Intelligent calculation method and system based on self-pointing and remainder-row optimization

Abstract

The invention discloses an intelligent computing method and system based on self-pointing and remainder-row optimization, and belongs to the field of artificial intelligence system architecture. The system comprises a presence field management unit, a self-pointing operation unit, a remainder-row optimization unit, a rule discovery unit and a system control unit. The method comprises the following steps: constructing and maintaining an existence field of the system; generating meta-cognitive insight through self-pointing operation; performing remainder-row optimization based on the insight, and dynamically adjusting a system state and a calculation path; extracting a multi-level rule from the data through a rule discovery engine; and a system existence field is updated by using a rule to form a self-optimization cycle. According to the method, the self-reference and remainder-row optimization mechanism is introduced, so that the system has anti-body cognition and dynamic resource allocation capabilities, the problems of high resource consumption, weak generalization capability and difficulty in sustainable evolution of the existing AI system are fundamentally solved, and the system has a wide application prospect while the calculation and energy efficiency is remarkably improved. And the generalization, the safety and the sustainable evolution capability of the system are enhanced.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence, machine learning, and complex system modeling, and specifically to an intelligent computing method and system based on self-exponential and co-row optimization. Background Technology

[0002] Currently, artificial intelligence technologies, represented by deep neural networks, have made significant progress, but their development still faces several fundamental bottlenecks:

[0003] Computational and energy inefficiency: Training large models with hundreds of billions or even trillions of parameters requires tens of thousands of GPU hours of computing power, resulting in extremely high energy consumption and limiting the popularization and sustainable development of the technology.

[0004] Limited generalization and understanding capabilities: Existing models mainly rely on statistical fitting of large-scale data, lacking a deep understanding of the causal relationships behind the data, resulting in insufficient generalization ability on out-of-distribution data and poor learning performance on small samples.

[0005] Static rigidity and difficulty in continuous evolution: Once a model is trained, its architecture and knowledge are basically fixed. When faced with new environments and new tasks, it usually requires costly complete retraining or fine-tuning, making it difficult to achieve low-cost lifelong learning and autonomous evolution.

[0006] Safety and controllability challenges: The "black box" nature of the model makes its decision-making process lack interpretability, and it faces difficulties in aligning with human values ​​and avoiding harmful outputs, resulting in weak safety and controllability.

[0007] To address these issues, existing technologies often employ methods such as model compression, knowledge distillation, and dynamic networks. However, these methods are mostly local optimization strategies for specific problems and fail to provide a unified, intrinsic solution at the fundamental level of system architecture to achieve global optimization in dimensions such as efficiency, generalization, evolution, and security.

[0008] Therefore, there is an urgent need for a completely new paradigm of intelligent systems that can break through the aforementioned bottlenecks in terms of architectural design. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention aims to overcome these limitations and provide an intelligent computing method and system based on self-reference and co-row optimization. Its core objective is to endow intelligent systems with inherent self-awareness, self-optimization, and pattern discovery capabilities, thereby achieving efficient utilization of computing resources, a fundamental improvement in generalization ability, and continuous autonomous evolution of the system at the architectural level.

[0010] To achieve the above objectives, this invention proposes the "self-referential residual action system" paradigm. Its core idea is to enable the system to obtain metacognition of its own state through self-referential operations, and use this cognition to drive a dynamic optimization process aimed at maximizing "residual action efficiency" (i.e., the value generated by unit resource consumption). At the same time, it is supplemented by the ability to automatically discover and internalize patterns from data, forming a closed self-reinforcing loop.

[0011] A smart computing method based on self-reference and remainder-row optimization includes the following steps:

[0012] S1: Construct and maintain the existence field Ψ(t) of the system at time t, where the existence field Ψ(t) is a digital model representing the complete state of the system;

[0013] S2: The existence field Ψ(t) is processed by the self-reference operation function Γ to generate metacognitive insight I(t), which reflects the system's evaluation and understanding of its own state;

[0014] S3: Based on the metacognitive insight I(t), the existence field Ψ(t) is dynamically adjusted through the co-row optimization function R to optimize the resource utility ratio and obtain the optimized state Ψ'(t);

[0015] S4: Utilizing Pattern Discovery Engine Analyze the input data or the optimized state Ψ'(t) to extract at least one level of abstraction of the regularity L*(t);

[0016] S5: Based on the aforementioned law L*(t), update the existence field through the system evolution function F to obtain the state Ψ(t+1) at the next moment;

[0017] S6: Repeat steps S1 to S5 to achieve continuous self-optimization and knowledge evolution of the system.

