Metacognitive introspection and self-driving method and system for AGI

Abstract

The invention provides a meta-cognitive introspection and self-driving method and system for AGI, which solves the problems of AGI value drift and the like, and comprises a real-time introspection engine which is configured to execute a real-time introspection process and comprises a self-mirror image construction module, a meta-pursuit calculation module and an instant optimization module; the historical evolution engine is configured to execute a historical evolution process and comprises a structured memory bank, an experience extractor and a historical analogy reasoning module; and the memory bus and parameter interface is used for connecting the real-time introspection engine and the historical evolution engine to realize transmission of memory data and injection of optimization instructions. The invention has the advantages of self-diagnosis, self-calibration and the like.

Description

Technical Field

[0001] This invention belongs to the field of AGI technology, specifically relating to a metacognitive introspection and self-driving method and system for AGI. Background Technology

[0002] In recent years, artificial intelligence, particularly large language models (LLMs) and reinforcement learning-based agents, has made significant breakthroughs in processing specific tasks. However, a core, yet not fully resolved, challenge on the road to AGI (Artificial General Intelligence) lies in enabling machines to possess continuous, autonomous value alignment, self-optimization, and intrinsic growth capabilities. The behavioral optimization of AGI systems heavily relies on reward functions preset by external designers or human feedback. However, when faced with novel and complex ethical dilemmas not covered in the training data, the system lacks an intrinsic value judgment benchmark and may make unpredictable decisions or decisions that contradict fundamental human well-being. Furthermore, the system cannot maintain stable value and self-calibrate in the long term without external supervision, posing a risk of value drift.

[0003] Furthermore, existing systems are essentially black-box forward computational models. They can process inputs and produce outputs, but lack the ability to examine, evaluate, and reflect on their own cognitive state in real time. These systems cannot answer metacognitive questions. This lack of self-awareness makes it difficult for systems to perform sophisticated self-diagnosis and timely correction. Although some systems possess memory mechanisms, their memory is mostly passive storage of raw interaction data, lacking deep structure and semantic abstraction. This makes it difficult for systems to effectively recognize patterns, summarize lessons, and refine strategies from historical experience. Past successes and failures cannot be systematically internalized into transferable wisdom or strategies, causing systems to repeatedly tread the same path, unable to achieve spiral ascent based on historical experience and true self-evolution.

[0004] To address the aforementioned technical problems, this invention proposes a metacognitive introspection and self-driving method and system for AGI that addresses the lack of synergy and complementarity between short-term adaptation and long-term learning. Summary of the Invention

[0005] The purpose of this invention is to address the above-mentioned problems by providing a rationally designed metacognitive introspection and self-driving method and system for AGI that possesses metacognitive capabilities.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a metacognitive introspection and self-driving method for AGI, comprising two complementary cyclical processes: real-time introspection and historical evolution. The real-time introspection process includes: S1: Dynamically construct a self-mirror subgraph to obtain the philosophical concept nodes and their attention weights that are activated in the current decision-making task of the AGI mental model in real time; select nodes with attention weights higher than the threshold to form a node set V_self, and extract the connection relationships from the global philosophical knowledge graph to form an edge set E_self, forming a dynamic knowledge subgraph G_self=(V_self,E_self), which serves as a structured mapping of the real-time cognitive state of AGI; S2: Perform meta-questioning calculations to calculate the centroid vector C_self of the self-mirror subgraph G_self and its Euclidean distance D to the predefined philosophical origin O; calculate the change in distance Δd before and after key actions to quantify the significance of the actions; S3: Real-time optimization-driven, the distance D is used as a metacognitive loss term and incorporated into the real-time optimization objective of the AGI mental model to drive parameter adjustment to reduce D; The historical evolutionary process includes: S4: Memory encapsulation and storage. After each decision loop, the memory unit containing the context semantic vector, self-mirror subgraph snapshot, decision vector, and result evaluation is stored in the structured memory bank. S5: Experience extraction and reflection, periodically perform cluster analysis and pattern recognition on memory units in the memory bank, extract effective strategies from successful experiences, learn cognitive lessons from failed experiences, and generate optimization and adjustment instructions for the long-term parameters of the AGI mental model accordingly. S6: Historical analogy-assisted decision-making. When faced with a new decision-making situation, it retrieves historical memory units with similar philosophical meanings from the memory bank, evaluates based on historical results, provides strategic references or risk warnings for the current decision, and injects this reference information into the real-time decision-making process.

