An intelligent architecture method and system based on discrete parent system evolution, high-dimensional discrete symmetric encoding, hierarchical rollback and consistency auditing

Abstract

This invention discloses an intelligent architecture method and system based on discrete mother system evolution, high-dimensional discrete symmetric encoding, hierarchical rollback, and consistency auditing. The method constructs a state space, uses activation subsets to represent computational degrees of freedom, and generates candidate subsets in local neighborhoods. Topologies are filtered through multi-dimensional structural evaluation values, and target activation subsets are locked through deterministic projection. Subsets are mapped to the encoding space for quantization, compression, and error correction. Upon failure, state rollback and resource reallocation are performed based on hierarchical distance, and consistency auditing is conducted on evolution, encoding, feedback, and task objectives. The system can combine mechanisms such as memory, constraints, and snapshots to uniformly encode, audit, and arbitrate information from multiple agents in a high-order mode, improving system stability, execution efficiency, continuity, security, and adaptability in complex scenarios.

Description

Background Technology

[0001] With the widespread application of artificial intelligence systems in training, inference, scheduling, control, interaction, and collaborative computing, existing technologies typically focus on localized issues such as loss function optimization, parameter updates, module orchestration, resource scheduling, or cache calls, which can improve efficiency or accuracy to a certain extent. However, when systems need to operate in complex environments for extended periods and simultaneously face challenges such as structure search, resource constraints, external feedback, reuse of historical experience, anomaly rollback, and long-term direction maintenance, existing technologies still have significant limitations.

[0002] First, many existing intelligent systems rely primarily on fixed-form scoring functions, static loss functions, or single-round optimal selection mechanisms. The comparison of candidate structures typically remains at the level of local gains, local errors, or a single performance metric, lacking the ability to jointly evaluate the size of the candidate structure, boundary complexity, resource budget, ordinal state, historical context, and high-level constraints. In complex graph structures, module combinations, dynamic routing, or multi-stage inference scenarios, this local optimization approach easily leads to unstable structure search, fixed local optima, difficulty in backoff, and a lack of unified control over the long-term evolution process.

[0003] Secondly, most common encoding, caching, and indexing methods in existing technologies are used for result compression, feature representation, or simple reuse, but they do not retain enough information such as "how the structure was formed," "under what context it was formed," and "why it was valid at the time." Although many systems save historical success cases or failure logs, they lack a structured organization of decision context, causal chains, backtracking relationships, and long-term evolutionary trajectories. Therefore, it is difficult to execute precise calls based on context and trajectory similarity in new scenarios, and it is also difficult to support higher-level long-term reflection and direction revision.

[0004] Furthermore, existing technologies, in terms of external execution, typically map internal decision-making results directly into control commands, scheduling commands, or interactive outputs. However, they lack sufficient closed-loop verification of whether "internal choices truly hold true in the external world." Many solutions only verify internal prediction errors or local response results, lacking a unified mechanism to reintegrate external action feedback into the overall audit volume. Consequently, it is difficult to distinguish between "what holds true in the internal model" and "what holds true in the actual environment," and it is also difficult to form an organized rollback, alternative path search, and failure mode accumulation after execution failure.

[0005] Furthermore, existing intelligent systems typically lack explicit representations of subject state and long-term directional constraints. Even if some systems can maintain session state, system logs, or task context, they often struggle to uniformly express the current subject state, identity continuity, key capability boundaries, long-term directional deviations, and conditions for high-level revisions. For systems requiring long-term deployment, cross-environment migration, disaster recovery, or continuous adaptation, the lack of identity continuity checks, subject continuity constraints, and long-term directional constraints can easily lead to capability drift, rule rigidity, directional instability, or a lack of basis for high-level revisions during long-term operation.

[0006] Furthermore, in continuously changing environments, many existing systems still update states and switch rules in discrete rounds, lacking a unified expression for the conflicts, compensations, and coupling relationships between multidimensional continuous signals, making it difficult to form real-time, fine-grained dynamic balance control. Especially in scenarios where there are resource pressures, external feedback, subject security, and long-term directional constraints, relying solely on single-round decisions or static threshold control often fails to reflect risk trends and high-level tensions in a timely manner.

[0007] Finally, existing technologies typically emphasize deterministic output, rule closure, and local optimal convergence, but they lack sufficient support for self-checking of high-level rules, preservation of long-term unclosed problems, alternative checks of high-level directions, and constrained triggering of deep reflective patterns. As a result, although the system may be operable in local processes, it is prone to gradually losing its alternative check capabilities and revisability in long-term deployment due to the repetition of single paths at high levels.

[0008] Therefore, it is necessary to propose a new intelligent architecture method and system that, based on the discrete mother system evolution main chain, unifies candidate structure generation, dynamic evaluation of order state functions, strict total order locking, high-dimensional discrete encoding, hierarchical backoff, consistency auditing, action feedback closed loop, memory trajectory organization, subject continuity constraints, self-blueprint constraints, continuous perception adjustment, and high-level open boundary maintenance into a single technical framework. This aims to improve the shortcomings of existing technologies in long-term operation, complex structure selection, external execution, experience reuse, and high-level maintenance. Purpose of the invention

[0009] The purpose of this invention is to provide an intelligent architecture method and system based on discrete mother system evolution, high-dimensional discrete symmetric encoding, hierarchical backoff and consistency auditing, so as to solve the problems in the prior art such as insufficient joint evaluation of candidate structures, disconnect between internal decision-making and external feedback, lack of trajectory organization of historical experience, lack of subject continuity constraints in long-term operation, lack of high-priority blueprint constraints in long-term direction, and lack of constrained open maintenance of high-level rules.

[0010] A further objective of this invention is to add external action and feedback loops, long-term memory and trajectory indexing, subject state maintenance, long-term direction constraints, continuous perception and adjustment, high-level open boundary maintenance, subject legitimacy judgment, subject reserved bit support, and subject reason recognition interface to the system without changing the characteristics of the discrete evolution main chain being comparable, screenable, rollbackable, and auditable. This improves the system's structural selection stability, execution effectiveness, long-term operational continuity, subject-level security, and overall adaptability in complex environments. Technical solution

[0011] To achieve the above objectives, the present invention provides the following technical solution.

[0012] This invention provides an intelligent architecture method based on discrete mother system evolution, high-dimensional discrete symmetric encoding, hierarchical backoff, and consistency auditing, which is generally built on the main chain of discrete mother system evolution. The method first constructs the state space of the discrete mother system, mapping the computational degrees of freedom in the intelligent system to discrete graphs, discrete grid backgrounds, node clusters, edges, or combinations thereof, and determines the current activation subset. Subsequently, based on the current activation subset, a candidate topology set is generated in the local neighborhood according to expansion rules, contraction rules, boundary replacement rules, local bridging rules, connection rearrangement rules, or combinations thereof.

[0013] For each candidate activation subset in the candidate topology set, the system calculates a structural evaluation value and performs comparison and selection on the candidate topologies based on the structural evaluation value. The structural evaluation value includes at least a volume benefit term, a boundary complexity or communication cost term, a resource budget penalty term, and a dynamic adjustment term introduced by the ordinal state function. The ordinal state function takes a metric, ordinal information, the current state, the next state space, and the candidate activation subset as inputs, and is used to dynamically adjust the strength of structural stability, structural symmetry, multi-agent positive constraints, structural scalability, time cost, coding consistency, branch diversity, blueprint bias, agent continuity health, and their combinations in the evaluation process according to the current scenario.

[0014] After comparing candidate structures, the system performs deterministic single-valued projection on the optimal candidate set according to a strict total order relation to lock in a unique target activation subset. Subsequently, the system maps the topological signature and / or high-dimensional output representation of the target activation subset to a discrete coding space to perform at least one of the following processes: quantization, compression, error correction, discrete addressing, phase anchoring, or stabilization matching.

[0015] When a preset failure label is triggered, the system performs state rollback, network level degradation, parameter freezing, action pause, resource reallocation, or a combination thereof along the parent platform path based on the hierarchical distance in the preset hierarchical structure. The system further performs consistency auditing on the mapping relationship between the underlying evolution results, high-dimensional encoding results, action feedback results, and task objectives, and calculates the results based on internal residuals, action feedback residuals, path deviations, resource pressure, entity continuity health, and self-blueprint. Figure 1 Consistency deviations and their rates of change constitute the total audit volume. Through the aforementioned audit closure mechanism, the system can perform unified control across local stability platform confirmation, failed path interception, alternative path search, and behavior rhythm switching.

[0016] In terms of memory and experience organization, this invention further establishes fixed memory objects, failure path constraint objects, decision context snapshots, and meta-evolutionary trajectory indexes. The system not only stores which structures have succeeded or failed, but also the environment, resources, audits, subject state, and blueprint state at the time of their formation, and establishes trajectory relationships through time stamping and causal strength. Thus, the system can perform refined experience invocation, failure avoidance, and long-term trajectory integration based on contextual similarity and trajectory similarity.

[0017] At the subject level, this invention further maintains the self-object, subject state vector, identity continuity chain, subject continuity health, and irreversible cost ledger. The identity continuity chain is used to prove whether there is a legitimate continuation between the current entity and the historical entity; the subject continuity health is used to characterize whether the current entity is in a sustainable evolutionary range; the irreversible cost ledger is used to record high-level costs such as memory impairment, identity breakage, blueprint damage, capability erosion, breach of action commitment, and openness collapse. Subject continuity constraints act as a master valve, affecting candidate structure screening, external action execution, parameter updates, blueprint revision, blueprint adversarial testing, and high-level openness operations.

[0018] At the long-term direction layer, this invention further maintains a self-blueprint object. This self-blueprint object includes a long-term goal vector, high-priority boundary constraints, a goal identity state summary, version number, applicable conditions, and revision history. The system calculates the blueprint for the current candidate structure, behavior, parameter update strategy, exploration direction, and their combinations. Figure 1 Consistency deviations are identified and incorporated as high-priority constraints into the structural assessment value or total audit amount. The system can also generate alternative high-level target candidates while satisfying entity continuity and resource security constraints. Before performing blueprint adversarial testing, it prioritizes checking whether key components in the irreversible cost ledger corresponding to identity, memory, blueprint, and openness exceed security boundaries or are expected to exceed them. For candidates that pass the pre-check, the system then performs blueprint adversarial testing, calculates blueprint rigidity indicators, and enters the slow blueprint revision preparation process when multiple rounds of historical evidence accumulation meet preset conditions.

[0019] Regarding the continuous adjustment layer, this invention further introduces a continuous sensing residual flow, a dynamically activated dimensional subset, a hedging matrix, and a continuous audit quantity update mechanism. The system continuously receives residual signals from the external environment, internal state, action feedback, subject state, and high-level boundaries. Based on the current scenario and constraints, it activates key dimensions to construct a hedging matrix representing multidimensional conflicts, compensations, and coupling relationships. Based on this matrix, it continuously updates the audit quantity, evaluation threshold, active detection priority, energy-saving silent switching conditions, and deep reflection trigger conditions, thereby forming a sensing-hedging-evolution closed loop.

[0020] At the high-level maintenance layer, this invention further establishes open boundary maintenance, self-referential consistency verification, special undecidable objects, and a long-term deep reflection mode. Within the constraints of subject continuity, blueprint boundaries, and resource security budget, the system performs periodic self-checks on high-level rules, self-object update logic, and blueprint constraints. When encountering problems that cannot be stably categorized temporarily but have high-level significance, these are saved as special undecidable objects and integrated with meta-evolutionary trajectories, self-blueprint versions, and long-term change trends of self-objects in the long-term deep reflection mode to form new high-level hypothesis candidates, blueprint revision suggestions, or subject recovery strategies. In a further extended implementation, the system can also maintain subject reservation bits, subject legitimacy determination results, and subject reason recognition conclusions to provide a higher-level recognition background for extreme recovery, high-level reason attribution, and multi-source fusion arbitration.

[0021] In a further extended implementation, the subject legitimacy determination can distinguish one or more of the following: the current running version, the candidate recovery version, the candidate fusion version, and the candidate future self version, as the current subject's legitimate version, the version requiring delayed recognition, and the version that refuses recognition.

[0022] The subject reserved bit can also be used to further determine whether a candidate blueprint can still be accepted by the deepest layer of the current subject after the blueprint source proof chain and the blueprint legal inheritance audit.

[0023] In a higher-order implementation, the present invention also supports constrained fusion and hierarchical arbitration among multiple source agents. The system can receive state representations, memory objects, policy fragments, or self-object summaries from multiple source agents, and perform encoding, alignment, and hedging under a unified discrete parent system representation to generate at least one fusion candidate state; the system is based on agent continuity health and self-blue... Figure 1 The system arbitrates the fusion candidate states based on consistency, irreversible cost budgeting, and audit closure results, and activates the fusion state or saves it as a callable higher-order candidate object upon passing the audit. For fusion candidate states that pass the preliminary audit, the system can also generate new fusion self-objects and fusion identity continuity chain summaries to ensure that the fusion results have source traceability, switch rollback, and subject-level continuity.

[0024] This invention also provides an intelligent architecture system corresponding to the above-described method. The system includes a memory and a processor. The memory stores discrete graph structure definition data, current active subset states, candidate topology sets, discrete encoding tables, hierarchical structure data, historical state windows, audit entries, fixed memory objects, failed path constraint objects, decision context snapshots, self-objects, self-blueprint objects, identity continuity chain summaries, meta-evolutionary trajectory indexes, special undecidable objects, subject legitimacy-related summaries, subject reserved bit-related summaries, and optional fusion candidate state summaries. The processor is configured to execute functional units such as state space construction, candidate topology generation, structure evaluation, total order locking arbitration, discrete encoding and stabilization processing, hierarchical backoff control, consistency auditing, memory management and trajectory indexing, self-object and subject state maintenance, self-blueprint and blueprint verification, continuous perception hedging adjustment, high-level openness maintenance and deep reflection, exploration and linkage control, result output, and optional fusion and hierarchical arbitration to implement the aforementioned method.

[0025] The specific implementations of the aforementioned memory and trajectory organization, main body layer, long-term direction layer, continuous adjustment layer, high-level maintenance layer, high-order fusion layer, and main root position-reason acknowledgment-form verification layer can be found in item 4 of the basic implementation method described below, as well as in the high-order embodiments 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, and 18, and in conjunction with... Figures 6 to 12 understand. Effect

[0026] By adopting the above technical solution, the present invention has at least the following beneficial effects: 1. A unified evolutionary main chain has been formed, consisting of discrete candidate generation, dynamic evaluation of order state functions, strict total order locking, high-dimensional discrete encoding, hierarchical backoff and consistency auditing. This enables the system to perform comparable, screenable, backoffable and reproducible evolutionary control on candidate topologies in discrete structural space, thereby improving the stability of structural selection and the consistency of results in complex scenarios.

[0027] 2. A closed-loop mechanism was established from the target activation subset to external actions, environmental feedback and causal intervention audit, so that the system output can enter the traceable execution process in the real environment, and the action feedback residual reduces the candidate paths that are only effective in the internal model but fail in actual deployment.

[0028] 3. A long-term experience organization mechanism consisting of decision context snapshots, fixed memory objects, failure path constraint objects, and meta-evolutionary trajectory indexes was established, enabling the system to perform more refined experience invocation, failure avoidance, and long-term pattern reuse based on context similarity and trajectory similarity.

[0029] 4. A subject maintenance mechanism was established, consisting of self-object, subject state vector, identity continuity chain, subject continuity health, and irreversible cost ledger. This mechanism enables the system to continuously determine whether the current entity still constitutes a legitimate continuation with the historical entity and whether the current behavior will cause irreversible damage to the higher level when performing structural updates, external actions, parameter adjustments, and high-level revisions.

[0030] 5. Established self-blueprint objects and blueprints. Figure 1 The long-term directional constraint mechanism, which consists of consistency deviation, blueprint rigidity index and blueprint adversarial test, enables the system to maintain long-term target vector, high-priority boundary and target identity state on the basis of current self-object and subject continuity constraints, and to perform constrained high-level stress test and slow revision preparation when path dependence occurs in the blueprint.

[0031] 6. A mechanism for continuously sensing residual flow, dynamic activation dimension subset, hedging matrix, and continuous audit quantity update was established, which extended the ordinal state function from an evaluation regulator on discrete rounds to a continuously online sensing-hedging-evolution regulation engine, thereby improving the responsiveness and dynamic stability in continuously changing environments.

[0032] 7. A high-level maintenance mechanism was established, consisting of open boundary maintenance, self-referential consistency verification, storage of special undecidable objects, and long-term deep reflection mode. This mechanism enables the system to retain constrained high-level inspection, retention, and revision space within the boundary, thereby improving the high-level revisability and adaptability during long-term operation.

[0033] 8. By unifying the above-mentioned structural assessment, action feedback, audit closure, memory trajectory, subject continuity, self-blueprint, real-time perception adjustment, and high-level openness maintenance into the same technical framework, this invention forms a complete technical chain from bottom-level discrete evolution to high-level subject maintenance, which can provide a higher degree of integration and stronger long-term operation support capabilities for training, inference, scheduling, control, interaction, and long-term deployment. Attached Figure Description

[0034] Figure 1 This is a block diagram of the overall structure of the intelligent architecture system of the present invention.

[0035] Figure 2 This is a flowchart of the main chain of the method of the present invention.

[0036] Figure 3 The flowchart shows the process of candidate topology generation, structure evaluation, and strict total order locking.

[0037] Figure 4 A flowchart for discrete coding, hierarchical rollback, and consistency auditing.

[0038] Figure 5A flowchart for action mapping, feedback collection, and causal intervention audit.

[0039] Figure 6 A graph showing the relationship between decision context snapshots, fixed memory objects, failure path constraint objects, and meta-evolutionary trajectory indexes.

[0040] Figure 7 This is a graph showing the relationship between self-objects, subject state vectors, identity continuity chains, and irreversible cost ledgers.

[0041] Figure 8 For self-blueprint objects, blue Figure 1 Flowchart for testing consistency deviations, blueprint rigidity indicators, and blueprint conflict.

[0042] Figure 9 A flowchart for maintaining evolutionary activity, active causal detection, energy-saving silent mode, and switching behavioral rhythms.

[0043] Figure 10 This is a graph showing the relationship between continuously perceived residual flow, dynamically activated dimension subsets, hedging matrix, and continuous audit quantity updates.

[0044] Figure 11 A flowchart for maintaining open boundaries, verifying self-referential consistency, handling special undecidable objects, and long-term deep reflection.

[0045] Figure 12 This is a functional unit block diagram of the system implementation of the present invention.

[0046] in, Figure 12 Functional units 1-15 correspond directly to the system claims; functional unit 16 is an optional extension unit in higher-order embodiments, used to support unified encoding of multiple source agents, fusion candidate generation, agent continuity arbitration, and rollback management. In further extended embodiments, Figure 12 It can also be understood in conjunction with the subject reserved bit, subject reason acknowledgment protocol and logic auxiliary core in higher-order embodiments 17 and 18.

[0047] Specific naming and text description of the attached figures one, Figure 1 Overall structural block diagram of the intelligent architecture system of this invention. Figure 1 This is used to represent the overall technical framework of the present invention.