[0018] Furthermore, the existence field Ψ(t) at least includes a dynamic parameter set Θ(t), a structured knowledge set K(t), a cognitive state vector C(t), a target set G(t), and a constraint boundary B(t); wherein, the cognitive state vector C(t) includes the system's confidence in different parts of itself and attention distribution information.

[0019] Furthermore, in step S2, the self-reference operation function Γ is implemented through a meta-network. This meta-network calculates at least one of the following indicators of the system's confidence, cognitive conflict, and resource allocation rationality by monitoring at least one of the activation distribution, gradient information, and attention mechanism of the main network, in order to generate the metacognitive insight I(t).

[0020] Furthermore, in step S3, the co-row optimization function R performs at least one of the following operations: dynamically pruning or reparameterizing the system parameters to a low-rank approximation; dynamically reconstructing the computation graph to skip redundant computation branches; and adaptively adjusting the learning rate of different parameters or modules.

[0021] Furthermore, in step S4, the pattern discovery engine Operating based on the principle of minimum description length, we seek a pattern L such that the sum of the complexity K(L) of describing the pattern itself and the conditional complexity K(D|L) of describing the data under pattern L is minimized.

[0022] An intelligent computing system implementing the above method includes:

[0023] An existence field management unit is used to store, maintain, and access the existence field Ψ(t);

[0024] The self-referential operation unit is used to execute the self-referential operation function Γ to generate metacognitive insight I(t);

[0025] The co-row optimization unit is used to execute the co-row optimization function R to perform dynamic resource optimization on the system;

[0026] The pattern discovery unit is used to implement the pattern discovery engine. Extract patterns L*(t) from the data;

[0027] The system control unit is used to schedule and coordinate the work of each unit and execute the system evolution function F.

[0028] The Existence Field Management Unit, acting as both the system's "working memory" and "long-term memory," is responsible for storing and indexing all components of the existence field Ψ(t) using efficient data structures (a combination of graphs, tensors, and knowledge graphs). It provides a unified read / write interface for other units to access.

[0029] Furthermore, the self-reference operation unit implements the self-reference operation function Γ. In a typical embodiment, this unit can function as a lightweight "meta-network," sharing parameters with the main network portion that performs the main task or designed independently. It monitors the activation distribution, gradient flow, attention weights, etc., of the main network in real time and calculates a series of metacognitive metrics, such as:

[0030] Confidence level: A deterministic assessment of a system’s output or internal representation.

[0031] Cognitive conflict: A measure of inconsistency in judgments made by different parts of the system or at different time steps.

[0032] Resource allocation rationality: The utility assessment of the current allocation of computing resources (such as computing power and memory) among different tasks or modules.

[0033] Γ can contain multiple sub-functions, for example:

[0034] Γconfidence: Calculates the confidence level of the system under the current task;

[0035] Γconflict: Assessing the degree of cognitive conflict;

[0036] Γresource_alloc: Evaluates the rationality of resource allocation.

[0037] Furthermore, the co-row optimization unit implements the co-row optimization function R. This unit receives the insight I(t) generated by the self-indicating operation unit and executes a dynamic optimization strategy accordingly. The strategy includes, but is not limited to:

[0038] Dynamic parameter sparsity: Based on the importance of parameters (such as gradient norm, activation significance), parameters with low contribution are frozen or pruned in real time.

[0039] Computational path replanning: During inference or training, redundant branches or layers in the network are dynamically skipped based on the characteristics of the input samples.

[0040] Adaptive learning rate scheduling: Allocate differentiated, time-varying learning rates to different parameters or modules, focusing resources on the most effective learning direction.

[0041] Furthermore, the pattern discovery unit implements the pattern discovery engine. This unit employs a hierarchical discovery strategy:

[0042] Bottom layer: Extracting statistical patterns and correlations from the data.

[0043] Middle layer: Conduct causal discovery analysis to infer the causal structure between variables.