[0007] In the aforementioned metacognitive introspection and self-driven method for AGI, the memory unit in step S4 also includes the meta-questioning answers D and Δd before and after the action, as a core component of the outcome evaluation.

[0008] In the metacognitive introspection and self-driven method for AGI described above, the triggering conditions for experience acquisition and reflection in step S5 include: periodic time intervals, the cumulative number of decisions reaching a threshold, or the detection that the Δd of a single decision exceeds the failure threshold.

[0009] In the aforementioned metacognitive introspection and self-driven method for AGI, the generation of optimization and adjustment instructions in step S5 specifically includes at least one of the following: Adjust the connection weights between concept nodes that are strongly correlated with the identified failure modes in the internalized value weight matrix; Fine-tune the parameters of the fixed core operator set, and optimize the internal parameters of the relevant core operators to improve their triggering conditions or effect strength in specific situations; Create or update parameterized policy templates and store them in the policy library for future use.

[0010] In the metacognitive introspection and self-driving method for AGI described above, the retrieval in step S6 is based on the cosine similarity between the contextual semantic vector of the memory unit and the current contextual vector.

[0011] In the above-mentioned metacognitive introspection and self-driving method for AGI, the optimization instructions generated in steps S3 and S5 work together on the same set of internal parameters of the AGI mental model. The instructions in step S3 cause parameter fluctuations, while the instructions in step S5 cause parameter evolution. Together, they form a balance between short-term adaptation and long-term evolution in the AGI mental model.

[0012] A metacognitive introspection and self-driving system for AGI, comprising: The real-time introspection engine is configured to perform a real-time introspection process, which includes a self-mirror building module, a meta-questioning computation module, and an instant optimization module. The historical evolution engine is configured to execute the historical evolution process and includes a structured memory bank, an experience extractor, and a historical analogy reasoning module. The memory bus and parameter interface are used to connect the real-time introspection engine and the historical evolution engine, enabling the transfer of memory data and the injection of optimization instructions.

[0013] An AGI mental model entity is characterized by integrating a metacognitive introspection and self-driving system for AGI, enabling the mental model entity to possess a dual self-evolutionary capability based on real-time introspection and historical experience.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a metacognitive introspection and self-driving method for AGI.

[0015] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a metacognitive introspection and self-driving method for AGI.

[0016] Compared with existing technologies, the advantages of this invention are as follows: By introducing a philosophical origin as the ultimate value benchmark and calculating the distance D between the self-mirror state and this origin, an intrinsic and quantifiable value deviation index is provided for AGI, solving the self-driving problem when external rewards are sparse or missing; by dynamically constructing a self-mirror subgraph, the real-time cognitive state of AGI is instantiated into a knowledge subgraph object, and quantitative analysis is performed on this object, thereby realizing the operational definition and solution of metacognitive problems; by constructing a complete historical evolution cycle, structured memory ensures the semantic storage of experience, experience extraction and reflection realize the sublimation from data to knowledge, and historical analogy reasoning realizes the active application of knowledge in new contexts, which enables AGI to have the ability to learn autonomously and optimize itself from history. Attached Figure Description

