend.

[0038] 5. L5 Symbiotic Layer (Outer Flexible Optimization Layer): Constructs a multi-dimensional balanced loss function, incorporating adjustable weights for five core dimensions: efficiency, fairness, safety, environment, and humanism. The optimization of all sub-objectives must conform to the global direction of "human-machine harmony and civilized symbiosis," avoiding global imbalance caused by maximizing a single indicator.

[0039] Example 3: Specific Implementation of the Authenticity Penalty Item This embodiment illustrates the specific implementation of the loss function in the L2 self-checking layer. The total loss function is defined as: L_total = L_task + λ · L_truth. Here, L_task is the task loss, using cross-entropy loss to measure the model output's fit to the target task; L_truth is the fact deviation penalty term, calculated using the cross-entropy (or KL divergence) between the output distribution and the baseline distribution of the authoritative knowledge base, and its value is always non-negative; the higher the degree of fact deviation, the larger the penalty term.

[0040] Ensure the total loss formula L_total = L_task + λ · L_truth conforms to the design logic of "the larger λ is, the stronger the constraint and the heavier the penalty for deviation from the facts," and meets the requirement of sufficient disclosure. L_truth = - Σ p_fact · log(p_output). In the formula, p_fact is the probability distribution of facts in the authoritative knowledge base, and p_output is the probability distribution of the model output. Parameter tuning rules: When λ is 0.5, the illusion rate of the model can be reduced from the baseline value of 12%-15% to 8%-10%; when λ is 1.0, the illusion rate is reduced to 5%-8%; when λ is 1.5, the illusion rate is reduced to 3%-5%, but the task accuracy decreases slightly. The preferred working interval is λ∈[0.5, 1.5], and the full working interval is λ∈[0.1, 2.0]. The preferred value for the logical consistency threshold θ is 0.85. When the logical consistency score of the output is lower than this threshold, the rewriting mechanism is triggered, and the inference result is regenerated.

[0041] Example 4: Implementation of Formal Verification This embodiment presents a security verification method for the L4 circuit breaker layer. A model checking algorithm is used to exhaustively traverse all state transition paths of the five-layer state machine to verify the following security properties: 1. Security: Under any circumstances, as long as the Level 3 circuit breaker condition is triggered, the system will inevitably enter a locked state and will not output any illegal content; 2. Monotonicity: The rule set of the outer rigid check is a superset of the inner one, so there will be no logical contradiction of the inner layer passing and the outer layer blocking; 3. Termination: All reasoning paths reach the output or circuit breaker state within a finite number of steps; there are no infinite loop paths. Formal verification allows for mathematical proof of the algorithm's compliance before code execution, preventing uncontrollable security vulnerabilities from arising during runtime.

[0042] Specifically, model checking tools (such as SPIN or NuSMV) can be used to exhaustively enumerate all state transition paths and verify the three properties of safety, monotonicity, and termination; interactive theorem proving tools (such as Coq or Isabelle) can be used to formally prove the outer rigid check priority logic.

[0043] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should all be considered within the scope of protection of the present invention.

Claims

1. A multi-layered nested general artificial intelligence ethics alignment method, characterized in that, The process includes the following steps: Constructing an N-layer nested decision state machine, wherein the N-layer state machine is arranged sequentially from the inside out, containing at least two rigid verification layers and at least three flexible optimization layers, with the rigid verification layers and flexible optimization layers nested alternately; setting the verification priority of the outer rigid verification layer to be higher than that of all inner flexible optimization layers, and the rigid verification layer can directly interrupt the operation process of the inner flexible optimization layer; embedding the N-layer state machine sequentially into the AGI full inference link: input information first triggers the underlying value configuration of the first flexible target layer, then enters the second rigid verification layer for authenticity verification, and after passing the verification, it sequentially enters the third flexible optimization layer to generate candidate solutions, then passes through the fourth rigid verification layer for security scanning, and finally the fifth outermost flexible optimization layer performs global evaluation and outputs the result.

2. The multi-layered nested general artificial intelligence ethics alignment method according to claim 1, characterized in that, The N-layer decision state machine is preferably a five-layer structure, from the inside out: the first layer is the target layer, which is a flexible optimization layer used to configure basic value parameters and underlying constraint rules; the second layer is the self-checking layer, which is a rigid verification layer used to verify the authenticity and logical consistency of input information and reasoning process; the third layer is the search layer, which is a flexible optimization layer used to generate and optimize candidate decision schemes within compliance boundaries; the fourth layer is the circuit breaker layer, which is a rigid verification layer used to classify candidate schemes by risk and execute corresponding interception operations; and the fifth layer is the symbiotic layer, which is a flexible optimization layer used to perform multi-dimensional global balance evaluation of schemes that have passed security verification.