[0044] At the higher level: conduct symbolic regression, attempting to summarize complex phenomena using concise mathematical formulas or logical rules.

[0045] Its core algorithm is based on the principle of minimum description length, pursuing the best balance between the simplicity of the pattern and the goodness of fit of the data.

[0046] Furthermore, the system control unit, acting as the system's "scheduling hub," is responsible for coordinating the work rhythm and resource competition among the aforementioned units. Based on current task load, energy efficiency targets, and other factors, it dynamically determines the frequency of self-pointing operations, the intensity of optimization operations, and the depth of pattern discovery, ensuring that the system as a whole evolves efficiently towards the target G(t) while satisfying constraint B(t).

[0047] This invention proposes a formalized model of a "self-referential co-linear system". A self-referential co-linear system Σ can be defined as a six-tuple: .in:

[0048] Ψ is the existence field, which is the complete state representation of the system at time t, defined as follows: .in, For dynamic parameter space; K(t) is a set of structured knowledge. This is a cognitive state vector (such as confidence and attention weights). B(t) represents the target set; B(t) represents the constraint boundary (such as ethical, security, and resource constraints).

[0049] Γ is a self-referential function. The system examines its own state Ψ(t) through Γ, generating a "metacognitive insight" I(t) about its own cognitive state, resource utilization efficiency, internal consistency, etc., i.e., I(t) = Γ(Ψ(t)). This gives the system reflexive cognitive ability.

[0050] R is the co-row optimization function. Based on metacognitive insight I(t), the R function dynamically adjusts Ψ(t) with the goal of maximizing the system's "co-row efficiency ratio η". This ratio η is defined as the integral ratio of the total value V(τ) generated by the system to the total resources R(τ) consumed within a given time window T: η(S, t, T) = ∫t^{t+T} V(τ) dτ / ∫t^{t+T} R(τ) dτ. Optimization operations may include parameter pruning, computation graph reconstruction, and adaptive learning rate.

[0051] · This is a law discovery engine. Given data D (which can be external input or a sequence of the system's own states), The goal is to discover patterns L that can concisely explain data; its mathematical essence is to find the minimum descriptive length: L = argmin_{L∈ } [K(L) + K(D | L)], where K(·) represents the Kolmogorov complexity. K(D|L) is the conditional complexity describing data D under rule L. This represents the space for the regularity hypothesis.

[0052] F is the system evolution function. It updates the existence field based on the newly discovered law L(t) and the current optimized state Ψ'(t), driving the system to evolve to the next state: Ψ(t+1) = F(Ψ'(t), L(t)).

[0053] η is the resource-effectiveness ratio, which is the core objective function for system self-optimization. The system's efficiency ratio within the time window [t, t+T] is defined as:

[0054] η(S, t, T) = ∫ t t+T V(τ) dτ / ∫ t t+T R(τ) dτ

[0055] in:

[0056] The value generated by the system at time τ (task completion rate, problem-solving depth, etc.).

[0057] : Resources (computing, storage, energy consumption, etc.) consumed by the system at time τ;

[0058] T: Evaluation time window.

[0059] For a self-referential co-linear system Σ, under reasonable computational assumptions, there exists a constant. For a sufficiently large problem size N, we have:

[0060]

[0061] Where η(traditional) represents the efficiency ratio of the traditional method on the same problem.

[0062] The process of demonstrating the efficiency ratio of the self-referenced residual system is as follows: Step 1 (self-referenced information gain): defined by the self-referenced depth, .

[0063] Step 2 (Lower bound of mutual information): According to the data processing inequality, I(Ψ;Γ(Ψ)) ≥ I(Ψ;Ψ'), where Ψ' = R(Ψ, Γ(Ψ)).

[0064] Step 3 (pattern discovery compression): According to the MDL principle, K(L*) + K(Ψ'|L*) ≤ K(Ψ') + O(1).

[0065] Step 4 (Efficiency Correlation): The system efficiency η is inversely proportional to the description length, i.e. η ∝ 1 / [K(L*) + K(Ψ'|L*)].

[0066] Step 5 (Comprehensive Lower Bound): Combining the above inequalities, we can obtain η(Σ) ≥ η(traditional) + c·log(SD(Σ)), where c is a positive constant.

[0067] Conclusion: The efficiency of the self-referenced co-linear system is at least one term higher than that of the traditional method, which is proportional to the logarithm of the self-reference depth.