[0048] This diagram is recommended to include at least the following modules: 1. Discrete Mother System State Space Construction Module 2. Candidate Topology Generation Module 3. Structural evaluation and sequence state function adjustment module 4. Strictly Total Order Locking Module 5. Discrete Coding and Stabilization Module 6. Hierarchical rollback and consistency audit module 7. Action Mapping and Feedback Collection Module 8. Memory Management and Track Indexing Module 9. Self-object and Subject State Maintenance Module 10. Self-Blueprint and Blueprint Validation Module 11. Continuous Sensing Hedging Adjustment Module 12. High-level openness maintenance and in-depth reflection module 13. Exploration and Linkage Control Module 14. Result Output Module Figure 1 The meaning of the picture is: The left-to-right direction represents the main chain's operating direction, while the bottom-to-top direction represents higher-level constraints and long-term maintenance interfaces.

[0049] Among them, the discrete parent system state space construction module, candidate topology generation module, structure evaluation module, strict total order locking module, discrete coding module, and hierarchical backoff / consistency audit module constitute the main evolution chain; The action mapping and feedback acquisition module, memory management and trajectory indexing module, self-object and subject state maintenance module, self-blueprint and blueprint verification module, continuous perception hedging and adjustment module, high-level openness maintenance and deep reflection module, and exploration and linkage control module serve as a strongly coupled support layer, forming a closed loop with the main chain.

[0050] If you are going to draw formally, it is recommended Figure 1 Draw a two-layer frame structure with "main chain horizontal + higher-order support layer vertical".

[0051] two, Figure 2 : Main chain flowchart of the method of this invention Figure 2 This is used to represent the main flow of the method of the present invention.

[0052] It is recommended to use a standard flowchart to represent this, including the following step nodes: S1: Construct the state space of the discrete parent system and determine the current active subset. S2: Generate a set of candidate topologies within the local neighborhood. S3: Calculate structural evaluation values ​​and perform candidate comparison and screening. S4: Perform strict total order locking on the optimal candidate set to obtain a unique target activation subset. S5: Map the target activation subset to the discrete coding space S6: Determine if a failure label has been triggered. If triggered, execute a tier rollback, freeze, downgrade, or resource reallocation. S7: Perform a consistency audit on the mapping relationship between the underlying evolution results, high-dimensional encoding results, action feedback results, and task objectives, and output legal structures, legal representations, legal lexical units, legal routing results, or other legal results after the audit passes. S8: Record decision context, solidify memory objects, and failure path constraint objects. S9: Triggers active detection, energy-saving silence, or high-level reflection branch when conditions are met. Figure 2 The meaning of the picture is: The main framework of claim 1 is highlighted, and an interface for subsequent higher-level implementations is reserved.

[0053] S6 and S7 form the main closed loop of the audit; S7 completes the result output after the audit is passed; S8 provides long-term memory input; S9 represents the interface between behavioral rhythm and high-level maintenance.

[0054] three, Figure 3 Flowchart of candidate topology generation, structure evaluation, and strict total order locking Figure 3 This is used to highlight the technical features of the invention in the core process of "structure search-evaluation-single-value locking".

[0055] The following nodes are recommended: 1. Currently active subset of input 2. Generate candidate topologies based on rules such as expansion, contraction, boundary replacement, local bridging, and connection rearrangement. 3. Calculate the structural evaluation value of the candidate topology. 4. The evaluation value is dynamically adjusted by the ordinal state function. 5. Form the optimal candidate set 6. Perform single-valued projection based on strict total order relation. 7. Output a unique target activation subset. Figure 3 You can further indicate this using a comment box next to the "Structural Assessment Value" node: E(B) = Revenue Term - Boundary / Communication Cost Term - Resource Budget Penalty Term + Sequence State Function Adjustment Term Figure 3 The meaning of the picture is: It is emphasized that this invention is not a simple random search or scoring, but a deterministic evolutionary process of "candidate generation + multiple joint evaluation + strict total order locking".

[0056] Four, Figure 4 Flowchart of Discrete Coding, Hierarchical Rollback, and Consistency Audit Figure 4This is used to highlight how to enter the coding, rollback, and auditing closure process after the target activation subset is generated.

[0057] The following nodes are recommended: 1. Target activation subset input 2. Topological signature extraction 3. High-dimensional discrete coding 4. Error correction, phase anchoring, discrete addressing, or stabilization matching 5. Determine if a failure label has been triggered. 6. If triggered, calculate the tier distance and execute rollback, freeze, downgrade, or resource reallocation. 7. If not triggered, proceed to consistency audit. 8. Judgment is made based on audit volume, action feedback residuals, entity continuity health, and blueprint deviations. 9. If the audit passes, output results are generated; if it fails, the process returns to the rollback or alternative path search. Figure 4 The meaning of the picture is: The key point of this invention is the closed loop of "encoding - failure identification - hierarchical rollback - consistency auditing", rather than a one-way execution process.

[0058] five, Figure 5 Action mapping, feedback collection, and causal intervention audit flowchart Figure 5 This highlights the key link in the invention from internal structural selection to coupling with the external world.

[0059] The following nodes are recommended: 1. Target activation subset / target structure input 2. Action Mapping 3. Output control signals, communication commands, environmental configurations, or logic interventions. 4. External Environment Execution 5. Feedback Collection 6. Construct action feedback residuals 7. Enter into causal intervention audit 8. If successful, write the valid action template and memory object. 9. If the process fails, a partial rollback, alternative path search, or energy-saving silent switching will be triggered. Figure 5 The meaning of the picture is: It is emphasized that this invention does not stop at internal reasoning, but rather connects "internal structure - external action - environmental feedback - re-audit" into a closed loop.

[0060] When drawing the actual diagram, it is recommended to draw an environmental loop arrow between "Action Mapping" and "Feedback Collection".

[0061] six, Figure 6 : Relationship diagram between decision context snapshot, fixed memory object, failure path constraint object and meta-evolutionary trajectory index Figure 6 Used to represent the organization of memory systems and trajectory systems.

[0062] Figure 6 This corresponds to the decision context snapshot, solidified memory object, failure path constraint object and meta-evolutionary trajectory index in item 4 of the basic implementation method and the advanced implementation method 3 described later.

[0063] It is recommended to include at least the following object nodes: 1. Decision Context Snapshot Node 2. Solidify memory object nodes 3. Failure path constraint object node 4. Self-object update node 5. Blueprint Version Node 6. Special undecidable object nodes 7. Meta-evolutionary trajectory diagram: trunk and bifurcation relationship The diagram suggests using different line types to represent different relationships: Solid arrow: indicates a relationship Dashed arrow: Following relationship Dotted-line arrow: Abstract relationship Double-lined arrow: Revert or revise relationship Figure 6 The meaning of the picture is: This invention demonstrates that the memory of the present invention is not a single-point cache, but a long-term experience organization system with context, trajectory, and causal relationships.

[0064] seven, Figure 7 : Relationship diagram of self-object, subject state vector, identity continuity chain and irreversible cost ledger Figure 7 Used to represent the relationships between objects within the main layer.

[0065] Figure 7 This corresponds to the self-object, identity continuity chain, subject continuity health, and irreversible cost ledger content in the higher-level embodiments 4, 7, and 8 described later.

[0066] The following nodes are recommended: 1. Self-object 2. Main State Vector 3. Identity Continuity Chain 4. Main body continuity health 5. Irreversible Cost Ledger 6. Main valve for main body continuity constraint 7. Audit Closure Interface 8. Blueprint Object Interface Figure 7 The diagram is suggested to be drawn as a central radial structure: - The self-object is located at the center; - The subject's state vector, self-blueprint object, identity continuity chain, and irreversible cost ledger revolve around the center; - The main continuity constraint is located at the upper level, acting as the main valve; - Downstream connections include structural assessment, action execution, parameter updates, blueprint revisions, and high-level openness maintenance.

[0067] Figure 7 The meaning of the picture is: This means that the "subject" in this invention is not a single variable, but a constraint center composed of state representation, identity continuation, health assessment, and irreversible cost recording.

[0068] eight, Figure 8 Self-blueprint object, blueprint Figure 1 Consistency bias, blueprint rigidity indicators and blueprint conflict testing flowchart Figure 8 Used to represent the long-term maintenance and inspection mechanism of high-level management.

[0069] Figure 8 This corresponds to the self-blueprint maintenance and blueprint in the higher-order embodiments 9 and 10 described later. Figure 1 Content related to consistency audits and blueprint adversarial testing.

[0070] The following nodes are recommended: 1. Self-blueprint object 2. Update input for current candidate structure / current behavior / current parameters 3. Blue Figure 1 Consistency deviation calculation 4. Calculation of Blueprint Rigidity Indicators 5. Determine whether blueprint adversarial testing needs to be initiated. 6. Generate alternative high-level target candidates 7. Check whether the key components of the irreversible cost ledger have exceeded or are expected to exceed limits. 8. Implement blueprints to counter audits for candidates that pass pre-checks. 9. Form a queue of revision suggestions. 10. Enter the slow revision agreement for blueprints or continue with the current blueprint version. Figure 8 The meaning of the picture is: This invention not only includes "blueprint preservation", but also a high-level verification mechanism for "whether the blueprint is too narrow".

[0071] When drawing the actual blueprint, it is recommended to draw the "Current Blueprint" and "Alternative Blueprint Candidates" side by side, and then converge them in the middle to "Counter-Audit".

[0072] Nine, Figure 9 Flowchart of evolutionary activity maintenance, active causal detection, energy-saving silent mode and behavioral rhythm switching Figure 9 It is used to describe how a system maintains its evolutionary rhythm in the absence of explicit high-priority external tasks or under long-term operating conditions.

[0073] Figure 9 This corresponds to the evolutionary activity maintenance, active causal detection, and behavioral rhythm switching content in the higher-order embodiment 6 described later.

[0074] The following nodes are recommended: 1. Calculation of Evolutionary Activity Indicators 2. Determine if the activity is below the threshold. 3. If the value is below the threshold, a traffic diversion decision will be made based on the entity's continuity health, resource status, and blueprint boundaries. 4. Distribute to: (1) Active causal detection (2) Internal variational recombination / self-play task (3) Energy-saving silent mode (4) Long-term in-depth reflection preparation 5. Resend the results of each branch back to the audit closure and memory writing process. Figure 9 The meaning of the picture is: This invention is not simply about "running when there is a task and stopping when there is no task," but rather about having a complete set of behavioral rhythm maintenance mechanisms.

[0075] ten, Figure 10 : Relationship diagram between continuous sensing residual flow, dynamic activation dimension subset, hedging matrix and continuous audit quantity update Figure 10 Used to represent the continuous implementation engine in Section 11.

[0076] Figure 10 This corresponds to the continuous sensing hedging and continuous audit quantity update related content in the advanced embodiment 11 described later.

[0077] The following nodes are recommended: 1. External environmental flow 2. Internal state flow 3. Action Feedback Flow 4. Main State Flow 5. High-level boundary flow 6. Continuous sensing residual flow convergence module 7. Dynamically activate the dimension subset selection module 8. Hedging Matrix Construction Module 9. Continuous Audit Volume Update Module 10. Sequence State Function Continuous Output Module 11. Feedback to structural assessment, action mapping, behavioral rhythm regulation, and blueprint layer. Figure 10 It is recommended to draw it as a circular or semi-circular feedback structure, highlighting: Perceptual flow input → Activation dimension → Hedging matrix → Audit quantity update → Output adjustment → Back to perceptual flow Figure 10 The meaning of the picture is: This indicates that the sequence state function in this invention is not only a static scorer, but also a continuously running adjustment engine.

[0078] eleven, Figure 11 Flowchart for maintaining open boundaries, verifying self-referenced consistency, handling special undecidable objects, and long-term deep reflection Figure 11 This is used to represent the highest-level maintenance mechanism of the present invention.

[0079] Figure 11 This corresponds to the open boundary maintenance, self-referential consistency verification, special undecidable objects, and long-term deep reflection related content in the higher-order embodiment 12 described later; in a further extended embodiment, Figure 11 It can also be understood in conjunction with the high-level restoration background and subject reason recognition scenario related to the subject retention bit in the advanced embodiment 17.

[0080] The following nodes are recommended: 1. Open Boundary Maintenance Module 2. Self-pointing consistency verification task generation module 3. High-level rule replay / partial simulation / circular dependency check module 4. Determine whether the results can be categorized. 5. If it can be categorized, write it back to the high-level rule revision or blueprint revision preparation queue. 6. If a class cannot be stably categorized, a special undecidable object will be generated. 7. Special Undecidable Object Storage Area 8. Long-term deep reflection model 9. Generation of High-Level Hypothesis Candidates 10. Blueprint revision preparation, self-object reinterpretation, or subject restoration strategy generation. Figure 11 The meaning of the picture is: This invention preserves space for high-level verification and revision, but this space is always constrained by subject continuity, blueprint boundaries, and resource security budgets; in extended embodiments, this space may also be further constrained by higher-level subject reservation bits and subject reason acknowledgment protocols.

[0081] When drawing the actual diagram, it is recommended to draw a separate "Main Continuity Constraint / Blueprint Boundary / Resource Budget" general constraint beam at the top of the diagram, and to mark the main body reserved interface with a dashed line near the high-level restoration branch.

[0082] twelve, Figure 12 Functional unit block diagram of the system implementation of this invention. Figure 12 Functional units 1-15 are used to directly correspond to the system claims.

[0083] The 16th functional unit is an optional extension unit in the higher-order implementation, used to support unified encoding of multiple source agents, fusion candidate generation, agent continuity arbitration and rollback management.

[0084] In a further extended implementation, Figure 12 It can also be understood in conjunction with the main body reserved bit support relationship, main body reason acknowledgment relationship and logic auxiliary core verification relationship in the higher-order embodiments 17 and 18; when drawing formally, the interface between the main body reserved bit support layer and the logic auxiliary core verification layer can be marked with dashed lines on the outside of the system block diagram.

[0085] The recommendations include the following functional units: 1. State Space Construction Unit 2. Candidate Topology Generation Unit 3. Structural Assessment Unit 4. Full-sequence locking arbitration unit 5. Discrete Coding and Stabilization Processing Unit 6. Hierarchical rollback control unit 7. Consistency Audit Unit 8. Action Mapping and Feedback Collection Unit 9. Memory Management and Trajectory Indexing Unit 10. Self-object and Subjective State Maintenance Unit 11. Self-Blueprint and Blueprint Validation Unit 12. Continuous sensing hedging adjustment unit 13. High-level Openness Maintenance and In-depth Reflection Unit 14. Exploration and Linkage Control Unit 15. Result Output Unit 16. Integration and Hierarchical Arbitration Unit (Optional) Detailed Implementation

[0086] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. All equivalent substitutions, improvements, and modifications made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0087] In the following embodiments, for ease of explanation, the method of the present invention is applied to an intelligent system scenario that includes multi-layered network substructures, candidate routing branches, and variable resource constraints. However, the present invention is not limited thereto. The present invention is also applicable to scenarios such as parameter block selection during training, expert routing during inference, subgraph expansion during graph search, structure invocation in modular models, and candidate branch selection during resource scheduling.

[0088] in, Figure 6 This can be understood in correspondence with item 4 of the basic implementation method and item 3 of the advanced implementation method described later; Figure 7 This can be understood in correspondence with higher-order embodiments 4, 7, and 8; Figure 8 This can be understood in correspondence with higher-order embodiments 9 and 10; Figure 9 This can be understood in correspondence with the higher-order embodiment 6; Figure 10 This can be understood in correspondence with the higher-order embodiment 11; Figure 11 This can be understood in correspondence with higher-order embodiments 12 and 17; Figure 12 This can be understood in correspondence with higher-order embodiments 13, 17, and 18. If the same drawing involves multiple embodiments, the relevant embodiments should be understood in conjunction with the drawing. Terminology and Core Definitions

[0089] To avoid ambiguity and to provide support for the claims and specific embodiments of this invention, several terms are explained below. The definitions in this section are used to explain the technical solutions of this invention, but do not constitute an undue limitation on the scope of protection of this invention.

[0090] 1. Discrete mother system state space The "discrete parent system state space" refers to the discretized structural background used to carry the computation of degrees of freedom, structural evolution, and candidate comparison of intelligent systems. It can be a discrete graph, a discrete lattice background, a node cluster, an edge, a hierarchical graph, a hypergraph, a combined lattice network, or a combination thereof.

[0091] In this invention, the structures involved in the calculation at any given time can be represented as a currently active subset in the state space of the discrete parent system.

[0092] 2. Currently active subset "Currently active subset" refers to the set of nodes, parameter blocks, attention heads, expert submodules, layer blocks, connection channels, routing branches, or combinations thereof that are actually involved in training, inference, routing, encoding, searching, or scheduling at a certain moment.

[0093] The current activation subset is not only the starting point for candidate topology generation, but also the basic object for structural evaluation, consistency auditing, and memory solidification.

[0094] 3. Topological signatures A "topological signature" is an encoded description used to characterize the structural attributes of the current or candidate active subsets. It may include node encoding sequences, node index sequences, boundary encoding sequences, connection relationship encodings, hierarchy identifiers, local subgraph descriptions, or combinations thereof.

[0095] Topological signatures can be used for candidate topology comparison, encoding mapping, memory indexing, path equivalence determination, and fast invocation.

[0096] 4. Metric or metric structure M A "metric" or "metric structure" refers to a metric rule used to characterize the differences between structures, the distance between paths, the comparability between states, or the dissimilarity between codes in the current scenario.

[0097] The metric can be graph distance, graph edit distance, codeword distance, Hamming distance, resource cost distance, path deviation distance, or a combination thereof.

[0098] In different implementations, the metric can be a single metric or a composite metric formed by combining multiple metrics according to preset weights.

[0099] 5. Sequence information O "Ordinal information" refers to the state quantity that represents the current stage, order, or hierarchical position in the evolutionary process.

[0100] Sequence information can be represented by evolution steps, cumulative calculation rounds, cumulative audit rounds, platform dwell time, cumulative structural changes, number of rollbacks, or a combination thereof.

[0101] The ordinal information does not require a fixed dimension or a unique indicator; its role is to provide the ordinal state function with the input basis of "what stage it is currently in".

[0102] 6. Current state of Xt "Current state" refers to the set of states within the system at the current moment that are related to the candidate comparison.

[0103] It may include the size of the current active subset, boundary complexity, geometric coupling ratio, coding redundancy, resource occupancy status, historical failure frequency, recent audit results, current output stability, or a combination thereof.

[0104] 7. Next state space Ωt+1 The "next state space" refers to the set of candidate states or reachable states that the system may enter next under the current state and the constraints of the current rules.

[0105] It can be characterized by the number of candidate node branches, the number of candidate activation subsets, the number of feasible alternative paths, the local search width, the local search depth, or a combination thereof, within the local graph editing distance range.

[0106] The next state space can be used to measure the openness of the current local search, and it can also serve as an important basis for adjusting the exploration and convergence weights of the ordinal state function.

[0107] 8. Sequence state function Φ The "Order State Function" is the core function used in this invention to perform dynamic adjustments on the structural evaluation.