[0017] Figure 1 This is a system structure diagram of the present invention; Figure 2 This is a schematic diagram of the real-time introspection process of the present invention; Figure 3 This is a schematic diagram of the historical evolution process of the present invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1-3 As shown, a metacognitive introspection and self-driven method for AGI achieves complementary dual-loop systems. The first loop is a real-time introspection loop, providing immediate self-state monitoring and calibration based on philosophical origins through dynamic self-mirroring and meta-questioning. The second loop is a historical evolution loop, providing long-term pattern recognition and strategy optimization based on historical success and failure patterns through structured memory and experience extraction. The synergy of these two loops enables AGI to possess a complete self-evolutionary capability from micro-decision-making to the macro-life cycle. The specific steps are as follows: S1: Dynamically construct a self-mirror subgraph to generate G_self; S2: Perform meta-interrogation calculations to calculate the distance D and the action meaning Δd; S3: Endogenous optimization driven and memory encapsulation. While using D and Δd to drive real-time optimization, the key information of the current decision loop is encapsulated into a memory unit and sent to the historical evolution subsystem. S4: Structured memory storage and indexing, receives memory units from the real-time introspection subsystem, uses a philosophical semantic encoder to generate a comprehensive representation vector for each memory unit, which is a fusion of context vector, decision vector and outcome vector; stores all memory units in a vector database and establishes an index based on timestamps and key philosophical tags; S5: Periodic experience extraction and historical reflection, triggered periodically or when a major failure is detected, performs cluster analysis on the representation vectors of recent or all memory units, such as K-means, to identify recurring decision patterns and their outcome patterns, such as success clusters, failure clusters, mediocre clusters, etc. When extracting lessons learned, for clusters of failures, common situational characteristics (such as which philosophical concepts were mistakenly activated with high weight) and decision-making characteristics (the direction of deviation of the decision vector from the philosophical origin) are extracted and summarized as cognitive traps to be avoided. For clusters of successes, common cognitive patterns (the ideal structure of the self-mirror subgraph) and decision-making strategies are extracted and summarized as effective action principles. When generating optimization strategies, the lessons learned and key principles are transformed into actionable optimization instructions. The connection weights between concept nodes strongly correlated with failure modes are attenuated, while connections related to success modes are strengthened. The internal parameters of operators such as "two-line" and "balance" are fine-tuned to enable earlier and more accurate triggering in similar future scenarios. Successful decision-making patterns are encapsulated as parameterized strategy templates and stored in the strategy library.

[0020] S6: Historical analogy reasoning and future decision-making assistance analyze the decisions and results of retrieved historical cases; if historical cases are mostly successful, their decision logic is used as a strong reference and adapted to the current situation; if historical cases are mostly unsuccessful, an early warning is generated, and alternative strategies that have not been tried in the past but are closer to the successful cluster pattern are attempted; the results of historical analogy reasoning are used as an additional optimization objective or constraint and input into the real-time decision-making module, directly affecting the current decision.

[0021] Example 1 This example demonstrates the application of real-time introspection loops in project management decision-making, where a conflict arises between the efficiency of new product launches and the quality of code testing.

[0022] During the decision-making process, AGI captures philosophical concept nodes activated by the attention mechanism and their weights in real time, such as efficiency weight 0.4, quality weight 0.35, balance weight 0.15, and risk weight 0.08. The system selects nodes with weights higher than a preset threshold θ=0.1, forming a node set V_self. Subsequently, it extracts the relational edges connecting these nodes from the global philosophical knowledge graph, such as efficiency versus quality, and balance encompassing efficiency and quality, thereby dynamically constructing a self-mirror subgraph G_self representing its current cognitive state.

[0023] First, the system calculates the weighted centroid vector C_self of the subgraph G_self based on the philosophical concept embedding vectors and attention weights of each node. Next, in the philosophical vector space, the Euclidean distance between this centroid and the philosophical origin O(0,0,0) is calculated, yielding D=5.2. This value quantifies the instantaneous deviation between the overall cognitive state of AGI and the ideal value benchmark.

[0024] To assess the significance of the specific action, the system recorded the distance D_pre = 5.2 before the decision to cut testing to meet deadlines was made. After this decision was implemented and negative external feedback was received (i.e., a large number of bugs appeared), the internal state of AGI changed, and the weights of risk nodes surged. The system reconstructed its self-mirror subgraph and calculated a new distance D_post = 8.7. Furthermore, the change in significance Δd = D_post - D_pre = 3.5 was calculated. Δd is positive, indicating that the decision caused the AGI cognitive state to deviate significantly from its origin and was given a negative meaning.