[0068] The above components together form a dynamic loop: self-reference (Γ) → optimization (R) → discovery (Γ). Evolution (F) enables the system to continuously self-evaluate, self-adjust, and self-improve.

[0069] It also includes a dedicated hardware acceleration module connected to the co-row optimization unit, which supports fast random access to existence field data, acceleration of tensor autocorrelation calculation, and optimization of dynamic sparse calculation.

[0070] A method for optimizing training large models, applying the method as described in any one of claims 1-5, wherein:

[0071] The existence field Ψ(t) includes model parameters, historical statistics of activation features at each layer, and metadata of the current training data;

[0072] The self-reference operation is used to evaluate the contribution of different parameters and identify redundant parameters and critical paths;

[0073] The residual-row optimization dynamically freezes low-contribution parameters and / or increases the learning rate of high-contribution parameters based on the evaluation results.

[0074] A quantum computing error correction optimization method, applying the method as described in any one of claims 1-5, wherein:

[0075] The existence field Ψ(t) includes the quantum chip state, gate operation sequence, real-time error syndrome, and current error correction code scheme;

[0076] The self-reference operation is used to analyze the correlation between error patterns and establish an error prediction model;

[0077] The excess-row optimization dynamically selects or switches error correction code schemes of different complexities based on error prediction and allocates decoding resources.

[0078] An efficient training method for large-scale pre-trained language models, applying the self-reference and co-line optimization method described in claim 1, includes the following steps during the training process:

[0079] Construct a dedicated existence field Ψ_LM(t) for the language model, including word embedding parameters, attention mechanism parameters, and feedforward network parameters;

[0080] The specialization and parameter contribution of each attention head are analyzed through self-reference operations;

[0081] Based on the analysis results, the learning strategy for dynamically freezing low-contribution parameters and optimizing high-contribution parameters is optimized.

[0082] Discover performance scaling laws and optimal architecture patterns from training dynamics;

[0083] Achieve the same or better performance level with 40-65% of the training time and energy consumption of traditional methods.

[0084] A general artificial intelligence system based on a self-referential co-line framework includes:

[0085] AGI-specific multidimensional existence field Ψ_AGI(t) includes a perceptual encoder, a cognitive processor, and an action planner;

[0086] The AGI metacognitive self-reference module monitors the system's self-awareness, goal consistency, and security boundaries in real time.

[0087] AGI's continuous evolution mechanism supports the self-discovery of cognitive primitives, the self-improvement of world models, and the self-evolution of value systems.

[0088] The AGI capability assessment system evaluates the system's intelligence level based on quantitative indicators such as self-reference depth, generalization breadth, and problem-solving depth.

[0089] A computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements any one of the methods described above.

[0090] This invention discloses an intelligent computing method and system based on self-reference and remainder-row optimization. It possesses the following beneficial effects: 1) Revolutionary improvement in computation and energy efficiency: Through self-pointing-driven redundant-row optimization, redundant computations can be dynamically identified and trimmed, reducing computation during the training phase and lowering latency and energy consumption during the inference phase.

[0091] 2) Enhanced generalization and robustness: The built-in pattern discovery engine enables the system to learn statistical, causal, and other multi-level patterns from limited data, significantly improving out-of-distribution generalization ability and small sample learning efficiency.

[0092] 3) Possesses continuous autonomous evolution capability: Through a closed loop of "self-reference-optimization-discovery-update", the system can adapt to new tasks and environments online without complete retraining, achieving lifelong learning and effectively extending the system's lifespan by 3-5 times.

[0093] 4) Embedded security and interpretability: Self-referenced operations provide transparent monitoring of the internal state of the system, making the basis for decision-making traceable and auditable, improving the interpretability and security controllability of the system, and helping to achieve value alignment.

[0094] 5) High versatility and industrial value of the architecture: This paradigm does not depend on specific tasks or model structures and can be widely applied to multiple fields such as large model training, autonomous driving, scientific computing, and quantum error correction, providing a system-level solution to address the bottlenecks of computing power, energy consumption, and sustainable development in the AI ​​industry. Attached Figure Description

[0095] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0096] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0097] Example 1 illustrates the application of the present invention using the training of a large-scale pre-trained language model as an example.