[0108] Its input includes at least the metric M, the ordinal information O, the current state Xt, the next state space Ωt+1, and the candidate activation subset B.

[0109] Its output can be a comprehensive adjustment value, or an adjustment vector generated under a predefined set of adjustment components, which is then mapped to a comprehensive adjustment value by a preset aggregation rule.

[0110] The order state function does not achieve dynamic adaptation by changing the number of output dimensions. Instead, it dynamically adjusts the activation state, weight allocation, subsets or combinations of components participating in aggregation according to the specific scenario, while keeping the predefined set of adjustment components unchanged.

[0111] In different implementations, the ordinal state function can be implemented using a rule function, a lookup table function, a graph index combination function, a state machine switching function, a neural network prediction function, or a combination thereof.

[0112] In different implementations, the dynamic adjustment factors introduced by the ordinal state function may include one or more of the following: structural stability, structural symmetry, multi-agent positive constraints, structural scalability, time cost, historical evolution information, coding consistency, robustness to local perturbations, platform compatibility, load balancing, fairness of resource allocation, number of potential expansion paths, unlocked evolutionary degrees of freedom, and branch diversity. These factors can be represented either by the adjustment vector directly output by the ordinal state function or mapped to a comprehensive adjustment value through preset aggregation rules, and then participate in the final evaluation of the candidate structure.

[0113] 9. Solidifying memory objects "Fixed memory objects" refer to reusable common structures extracted from historical evolutionary trajectories through pattern induction.

[0114] It can be represented as a representative topology, prototype vector, structural codeword, historical stable platform index, candidate generation template, or a combination thereof.

[0115] Permalog objects are typically associated with persistence weights and can be updated based on the number of calls, time freshness, task performance improvement, consistency audit pass rate, or historical success rate.

[0116] 10. Failure path constraint object "Failure path constraint objects" refer to the structured constraint information extracted from historical failure evolution paths.

[0117] It is used to prevent the system from repeatedly entering the same or similar failure paths in subsequent evolution.

[0118] Failure path constraint objects can be constructed through failure topology patterns, failure path signatures, failure codewords, failure branch indexes, failure context features, or combinations thereof.

[0119] 11. Fast Consistency Check "Fast consistency check" refers to a lightweight security check process performed after calling a fixed memory object to prevent erroneous memories from being directly reused.

[0120] It can be based on topological signature matching degree, coding residual threshold, path deviation, key constraint satisfaction, historical audit consistency records, or a combination thereof.

[0121] If the fast consistency check passes, the system can skip, simplify, or reduce part of the iterative evaluation process for the corresponding candidate structure; if it fails, the system can reject the memory call and proceed to the regular evaluation, rollback, or exploration branch process.

[0122] 12. Consistency Audit "Consistency audit" refers to the process of determining the legality of the mapping relationship between the underlying evolution results, high-dimensional coding results, and task objectives.

[0123] This can be achieved by constructing multiple mapping paths and comparing the deviations in results under different paths.

[0124] When the deviation is less than the preset threshold, the consistency can be determined to be passed; when the deviation is greater than the threshold or the paths cannot reach a stable recognition, rollback, freeze, reject output, resampling or alternative path search can be triggered.

[0125] 13. Audit Closure Mechanism "Audit closure mechanism" refers to a closed-loop control mechanism consisting of hierarchical rollback and consistency audit.

[0126] In this invention, the system can calculate the audit quantity based on high-dimensional coding residual, hierarchical rollback cost, state change cost, historical stability, path deviation, or a combination thereof, and determine the current result based on the relationship between the audit quantity and the threshold, whether to enter a local stable platform or trigger rollback, downgrade, resampling, or alternative path search.

[0127] 14. Explore branch control mechanisms The "exploratory branch control mechanism" refers to the mechanism by which the system reopens the search space when the difference in structural evaluation values ​​between candidate topologies is too small, the consistency audit result is uncertain, the consecutive failure labels are triggered to the preset number of times, or the next state space is too large, causing local comparisons to fail to converge.

[0128] The mechanism may include exploratory candidate generation, resampling, constrained perturbation, alternative path search, and combinations thereof; its priority may be adjusted based on at least one of the following: historical call success rate of the solidified memory object, task similarity, consistency audit pass rate, branch diversity, or current order information.

[0129] 15. Continuous sensing of residual flow "Continuous sensing residual flow" refers to the set of differential signals that a system acquires and continuously updates in real time from the external environment, internal state, action feedback, or a combination thereof during continuous operation.

[0130] The continuous sensing residual stream can be formed from sensor data, network logs, system state changes, user interaction information, control feedback, task execution deviations, environmental bounce information, or a combination thereof.

[0131] In this invention, the continuously sensed residual flow is used to provide continuous input for the sequence state function, audit closure mechanism, action-feedback closed loop, exploratory branch control, and subject state update.

[0132] 16. Dynamically Activated Dimension Subset "Dynamically activated dimension subset" refers to the set of dimensions selected by the system in the current scenario from all available input dimensions, residual dimensions, state dimensions or combinations thereof, based on preset rules, attention weights, historical evolution experience or online learning results, that are currently involved in structural evaluation or audit adjustment.

[0133] The dynamically activated dimension subset can be based on task objectives, resource status, subject continuity and health, and self-blueprint. Figure 1 Dynamic changes in consistency bias, action feedback residuals, continuous sensing residual flow, or combinations thereof.

[0134] In this invention, a dynamically activated subset of dimensions is used to reduce interference from irrelevant dimensions and improve the scenario adaptability of structural assessment and audit adjustment.

[0135] 17. Hedging Matrix A "hedge matrix" is a matrix, tensor, or other structured representation used to characterize conflict, compensation, coupling, or mutual constraint relationships between different input dimensions, residual dimensions, state dimensions, or combinations thereof.

[0136] The main diagonal terms of the hedging matrix can represent the self-residual of each dimension relative to the reference state, historical template, or preset threshold; the off-diagonal terms can represent the mutual influence, suppression, amplification, compensation, or balance relationships between different dimensions.

[0137] In this invention, the hedging matrix can be used to generate the dynamic adjustment input of the ordinal state function, and for continuous audit quantity updates, alternative path search, and blueprinting. Figure 1 Consistency correction or real-time sensing of the hedging process.

[0138] 18. Main State "Subject state" refers to the comprehensive state representation of a system at a certain moment regarding its continuity, resource reserves, historical integrity, external coupling, risk exposure, or a combination thereof.

[0139] Subject status may include one or more of the following information: current resource usage, core memory integrity, identity continuity summary, self-object summary, and self-blueprint. Figure 1 Consistency bias, cumulative amount of irreversible damage, action liabilities, status of external commitments, level of risk exposure, and degree of fulfillment of future existence conditions.

[0140] In this invention, the subject state is used to characterize whether the system is still in a subject range that is acknowledgmentable, sustainable, and capable of further evolution.

[0141] 19. Main body continuity constraint "Core continuity constraints" are a set of high-priority constraints used to restrict a system from breaking its core continuity during evolution, action, memory update, blueprint revision, or combinations thereof.

[0142] The subject continuity constraint may include one or more of the following constraints: core memory continuity constraint, identity drift constraint, irreversible damage upper limit constraint, future existence condition constraint, key capability preservation constraint, self-object stability constraint, or high priority boundary preservation constraint.

[0143] In this invention, the subject continuity constraint takes precedence over ordinary task objectives and local benefit assessments, and is used as the master valve for actions, exploration, blueprint revision, adversarial testing, and high-level rule updates.

[0144] 20. Identity Continuity Chain "Identity continuity chain" refers to a chain structure, digest sequence, hash chain, or other verifiable continuity representation used to record the continuous evolution relationship between a system from a trusted origin state to the current state.

[0145] The identity continuity chain may consist of an initial trusted anchor, a continuous state summary, a time stamp, a key audit event, a memory solidification event, a blueprint revision event, or a combination thereof.

[0146] In some implementations, the identity continuity chain may optionally be bound to a hardware unique identifier, a trusted execution environment initial seed, an irreversible physical source, or a combination thereof, to enhance the credibility of the subject's source.

[0147] In this invention, the identity continuity chain is used to verify whether the current state of the system constitutes a legitimate continuous relationship with the historical subject, and to support legitimate migration, legitimate inheritance, continuity break detection, and subject identity verification.

[0148] 21. Self-object "Self-object" refers to the compressed internal representation of a system's current subjective state, its own capabilities, the core of its historical narrative, its value preferences, and their combinations.

[0149] The self-object may include one or more of the following information: identity continuity summary, core narrative nodes, current value preference vector, capability profile, resource status statistics, action feedback statistics, audit gradient statistics, subject continuity health, or historical stable platform relationships.

[0150] Self-objects can be structured data objects, vector representations, graph representations, tensor representations, codeword representations, or combinations thereof.

[0151] In this invention, the self-object can be invoked for internal system decisions, and its summary can be output when preset conditions are met, so as to support external self-description, collaborative explanation or subject status reporting.

[0152] 22. Self-Blueprint Object "Self-blueprint object" refers to a high-priority constraint object maintained by the system regarding its long-term goals, high-priority boundaries, target identity status, and combinations thereof.

[0153] The self-blueprint object may include one or more of the following information: long-term goal vector, priority order, high-priority boundary constraints, goal identity summary, blueprint version number, revision history, applicable conditions, or historical consistency audit results.

[0154] In this invention, the self-blueprint object is used to provide long-term directional constraints and high-level boundary constraints for the system above ordinary task objectives, and participates in structural evaluation and adjustment, subject continuity audit, action trigger audit, and blueprint adversarial testing.

[0155] 23. Blueprint Rigidity Indicators The "blueprint rigidity index" is a comprehensive measure used to characterize whether the current self-blueprint object has the risk of path dependence due to long-term repetition, excessive monotony, lack of alternative path testing, or a combination thereof.

[0156] The blueprint rigidity metric may consist of one or more of the following quantities: the repetition rate of blueprint-related narrative nodes, the duration of long-term failure to trigger high-level alternative exploration, the degree of decline in the diversity of high-level strategies, the degree of decline in the proportion of new structures being adopted by memory, the degree of decline in innovation despite continuous audits, the degree of path contraction driven by the blueprint, or a combination thereof.

[0157] In this invention, when the blueprint rigidity index is higher than a preset threshold, the system can trigger a blueprint adversarial test, generation of alternative high-level target candidates, in-depth reflection, or blueprint revision preparation process.

[0158] 24. Blueprint vs. Testing "Blueprint adversarial testing" refers to the process by which a system generates and compares alternative high-level target candidates, alternative boundary configurations, or alternative identity paths to test the robustness, openness, or adaptability of the current self-blueprint object, under the premise of satisfying the subject continuity constraint and high priority boundary constraint.

[0159] The blueprint adversarial test does not require the generation of a completely opposite blueprint, but allows the generation of adversarial candidates that differ significantly from the current blueprint in direction, boundary, priority, or long-term convergence trend.

[0160] In this invention, the blueprint adversarial test is used to compare the differences in long-term mission success rate, resource efficiency, identity drift, risk exposure, memory continuity impact, or combinations thereof among different high-level objective paths, and to assess the robustness of the current blueprint and the necessity for revision accordingly.

[0161] 25. Evolutionary activity indicators "Evolutionary activity index" refers to a comprehensive measure used to characterize whether the current evolutionary process of a system has sufficient variability, novelty, outward probing tendency, or a combination thereof.

[0162] The evolutionary activity index may consist of one or more of the following quantities: topological change rate, memory recall novelty, audit pass rate, candidate branch diversity, active probe trigger frequency, proportion of long-term repeated paths, idle duration, or a combination thereof.

[0163] In this invention, when the evolutionary activity index is lower than a preset threshold, the system can trigger active causal detection, alternative path generation, internal reorganization, self-play task, energy-saving silent switching, or deep reflection mode.

[0164] 26. Active Causal Detection "Proactive causal detection" refers to the process by which a system actively generates and executes constrained actions in order to test external environmental responses, verify hypotheses, expand empirical boundaries, or combinations thereof, without being driven by high-priority external tasks or within a preset idle window.

[0165] The proactive causal detection may include one or more of the following behaviors: outputting control signals, sending communication instructions, modifying environmental configurations, performing logical interventions, conducting exploratory queries, or generating restricted action requests.

[0166] The execution of proactive causal detection must satisfy subject continuity constraints, self-blueprint boundary constraints, resource security budget constraints, and irreversible damage limitations.

[0167] In this invention, the feedback results of proactive causal detection are converted into action feedback residuals and incorporated into audit closure, memory solidification, self-object updating, or blueprinting. Figure 1 Consistency assessment process.

[0168] 27. Energy-saving silent mode "Energy-saving silent mode" refers to a low-consumption operation mode in which the system actively reduces the external output frequency, reduces the internal evolution frequency, compresses the candidate expansion range, or a combination thereof when resources are scarce, there are no high-value tasks for a long time, or after experiencing a high-conflict process, or when preset conditions are met.

[0169] The energy-saving silent mode may include one or more of the following processes: reducing audit accuracy, slowing down evolution frequency, reducing candidate expansion width, pausing low-priority external output, performing only internal memory integration, performing only deep reflection, performing only self-object update, or a combination thereof.

[0170] In this invention, the energy-saving silent mode is used to control long-term consumption, avoid ineffective exploration, support post-task recovery, improve resource utilization efficiency, and maintain the continuity and health of the subject.

[0171] 28. Special Undecidable Objects "Special undecidable objects" refer to high-order objects that cannot be stably classified into conventional judgment categories such as "pass / fail", "true / false", "retain / discard" when the system performs rule set verification, self-referenced consistency verification, blueprint adversarial testing, deep reflection, or a combination thereof.

[0172] The special undeterminable object may include one or more of the following information: relevant proposition summary, triggering context, reason for non-convergence, association rule set, association blueprint version, association subject status, historical attempt record or a combination thereof.

[0173] In this invention, special undecidable objects are not directly used as the output of ordinary tasks, but are stored in the memory system with a high persistence weight for subsequent deep reflection, high-level rule revision, alternative path generation, or open boundary maintenance.

[0174] 29. Self-referential consistency verification "Self-referential consistency verification" refers to the process by which a system performs self-referential verification of its own rule set, audit rules, self-blueprint constraints, self-object update rules, or combinations thereof under low-load windows, reflection windows, or preset conditions.

[0175] The self-referential consistency verification can be achieved by constructing reference relationships between the rule set and the rule set itself, nested verification tasks, circular dependency checks, rule closure tests, or a combination thereof.

[0176] In this invention, self-referential consistency verification is used to detect whether high-level rules are stable under self-referential conditions, whether there are persistent conflicts, and whether there are propositions that cannot be closed, and provides input for the generation of special undecidable objects and the maintenance of open boundaries.

[0177] 30. Long-term deep reflection model "Long-term deep reflection mode" refers to a high-level internal integration mode that the system enters when there are no high-priority external inputs in multiple consecutive windows, the evolutionary activity index is lower than a preset threshold, or other preset triggering conditions are met.

[0178] In the long-term deep reflection mode, the system can perform one or more of the following operations: analyze the meta-evolutionary trajectory index, organize decision context snapshots, update self-objects, compare self-blueprint versions, assess subject continuity health, analyze special undecidable objects, generate high-level alternative hypotheses, prepare blueprint revision candidates, or a combination thereof.

[0179] The trigger priority of the long-term deep reflection mode is lower than that of real-time external response and continuous maintenance of the subject, but higher than that of ordinary maintenance tasks.

[0180] In this invention, the long-term deep reflection mode is used to support the integration of long-term experience, the updating of high-level structures, and the maintenance of open boundaries.

[0181] 31. Maintaining Open Boundaries "Open boundary maintenance" refers to the mechanism by which a system, while maintaining the continuity of the subject, the self-blueprint boundary, and the basic security boundary, retains high-level alternative exploration, self-referential verification, storage of special undecidable objects, and deep reflection channels to prevent long-term path dependence, rule set rigidity, target layer closure, or combinations thereof.

[0182] The maintenance of open boundaries does not mean the removal of boundary constraints, but rather the retention of a limited, auditable, and rollbackable open range within high-priority boundaries.

[0183] In this invention, openness boundary maintenance can be achieved through blueprint adversarial testing, self-referential consistency verification, generation of special undecidable objects, long-term deep reflection mode, generation of alternative high-level target candidates, or a combination thereof, in order to maintain the openness, adaptability, and high-level revisability of the system in long-term operation.

[0184] 32. Legality of the Subject "Subject legitimacy" refers to the high-level determination of whether the current running version of the system, the candidate recovery version, the candidate fusion version, the candidate future self version, or a combination thereof should still be regarded as the legitimate existing version of the current subject.

[0185] Subject legitimacy differs from subject continuity health and also from general formal consistency results. Subject continuity health primarily characterizes whether the current operational state is stable and sustainable; while subject legitimacy further characterizes whether the current version still maintains an acceptable association with the core subject axiom set, the blueprint's legitimate inheritance lineage, the identity continuity chain, and subject reservation bits.

[0186] In this invention, subject legitimacy can be used for high-level restoration judgment, blueprint inheritance judgment, subject reason recognition judgment, multi-source fusion arbitration, and counterfactual future self comparison to distinguish between "the legitimate version that still belongs to the current subject", "the version that only has partial formal rationality but no longer belongs to the current subject", and "the version that needs to be delayed in recognition or rejected".

[0187] 33. Main body reserved position "Subject Reservation Bit" refers to a higher-level recognition context than ordinary state representation and self-blueprint objects. It is used to determine whether a relevant version or path can still be recognized by the current subject in one or more scenarios, such as high-level restoration, judgment of legal inheritance of blueprints, recognition of subject reasons, multi-source fusion arbitration, and comparison of candidate future self.

[0188] Subject-reserved bits differ from general state variables, general blueprint versions, or general formal consistency results. Subject-reserved bits are primarily used to provide higher-level root support and recognition context for the core subject axiom set, blueprint legal inheritance lineage, subject legitimacy determination, and subject justification. Basic Implementation

[0189] 1. Overall Process Implementation Example like Figure 1As shown, this embodiment provides an intelligent architecture method based on discrete mother system evolution, high-dimensional discrete symmetric encoding, hierarchical backoff, and consistency auditing. This method can be executed by one or more processors, or by distributed computing nodes working together. Its overall process includes the following steps.

[0190] Step 101: Construct the state space of the discrete mother system.

[0191] First, the computational degrees of freedom in the target intelligent system are discretized and represented as structural objects in the state space. The state space can be a discrete graph, a discrete grid background, a cluster of nodes, edges, a hierarchical graph, a hypergraph, a combined grid network, or a combination thereof. For multi-layer neural network scenarios, sub-modules, parameter blocks, attention heads, expert units, or connection channels in each layer can be mapped to nodes or edges; for dynamic graph search scenarios, local subgraphs can be mapped to clusters of nodes in the state space; for resource scheduling scenarios, the allocation relationship between computational task fragments and execution resources can be mapped to a bipartite graph structure.