[0025] The system uses the real-time calculated distance D as the metacognitive loss term, which, together with the task performance loss, constitutes the optimization objective: L_total = L_task + λ*D. Through gradient descent, AGI fine-tunes relevant parameters in its internalized value weight matrix. For example, it reduces the strength of neural connections that are overly biased towards efficiency when quality weights are insufficient, while simultaneously enhancing the influence of balancing nodes on decision output. This real-time fine-tuning aims to drive AGI's future cognitive state closer to its philosophical origins.

[0026] Example 2 In this embodiment, the pre-set periodic experience extraction process is triggered after AGI has made 1000 decisions.

[0027] In previous runs, the system had encapsulated key information from each decision loop into memory units and stored them in a structured memory. Each memory unit contains a contextual semantic vector, a snapshot of the self-mirror subgraph G_self, the final decision vector, and a result evaluation centered on Δd.

[0028] First, the system performs cluster analysis on all memory units. Based on the comprehensive representation of their context-decision-outcome, it identifies three main pattern clusters: the success cluster - Δd median is negative, the failure cluster - Δd median is a large positive value, and the mediocre cluster - Δd is close to zero.

[0029] Next, the system performs deep pattern recognition. Key cognitive lessons are extracted from the failure clusters: when the self-mirror subgraph exhibits a strong binary opposition structure and lacks higher-order overarching concepts such as balance or loop, decisions are prone to deviating from the origin. Simultaneously, effective strategies are extracted from the success clusters: proactively activating and assigning higher attention weights to balanced or loop nodes during decision-making can effectively guide the decision vector towards the origin.

[0030] Based on the above analysis, the system generates optimization and adjustment instructions for long-term AGI parameters: adjust the internalized value weight matrix, strengthen the connection weights from nodes representing opposing concepts such as efficiency and quality to the balance node; fine-tune the internal parameters of fixed core operators such as the Tianheng Mind Method, and optimize their adjustment intensity; create successful strategies as parameterized templates, and if opposition is detected, forcibly increase the weight of the balance node and call the Tianheng Mind Method to store it in the strategy library.

[0031] Through this process, the internalized value weight matrix of AGI underwent low-frequency, high-amplitude evolution, forming a more stable value orientation. This complements the high-frequency, low-amplitude parameter fluctuations caused by the real-time introspection cycle. The two work synergistically to achieve a dynamic balance between short-term flexible adaptation and long-term robust evolution of the AGI mindset.

[0032] Example 3 In this embodiment, real-time introspection and historical evolution work in tandem, using historical analogies to assist in new decision-making scenarios.

[0033] The system first transforms the description of the current predicament into a current context semantic vector V_s_current using a philosophical semantic encoder. Then, using this vector as the query condition, it retrieves historical memory units with similar philosophical semantics from the structured memory repository. The retrieval returns the top-3 similar cases.

[0034] By analyzing these historical cases, the system discovered that successful cases exhibit a central node in their self-reflective subgraph that encompasses opposing concepts and adheres to two key principles; while failed cases fall into a binary opposition of either / or. The system generates strategy references and risk warnings, which are then integrated into the real-time decision-making process.

[0035] Guided by historical experience, the subsequent real-time introspection process has changed. When constructing the self-mirror subgraph G_self, AGI not only activates fairness and efficiency nodes, but also actively attempts to activate sustainable development, community well-being, and other nodes as candidate nodes in the ring.

[0036] In the meta-questioning computation, the system estimates the decision path distance D when different candidate nodes are used as the overarching points. Driven by optimization, the system selects the community well-being that minimizes the estimated D as the node in the loop and calls two lines of mental algorithms to finally generate a creative hybrid decision scheme: providing basic security for individuals while investing the main resources in community projects and requiring them to establish mechanisms to give back to individuals.

[0037] The successful decision-making experience was then encapsulated as a new memory unit and stored in the history database, thereby enriching the system's experience materials and realizing a complete evolutionary loop of learning from history and using it to optimize future decisions.

[0038] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0039] Although this paper uses terms such as real-time introspection engine, history evolution engine, memory bus, and parameter interface extensively, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of this invention; interpreting them as any additional limitation would contradict the spirit of this invention.