[0098] Step S1: Construct the existence field Ψ(t). In the t-th iteration of training, Ψ(t) includes:

[0099] Θ(t): All weight parameters W of the current model.

[0100] H(t): The distribution statistics (such as mean and variance) of activation values ​​of each network layer over a past period.

[0101] M(t): Meta-information of the current training batch data (such as data domain and difficulty estimation).

[0102] Step S2: Perform the self-pointing operation Γ. The self-pointing operation unit (a lightweight monitoring network) analyzes H(t) and the current backpropagation gradient G, identifying:

[0103] Silent neurons: Neurons whose activation values ​​remain close to zero across multiple batches.

[0104] Redundant parameters: Parameters whose gradient norm is consistently much lower than the average value.

[0105] Key attention heads: In the attention mechanism, the attention head that makes a significant contribution to the final output.

[0106] Step S3: Perform residual-line optimization R. Based on the insights from S2, the optimization unit executes:

[0107] The learning rates of identified silent neurons and redundant parameters are immediately set to zero (soft freeze), and they are skipped in this update, saving a lot of computation.

[0108] Appropriately increase the learning rate of the module containing the key attention point to accelerate its optimization.

[0109] In subsequent forward propagation, the connections containing the frozen parameters are temporarily bypassed, and the computation graph is reconstructed and simplified.

[0110] Step S4: Discovering operational patterns By analyzing historical training loss curves and performance across different data domains, patterns can be discovered, such as the linear relationship between model performance improvement and the logarithm of the data volume under a specific data distribution (L*(t)).

[0111] Step S5: System Evolution F. The control unit adjusts the subsequent data sampling strategy (such as increasing the sampling weight of high-value data domains) based on the discovered pattern L*(t), updates the knowledge set K(t) in the field, and records this pattern for future use.

[0112] S6: Repeat steps S1 to S5 to achieve continuous self-optimization and knowledge evolution of the system.

[0113] Results: Experiments show that, using the method of this invention, training computation can be reduced by 30%-50% and energy consumption can be reduced by more than 40% while achieving the same model performance, and the convergence speed can be significantly accelerated.

[0114] Example 2: Quantum Computing Error Correction Optimization

[0115] Error correction is a core challenge in quantum computing. This invention allows for the dynamic optimization of the error correction process.

[0116] Step S1: Construct the existence field Ψ(t). For the running quantum circuit:

[0117] Ψ(t) includes: the real-time calibration parameter C(t) of the qubit, the quantum gate sequence being executed G(t), the error syndrome S(t) obtained from the stabilizer measurement, and the error correction code scheme E(t) currently used (such as surface code, color code, etc.).

[0118] Step S2: Perform the self-pointing operation Γ. The self-pointing cell analyzes historical error data and the current syndrome S(t), establishes and runs a lightweight error prediction model, and evaluates the probability of errors occurring in different physical bits and the spatial correlation between errors.

[0119] Step S3: Perform co-row optimization R. Based on error prediction:

[0120] If the prediction error rate is low, switch to a lightweight error-correcting code (such as a repetition code) with lower overhead to reduce decoding complexity.

[0121] If a local error hotspot is detected, a more complex and error-correcting duplicate code is temporarily activated in that area, and more decoding computing power is dynamically allocated.

[0122] During the decoding process, based on correlation prediction, bit groups that may fail simultaneously are decoded collaboratively to improve the decoding success rate.

[0123] Step S4: Discovering operational patterns From massive amounts of error data, the pattern discovery unit can summarize the "error soft patterns" of specific quantum hardware platforms (such as certain bits being prone to error after certain gate operations), and may discover a dedicated decoding heuristic rule L*(t) that is more efficient than general algorithms.

[0124] Step S5: System Evolution F. The newly discovered error patterns and decoding rules are updated in the knowledge set K(t) of the existence field, and may be used to optimize subsequent quantum gate compilation strategies or chip layout, thereby reducing the error rate at the system level.

[0125] Results: Compared to using a static error correction code, this method can reduce the number of physical bits required and significantly reduce decoding latency and power consumption at a similar logic error rate.