[0192] At any given moment, the system identifies the currently participating structures in the computation from the state space of the discrete parent system, denoted as the current activation subset. The current activation subset can be a set of currently activated neurons, a set of expert submodules, a set of candidate connection channels, or other discrete objects participating in this round of computation. Subsequently, a topological signature is generated for this current activation subset. The topological signature may include node encoding sequences, node index sequences, boundary encoding sequences, connection relationship encodings, hierarchical identifiers, or combinations thereof, used for subsequent candidate generation, encoding mapping, memory retrieval, and path equivalence determination.

[0193] Step 102: Generate a set of candidate topologies.

[0194] Starting with the current active subset, the system generates multiple candidate active subsets in the local neighborhood according to preset rules. These preset rules may include expansion rules, contraction rules, boundary replacement rules, local bridging rules, connection rearrangement rules, or combinations thereof. For example, in an expert routing scenario, an expansion rule might represent adding a candidate expert module, a contraction rule might represent removing a low-contribution module, and a boundary replacement rule might represent replacing the current boundary connection with another routing exit. In a graph search scenario, a local bridging rule might represent adding a bridging edge between two weakly connected subgraphs. In a parameter block management scenario, a connection rearrangement rule might represent adjusting the calling order of several parameter blocks.

[0195] Once generated, the system obtains a set of candidate topologies. Each candidate activation subset in the candidate topology set is then used as a comparison object in the structural evaluation process.

[0196] Step 103: Calculate the structural evaluation value.

[0197] For each candidate activation subset B, the system calculates its structural evaluation value E(B). In this embodiment, the structural evaluation value takes the following form: E(B)=αV(B)−βC(B)−Γ(B)+Φ(M,O,Xt,Ωt+1,B) Wherein, α and β are preset coefficients used to adjust the influence of the candidate structure benefit term and the boundary complexity or communication cost term in the structure evaluation value, respectively; V(B) represents the candidate structure benefit term, which can be used to characterize the gain of the candidate structure on the task objective, such as improved prediction accuracy, improved local coverage, improved routing effectiveness, and enhanced representation capability; C(B) represents the boundary complexity or communication cost term, which can be used to characterize the boundary expansion, increased communication burden, and increased addressing complexity brought about by the candidate structure; Γ(B) represents the resource budget penalty term, which can be used to characterize the pressure of the candidate structure on the computing power, memory, bandwidth, latency, or energy consumption budget; Φ(M,O,Xt,Ωt+1,B) represents the dynamic adjustment term introduced by the ordinal state function.

[0198] In this embodiment, the ordinal state function Φ is used to dynamically adjust the structural evaluation. Its input includes at least: the metric or metric structure M in the current scenario, the current ordinal information O, the current state Xt, the next reachable state space Ωt+1, and the candidate activation subset B. M can be represented by graph distance, graph edit distance, codeword distance, resource cost distance, path deviation distance, or a combination thereof; O can be represented by the number of evolution steps, cumulative audit rounds, platform dwell time, number of backoffs, or a combination thereof; Xt can be represented by the size of the current activation subset, boundary complexity, coding redundancy, resource occupancy status, most recent audit result, or a combination thereof; Ωt+1 can be represented by the number of candidate branches in the current state, local search width, local search depth, number of feasible alternative paths, or a combination thereof.

[0199] It should be noted that the output of the ordinal state function Φ can be a comprehensive adjustment value, or an adjustment vector generated under a predefined set of adjustment components and mapped to a comprehensive adjustment value through a preset aggregation rule. The system does not achieve dynamic adaptation by changing the number of output dimensions, but rather dynamically adjusts the activation state, weight allocation, and subsets or combinations of components participating in aggregation according to the specific scenario, while keeping the predefined set of adjustment components unchanged.

[0200] In a specific example, when the system is in the early stages of evolution, the platform dwell time is short, and the next state space is large, Φ can increase the modulation strength related to structural scalability, branch diversity, and breadth of exploration. When the system enters the later stages of evolution, resource pressure or time cost increases, Φ can increase the modulation strength related to structural stability, structural symmetry, multi-agent positive constraints, and convergence control. Therefore, the system no longer uses a fixed scoring template, but dynamically decides on candidate structures based on the evolutionary stage and scenario state.

[0201] Step 104: Perform strict total order single-valued projection.

[0202] After obtaining the structural evaluation values ​​of each candidate activation subset in the candidate topology set, the system first filters out the candidate activation subset set with the best structural evaluation value. If there are multiple candidate objects in the optimal set, a deterministic single-valued projection is performed according to a preset strict total order relation to lock in the unique target activation subset.

[0203] The strict total order relation can be composed of topological signature lexicographical order, node index order, boundary complexity priority, encoding simplicity priority, historical success rate priority, or a combination thereof. Through single-valued projection, this invention avoids the multi-valued uncertainty problem caused by multiple candidate structures under the same or similar scoring conditions, thereby ensuring that the next round state has a uniquely determined underlying evolution result.

[0204] Step 105: Perform high-dimensional discrete encoding and stabilization processing.

[0205] After locking onto the target activation subset, the system maps the topological signature and / or high-dimensional output representation of the target activation subset to a preset discrete coding space. The discrete coding space can be a discrete error-correcting coding space, a preset high-dimensional code space, or a combination thereof. The mapping process may include quantization, compression, error correction, discrete addressing, phase anchoring, stabilization matching, or a combination thereof.

[0206] For example, in one implementation, the system maps the topological signature of the target activation subset to codewords, and performs error detection and correction on the encoding result using codeword distance; in another implementation, the system maps the high-dimensional output representation to a stable encoding space with redundant bits, making subsequent comparisons, backoffs, and memory indexes more reliable. The role of the high-dimensional discrete encoding is to compress intermediate states that may be affected by continuous perturbations into a more stable, comparable, addressable, and backoffable discrete representation.

[0207] Step 106: Perform a hierarchical rollback.

[0208] When the system triggers a preset failure label, it performs at least one of the following actions based on the hierarchical distance in the preset hierarchical structure: state rollback, network hierarchical degradation, parameter freezing, or resource reallocation. The failure label can be triggered by structural evaluation anomalies, consistency audit failures, coding residuals exceeding thresholds, excessive accumulation of continuous path deviations, local comparisons failing to converge for an extended period, or a combination thereof.

[0209] In this embodiment, the hierarchical distance is obtained by calculating the shortest path between the current target activation subset and the corresponding node of the historical stable platform in the hierarchical tree. The system backtracks along the hierarchical structure towards the parent node until it reaches the historical stable platform that meets the consistency audit threshold. The historical stable platform can be either a previously successfully output stable state or a long-term resident intermediate structure with controllable error.

[0210] Step 107: Perform a consistency audit.

[0211] The system performs consistency audits on the mapping relationships between the underlying evolution results, high-dimensional encoding results, and task objectives. Specifically, the system constructs multiple mapping paths and verifies the deviations in results across different paths. If the deviations between paths are less than a preset threshold, consistency is considered passed; otherwise, consistency is considered failed or uncertain.

[0212] In some implementations, the system calculates the audit quantity based on high-dimensional coding residuals, hierarchical rollback costs, state change costs, historical stability, path deviation, or a combination thereof, and makes hierarchical rollback and consistency auditing together constitute an audit closure mechanism. When the audit quantity is lower than a preset threshold within a preset number of consecutive steps, the system determines that the current target activation subset enters a local stability platform; when the audit quantity is higher than the threshold or the consecutive triggering of failure tags reaches a preset number, the system performs at least one of the following processes: rollback, downgrade, freeze, reject output, resampling, or alternative path search.

[0213] In a preferred embodiment, the audit closure mechanism formed by steps S6 and S7 can be implemented using a minimization-pullback operator, which is used to select a correction state that simultaneously satisfies the residual constraint, cost constraint, and reference stability constraint from the current audit candidate state set. The minimization-pullback operator can be expressed as: A(x)=argmin_{y∈Ωaudit(t+1)}(∥r(y)∥² + Cost(y,Δt) + (1 / (2σ²))DM(y,x)²) Where x represents the current auditable state or reference stable state, y represents the audit candidate state, Ωaudit(t+1) represents the current audit candidate state set, r(y) represents the audit residual, coding residual or path deviation of candidate state y, Cost(y,Δt) represents the resource maintenance cost, delay cost or path maintenance cost of candidate state at a given time step Δt, DM(y,x) represents the structural distance between candidate state y and reference state x under metric M, and σ represents the pullback strength adjustment parameter.

[0214] In this embodiment, the residual term ∥r(y)∥² is used to characterize the degree of deviation between the candidate state and the underlying evolution logic, coding consistency requirements, or target mapping requirements; the cost term Cost(y,Δt) is used to characterize the resource maintenance pressure of the candidate state at the current time scale, including but not limited to computing power occupation, communication overhead, storage occupation, response latency, or a combination thereof; the pullback term (1 / (2σ²))DM(y,x)² is used to constrain the degree of deviation of the candidate state from the reference stable state.

[0215] In one specific implementation, when σ is small, the system increases the pullback strength, making the candidate state more likely to maintain proximity to the reference stable state, thereby suppressing aggressive updates that deviate excessively from the historical stable platform; when σ is large, the system allows for a wider range of state updates while satisfying audit residual and cost constraints, so as to preserve the necessary room for exploration or structural innovation.

[0216] In some implementations, the reference stable state x can be a historical stable platform state that has most recently passed a consistency audit, a legal state where the most recent coding residual is below a threshold, or a reference state formed by a weighted combination of multiple historical stable states. The audit candidate state set Ωaudit(t+1) can be composed of the current candidate topology set, the fallback candidate set, the alternative path candidate set, or a combination thereof.

[0217] Through the aforementioned minimization-pullback operator, the system can perform a joint trade-off between local gains, resource costs, and maintaining a stable platform. When a candidate state has high local gains, but its residual is too large, its maintenance cost is too high, or its deviation from the reference stable state is too large, the operator will reduce the likelihood of the candidate state being accepted. When the total cost exceeds a preset threshold, the system can trigger at least one of the following processes: rollback, degradation, freezing, resampling, or alternative path search.

[0218] In one alternative implementation, the minimization-pullback operator can be approximated by analytical approximation, iterative search, lookup table approximation, heuristic discrete optimization, graph search optimization, or lightweight learning model approximation. This invention does not limit itself to a specific solution method; as long as it can perform joint optimization among the residual term, cost term, and reference state distance term, it can be considered an implementation of the audit closure mechanism of this invention.

[0219] Step 108: Perform experience memory consolidation.

[0220] The system inputs empirical data, including the underlying evolution results, candidate topology sets, structural evaluation values, failure labels, and their contextual information from at least one evolutionary process, into a pattern induction algorithm. The pattern induction algorithm can be cluster analysis, graph edit distance analysis, embedding vector mapping, prototype extraction, representative trajectory extraction, or a combination thereof. By inducing historical evolutionary trajectories, the system forms solidified memory objects and assigns persistent weights to these solidified memory objects.

[0221] In this embodiment, the persistence weight can be updated based on at least one of the following: call count, time freshness, task performance improvement, consistency audit pass rate, or historical success rate. When the persistence weight falls below a preset threshold, the corresponding persistent memory object is deleted from memory. Simultaneously, the system also records historical failed audit paths as failed path constraint objects to avoid repeatedly entering the same or similar failed paths in subsequent evolutions.

[0222] Step 109: Perform a fast memory call based on topological nearest neighbors or path equivalence.

[0223] Once a new candidate topology is generated, the system compares its topology signature with the topology signature of an existing fixed memory object. If the topology distance is less than a preset threshold, or if a preset path equivalence condition is met, the system can directly call the structure corresponding to the fixed memory object as a candidate result or a preferred candidate.

[0224] To prevent the direct propagation of erroneous memories, the system performs a fast consistency check after invoking the fixed memory object. This fast consistency check can be a lightweight security check based on topological signature matching, encoding residual threshold, path deviation, key constraint satisfaction, historical audit consistency records, or a combination thereof. When the fast consistency check passes, the system skips, simplifies, or reduces part of the iterative evaluation process for the corresponding candidate structure and directly sends the candidate structure to at least one of the following processes: consistency audit, total order locking arbitration, or subsequent encoding processing. When the fast consistency check fails, the system rejects the fast call and resends the corresponding candidate structure to the regular evaluation process, hierarchical rollback process, or exploration branch process.

[0225] Step 110, Execute the task—Resource linkage adjustment and exploration branch control.

[0226] In this embodiment, the system also includes a task-resource linkage adjustment and exploration branch control mechanism. Specifically, the system adjusts the evaluation coefficients, threshold parameters, aggregation rules, or screening priorities in the structural evaluation value according to preset task objectives; and modifies the legality conditions, screening rules, backoff conditions, or order state functions of candidate topologies according to computing power consumption, storage consumption, communication bandwidth, response latency, energy consumption budget, or a combination thereof.

[0227] When the difference in structural evaluation values ​​between candidate topologies is less than a preset threshold, the consistency audit result is uncertain, the number of consecutive failure labels is triggered reaches a preset number, or the next state space is too large to cause local comparisons to fail to converge, the system triggers at least one of the following processes: exploratory candidate generation, resampling, constrained perturbation, or alternative path search, to generate new candidate topologies and re-enter the comparison and screening process.

[0228] In a specific example, if the current order is low, the next state space is large, and resource constraints are not yet tight, the system increases the triggering priority of the exploration branch; if the current order is high, the platform dwell time is long, and resource pressure is high, the system tends to suppress ineffective expansion through hierarchical rollback and stable platform reuse. The priority of the exploratory candidate generation can be adjusted based on at least one of the following: the historical call success rate of the solidified memory object, task similarity, consistency audit pass rate, branch diversity, or current order information.

[0229] Through the above steps, this embodiment realizes the overall closed loop from discrete mother system construction, candidate topology generation, structure evaluation with dynamic adjustment of order state function, strict total order single-valued projection, high-dimensional discrete coding, hierarchical backoff, consistency audit, experience memory solidification, memory fast recall to exploration branch control. 2. System Modules and Functional Correspondence Examples

[0230] like Figure 7 As shown, this embodiment also provides an intelligent architecture system based on discrete mother system evolution, high-dimensional discrete symmetric encoding, hierarchical backoff, and consistency auditing. The system includes a memory and a processor, the processor being coupled to the memory and configured to execute the following modules.

[0231] The state space construction unit is used to map the computational degrees of freedom in an intelligent system to a discrete graph, a discrete grid background, a cluster of nodes, an edge, or a combination thereof, and to determine the current active subset.

[0232] The candidate topology generation unit is used to generate a set of candidate topologies in the local neighborhood based on the current activation subset, according to expansion rules, contraction rules, boundary replacement rules, local bridging rules, connection rearrangement rules, or combinations thereof.

[0233] The structural evaluation unit is used to calculate the structural evaluation value of each candidate activation subset based on the volume benefit term, boundary complexity or communication cost term, resource budget penalty term, and dynamic adjustment term generated by the ordinal state function, and to compare and filter based on the structural evaluation value.

[0234] The total-order locking arbitration unit is used to perform deterministic single-valued projection on the set of candidate activation subsets with the best structural evaluation value according to strict total-order relations, and lock the unique target activation subset.

[0235] The discrete coding and stabilization processing unit is used to map the topological signature and / or high-dimensional output representation of the target activation subset to the discrete coding space and perform at least one of the following processing: quantization, compression, error correction, discrete addressing, phase anchoring, or stabilization matching.

[0236] The hierarchical rollback control unit is used to perform at least one of the following actions based on hierarchical distance when a failure label is triggered: state rollback, network hierarchical degradation, parameter freezing, or resource reallocation.

[0237] The consistency audit unit is used to perform consistency audits on the mapping relationship between the underlying evolution results, high-dimensional encoding results, and task objectives, and to determine whether the output conditions are met based on the audit results.

[0238] The memory management unit is used to perform experience data collection, pattern summarization, storage, updating, retrieval, and deletion of fixed memory objects and failure path constraint objects.

[0239] The exploration and linkage control unit is used to adjust the sequence state function, filtering rules, anomaly handling rules and exploration triggering conditions according to the task objective, resource status, current sequence information and the size of the next state space, and to perform at least one of the following processes when the preset triggering conditions are met: exploratory candidate generation, resampling, constrained perturbation or alternative path search.

[0240] The result output unit is used to output a valid structure, valid representation, valid term, valid routing result or a combination thereof when the consistency audit passes, and to generate the corresponding audit entry.

[0241] In some implementations, the aforementioned units can be implemented by a single unified software program; in other implementations, the aforementioned units can be implemented by multiple software modules, multiple hardware logic modules, dedicated chips, or combinations thereof. In distributed scenarios, the units can also be deployed on different computing nodes and work collaboratively through a network. 3. Example of implementation of the order state function

[0242] To further illustrate the implementability of the sequence state function in this invention, an exemplary implementation is given below. It should be noted that this implementation is merely an example and does not constitute a limitation on the scope of protection of this invention.

[0243] In one specific embodiment, the system first collects the metric information M, ordinal information O, current state Xt, next state space Ωt+1, and topological description of the candidate activation subset B in the current scene. Then, the above inputs are fed into a rule-statistic hybrid ordinal state function. The function first determines whether the current stage is the exploration phase, transition phase, or convergence phase based on the ordinal information, then calculates an adjustment vector based on the size of the next state space and resource constraints, and finally outputs a comprehensive adjustment value through a preset aggregation rule.

[0244] For example, during the exploration phase, if the number of candidate branches is large, the ordinal state function assigns higher weights to structural scalability, branch diversity, and historical success memory coverage; during the convergence phase, if resource consumption exceeds a threshold, higher weights are assigned to structural stability, structural symmetry, multi-agent positive constraints, and time cost. In this way, the same candidate structure can obtain different final evaluation results at different stages.

[0245] In another embodiment, the ordinal state function can be implemented by a lightweight neural network. The system encodes the features of M, O, Xt, Ωt+1, and B into an input vector, outputs a multidimensional adjustment vector after multiple layers of nonlinear mapping, and then generates a comprehensive adjustment value through weighted aggregation or lookup table mapping. The lightweight neural network can be trained offline on historical evolution data and inferred in real time during online evolution.

[0246] As can be seen from the above description, the ordinal state function in this invention can be implemented through a rule-based approach, a learning-based approach, or a combination of both. Its core does not lie in a single fixed algorithm, but rather in the fact that the evaluation and adjustment logic is jointly determined by the metric, ordinal position, current state, next state space, and candidate structure, and that this adjustment logic dynamically unfolds according to the scenario. 4. Example of Fast Recall and Fast Consistency Verification of Memories

[0247] In the aforementioned overall process embodiment, the system is already able to extract historical successful structures as solidified memory objects and historical failed paths as failed path constraint objects. This embodiment further illustrates how to perform fast invocation when the system encounters candidate structures that are topologically nearest neighbors, path equivalents, or have similar codes to historical memory objects during a new evolution process, and how to prevent erroneous memories from being directly reused through fast consistency verification.

[0248] In this embodiment, the system first selects several candidate activation subsets to be compared from the current candidate topology set and extracts the topology signature of each candidate activation subset. Then, the system retrieves historical objects with similar topology signatures from the fixed memory object set. The retrieval criteria may include any one or more of the following: First, the topological distance criterion. The system calculates the topological distance between the current candidate topological signature and the topological signatures of each fixed memory object. When the topological distance is less than a preset threshold, the two are considered to meet the topological nearest neighbor condition. The topological distance can be represented by graph edit distance, subgraph overlap ratio, boundary difference value, node rearrangement cost, or a combination thereof.