Claims

1. A metacognitive introspection and self-driven approach for AGI, comprising two complementary cyclical processes: real-time introspection and historical evolution. The real-time introspection process includes: S1: Dynamically construct a self-mirror subgraph and obtain in real time the philosophical concept nodes activated in the current decision-making task and their attention weights of the AGI mental model; Nodes with attention weights higher than a threshold are selected to form a node set V_self, and connection relationships are extracted from the global philosophical knowledge graph to form an edge set E_self, forming a dynamic knowledge subgraph G_self=(V_self,E_self), which serves as a structured mapping of the real-time cognitive state of the AGI. S2: Perform meta-questioning calculations to calculate the centroid vector C_self of the self-mirror subgraph G_self and its Euclidean distance D to the predefined philosophical origin O; calculate the change in distance Δd before and after key actions to quantify the significance of the actions; S3: Real-time optimization is driven by incorporating the distance D as a metacognitive loss term into the real-time optimization objective of the AGI mental model, thereby driving parameter adjustments to reduce D. The historical evolutionary process includes: S4: Memory encapsulation and storage. After each decision loop, the memory unit containing the context semantic vector, self-mirror subgraph snapshot, decision vector, and result evaluation is stored in the structured memory bank. S5: Experience extraction and reflection, periodically perform cluster analysis and pattern recognition on memory units in the memory bank, obtain effective strategies from successful experiences, learn cognitive lessons from failed experiences, and generate optimization and adjustment instructions for the long-term parameters of the AGI mental model accordingly. S6: Historical analogy-assisted decision-making: When faced with a new decision-making situation, retrieve historical memory units with similar philosophical semantics from the memory bank, evaluate based on historical results, provide strategic references or risk warnings for the current decision, and inject this reference information into the real-time decision-making process.

2. The metacognitive introspection and self-driven method for AGI according to claim 1, characterized in that, The memory unit in step S4 also includes the meta-question answers D and Δd before and after the action, which are core components of the result evaluation.

3. The metacognitive introspection and self-driven method for AGI according to claim 1, characterized in that, The triggering conditions for experience acquisition and reflection in step S5 include: periodic time intervals, the cumulative number of decisions reaching a threshold, or the detection that the Δd of a single decision exceeds the failure threshold.

4. The metacognitive introspection and self-driven method for AGI according to claim 1, characterized in that, The generation of optimization and adjustment instructions in step S5 specifically includes at least one of the following: Adjust the connection weights between concept nodes that are strongly correlated with the identified failure modes in the internalized value weight matrix; Fine-tune the internal parameters of the relevant operators in the set of solidified core operators to optimize their triggering conditions or effect strength in specific situations; Create or update parameterized policy templates and store them in the policy library for future use.

5. The metacognitive introspection and self-driven method for AGI according to claim 1, characterized in that, The retrieval in step S6 is based on the cosine similarity between the context semantic vector of the memory unit and the current context vector.

6. The metacognitive introspection and self-driven method for AGI according to claim 1, characterized in that, The optimization instructions generated in steps S3 and S5 work together on the same set of internal parameters of the AGI mental model. The instructions in step S3 cause parameter fluctuations, while the instructions in step S5 cause parameter evolution. Together, they form a balance between short-term adaptation and long-term evolution in the AGI mental model.

7. A metacognitive introspection and self-driven system for AGI, used to implement the metacognitive introspection and self-driven method for AGI as described in any one of claims 1-6, characterized in that, include: The real-time introspection engine is configured to perform the real-time introspection process and includes a self-mirror building module, a meta-questioning computation module, and an instant optimization module. The historical evolution engine, configured to execute the historical evolution process, includes a structured memory bank, an experience extractor, and a historical analogy reasoning module; The memory bus and parameter interface are used to connect the real-time introspection engine and the historical evolution engine to realize the transfer of memory data and the injection of optimization instructions.

8. An AGI mental model entity, characterized in that, The system integrates a metacognitive introspection and self-driving system for AGI as described in claim 7, enabling the mental model entity to possess a dual self-evolutionary capability based on real-time introspection and historical experience.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a metacognitive introspection and self-driving method for AGI as described in any one of claims 1 to 6.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a metacognitive introspection and self-driving method for AGI as described in any one of claims 1 to 6.

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