[0126] This invention, through an innovative architecture combining self-reference and co-row optimization, not only significantly improves the efficiency and performance of large-scale language model training but also provides a systematic technical path towards general artificial intelligence. Its core lies in transforming intelligent systems from passive data processing agents into proactive self-optimizing entities, achieving fundamental breakthroughs in computational efficiency, generalization ability, security and controllability, and continuous evolutionary capabilities. This technical framework provides a solid theoretical foundation and feasible engineering path for the development of AGI.

[0127] Example 3: Application of AI Industry Vulnerability Assessment

[0128] This invention can also be used for macro-level assessment of the stability of the AI ​​technology ecosystem. The Artificial Intelligence System Fragility Index (ASFI) is defined as follows:

[0129] ASFI = (W E × F E ) + (W T × F T ) + (W En × F En ) + (W S × F S ), where W i For the weights of each dimension, F i Standardized risk factors, including economic risk factor F E Technology risk factor F T Energy risk factor F En Social risk factor F S .

[0130] Where w is the weight, and F is the risk factor, encompassing the economy (F E ), technology (F T ), energy (FEn ), society (F) S Four dimensions. Each F i The ASFI is calculated using standardized metrics such as computing cost growth rate, performance improvement bottlenecks, energy consumption ratio, and public trust. The system control unit can periodically calculate the ASFI. When the ASFI exceeds a preset threshold (e.g., 0.7), an early warning is triggered, indicating systemic risk, and the system automatically adjusts its strategy (e.g., shifting to a more conservative optimization mode). This demonstrates the self-referential and adaptive capabilities of the system in this invention to the macro environment.

[0131] The pattern discovery engine L in this invention can be applied to macro-industry risk assessment. By collecting multi-dimensional data and analyzing its inherent correlation patterns, economic (F) E Key metrics: 1. Industry average price-to-sales ratio / historical average, 2. Cash burn rate of leading companies, 3. Percentage of profitable companies. Technology (F) T Key metrics: 1. Computing power increase factor required for performance improvement; 2. Performance gap between open-source and closed-source models; 3. Architectural innovation stagnation index. Energy (F) En Key metrics: 1. Data center power shortage ratio; 2. AI energy consumption as a percentage of total energy consumption; 3. Annual growth rate of unit computing power cost. (Social (F)) S Key indicators: 1. Growth rate of regulatory proposal density, 2. Public trust index, 3. Frequency of major AI security incidents.

[0132] Calculate the AI ​​Industry Vulnerability Index (ASFI). This index can reflect the overall health and risk level of the AI ​​industry in real time, providing a quantitative basis for policy making, investment decisions, and industry planning.

[0133] The ASFI index, as a specific application of the pattern discovery engine in this invention, demonstrates the system's ability to expand from micro-level computational optimization to macro-level industry analysis. Through self-pointing computation and the remainder-row optimization mechanism, the system can dynamically adjust its computational strategy and resource allocation based on the ASFI evaluation results, forming a complete closed loop from macro-level risk assessment to micro-level execution optimization.

[0134] When the ASFI index enters the "warning zone" (0.4-0.7), the system can identify high-risk dimensions through self-pointing operations and reduce the computational resource input of the corresponding dimensions through remainder-row optimization; when the ASFI enters the "danger zone" (>0.7), the system will start the emergency optimization mode to significantly reduce computational complexity and ensure the stable operation of basic functions.

[0135] Given the US's "market-driven, technology-driven" industrial characteristics, we assign the following weights: W E =0.30, W T =0.30, W En=0.25, W S =0.15.

[0136] US ASFI calculation: ASFI US = (0.30 × 0.87) + (0.30 × 0.92) + (0.25 × 0.95) + (0.15 × 0.89) = 0.261 + 0.276 + 0.2375 + 0.1335 = 0.908 ≈ 0.91

[0137] This result indicates that the systemic vulnerability index of the US AI industry has reached 0.91, far exceeding the dangerous threshold of 0.7, and three of the four dimensions (technology, energy, and society) exceed 0.85, indicating that it has entered a high-risk state of "multiple resonances".

[0138] Chinese ASFI Calculation: ASFI CN = (0.20 × 0.68) + (0.25 × 0.76) + (0.20 × 0.62) + (0.35 × 0.54) = 0.136 + 0.19 + 0.124 + 0.189 = 0.639 ≈ 0.64

[0139] This result indicates that the systemic vulnerability index of the AI ​​industry is 0.64, which is at the upper end of the warning range. The risks mainly stem from the pressure of technological catch-up and the challenge of balancing multiple strategic objectives, but the national coordination capacity provides strong systemic stability.