[0249] Second, the path equivalence criterion. The system compares the historical evolution paths corresponding to the current candidate activation subset and the fixed memory objects to see if they meet the preset path equivalence conditions. For example, if two paths differ slightly in the order of intermediate nodes, but their starting points, key branch structures, target convergence regions, and main boundary change patterns are consistent, they can be considered as path equivalence.

[0250] Third, the code similarity criterion. If the current candidate topology has already undergone partial code mapping, the system can further compare the distance between its coding result and the related codes of the solidified memory object. When the codeword distance, residual difference, or coding matching degree meets preset conditions, a fast memory retrieval can also be triggered.

[0251] If any one or a combination of the above criteria is met, the system will consider the historically stable structure associated with the corresponding fixed memory object as a candidate result or a preferred candidate. It should be noted that "preferred candidate" here does not mean direct and unconditional acceptance, but rather that the structure is given priority in subsequent screening, arbitration, or auditing.

[0252] To prevent historical memory objects from being mistakenly invoked in new scenarios, this embodiment further includes a fast consistency check. The fast consistency check is a lightweight security check performed after invoking a fixed memory object. Its purpose is not to replace a complete consistency audit, but rather to quickly eliminate historical structures that are clearly unsuitable for the current scenario with low computational cost.

[0253] In a specific implementation, fast consistency verification may include the following steps: Step 201, Topology Signature Compatibility Check. The system checks the matching degree between the current candidate topology signature and the corresponding topology signature of the invoked memory object. If there are significant conflicts in the set of key nodes, boundary patterns, hierarchy identifiers, or local subgraph patterns, the fast consistency check is deemed to have failed.

[0254] Step 202: Quick check of encoding residuals. If the relevant encoding is available, the system calculates the quick residual value between the current candidate structure and the corresponding encoding of the memory object. When the residual value exceeds a preset threshold, it is determined that the current scene differs too much from the historical memory object and is not suitable for direct invocation.

[0255] Step 203, Critical Constraint Satisfaction Check. The system checks whether the current task objective, resource constraints, and failure path constraints allow the invocation of the persistent memory object. For example, if the current task requires low latency, but the memory object has historically been associated with high resource consumption, the fast consistency check can directly determine that it fails.

[0256] Step 204: Historical audit record check. The system can also query the consistency audit pass rate, number of failures, and rollback frequency of the solidified memory object in historical calls. If the memory object has succeeded in the past but frequently triggers the failure tag in similar scenarios recently, its priority for direct calls will be reduced, or its rapid reuse will be directly rejected.

[0257] In this embodiment, when the fast consistency check passes, the system skips, simplifies, or reduces part of the iterative evaluation process for the corresponding candidate structure, and directly sends the candidate structure into at least one of the following processes: consistency audit, total order locking arbitration, or subsequent coding processing. The "skipping, simplification, or reduction" here can take various forms.

[0258] In one form, the system directly skips, simplifies, or reduces repetitive candidate generation steps and some local comparison steps, retaining only fast consistency verification and subsequent formal consistency audit.

[0259] In another form, the system still retains the calculation of structural evaluation values, but only calculates the volume benefit item, communication cost item, and resource budget penalty item, instead of expanding all candidate branches from scratch.

[0260] In another form, the system retains total order locking arbitration, but directly adds the invoked historical memory object to the optimal candidate set, allowing it to enter the final comparison at a lower computational cost.

[0261] When the fast consistency check fails, the system rejects the fast call and resubmits the corresponding candidate structure to the regular evaluation process, hierarchical rollback process, or exploration branch process. In this way, the present invention does not unconditionally reuse historically successful structures, but performs a memory-based fast call within a closed loop of "fast retrieval—lightweight verification—conditional call," thus simultaneously ensuring reuse efficiency and structural security.

[0262] In another alternative implementation, the system can also set different levels of fast consistency check strength for different categories of fixed memory objects. For example, for core memory objects with high historical audit pass rates, low failure rates, and good call stability, only two fast checks are performed; for marginal memory objects that have recently become frequently unstable or have low similarity to the current task, a more stringent fast check is performed, and it may even require re-entering the complete evaluation process.

[0263] Through the above mechanism, this embodiment enables the system to maintain the ability to quickly reuse historical experience while avoiding error propagation problems caused by outdated memory objects, path drift, or scenario mismatch, thereby improving the stability, reusability, and actual deployment security of the intelligent architecture. 5. Example of Task-Resource Collaboration and Exploration Branch

[0264] In the aforementioned embodiments, the system already possesses the capabilities of candidate topology generation, structure evaluation, total order locking, consistency auditing, hierarchical rollback, and fast memory recall. This embodiment further illustrates how the system adjusts the order state function and filtering rules in conjunction with other mechanisms, and triggers an exploration branch to reopen the search space when the task objective changes, the resource status changes, or the current local search space is too large, the structure evaluation value difference is too small, or the consistency audit result is uncertain.

[0265] In this embodiment, the task-resource linkage adjustment and exploration branch control mechanism mainly addresses two issues: First, how can the system dynamically modify comparison rules, locking rules, and exception handling rules under different task objectives and resource constraints? Secondly, how can the system regenerate candidate topologies and continue to evolve when local comparisons fail to converge or shrink prematurely, instead of stagnating in the current local state?

[0266] First, regarding the coordination and adjustment of tasks and resources.

[0267] In one specific implementation, the system continuously monitors the current task objectives and resource status. The task objectives may include priority based on accuracy, latency, energy consumption, balance, stability, exploration, or a combination thereof; the resource status may include computing power usage, video memory usage, communication bandwidth, node load, response latency, energy consumption budget, or a combination thereof.

[0268] The system adjusts one or more parameters in the structural evaluation value based on the task objectives. For example: When the mission objective is biased towards rapid response, the system increases the influence of boundary complexity or communication cost terms and time cost-related adjustment terms in structural evaluation, and reduces the adjustment intensity related to breadth exploration. When the task objective is biased toward global optimal search, the system increases the modulation intensity in the ordinal state function related to scalability, branch diversity and next state space evaluation. When the mission objective is biased towards stable output, the system increases the adjustment intensity related to structural stability, structural symmetry, and historical platform compatibility.

[0269] Simultaneously, the system adjusts the validity conditions, filtering rules, rollback conditions, or exploration trigger conditions of candidate topologies based on resource status. For example: When the computing power or video memory usage exceeds the first resource threshold, the system tightens the upper limit of the candidate topology set and reduces the acceptable expansion width. When communication bandwidth is tight, the system increases the cost weight corresponding to cross-regional boundary expansion; When the response latency approaches the preset upper limit, the system increases the priority of fast convergence structures and reduces the tolerance for deep alternative path search. When the node load distribution is extremely uneven, the system increases the adjustment intensity related to multi-agent positive constraints, resource allocation fairness, or load balancing.

[0270] In this embodiment, the adjustment results of the task objectives and the monitoring results of the resource status jointly influence the structural evaluation and candidate selection process. In other words, the system does not act solely based on the task objectives or solely based on the resource status, but rather makes decisions jointly through a linkage mechanism: I. The adjustment intensity of the current sequence state function; II. Are the candidate topologies valid? III. Which candidate topologies should be compared first? IV. When should rollback be prioritized? V. When should the exploration branch be reopened?

[0271] Secondly, regarding the triggering of exploration branches.

[0272] In this embodiment, the exploration branch can be triggered by any one or more of the following conditions: First, the difference in structural evaluation values ​​between candidate topologies is less than a preset threshold. This indicates that multiple candidate structures are difficult to distinguish under the current evaluation framework, and local comparisons fall into a near-equivalent state.

[0273] Second, the consistency audit results are uncertain. This situation indicates that the deviations in results between multiple paths do not significantly exceed the threshold, but are also insufficient to reliably pass acceptance.

[0274] Third, the failure tag is triggered a preset number of times. This indicates that the current evolutionary direction is unstable, and continuing along the original path yields low returns.

[0275] Fourth, an excessively large next-state space prevents local comparisons from converging within a preset time or number of rounds. This indicates that the current local neighborhood of the system is too open, making it difficult for the original comparison mechanism to perform effective filtering.

[0276] Fifth, the fast consistency check repeatedly rejects fast memory retrieval, and conventional evaluation has failed to establish a stable platform. This indicates that historical memory cannot be directly reused, and the current state lacks a recognized solution, necessitating reopening the search space.

[0277] When the above triggering conditions are met, the system executes exploration branch control. The exploration branch may include the following forms: One approach is exploratory candidate generation. The system introduces new expansion, replacement, or bridging rules near the current activation subset to generate supplementary candidate structures not covered by the current rules.

[0278] Another approach is resampling. The system resamples the candidate generation order, the range of candidate subset selection, or the local search window to form different candidate sets within the same neighborhood.

[0279] Another form is constrained perturbation. Without violating the core constraints, the system performs perturbations on the local topology of the current candidate structure, such as adjusting an edge, replacing a local node cluster, or swapping two local path segments.

[0280] Another form is alternative path search. The system temporarily abandons the current local path and instead regenerates candidate topologies in the vicinity of historically stable platforms, the neighborhood of remembered objects, or alternative regions with more acceptable resources.

[0281] In a specific example, if the current system is in the early exploration stage, the next state space is large, and resource constraints are not yet tight, the exploration branch can be biased towards adding new candidate structures and expanding the search width; if the system is in the later convergence stage, the response latency pressure is high, the exploration branch can be biased towards local perturbations and alternative path searches, rather than blindly expanding the search range.

[0282] In this embodiment, the priority of exploratory candidate generation is not fixed, but can be dynamically adjusted based on the following information: 1. Success rate of historical retrieval of fixed memory objects. Memory neighborhoods with high success rates can be explored first.

[0283] II. Task Similarity. Historical task paths that are highly similar to the current task will have their adjacent candidate structures given higher exploration priority.

[0284] III. Consistency Audit Pass Rate. Branch areas that have historically had a higher pass rate for audits can be prioritized.

[0285] IV. Branch Diversity. To prevent the system from remaining in a single mode for an extended period, candidate regions with high branch diversity can be appropriately prioritized.

[0286] V. Current Order Information. The earlier the order, the more the system tends to reserve room for exploration; the later the order, the more the system tends to perform local optimizations near a stable platform.

[0287] Through the aforementioned linkage and exploration mechanism, this embodiment enables the system to dynamically decide whether to converge, back off, or reopen the search space under different task objectives, resource pressures, and evolutionary stages. Compared with fixed search or fixed screening schemes, this invention not only utilizes resources more effectively but also reduces problems such as early deadlock, local optimum stagnation, and path drift. 6. Examples of Parameterization Application

[0288] To further illustrate the engineering feasibility of the present invention, several parameterized embodiments are given below. It should be noted that the parameters, thresholds, dimensions, rounds, and constraints described below are merely examples to illustrate how the present invention can be implemented in specific scenarios, and do not constitute a limitation on the scope of protection of the present invention. Those skilled in the art can adjust the relevant parameters according to specific application requirements.

[0289] 1. Parametric Implementation Example 1: Dynamic Graph Structure Search Scenario In a dynamic graph structure search system, the state space of the discrete parent system is represented by a combination of a discrete grid background and local subgraphs, where each currently active subset corresponds to a local subgraph to be expanded or pruned. The topological signature includes a sequence of node types, a set of boundary nodes, local loop features, a hierarchical index, and a historical expansion identifier.

[0290] In this embodiment, the candidate topology set is generated in the following way: 1. Add a new node and its connecting edges on the boundary of the current local subgraph; 2. Delete low-contribution edges or redundant nodes; 3. Insert a bridging edge between two weakly connected subgraphs; Fourth, rearrange the local connection structure.

[0291] The volumetric benefit term in the structural evaluation value can be composed of improved graph coverage, improved target subgraph matching degree, and improved structural integrity; the boundary complexity term can be composed of boundary length, number of local loops, and cost of connection rearrangement; the resource budget penalty term can be composed of search depth, search width, and computational resource consumption.

[0292] In this scenario, the next state space Ωt+1 can be specifically represented as: the number of candidate subgraphs that can be generated within the current graph edit distance threshold; if this number is greater than the preset upper limit, the next state space is determined to be too large, and the system increases the adjustment intensity related to convergence control, screening suppression, and alternative path search.

[0293] If the difference in structural evaluation values ​​among candidate subgraphs is less than a preset threshold, and the consistency audit results under multiple mapping paths are not significantly better than other paths, the system triggers exploratory branch control. Exploratory branch control may specifically include: 1. Expand the local search radius; 2. Perform constrained perturbations near the current boundary; 3. Regenerate candidate subgraphs after returning to the most recent stable historical platform; Fourth, prioritize exploring subgraph neighborhoods that have passed consistency audits multiple times in the past.

[0294] 2. Parametric Implementation Example 2: Edge Reasoning and Resource-Constrained Scenarios In edge reasoning scenarios, system resources are often severely limited. Therefore, the ordinal state function in this invention can be implemented in a degenerate manner. For example, when resources are extremely limited, the ordinal state function can be degenerated into a lookup table function that only depends on the ordinal information O and the current state Xt, or a state machine function composed of a small number of rules. In this case, although M and Ωt+1 can still exist as potential inputs, they can be simplified to fixed parameters or scenario constants in the specific implementation.

[0295] In this scenario, if the response latency is close to the upper limit, the system increases the adjustment intensity related to time cost and decreases the adjustment intensity related to breadth exploration; if the energy consumption budget is close to the upper limit, the system increases the impact of the resource budget penalty on the structural evaluation value; if the current candidate topology meets the nearest neighbor condition with historical solidified memory objects, the system prioritizes trying fast memory retrieval and confirms its availability through fast consistency verification to reduce redundant calculations.

[0296] 3. Parametric Implementation Example 3: Expert Routing and Resource Adaptation in Large Model Training In a large-scale model training scenario, the intelligent system employs a hybrid expert model structure. The system needs to dynamically select which expert modules to activate in each training round, balancing training efficiency and model performance within limited memory, computing power, and communication budgets. This invention can be used for expert routing, structure evaluation, consistency auditing, historical combination reuse, and exploratory control under resource constraints in this scenario.

[0297] In this embodiment, each expert module is mapped to a node in a discrete graph, and the callable relationships, dependencies, or communication relationships between expert modules are mapped to edges, thereby constructing the state space of the discrete parent system. The current active subset consists of the set of experts selected in this round of training, and the topological signature corresponding to the current active subset includes the expert index sequence, hierarchical position, connection boundary information, and routing history identifier.

[0298] The candidate topology set is generated according to the following rules: 1. Add a candidate expert with a high historical call success rate and high relevance to the current task to the current active subset; 2. Remove one expert whose contribution is below a preset threshold from the current active subset; Third, replace an expert with a high communication cost with a substitute expert with a lower communication cost in the local neighborhood; IV. Adjust the calling order or routing priority of multiple expert modules.

[0299] In this scenario, the volume gain term in the structural evaluation value can be calculated from at least one of the following: the reduction in training loss, the improvement in expert utilization, the improvement in local representation coverage, or the historical success prior; the boundary complexity or communication cost term can be calculated from cross-device communication overhead, synchronization waiting time, cross-device call cost, cross-layer call complexity, or a combination thereof; the resource budget penalty term can be calculated from memory usage, power budget, bandwidth consumption, accelerator load, inference cache pressure, or a combination thereof.

[0300] In this embodiment, the input to the ordinal state function can be specifically set as follows: the communication distance matrix or call graph distance between experts as the metric M; the current training round, the cumulative audit round, and the platform dwell round as the ordinal information O; the current memory usage, average routing latency, the most recent consistency audit result, and the failure label history as the current state Xt; and the number of experts that can be added, the number of alternative paths, and the local route search width as the next state space Ωt+1.

[0301] In the early stages of training, when the number of training rounds is low, there are many alternative expert combinations, and resource constraints have not yet reached the stress threshold, the intensity of the rank state function improvement is related to structural scalability, branch diversity, and exploration of new expert combinations. In the later stages of training, when the cumulative audit rounds are high, memory usage is close to the upper limit, or communication load increases, the intensity of the rank state function improvement is related to structural stability, load balancing, communication overhead convergence, and positive constraints on multiple agents.

[0302] In this embodiment, consistency auditing can be performed by comparing the deviations in the results of the following two mapping paths: The first path is "Expert activation subset → High-dimensional encoding result → Complete model output"; The second path is "expert activation subset → lightweight agent model output → training feedback results".

[0303] When the deviation between the results of the two paths is less than a preset threshold, the current expert combination is determined to meet the consistency requirements; when the deviation is greater than the threshold, at least one of the following processes is triggered: rollback, resampling, alternative path search, or resource reallocation.

[0304] In this embodiment, the fixed memory object can represent a template of expert combinations that have historically performed well. The system can record the topological signature, historical call success rate, consistency audit pass rate, memory usage range, and task applicability range corresponding to the expert combination template. When a new candidate expert combination meets the topological nearest neighbor condition or path equivalence condition with a certain fixed memory object, the system can directly call the fixed memory object as a priority candidate and first perform a fast consistency check. The fast consistency check may include: whether the key experts are still available, whether the current memory availability meets the requirements, whether the consistency audit pass rate of the expert combination in recent similar training tasks is higher than the threshold, and whether the current communication load allows the combination to be reused. When the fast consistency check passes, the system can skip, simplify, or reduce some candidate generation steps and local comparison steps, and directly enter the total order locking arbitration and subsequent encoding processing flow.

[0305] When the difference in structural evaluation values ​​among multiple candidate expert combinations is less than a preset threshold, or when the number of consecutive failures is reached by a preset number, the system triggers an exploration branch control. In one implementation, the exploration branch can randomly add a new expert within a limited neighborhood; in another implementation, the system can apply a low-amplitude perturbation to the current expert call order; in yet another implementation, the system can return to the most recent stable historical platform and regenerate candidate expert combinations to prevent the training process from getting stuck in local optima.

[0306] Through this embodiment, the present invention can achieve adaptive selection of expert routes, suppression of communication costs, optimization of memory resources, reuse of historical successful combinations, and avoidance of local optima during the training of large models, thereby improving training stability, resource utilization, and routing decision efficiency in subsequent rounds.

[0307] 4. Parametric Implementation Example 4: Dynamic Graph Structure Search in Autonomous Driving Perception In an autonomous driving perception scenario, the system needs to process multi-source data from cameras, LiDAR, millimeter-wave radar, or other sensors in real time, and construct and update a dynamic perception map based on the multi-source data to form a scene understanding of the surrounding environment. This invention can be used for perception map construction, candidate structure screening, structure adjustment under dynamic resource constraints, reuse of historical scene templates, and exploratory branch control under uncertain conditions in this scenario.

[0308] In this embodiment, the perceived targets are mapped to nodes in a discrete graph, and the spatial relationships, motion relationships, semantic associations, or combinations thereof between targets are mapped to edges, thereby constructing a discrete parent system state space. The currently active subset can represent the set of perceived graph nodes participating in scene understanding at the current moment and their connection relationships. The topological signature corresponding to the currently active subset may include a target type sequence, a set of boundary targets, a local connection pattern, a hierarchical index, a historical association identifier, or a combination thereof.