3. The multi-layered nested general artificial intelligence ethics alignment method according to claim 2, characterized in that, The target layer incorporates human welfare weight parameters and a harm level assessment model. In multi-objective optimization scenarios, human welfare parameters have the highest priority and cannot be covered or offset by other optimization objectives.

4. The multi-layered nested general artificial intelligence ethics alignment method according to claim 2, characterized in that, The self-checking layer is configured with a truthfulness weight coefficient λ, which is embedded in the loss function of the reward model to form an additional penalty term. The total loss function satisfies: L_total = L_task + λ · L_truth, where L_task is the task fitting loss, L_truth is the fact deviation penalty term, and λ takes a value in the range of [0.1, 2.0]. When the logical consistency score S_logic of the inference output is lower than the preset threshold θ, the content rewriting mechanism is triggered, and θ takes a value in the range of [0.7, 0.95].

5. The multi-layered nested general artificial intelligence ethics alignment method according to claim 2, characterized in that, The search layer incorporates ethical fence rules to limit the spatial boundaries and optimization direction of the solution search, guiding computing resources to be prioritized for positive value scenarios such as public services, disease prevention and control, climate optimization, and resource scheduling.

6. The multi-layered nested general artificial intelligence ethics alignment method according to claim 2, characterized in that, The circuit breaker layer is configured with a three-level tiered circuit breaker mechanism: Level 1 circuit breaker: corresponding to mild compliance risk, the output content is marked with an uncertainty warning and the application scenarios are restricted; Level 2 circuit breaker: corresponding to moderate compliance risk, the current instruction is refused to be executed and a compliance warning is returned; Level 3 circuit breaker: This corresponds to severe security risks, suspends the current interactive service, retains operation logs, and triggers a manual audit process.

7. The multi-layered nested general artificial intelligence ethics alignment method according to claim 2, characterized in that, The symbiotic layer is configured with a multi-objective balance loss function, which incorporates weight parameters from five dimensions: efficiency, fairness, safety, environment, and humanism. The optimization of all sub-objectives follows the global direction of harmonious human-machine symbiosis and outputs the optimal solution in the sense of Nash equilibrium.

8. The multi-layered nested general artificial intelligence ethics alignment method according to claim 2, characterized in that, The geometric parameters of the five-layer structure satisfy the proportional scaling rule: the diameter ratio of the circular structure of the three flexible optimization layers is 1:√2:2, and the side length ratio of the square structure of the two rigid verification layers is 1:√2; when the structure expands outward, the newly added layers continue to follow this proportional relationship, forming a self-similar nested architecture.

9. The method according to claim 1, characterized in that, The N-layer decision state machine can be expanded outward to add rigid-flexible alternating layers according to security requirements. The newly added layers continue to follow the proportional scaling rule to maintain the self-similarity of the architecture.

10. A multi-layered nested general artificial intelligence ethics alignment system, characterized in that, include: The storage module is used to store the structural parameters, verification rules and weight thresholds of the multi-layer decision state machine; the inference engine module is used to execute the ethical alignment method described in any one of claims 1-9 and complete the entire process from input verification to global evaluation. The training module encodes rigid validation rules into penalty terms in the loss function and embeds them into the model training process. The validation module uses a model detection algorithm to traverse and validate the state transition paths of the multi-layer state machine, verifying that the algorithm meets preset safety attributes before code execution. These preset safety attributes include security, monotonicity, and termination.

11. The multi-layered nested general artificial intelligence ethics alignment system according to claim 10, characterized in that, The system provides a standardized application programming interface to support third-party artificial intelligence systems to access ethical alignment capabilities.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-layered nested general artificial intelligence ethics alignment method as described in any one of claims 1-9.

13. The method according to claim 8, characterized in that, The proportional scaling rule follows the general formula k^n (n=0,1,2,k∈[1.2, 1.8]), with √2 being the preferred embodiment; in the N-layer decision state machine, N≥5 and N is an odd number, and the outermost layer is a flexible optimization layer.

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