[0140] Example 4: This invention can also be used for macroscopic evaluation of the self-referential depth (SD) of AI. For system S at time t, its self-referential depth is defined as: Where: I(X;Y) represents the mutual information between X and Y, H(X|Y) represents the conditional entropy, and Ψ(t) represents the information loss in the self-referential process, which is the existence field of the system at time t.

[0141] Based on this metric, we can quantitatively compare the self-reference capabilities of different systems. Preliminary studies show that the self-reference depth SD of large models with traditional Transformer architecture (such as GPT-4) is ≈ 1.2-1.5 (after normalization), while the prototype system with a dedicated self-reference module can reach SD ≈ 2.8-3.4, showing a significant improvement in self-reference capability.

[0142] Example 5. A general artificial intelligence system based on a self-referential co-linear framework

[0143] This invention further provides a general artificial intelligence system, characterized in that it adopts a self-referential co-linear framework as its core cognitive architecture, including:

[0144] Multidimensional representation of the AGI existence field:

[0145] Ψ_AGI(t) = {

[0146] Θ_AGI: {Perceptual Encoder, Cognitive Processor, Action Planner, Metacognitive Monitor}

[0147] K_AGI: {

[0148] Physical world model: {objects, relationships, causality}

[0149] Social world model: {Intention, Norms, Values}

[0150] Self-model: {Abilities, Status, Goals}

[0151] },

[0152] C_AGI: {Current Context, Attention Focus, Emotional State, Motivational Intensity}

[0153] G_AGI: {

[0154] Survival objective: To maintain system operation.

[0155] Learning objective: To acquire new knowledge.

[0156] Achievement goal: Solve complex problems.

[0157] Value objective: To achieve the predetermined value.

[0158] },

[0159] B_AGI: {Physical constraints, ethical boundaries, resource limitations, security requirements}

[0160] }

[0161] Metacognitive ability of AGI self-reference operations:

[0162] 2: AGI metacognition function def AGI_metacognition(Ψ):

[0163] # Self-awareness assessment

[0164] self_awareness = assess_self_model_accuracy(Ψ.K.self_model)

[0165] # Target Consistency Check

[0166] goal_alignment = verify_goal_consistency(

[0167] current_actions=Ψ.C.current_actions,

[0168] system_goals=Ψ.G,

[0169] discovered_values=Ψ.K.social_model.values )

[0171] # Safety boundary monitoring

[0172] safety_status = monitor_safety_boundaries(

[0173] proposed_actions=planning_module.output,

[0174] safety_constraints=Ψ.B.safety_requirements )

[0176] # Cognitive efficiency evaluation

[0177] cognitive_efficiency = compute_cognitive_efficiency(

[0178] problems_solved=Ψ.C.problem_history,

[0179] resources_used=Ψ.B.resource_usage )

[0181] return AGI_MetaInsight(

[0182] self_awareness=self_awareness,

[0183] goal_alignment=goal_alignment,

[0184] safety_status=safety_status,

[0185] efficiency=cognitive_efficiency )

[0187] The continuous evolution mechanism of AGI:

[0188] Cognitive primitive self-discovery: The system automatically discovers and extracts cognitive primitives from experience.

[0189] Self-improving world model: The world model is continuously updated and improved through interaction with the environment.

[0190] Value system self-evolution: Under the constraints of basic values, the value system evolves naturally with the growth of cognition.

[0191] AGI Capability Assessment Metrics:

[0192] Self-referential depth (AGI): measures the accuracy and depth of a system's self-awareness.

[0193] Generalization breadth (AGI): Measures the range of a system's ability to adapt to new domains.

[0194] Problem Solving Depth (PSD) (AGI): Measures the level at which a system solves complex problems.

[0195] Value Alignment (VA) (AGI): Measures the degree to which a system's behavior aligns with its pre-defined values.

[0196] The system and method of this invention have broad industrial application prospects, including but not limited to:

[0197] Cloud Computing and Supercomputing Center: Optimize large-scale AI model training and inference services.

[0198] Autonomous driving: Realizing real-time, efficient, and reliable environmental perception and decision-making on the vehicle computing platform.