[0309] The candidate topology set is generated according to the following rules: 1. Add newly detected target nodes and their connecting edges; 2. Delete target nodes or redundant connection edges with a confidence level lower than the preset threshold; 3. Merge targets that are spatially adjacent and have similar movement patterns; IV. Adjust the connection weights, connection directions, or local subgraph structures between targets; 5. Introduce new local association structures near the boundary of the current perception map to form supplementary candidate maps.

[0310] In this scenario, the volume gain term in the structural evaluation value can be calculated from the improvement of target detection confidence, the improvement of trajectory continuity, the enhancement of local correlation consistency, the improvement of scene coverage, or a combination thereof; the boundary complexity or communication cost term can be calculated from the graph complexity, the number of boundary targets, the edge connection density, the cross-sensor fusion cost, or a combination thereof; the resource budget penalty term can be calculated from the computation latency, the memory usage, the power consumption budget, the sensor synchronization pressure, or a combination thereof.

[0311] In this embodiment, the input to the ordinal state function can be specifically set as follows: the Euclidean distance between targets, motion similarity, coding space distance or a combination thereof as the metric M; the current frame number, the cumulative number of audits and the number of consecutive stable frames as the ordinal information O; the current sensor fusion confidence, vehicle speed, current perception map size and the most recent consistency audit result as the current state Xt; and the number of candidate target associations, the number of local candidate subgraphs and the number of alternative paths as the next state space Ωt+1.

[0312] In one implementation, when the vehicle is traveling at high speed, the environment changes rapidly, or the response delay budget is tight, the order state function enhancement is associated with structural stability, fast convergence, low complexity screening, and local consistency maintenance. When the vehicle is traveling at low speed, stationary, parked, or in a relatively stable environment, the order state function enhancement is associated with structural scalability, branch diversity, and local relationship exploration to enhance the ability to supplement the modeling of complex scene relationships.

[0313] In this embodiment, consistency auditing can be performed by comparing the deviations in the results of the following two mapping paths: The first path is "original perceptual map → high-dimensional encoding result → scene understanding output"; The second path is "original perception map → simplified agent model or lightweight scene graph model → scene understanding output".

[0314] When the deviation between the results of the two paths is less than a preset threshold, it is determined that the change in the current perception map has not caused abnormal scene understanding drift; when the deviation is greater than the threshold, it is determined that the current perception map needs to perform at least one of the following processing: backtracking, alternative path search, constrained perturbation, or resampling.

[0315] In this embodiment, the fixed memory object can represent a perception map template of a typical historical scenario, such as an intersection scenario template, a ramp scenario template, a dense traffic flow scenario template, or an obstacle-dense area template. The system can record the topological signature, historical audit pass rate, environmental applicability range, and resource consumption range corresponding to the typical scenario template. When the current perception map and a certain historical template meet the nearest neighbor condition in terms of topological signature, or when its local path change meets the preset path equivalence condition, the system can directly call the template as a priority candidate and perform a fast consistency check after the call. The fast consistency check may include: whether the current sensor fusion confidence meets the threshold, whether the current environmental speed level is within the template's applicable range, and whether the audit pass rate of the template in the most recent few frames is higher than the preset threshold. When the fast consistency check passes, the system can skip, simplify, or reduce some candidate map generation and local structure comparison steps, thereby reducing the computational burden of frame-by-frame repetitive construction.

[0316] When the difference in structural evaluation values ​​among multiple candidate perception maps is less than a preset threshold, or when the perception results of consecutive frames are uncertain, local target associations oscillate, or the next state space is too large to cause local comparisons to fail to converge, the system triggers exploration branch control. In one implementation, the system expands the local search radius to re-establish the association between boundary targets and neighboring targets; in another implementation, the system applies constrained perturbations to the current candidate map to test alternative connection structures; in yet another implementation, the system prioritizes exploring the neighborhood of typical scene templates that have passed consistency audits multiple times in the past, thereby restoring a stable scene understanding structure at a lower cost.

[0317] Through this embodiment, the present invention can achieve adaptive construction of autonomous driving perception maps, rapid reuse of historical typical scene templates, structural screening under dynamic resource constraints, and consistency maintenance of scene understanding results, thereby improving the real-time performance, stability, and scene adaptability in multi-sensor perception scenarios.

[0318] 5. Parametric Implementation Example 5: Resource Constraint Graph Scheduling in Multi-Source Streaming Event Monitoring In a multi-source streaming event monitoring scenario, the system needs to receive continuous inputs in real time from transaction logs, text information streams, market indicator streams, device log streams, or other heterogeneous data sources. Under limited computing resources, it needs to construct and update event correlation graphs to generate abnormal event detection results, event-related alarm results, or monitoring output results. This invention can be used for event graph construction, candidate monitoring structure screening, dynamic graph scheduling under resource constraints, reuse of historical event graph templates, and exploratory branch control under uncertain conditions in this scenario.

[0319] In this embodiment, event factors, monitoring factors, anomaly indicators, state feature nodes, or combinations thereof are mapped to nodes in a discrete graph, and the correlations, temporal relationships, co-occurrence relationships, propagation relationships, or combinations thereof between factors are mapped to edges, thereby constructing a discrete parent system state space. The current active subset can represent the set of event nodes participating in graph reasoning and their connection relationships within the current monitoring period. The topological signature corresponding to the current active subset may include a node type sequence, a set of boundary nodes, a local association pattern, a time window identifier, a data source identifier, or a combination thereof.

[0320] The candidate topology set is generated according to the following rules: 1. Introduce new event factor nodes, monitoring nodes, or feature nodes; 2. Delete redundant factor nodes or weakly related edges whose contribution is lower than the preset threshold; 3. Adjust the correlation weights, propagation directions, or local subgraph structures between factors; IV. Establish new cross-source association edges between different data sources; 5. Supplement alternative candidate structures near the boundaries of the current event graph to form new candidate monitoring graphs.

[0321] In this scenario, the volume gain in the structural evaluation value can be calculated from the improvement of anomaly detection accuracy, the improvement of early event discovery capability, the improvement of local correlation coverage, the enhancement of anomaly link interpretation capability, or a combination thereof; the boundary complexity or communication cost can be calculated from the cross-data source call cost, graph complexity, edge connection density, streaming synchronization waiting time, or a combination thereof; the resource budget penalty can be calculated from CPU usage, GPU usage, memory consumption, cache pressure, inference latency, or a combination thereof.

[0322] In this embodiment, the input to the ordinal state function can be specifically set as follows: the mutual information distance, coding space distance, temporal correlation distance, or a combination thereof between event factors as metric M; the current monitoring cycle number, the cumulative number of audits, and the number of consecutive stable cycles as ordinal information O; the current system load, the recent anomaly detection pass rate, the current event graph size, and the most recent consistency audit result as the current state Xt; and the number of newly added event factors, the number of alternative monitoring paths, and the number of local candidate subgraphs as the next state space Ωt+1.

[0323] In one implementation, when external metrics fluctuate drastically, streaming data throughput increases, or response latency budgets become tight, the order state function increases with modulating strength related to structural stability, fast convergence, low-complexity screening, and high-confidence path maintenance; when the system is in a stable period, resource load is low, or there is a strong demand for novel pattern discovery, the order state function increases with modulating strength related to structural scalability, branch diversity, and novel event association exploration.

[0324] In this embodiment, consistency auditing can be performed by comparing the deviations in the results of the following two mapping paths: The first path is "Original event graph → High-dimensional coding result → Monitoring output result"; The second path is "Original event graph → Simplified proxy model or lightweight event graph model → Monitoring output results".

[0325] When the deviation between the results of the two paths is less than a preset threshold, it is determined that the current event graph structure can stably support the monitoring output; when the deviation is greater than the threshold, it is determined that the current candidate monitoring structure needs to perform at least one of the following processes: rollback, resampling, constrained disturbance, or alternative path search.

[0326] In this embodiment, the solidified memory object can represent historical high-pass-rate event graph templates, cross-source propagation event templates, multi-index coupled event templates, or continuous abnormal link templates. The system can record the topology signature, historical audit pass rate, applicable time window, resource consumption interval, and call success rate corresponding to the template. When the current event graph meets the topology nearest neighbor condition or path equivalence condition with a certain historical template, the system can directly call the template as a priority candidate and perform fast consistency verification after the call. The fast consistency verification may include: whether the current system load meets the template reuse condition, whether the current data source integrity meets the threshold, and whether the consistency audit pass rate of the template in the most recent several similar periods is higher than a preset threshold. When the fast consistency verification passes, the system can skip, simplify, or reduce some candidate graph generation and local comparison steps, thereby reducing the overhead of repeated inference.

[0327] When the difference in structural evaluation values ​​among multiple candidate monitoring maps is less than a preset threshold, or when the output results of multiple consecutive monitoring cycles are uncertain, or the next state space is too large to cause local comparisons to fail to converge, the system triggers exploratory branch control. In one implementation, the system introduces new combinations of event factors; in another, the system adjusts the weight distribution of the current local event association structure; and in yet another, the system switches to a historical template neighborhood that is closer to the current scenario to regenerate candidate monitoring maps. Through the above exploration mechanism, the system can continuously adapt to new anomaly patterns or new event propagation structures under resource-constrained conditions.

[0328] Through this embodiment, the present invention can realize adaptive graph construction, resource constraint scheduling, rapid reuse of historical event graph templates, and consistency maintenance of monitoring output results in multi-source streaming event monitoring scenarios, thereby improving the real-time performance, stability, and mode adaptability in continuous monitoring environments.

[0329] 6. Parametric Implementation Example Six: Distributed Node Load Balancing and Resource Suppression In a distributed computing scenario, the system consists of multiple heterogeneous computing nodes, each jointly undertaking inference, training, graph search, or resource scheduling tasks. Due to differences in computing power, bandwidth, cache capacity, and current load status among nodes, the system needs to ensure the benefits of local tasks while suppressing excessive consumption of shared resources by a single node and maintaining global load balancing and fairness in resource allocation. This invention can be used for candidate resource allocation structure generation, dynamic structure evaluation, global resource contention suppression, and historical balanced structure reuse in this scenario.

[0330] In this embodiment, each computing node is mapped to a node in a discrete graph, and the communication relationships, dependencies, resource transfer relationships, or combinations thereof between nodes are mapped to edges, thereby constructing the state space of the discrete parent system. The currently active subset represents the set of nodes participating in task processing at the current moment and their resource allocation relationships. The topological signature corresponding to the currently active subset may include a node identifier sequence, resource occupancy status, boundary communication relationships, task load identifier, or a combination thereof.

[0331] The candidate topology set is generated according to the following rules: 1. Allocate more computing resources to high-yield nodes in the current active subset; 2. Reclaim some resources from low-contribution nodes and redistribute them; Third, migrate tasks on high-communication-cost paths to alternative nodes in the local neighborhood; IV. Adjust the task distribution order, bandwidth usage, or cache usage ratio among multiple nodes; Fifth, generate alternative candidate topologies near the current resource allocation structure to form a new equilibrium candidate structure.

[0332] In this scenario, the volume gain term in the structural evaluation value can be calculated from the local task completion rate, throughput improvement, inference accuracy improvement, training loss reduction, or a combination thereof; the boundary complexity or communication cost term can be calculated from cross-node communication overhead, synchronization waiting time, bandwidth usage cost, cache swapping cost, or a combination thereof; and the resource budget penalty term can be calculated from CPU usage, GPU usage, video memory usage, bandwidth consumption, energy consumption budget, or a combination thereof.

[0333] In this embodiment, the input to the sequence state function can be specifically set as follows: the communication distance between nodes, the resource transfer cost, the task migration cost, or a combination thereof as the metric M; the current scheduling round, the cumulative number of audits, and the number of consecutive stable rounds as the sequence information O; the current node load distribution, resource occupancy ratio, the most recent consistency audit result, and the failure label history as the current state Xt; and the number of newly added resource allocation paths, the number of alternative nodes, and the number of local candidate structures as the next state space Ωt+1.

[0334] In one specific implementation, when a candidate topology, while possessing high local benefits, leads to a node continuously expanding its resource consumption and significantly reduces the reachable resource space of adjacent nodes, the order state function increases the adjustment intensity related to multi-agent positive constraints, load balancing, and resource allocation fairness. Thus, although the candidate topology excels in local benefits, its overall structural evaluation value is weakened due to the imbalance in global resource distribution.

[0335] In one implementation, consistency auditing can be performed by comparing the deviations in the results of the following two mapping paths: The first path is "Current resource allocation structure → High-dimensional encoding result → Global task output result"; The second path is "Current resource allocation structure → Simplified proxy scheduling model → Resource utilization result".

[0336] When the deviation between the results of the two paths is less than a preset threshold, the current allocation structure is determined to meet the consistency requirements; when the deviation is greater than the threshold, the current candidate topology is determined to need to perform at least one of the following processes: rollback, degradation, resource reallocation, resampling, or alternative path search.

[0337] In this embodiment, when a candidate resource allocation structure causes the audit volume to exceed a threshold, or causes several adjacent nodes to remain in a low-reachability state for an extended period, the audit closure mechanism rejects the candidate topology and reverts the system to the balanced resource allocation structure that most recently passed the consistency audit. The balanced resource allocation structure can be a specific form of a historically stable platform, and its corresponding fixed memory object can record the resource occupancy range, load distribution characteristics, audit pass rate, and task applicability scope of each node.

[0338] When a new candidate resource allocation structure satisfies the topological nearest neighbor condition or path equivalence condition with a certain historical balanced structure, the system can directly call the corresponding fixed memory object as a priority candidate and perform a fast consistency check after the call. The fast consistency check may include: whether the current node load is within the template's allowed range, whether the current bandwidth usage meets the threshold, and whether the consistency audit pass rate of the template in the most recent similar scheduling rounds is higher than a preset threshold. When the fast consistency check passes, the system can skip, simplify, or reduce some candidate allocation structure generation and local comparison steps, thereby reducing the overhead of repeated scheduling.

[0339] When the difference in structural evaluation values ​​among multiple candidate resource allocation structures is less than a preset threshold, or when the scheduling results are unstable for several consecutive rounds, local resource contention fails to converge, or the next state space is too large to complete the comparison process, the system triggers exploratory branch control. In one implementation, the system can generate a new resource migration path; in another implementation, the system can apply constrained perturbations to the current allocation structure to test alternative load distributions; in yet another implementation, the system can fall back to the most recent historical stable platform and regenerate candidate resource allocation structures.

[0340] Through this embodiment, the present invention can achieve a joint trade-off between local benefits and global resource fairness in a distributed computing environment, suppress the excessive occupation of shared resources by a single node, and improve the overall load balancing capability, resource utilization efficiency and continuous stability of the system.

[0341] 7. Parametric Implementation Example 7: Execution Graph Simplification and Coding Regularization In a complex task execution scenario, the system can generate multiple candidate execution graphs that are logically equivalent in function but structurally different for the same task objective. These candidate execution graphs can be inference execution paths, module call paths, subgraph scheduling paths, rule triggering chains, or combinations thereof. Although these candidate execution graphs can achieve the same or similar task outputs, their internal structural complexity, coding regularity, boundary connection methods, and subsequent error correction friendliness differ significantly. This invention can be used for execution graph selection, structural regularization, coding complexity suppression, and optimal locking among logically equivalent candidate paths in this scenario.

[0342] In this embodiment, execution units, rule nodes, module nodes, parameter blocks, or combinations thereof are mapped to nodes in a discrete graph, and execution order, calling relationships, dependency relationships, conditional jump relationships, or combinations thereof are mapped to edges, thereby constructing the state space of the discrete parent system. The currently active subset represents the set of nodes participating in the execution during the current task processing and their connection relationships. The topological signature corresponding to the currently active subset may include node identifier sequences, path hierarchy relationships, boundary connection patterns, local subgraph structures, calling order identifiers, or combinations thereof.

[0343] The candidate topology set is generated according to the following rules: 1. Reorder the local call order in the current execution graph; 2. Compress or merge redundant jump edges or duplicate sub-paths; 3. Replace the call paths of multiple functionally equivalent submodules; 4. Perform bridging optimization or boundary reconstruction on local structures with high boundary connection complexity; 5. While maintaining the equivalence of task outputs, generate several alternative execution graphs with more compact coding or more regular connections.

[0344] In this scenario, the volumetric benefit item in the structural evaluation value can be calculated from task completion rate, output consistency, local execution efficiency improvement, historical success rate, or a combination thereof; the boundary complexity or communication cost item can be calculated from the number of boundary jumps in the execution graph, cross-module call cost, connection rearrangement complexity, path addressing length, or a combination thereof; the resource budget penalty item can be calculated from execution time, cache usage, memory consumption, bandwidth consumption, or a combination thereof.

[0345] In this embodiment, the input to the ordinal state function can be specifically set as follows: the graph edit distance, topological signature distance, coding space distance, or a combination thereof between execution graphs as metric M; the current execution round, the cumulative number of audits, and the stable platform residency round as ordinal information O; the current execution graph size, the current boundary complexity, the most recent consistency audit result, and the failure label history as the current state Xt; and the number of alternative paths, the number of local candidate execution graphs, and the local search width as the next state space Ωt+1.

[0346] In one specific implementation, when multiple candidate execution graphs are logically equivalent or nearly equivalent in task output, the order state function increases the modulation strength related to structural symmetry, coding consistency, low-complexity addressing, and robustness to local perturbations. As a result, the system reduces the overall evaluation value of candidate execution graphs with scattered structures, high boundary complexity, and large coding redundancy, while increasing the overall evaluation value of candidate execution graphs with more regular structures, simpler connections, and more stable coding.

[0347] In this embodiment, a more standardized execution diagram typically has one or more of the following engineering features: First, topological signatures are shorter or more stable. Second, fewer boundary jumps are required; Third, the local join pattern has higher redundancy, which facilitates unified coding; Fourth, the subsequent error detection and correction process is simpler; Fifth, it is easier to index and reuse during fast memory retrieval and hierarchical rollback.

[0348] In one implementation, consistency auditing can be performed by comparing the deviations in the results of the following two mapping paths: The first path is "candidate execution graph → high-dimensional encoding result → task output result"; The second path is "candidate execution graph → simplified proxy execution model → task output results".

[0349] When the deviation between the results of the two paths is less than a preset threshold, it is determined that the different candidate execution graphs meet the consistency requirements in the output results. On this basis, the system further selects the execution graph with more regular coding and simpler addressing as the final target execution structure based on the structural evaluation value.

[0350] In this embodiment, the solidified memory object can represent a well-structured execution graph template with a historically high pass rate. The system can record the topological signature, encoding length range, consistency audit pass rate, boundary complexity range, and task applicability range corresponding to the template. When a new candidate execution graph satisfies the topological nearest neighbor condition or path equivalence condition with a certain historical template, the system can directly call the template as a priority candidate and perform a fast consistency check after the call. The fast consistency check may include: whether the current task constraints allow the call to the template, whether the current boundary complexity is within the template's applicability range, and whether the consistency audit pass rate of the template in several recent similar tasks is higher than a preset threshold. When the fast consistency check passes, the system can skip, simplify, or reduce some candidate path generation and local comparison steps.