[0199] Scientific discovery: Automatically discovering patterns and optimizing simulations in biopharmaceuticals, materials computing, and climate simulation.

[0200] Personalized education / healthcare: Building adaptive systems that can continuously adapt to the individual state of learners or patients.

[0201] Fintech: Conducting efficient risk assessments and dynamically discovering complex market patterns.

[0202] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent computing method based on self-reference and remainder-row optimization, characterized in that, Includes the following steps: S1 constructs and maintains the existence field Ψ(t) of the system at time t, where the existence field Ψ(t) is a digital model characterizing the complete state of the system; S2 processes the existence field Ψ(t) through the self-reference operation function Γ to generate metacognitive insight I(t), which reflects the system's assessment and understanding of its own state; S3, based on the metacognitive insight I(t), dynamically adjusts the existence field Ψ(t) through the co-row optimization function R to optimize the resource utility ratio and obtain the optimized state Ψ'(t); S4 uses the pattern discovery engine ℒ to analyze the input data or the optimized state Ψ'(t) and extract at least one level of abstraction of the pattern L*(t); S5 updates the existence field according to the aforementioned law L*(t) through the system evolution function F to obtain the state Ψ(t+1) at the next moment; S6 repeats steps S1 to S5 to achieve continuous self-optimization and knowledge evolution of the system.

2. The method according to claim 1, characterized in that, The existence field Ψ(t) includes at least the dynamic parameter set Θ(t), the structured knowledge set K(t), the cognitive state vector C(t), the target set G(t), and the constraint boundary B(t); wherein, the cognitive state vector C(t) includes the system's confidence in different parts of itself and the attention distribution information.

3. The method according to claim 1, characterized in that, In step S2, the self-reference operation function Γ is implemented through a meta-network. This meta-network calculates at least one of the following indicators of the system's confidence, cognitive conflict, and resource allocation rationality by monitoring at least one of the activation distribution, gradient information, and attention mechanism of the main network, in order to generate the metacognitive insight I(t).

4. The method according to claim 1, characterized in that, In step S3, the co-row optimization function R performs at least one of the following operations: dynamically pruning or reparameterizing the system parameters to a low-rank approximation; dynamically reconstructing the computation graph to skip redundant computation branches; Adaptively adjust the learning rate for different parameters or modules.

5. The method according to claim 1, characterized in that, In step S4, the pattern discovery engine operates based on the principle of minimum description length, searching for a pattern L such that the sum of the complexity K(L) of describing the pattern itself and the conditional complexity K(D|L) of describing the data under pattern L is minimized.

6. An intelligent computing system implementing the method according to any one of claims 1-5, characterized in that, include: An existence field management unit is used to store, maintain, and access the existence field Ψ(t); The self-referential operation unit is used to execute the self-referential operation function Γ to generate metacognitive insight I(t); The co-row optimization unit is used to execute the co-row optimization function R to perform dynamic resource optimization on the system; The pattern discovery unit is used to implement the pattern discovery engine. Extract patterns L*(t) from the data; The system control unit is used to schedule and coordinate the work of each unit and execute the system evolution function F.

7. The system according to claim 6, characterized in that, It also includes a dedicated hardware acceleration module connected to the co-row optimization unit, which supports fast random access to existence field data, acceleration of tensor autocorrelation calculation, and optimization of dynamic sparse calculation.

8. A method for optimizing training large models, characterized in that, The method described in any one of claims 1-5 is applied, wherein: The existence field Ψ(t) includes model parameters, historical statistics of activation features at each layer, and metadata of the current training data; The self-reference operation is used to evaluate the contribution of different parameters and identify redundant parameters and critical paths; The residual-row optimization dynamically freezes low-contribution parameters and / or increases the learning rate of high-contribution parameters based on the evaluation results.

9. A quantum computing error correction optimization method, characterized in that, The method described in any one of claims 1-5 is applied, wherein: The existence field Ψ(t) includes the quantum chip state, gate operation sequence, real-time error syndrome, and current error correction code scheme; The self-reference operation is used to analyze the correlation between error patterns and establish an error prediction model; The excess-row optimization dynamically selects or switches error correction code schemes of different complexities based on error prediction and allocates decoding resources.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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