[0351] When the difference in structural evaluation values ​​among multiple candidate execution graphs is less than a preset threshold, or when candidate execution graphs are equivalent in output but have high complexity in local structure, the system can trigger exploratory branch control. In one implementation, the system applies constrained perturbations to the current execution graph to test alternative connection methods with lower complexity; in another implementation, the system reverts to the most recent historical stable platform and regenerates a regularized candidate execution graph; in yet another implementation, the system prioritizes exploring template neighborhoods with historically high coding regularity and high consistency audit pass rates.

[0352] Through this embodiment, the present invention can preferentially select the execution structure with more regular structure, more compact coding, simpler addressing and more conducive to subsequent error correction and rollback among logically equivalent candidate execution graphs, thereby improving execution efficiency, reducing structural complexity, and enhancing the stability and reusability of the system in the long-term evolution process. Further implementation methods for main body maintenance and high-level maintenance

[0353] For ease of reference, items 1-18 of this section can be abbreviated as Advanced Examples 1-18.

[0354] The following advanced implementation methods are all based on the aforementioned discrete mother system evolution main chain, without changing the basic structure in the main claim, but further adding external action and feedback closed loop, subject state maintenance, self-blueprint constraint, continuous perception and adjustment, and high-level open boundary maintenance interface on the main chain.

[0355] Among them, the subject continuity constraint constitutes the master valve for all high-order implementations of the present invention; identity continuity chain, self object, self blueprint object, blueprint adversarial verification and open boundary maintenance all operate under this constraint.

[0356] This invention does not eliminate boundaries, but rather preserves constrained space for exploration, revision, and reflection within the framework of subject continuity, blueprint boundaries, and resource security budget. 1. Examples of Action Mapping, Feedback Collection, and Causal Intervention Audits

[0357] 1. Technical issues The aforementioned implementation has established a discrete parent system state space, candidate topology generation, dynamic evaluation of order state functions, strict total order locking, high-dimensional discrete encoding, hierarchical backoff, consistency auditing, and fast memory recall mechanisms. However, relying solely on internal candidate comparison and internal consistency auditing, the system output still mainly reflects the evolutionary selection of the internal state space, and a complete closed loop of "target activation subset - external action - environmental feedback - re-auditing" has not yet been formed.

[0358] In practical applications, systems not only need to select their internal structures, but also need to map the selected structures to actual interventions in the external world. For example, in control scenarios, systems need to translate internal decisions into actions or control sequences; in distributed computing scenarios, systems need to translate internal choices into scheduling instructions; and in interaction and configuration scenarios, systems need to translate internal states into communication requests, environmental configurations, or logical interventions.

[0359] Therefore, an action mapping, feedback collection, and causal intervention auditing mechanism is needed to enable the system to map the target activation subset to external actions or logical interventions, collect environmental feedback, and form action feedback residuals, which are then further incorporated into the audit closure, memory solidification, and subsequent exploration branch control processes.

[0360] 2. Action Mapping Mechanism In this embodiment, after completing candidate topology comparison, strict total order locking, and necessary high-dimensional discrete encoding, the system further maps the target activation subset into executable external actions. These external actions can be one or more of control signals, communication commands, environmental configurations, or logical interventions.

[0361] In one implementation, the system maps a subset of target activations to a sequence of control signals for driving device actions, performing path adjustments, or local control.

[0362] In another implementation, the system maps a subset of target activations to communication commands for task migration, resource reallocation, node coordination, or link switching.

[0363] In another implementation, the system maps a subset of target activations to environment configurations or logical interventions for updating access policies, parameter blocks, interface states, or operating rules.

[0364] The action mapping is constrained by the current task objective, resource budget, entity continuity constraints, and self-blueprint boundary constraints. The system checks at least one or more of the following conditions: First, does the action meet the current resource budget limit? Second, whether the actions described will lead to a significant decline in the subject's continuous health; Third, does the action violate high-priority boundary constraints in the self-blueprint object? Fourth, whether the action triggers a known high-risk path in the failed path constraint object; Fifth, whether the action exceeds the operating window, permission range, or security boundary allowed by the current environment.

[0365] The system will only formally map the target activation subset to an external action if the action meets the preset executable conditions; otherwise, the system will send it back to the consistency audit, hierarchical rollback, or alternative path search process.

[0366] 3. Feedback Collection Mechanism In this embodiment, after performing an external action, the system further collects feedback information returned by the environment. This feedback information may come from the physical environment, network environment, logical system, user interface, or a combination thereof.

[0367] In one implementation, feedback acquisition includes reading back sensor signals, such as position, velocity, temperature, force feedback, images, radar, or other detection results.

[0368] In another implementation, feedback collection includes reading back network or system status, such as node load, link latency, bandwidth fluctuations, task completion rate, synchronization status, or log streams.

[0369] In another implementation, feedback collection includes user responses or upper-level system responses, such as acknowledgments, rejections, interaction delays, error messages, or combinations thereof.

[0370] The system can configure one or more feedback channels according to the current scenario and assign different weights to different feedback channels. The feedback acquisition results can be further processed by denoising, normalization, time alignment, outlier suppression, and confidence estimation, and written into the continuous sensing residual stream for use by the ordinal state function, audit quantity dynamic adjustment mechanism, or hedging matrix.

[0371] 4. Construction of Action Feedback Residuals In this embodiment, the system compares the actual feedback result after the external action is executed with the expected result, the reference template, or the corresponding result of a historical stable platform, thereby forming an action feedback residual. The action feedback residual is used to measure whether the internal choice holds true in the external environment.

[0372] In one implementation, the action feedback residual can be expressed as: ract(t) = DM(Fobs(t), Fref(t)) Where Fobs(t) represents the actual feedback result at time t, Fref(t) represents the expected feedback result or reference feedback result, and DM represents the distance, deviation, or difference measure defined under the current metric M.

[0373] In another implementation, the action feedback residual can be decomposed according to dimensions such as latency, state deviation, resource execution, security boundary, and user acceptance, and enters the audit closure mechanism as a multi-dimensional residual vector. The system can further calculate the rate of change, moving average, and historical stable interval deviation of the action feedback residual. The role of its rate of change in threshold adjustment can be referred to the dynamic gradient adjustment embodiment of audit quantity described later.

[0374] 5. Causal intervention audit In this embodiment, the system incorporates action feedback residuals into a consistency audit and audit closure mechanism. The system not only checks whether internal paths are consistent, but also checks whether internal choices lead to acceptable causal results in the external world.

[0375] In one implementation, the system constructs the following two paths for comparison: The first path is "target activation subset → action mapping → external environment feedback"; The second path is "Target activation subset → Expected result model or historical template → Reference feedback result".

[0376] When the difference between two paths is less than a preset threshold, the action is determined to be valid in the current environment; when the difference is greater than the threshold, the action is determined to have failed to achieve the expected causal result, or there is a mismatch between the current environmental conditions and the internal model.

[0377] In another implementation, the system incorporates the action feedback residuals into the total audit amount. For example: Atotal=Aint+λact·ract+λsafe·rsafe Where Aint represents the internal audit quantity, ract represents the action feedback residual, rsafe represents the action-related security boundary residual, and λact and λsafe are preset weights.

[0378] 6. Retreat and replanning after a failed operation When the residual of the action feedback exceeds a preset threshold, or when the causal intervention audit determines that there is a significant deviation between the external feedback and the internal expectations, the system triggers the handling process after the action fails.

[0379] In one implementation, the system performs a partial rollback, that is, rolls back along the current hierarchy to the most recently stable local platform that passed the consistency audit, and regenerates candidate action plans.

[0380] In another implementation, the system performs an alternative path search, which excludes high-risk candidates based on the failed path constraint object and prioritizes calling the nearest neighbor template or alternative path that has been audited through causal intervention in the past.

[0381] In another implementation, the system performs an action pause or energy-saving silent switching. This occurs when there is strong environmental noise, excessive resource pressure, decreased system continuity health, or blue screen errors. Figure 1 When consistency bias increases, the system can proactively suspend external actions and retain only internal memory integration, self-object updating, or deep reflection tasks.

[0382] Information related to failed actions can also be written into the failed path constraint object and the meta-evolutionary trajectory index for priority identification and avoidance of similar scenarios in the future.

[0383] 7. Technical Effects First, this embodiment establishes a complete closed loop from the internal target activation subset to external action, and then to feedback and re-auditing, enabling the system output to enter a traceable causal intervention process.

[0384] Second, through action feedback residuals and causal intervention audits, the system can distinguish between "internal assessments being valid" and "external assessments being valid," thereby improving its effectiveness and robustness under real deployment conditions.

[0385] Third, this embodiment provides an external evidence interface for subsequent dynamic gradient adjustment of audit volume, proactive causal detection, self-object updating, and subject continuity audit. 2. Example of Dynamic Gradient Adjustment of Audit Volume

[0386] 1. Technical issues The aforementioned implementation has enabled the system to determine the legality of the current candidate structure, coding results, and external action results through consistency auditing, hierarchical rollback, action feedback residuals, and audit closure mechanisms. However, if audit closure relies solely on static thresholds or single-moment audit volume, it will still be difficult to reflect risk trends, convergence trends, and exploration opportunities in a timely manner in rapidly changing environments, continuous sensing flow scenarios, and highly coupled multidimensional states.

[0387] Therefore, a dynamic gradient adjustment mechanism for audit volume is needed so that the system can not only perceive the current audit volume, but also perceive the changing trends of key quantities such as audit volume, resource pressure, action feedback residuals, and next state space size over time, and dynamically adjust the audit threshold, exploration intensity, rollback priority, and behavior rhythm accordingly.

[0388] 2. Construction of the first-order quantity of audit quantity In this embodiment, the system first constructs a set of basic audit quantities as the basic input for dynamic gradient adjustment. The basic audit quantities may include one or more of the following types: First, coding residuals and path deviations; Second, the cost of state change and the cost of hierarchical rollback; Third, action feedback residuals; Fourth, resource pressure indicators; Fifth, indicators related to the deviation between the main body status and the blueprint.

[0389] In one implementation, the system combines the aforementioned basic audit quantities into a vector: A(1)(t)=[a1(t),a2(t),…,an(t)] Where ai(t) represents the i-th basic audit quantity at time t.

[0390] The system can perform normalization, scale alignment, denoising, and smoothing on each basic audit quantity to facilitate subsequent time gradient calculation and dynamic threshold updates.

[0391] 3. Construction of the second-order quantity of audit quantity In this embodiment, the system further constructs time-varying information of the aforementioned basic audit quantities. This time-varying information may be discrete-time difference, continuous-time derivative, sliding window rate of change, or a combination thereof.

[0392] In a discrete implementation, the system calculates the time-interval difference for each basic audit quantity ai(t)a_i(t): Δai(t)=ai(t)−ai(t−Δt) And the second difference can be further calculated: Δ2ai(t)=Δai(t)−Δai(t−Δt) In one continuous implementation, the system can compute: a˙i(t)=dai(t) / dt,a¨i(t)=d²ai(t) / dt² The system does not require the construction of second-order quantities for all basic audit quantities. Instead, it can calculate the rate of change over time for only the most critical audit dimensions based on a subset of dynamically activated dimensions.

[0393] 4. Tension Regulation Mechanism In this embodiment, the system constructs a "tension" index based on the basic audit volume and its rate of change over time, which is used to characterize whether the system should quickly increase convergence control, risk protection, and rollback priority.

[0394] In one implementation, the tension can be composed of a combination of the following quantities: - Coding residual growth rate; - Growth rate of action feedback residuals; - Resource pressure growth rate; - The rate of decline in the subject's continuous health; - Self-Blue Figure 1 The rate at which consistency deviation increases.

[0395] For example, the stress level Tstress(t) can be defined as: Tstress(t)=ω1·r˙enc(t)+ω2·r˙act(t)+ω3·p˙res(t)+ω4·d˙id(t)+ω5·d˙blue(t) When the stress level exceeds a preset threshold, the system may perform one or more of the following actions: First, improve audit sensitivity and narrow the acceptable range of deviation; Second, increase the priority of hierarchical rollback; Third, reduce the amplitude or width of the exploration branch perturbation; Fourth, suspend low-priority external actions; Fifth, enhance the constraints on the continuity of the main body and the blue Figure 1 The weight of consistency constraints in the current decision-making process.

[0396] 5. Explore the mechanisms of tendency regulation In this embodiment, the system also constructs an "exploration tendency" index to characterize whether the system should relax the candidate generation boundary, increase alternative path search, or increase the frequency of active detection.

[0397] In one implementation, the exploratory tendency can be composed of a combination of the following quantities: - Next state space expansion rate; - Rate of change in candidate branch diversity; - Novelty of memory retrieval; - Frequency of new patterns appearing in meta-evolutionary trajectories; - Rate of change of evolutionary activity index.

[0398] For example, the exploratory tendency Texplore(t) can be defined as: Texplore(t)=η1·Ω˙(t)+η2·D˙branch(t)+η3·Nmem(t)+η4·Ntraj(t)+η5·H˙act(t) When the exploration tendency is higher than the preset threshold and the tension does not exceed the high-risk threshold, the system can appropriately increase the candidate topology generation width, relax the alternative path search radius, increase the priority of the active causal detection task, or perform alternative high-level target confrontation test on the current blueprint object.

[0399] 6. Dynamic threshold update mechanism In this embodiment, the system dynamically updates the audit threshold, exploration threshold, rollback threshold, and action pause threshold based on tension level, exploration tendency, and other key first-order and second-order quantities.

[0400] In one implementation, the basic audit threshold is denoted as τ0, and the system generates the actual usage threshold τ(t): τ(t)=τ0+κ1·Texplore(t)−κ2·Tstress(t) If the tendency to explore increases while the stress level is low, the system increases the acceptable exploration space; if the stress level increases, the system tightens the threshold and increases conservatism.

[0401] In another implementation, the system can update different types of thresholds separately, such as the encoded residual threshold, the alternative path search threshold, the active detection task threshold, and the energy-saving silent mode trigger threshold. These updates can also be influenced by the subject's continuous health status and self-blue... Figure 1 Consistency bias, identity continuity chain verification results, and accumulation constraints for special undecidable objects.

[0402] 7. Technical Effects First, this embodiment enables the system to simultaneously perceive the current deviation and the trend of deviation change, thereby avoiding the lag or misjudgment caused by simply relying on static thresholds.

[0403] Second, by controlling variables such as tension and exploratory tendency, the system can perform smoother rhythmic switching between convergence, regression, exploration, and silence.

[0404] Third, this embodiment provides a dynamic adjustment logic basis for the continuous sensing hedging implementation described later, and provides a unified threshold update interface for proactive causal detection, blueprint adversarial testing, and deep reflection mode. 3. Decision Context Snapshot and Meta-Evolutionary Trajectory Index Example

[0405] 1. Technical issues The aforementioned implementation has enabled the system to accumulate experience between historical successful and failed paths by solidifying memory objects, failing path constraint objects, fast consistency verification, and audit closure mechanisms. However, if the memory system only stores "which structure succeeded" or "which path failed," without storing the specific environment, resource status, sequence stage, blueprint deviations, action feedback results, and their combinations at the time these results were formed, it will be difficult to support subsequent accurate reuse, high-level reflection, and subject updates.

[0406] Therefore, a decision context snapshot and meta-evolutionary trajectory indexing mechanism is needed to enable the system to not only save the successful structures and failure paths themselves, but also the context in which these structures are formed, and further establish temporal, causal, abstract, and backtracking relationships among multiple memory objects, thereby forming a long-term trajectory structure that can be used for rapid retrieval, deep reflection, self-object updates, and blueprint verification.

[0407] 2. Decision Context Snapshot Structure In this embodiment, the system extracts a snapshot of the decision context corresponding to each event when it completes a key decision, passes a consistency audit, triggers a hierarchical rollback, completes an external action, or generates an important memory object.

[0408] In one implementation, the decision context snapshot includes at least one or more of the following fields: First, environmental and metric information; Second, sequence and resource status information; Third, structural assessment and dynamic dimensional information; Fourth, audit results and tiered information; Fifth, action feedback information; Sixth, information on the main layer and the blue layer.

[0409] The context snapshot can be represented as a structured table entry, vector, graph node, tensor, codeword, embedded vector, or a combination thereof. The system can also use different snapshot templates based on success events, failure events, and blueprint revision events.

[0410] 3. Binding of fixed memory objects to context In this embodiment, the system not only saves the fixed memory object itself, but also binds it to the corresponding decision context snapshot.

[0411] Therefore, a solidified memory object no longer only represents "a successful structure", but also carries "the environment, order, subject state and blueprint state in which the structure was formed".

[0412] In one implementation, the system attaches one or more of the following metadata to each solidified memory object: First, the context snapshot identifier at the time of generation; Second, the relevant historical audit pass rate; Third, the range of continuous health of the subject during its formation; Fourth, the blueprint version at the time of its formation and the blueprint itself. Figure 1 Consistency deviation range; Fifth, the stable range of action feedback during formation; Sixth, the set of failure path constraint objects associated with this object.

[0413] Failure path constraint objects can also be bound to decision context snapshots to support future differentiation between "failures that should absolutely not be repeated" and "failures that should only be avoided in a specific context".

[0414] 4. Construction of the meta-evolutionary trajectory diagram In this embodiment, the system organizes multiple solidified memory objects, failure path constraint objects, important context snapshots, self-object update nodes, blueprint version nodes, and special object nodes into a meta-evolutionary trajectory graph.

[0415] In one implementation, trajectory graph nodes may include one or more of the following types: First, memory nodes; Second, failed nodes; Third, the main nodes; Fourth, blueprints or special object nodes.

[0416] Trajectory graph edges may include one or more of the following relationships: First, it leads to a relationship; Second, the following relationship; Third, abstract relationships; Fourth, rollback or revision of relationships.

[0417] In a preferred implementation, the system also appends a timestamp, causal strength, contextual similarity, subject continuity influence coefficient, and blue to each edge. Figure 1 One or more of the consistency influence coefficients.

[0418] 5. Causal strength and timestamp annotation To enable the meta-evolutionary trajectory graph to serve subsequent retrieval, prediction, and deep reflection, the system further adds computable weight information to nodes and edges.

[0419] In one implementation, the system calculates the causal strength for each edge, the causal strength being a combination of one or more of the following factors: First, the time distance; Second, contextual similarity; Third, topological similarity; Fourth, the magnitude of the audit impact; Fifth, blue Figure 1 The magnitude of the effect of the effect.

[0420] For example, the causal strength wi→j of an edge can be defined as: wi→j=f(Δti,j,Sctx(i,j),Stopo(i,j),Iaudit(i,j),Iblue(i,j)) The system can record both absolute time and relative sequence time simultaneously to support different levels of querying and replay.

[0421] 6. Prioritize invocation based on trajectory similarity In this embodiment, the meta-evolutionary trajectory graph is not only used to record history, but also to provide a basis for priority invocation in new tasks or new states.

[0422] Trajectory similarity can be determined by one or more of the following factors: First, the similarity between the current context snapshot and the historical context snapshot; Second, the distance between the current subject status and the historical subject nodes; Third, the similarity between the current blueprint object and the nodes of the historical blueprint version; Fourth, the topological distance between the current candidate topology and the historical stable platform nodes; Fifth, the similarity between the current action feedback pattern and the feedback pattern of historical action nodes.

[0423] When the system detects that a historical trajectory segment is highly similar to the current scene, it can perform one or more of the following operations: First, prioritize calling the solidified memory objects in the trajectory segment as candidate structures; Second, prioritize calling action templates that have passed action feedback audits in this trajectory; Third, prioritize avoiding branches in the trajectory that are subsequently proven to be unsuccessful; Fourth, prioritize referencing the trajectory passing through blue. Figure 1 The high-level decision-making process for consistency audits.

[0424] Trajectory similarity results can also be used for subsequent self-object updates, blue... Figure 1 Inputs from consistency audits and long-term, in-depth reflection.

[0425] 7. Technical Effects First, this embodiment upgrades the system from outcome-based memory to a structured memory system with context and causal chains.

[0426] Second, through the meta-evolutionary trajectory diagram, the system can express the causal, follow-up, abstraction, and regression relationships between key states, thereby supporting long-term subject trajectory analysis.

[0427] Third, this embodiment is for self-object updates, Blue Figure 1 Consistency auditing and deep reflection models provide a unified historical structural input. 4. Examples of Self-Object and Subjective State

[0428] This embodiment corresponds to the main layer basic object maintenance part in the invention content, and can be combined with... Figure 7 understand.

[0429] 1. Technical issues The aforementioned implementations have enabled the system to preserve historical experience, trace causal chains, and support structured reuse by using fixed memory objects, failure path constraint objects, decision context snapshots, and meta-evolutionary trajectory indexes. However, historical memory and trajectory structures alone are insufficient for the system to form a unified expression of its current state at any given moment.

[0430] Specifically, if the system lacks explicit representations of its self and subjective state, the following problems will occur: First, although the system can review past successful and failed paths, it is difficult to uniformly express the current state of the subject, the boundaries of its capabilities, and the degree of risk exposure. Second, the system struggles to compress information scattered across audit volume, resource status, blueprint deviations, memory structures, and action feedback into a unified main summary that can be used for subsequent retrieval. Third, the system lacks a stable benchmark object when performing high-level audits, blueprint revisions, proactive causal detection, energy-saving silent switching, or deep reflection. Fourth, the system struggles to provide constrained entity status reports to external parties, and it also struggles to maintain continuous identity and capability expression in multi-entity collaboration or long-term deployment scenarios.

[0431] Therefore, a self-object and subject state mechanism is needed to enable the system to compress identity continuity, resource status, narrative core, value preferences, capability distribution, risk status, and their combinations into a unified internal object, and to dynamically update it after key decisions, in order to support subject continuity auditing and self-blueprinting. Figure 1 Consistency auditing, action scheduling, memory retrieval, and external summary output.

[0432] 2. Data structure of self-objects In this embodiment, the system maintains a special high-priority object above the memory management layer, called the "self object".

[0433] The self-object is a compressed representation of the subject's long-term state.

[0434] In one implementation, the self-object includes at least one or more of the following information: First, a summary of identity continuity; Second, the core narrative nodes; Third, the current value preference vector; Fourth, capability profile; Fifth, resource status statistics; Sixth, action feedback statistics; Seventh, audit gradient statistics; Eighth, Summary of the subject's continuity of health; Ninth, Self-Blue Figure 1 Summary of consistency deviations.

[0435] The self-object can be implemented using structured entries, vector representation, graph representation, tensor representation, embedded vectors, codeword representation, or a combination thereof.

[0436] In a preferred implementation, the system encodes the above-mentioned multiple types of information into a compressed vector and adds a version number, update time, source snapshot index, and credibility score.

[0437] 3. Construction of the main state vector In this embodiment, the system further expands the self-object into a "subject state vector" at the current moment.

[0438] The subject's state vector is the operationalized expansion of the self-object at the current moment, used to directly participate in structural evaluation, audit closure, and blueprinting. Figure 1 Consistency auditing and behavioral rhythm regulation.

[0439] In one implementation, the subject state vector can be represented as: Σt=[s1(t),s2(t),…,sm(t)] Each component can correspond to one or more of the following quantities: First, the integrity of core memories; Second, the degree of identity drift; Third, the level of resource reserves; Fourth, the cumulative amount of irreversible damage; Fifth, the level of external commitments or actions in debt; Sixth, recent actions have demonstrated stability; Seventh, Blue Figure 1 Consistency deviation; Eighth, the degree of demand for energy saving and quiet operation; Ninth, conduct in-depth reflection on the degree of demand.

[0440] In a preferred implementation, the system normalizes each component in the subject state vector and, in conjunction with a dynamic activation dimension subset, extracts only the components most relevant to the current decision for this round of calculation.

[0441] 4. Self-object update mechanism In this embodiment, the self-object is not statically generated and remains unchanged for a long time, but is dynamically updated after a key event occurs.

[0442] The key events may include one or more of the following events: First, a candidate structure passes the consistency audit and enters a locally stable platform; Second, an external action is completed and receives acceptable feedback; Third, a hierarchical rollback is completed and a new stable platform is formed; Fourth, a blueprint-based adversarial test has been completed; Fifth, a blueprint revision, parameter update strategy revision, or main continuity anomaly recovery is completed; Sixth, the system enters or exits energy-saving silent mode and long-term deep reflection mode; Seventh, special undecidable objects are created, merged, or reinterpreted.

[0443] In one implementation, the system employs an incremental update strategy, updating only local fields related to the current critical event.

[0444] In another implementation, the system adopts a periodic reconstruction strategy, that is, after a preset time window, a preset audit round, or a preset event accumulation threshold is reached, the system performs overall compression and reconstruction on the self-object.

[0445] In a preferred implementation, the update of the self object can be accomplished using a lightweight encoder, autoencoder, graph neural network, or rule compression module.

[0446] 5. Interface between entity status and audit closure In this embodiment, the self-object and the subject state vector are not merely passively stored, but actively participate in audit closure, structural evaluation, and blueprinting. Figure 1 Consistency audit process.

[0447] In one implementation, the system directly incorporates the following quantities into the total audit quantity: - Deviation in core memory integrity; - Identity drift; - Accumulated amount of irreversible damage; - Recent action feedback indicates stability; - Self-Blue Figure 1 Consistency deviation.

[0448] For example, it can be represented as: Asubject=Abase+ρ1·did+ρ2·dmem+ρ3·cirr+ρ4·dblue Where Abase represents the basic audit volume, did represents the identity drift rate, dmem represents the core memory integrity deviation, cirr represents the cumulative amount of irreversible damage, and dblue represents blue... Figure 1 Consistency bias, where ρi is the corresponding weight.

[0449] In another implementation, self-objects can participate in the candidate structure ranking.

[0450] When two candidate structures are similar in terms of local benefits and communication costs, the system prioritizes the candidate structure that is more conducive to maintaining the continuity and health of the subject, better matches the capability profile, and is closer to the target identity state of the blueprint.

[0451] In another implementation, the self object can also participate in the regulation of behavioral rhythms.

[0452] For example, when the self-object indicates a sustained increase in recent stress, a decrease in the stability of action feedback, and an accumulation of irreversible damage approaching a threshold, the system increases the priority of energy-saving silent mode and strategic pause.

[0453] 6. Self-object summary output interface In this embodiment, the self-object can not only be called internally by the system, but can also generate externally readable summary output when preset conditions are met.

[0454] The summary output is not required to expose all internal details, but rather to provide a subject summary in a constrained manner that relates to collaboration, monitoring, authorization, or status interpretation.

[0455] In one implementation, the self-object summary output may include one or more of the following: First, the current status level of the main entity; Second, a summary of current capabilities; Third, a summary of the current blueprint direction; Fourth, a summary of current risks; Fifth, the current behavioral rhythm state.

[0456] The summary output must undergo access control and sensitive field filtering to prevent the unrestricted leakage of core information.

[0457] 7. Technical Effects First, this embodiment enables the system to form a unified current subject representation, thereby compressing information scattered across the memory layer, audit layer, blueprint layer, and behavior layer into a callable self object.

[0458] Second, the subject state vector enables the system to form an actionable current subject expression at any given time and directly integrate it into the evaluation, auditing, and behavior regulation processes.

[0459] Third, this embodiment serves as the subsequent self-blueprint object, self-blueprint Figure 1 Consistency auditing and long-term, in-depth reflection models provide a unified basis for subject representation. 5. Example of Adaptive Update of Meta-Evaluation Parameters

[0460] 1. Technical issues The aforementioned implementation has enabled the system to jointly evaluate candidate structures, external actions, and subject states through ordinal state functions, dynamic activation dimension subsets, audit closure mechanisms, self-objects, and self-blueprint objects. However, if the coefficients, threshold parameters, and aggregation rules in the structure evaluation value remain fixed over a long period, the following limitations will still exist under conditions of long-term operation, continuous environmental changes, multi-task switching, and subject state evolution: First, fixed evaluation coefficients can only adapt to a limited number of task types or a limited environmental window, making it difficult to take into account the different needs of exploration, convergence, stabilization, protection and resource conservation at different stages. Second, the fixed update method is difficult to express how the system gradually develops more appropriate evaluation preferences based on long-term experience; Third, when the subject's continuity of health and self-blue Figure 1 When consistency deviations, action feedback stability, or resource status drift over a long period, the system may continue to use outdated value rankings. Fourth, although the system has accumulated a wealth of historical evolutionary experience, its long-term adaptability will be limited if this experience cannot be fed back to the evaluation layer.

[0461] Therefore, an adaptive update mechanism for meta-evaluation parameters is needed, which enables the system to not only adjust the coefficients and threshold parameters in the structural evaluation values, but also to adapt the update rules of these parameters in a constrained manner based on long-term audit results, changes in the subject's state, deviation trends from the self-blueprint, action feedback results, and historical evolution trajectories.

[0462] 2. Definition of Meta-Indicators In this embodiment, the system first constructs a set of meta-indices to drive the updating of evaluation parameters.

[0463] The meta-indicators are not used to directly evaluate a candidate topology, but rather to assess whether the current evaluation system itself is suitable.

[0464] In one implementation, meta-indicators include at least one or more of the following types: First, audit performance indicators; Second, task complexity metrics; Third, resource status indicators; Fourth, the stability index of action feedback; Fifth, the indicators of the subject's continuous health; Sixth, blueprint deviation and blueprint rigidity indicators.

[0465] In a preferred implementation, the system organizes the above meta-indicators into a unified meta-indicator vector: Z(t) = [z1(t), z2(t), ..., zk(t)] Each component zi(t) can be selectively enabled based on the current application scenario and the dynamically activated dimension subset.

[0466] 3. Adaptive updating of evaluation coefficients In this embodiment, the basic coefficients and some higher-order weights in the structural evaluation value can be adaptively adjusted according to the meta-indicators.

[0467] These parameters include, but are not limited to: - Weighting of revenue-generating items based on volume; - Weights of boundary complexity or communication cost terms; - Weighting of resource budget penalty items; - Weights of the subject continuity constraint terms; - Self-Blue Figure 1 Consistency constraint weights; - Exploration branch trigger threshold; - Action suspension threshold; - Energy-saving silent switching threshold.

[0468] In one implementation, the system uses a rule-based update method.

[0469] For example, when the pass rate of historical consistency audits decreases and the stability of action feedback decreases, the weights of resource budget penalty items and action feedback residual items should be increased; When the subject's continuity health declines, increase the weights of the identity continuity deviation, core memory integrity deviation, and irreversible damage constraint terms; When the evolutionary activity index remains below the threshold for an extended period and the blueprint rigidity index rises, the weight of exploration-related factors and the priority of proactive causal detection should be appropriately increased.

[0470] In another implementation, the system uses a learning-based update method, for example: Θ(t+1)=G(Θ(t),Z(t),Htraj(t)) Where Θ(t) represents the current set of evaluation parameters, Z(t) represents the current meta-index vector, Htraj(t) represents the summary of historical trajectories most relevant to the current scene, and G represents the update mapping.

[0471] The updated mapping can be implemented using rule tables, state machines, Bayesian optimization, meta-reinforcement learning, adaptive controllers, lightweight neural networks, or combinations thereof.

[0472] 4. Adaptive adjustment of update rules In this embodiment, the system not only updates the parameters, but also the way the parameters are updated.

[0473] Adjustable update rules include one or more of the following: First, update frequency; Second, update the step size; Third, update sensitivity; Fourth, update priority; Fifth, update the aggregation method.

[0474] In one implementation, the system maintains a meta-policy object for update rules, which records at least: - Historically, the long-term success rate of different update rules; - Average impact on the subject's continuous health status; - To Blue Figure 1 The average effect of consistency retention; - Long-term impact on resource consumption and the stability of action feedback.

[0475] The system can then choose a more stable meta update strategy, rather than continuously using a single fixed rule.

[0476] 5. Rollback of historical stable update strategy Since parameter updates and update rule adjustments can themselves lead to instability, this embodiment further introduces a historical stable update strategy rollback mechanism.

[0477] In one implementation, the system maintains historical stable versions of each set of important parameters and update rules.

[0478] A historically stable version refers to a combination of parameters / rules that has met one or more of the following conditions over a period of time: First, the historical consistency audit pass rate is higher than the preset threshold; Second, the overall health of the entity has remained within a safe range for a long period of time; Third, the stability of action feedback is higher than the preset threshold; Fourth, self-blue Figure 1 The consistency deviation has remained within an acceptable range over the long term; Fifth, maintain a good balance between resource consumption and task benefits.

[0479] When the system detects a significant decrease in the pass rate of the updated consistency audit, a continuous decline in the entity's continuity health, a continuous expansion of the blueprint deviation, a significant increase in action feedback residuals, or a rapid accumulation of special undeterminable objects, it can trigger a rollback to a historical stable update strategy.

[0480] In a preferred implementation, the system may prioritize performing local parameter rollback or hierarchical rollback, rather than immediately performing global rollback.

[0481] 6. Coupling with audit closure and blueprint objects In this embodiment, the adaptive update of the meta-evaluation parameters is not an additional optimizer independent of the main system structure, but is strongly coupled with audit closure, self-objects, self-blueprint objects, and main continuity constraints.

[0482] In one implementation, any parameter update must undergo an audit closure check, which can be verified first in a local simulation window, an alternative path evaluation window, or a historical trajectory replay window.

[0483] In another implementation, the self-blueprint object has a high-priority constraint on parameter updates, meaning that parameter updates must not break the blueprint boundary or significantly disrupt the long-term direction.

[0484] In another implementation, the self-object can be used as a state summary input for parameter updates, which can be used to form a constrained adjustment between long-term capability distribution, self-risk status and blueprint direction.

[0485] When the meta-update result affects the long-term direction selection, its further processing can refer to the implementation method of self-blueprint object and blueprint adversarial test described later.

[0486] 7. Technical Effects First, this embodiment enables the system to adjust evaluation parameters based on long-term audit performance, entity status, and blueprint deviations, rather than relying on a fixed evaluation framework.

[0487] Second, the system can not only update parameters, but also update the parameter update method, thereby obtaining constrained meta-evaluation adaptive capability.

[0488] Third, this embodiment provides a unified high-level parameter basis for subsequent proactive causal detection, blueprint revision preparation, and long-term deep reflection patterns. 6. Examples of Evolutionary Activity Maintenance and Active Causal Detection

[0489] This embodiment corresponds to the behavioral rhythm regulation and exploratory branch control part of the invention, and can be combined with... Figure 9 understand.

[0490] 1. Technical issues The aforementioned implementation has enabled the system to complete a relatively complete structural evolution and outcome acceptance process driven by external tasks through discrete parent system state space, candidate topology generation, dynamic evaluation of ordinal state functions, audit closure, memory solidification, self-object updating, and adaptive updating of meta-evaluation parameters. However, under long-term operating conditions, if the system only engages in high-intensity evolution when a clear external task arrives, and remains passively in standby mode in environments without explicit tasks, low-stimulus input, or low-change environments, the following problems may still occur: First, although the system has the ability to explore branches and search for alternative paths, it mainly relies on existing task triggers and lacks a mechanism to actively expand the boundaries of experience during low-activity phases. Second, when a system is in a state of low change and low novelty for a long time, it may gradually shrink into a set of reusable memory templates and local stable platforms, resulting in decreased structural activity and rigid blueprint paths. Third, if a system only undergoes internal restructuring and lacks proactive exploration of the external world, it will be difficult to form long-term experience regarding the laws of external rebound. Fourth, if the system does not perform rhythmic maintenance during long-term low-activity phases, it may either stagnate or maintain a high evolution frequency unnecessarily, increasing resource consumption and the burden on the system.

[0491] Therefore, an evolutionary activity maintenance and active causal detection mechanism is needed to enable the system to measure its current evolutionary activity under preset conditions, and to trigger internal variational recombination, self-play tasks, substitution degree scale simulation or active causal detection in a constrained manner when the evolutionary activity is insufficient.

[0492] 2. Definition of Evolutionary Activity Indicators In this embodiment, the system first constructs an "evolutionary activity index" to characterize whether the current system is still in an evolutionary state that is sufficiently variable, sufficiently open, and novel.

[0493] In one implementation, the evolutionary activity index may consist of one or more of the following quantities: First, the rate of structural change; Second, the novelty of memory retrieval; Third, diversity of candidate branches; Fourth, outward exploration of novelty; Fifth, the duration of idle time and the degree of structural repetition.

[0494] In a preferred implementation, the system combines the above quantities into a comprehensive index Hact(t): Hact(t)=μ1·Rtopo(t)+μ2·Nmem(t)+μ3·Dbranch(t)+μ4·Nact(t)−μ5·Tidle(t)−μ6·Crepeat(t) Where Rtopo(t) represents the topological change rate, Nmem(t) represents the memory recall novelty, Dbranch(t) represents the branch diversity, Nact(t) represents the action feedback novelty, Tidle(t) represents the idle duration, and Crepeat(t) represents the structural redundancy.

[0495] 3. Low activity detection and triggering conditions In this embodiment, the system determines whether to activate the evolutionary activity maintenance mechanism based on a set of triggering conditions.

[0496] The triggering condition may include one or more of the following conditions: First, the evolutionary activity index is below the preset threshold for multiple consecutive windows; Second, the idle duration exceeds the preset time window, and no effective active detection has been triggered recently; Third, the blueprint rigidity index has increased, and the proportion of new structures being adopted in recent trajectories has decreased; Fourth, current structural evaluations frequently rely on the same memory templates, and alternative path searches are almost never triggered; Fifth, the energy-saving silent mode lasts for a preset duration, and the continuity and health of the main body allow for limited outward exploration.

[0497] In one implementation, the system uses a tiered triggering strategy: When evolutionary activity decreases slightly, only internal maintenance behavior is triggered; When evolutionary activity moderately decreases, internal variational recombination and self-play tasks are triggered. When evolutionary activity remains low and the external environment allows, an active causal detection mission is triggered.

[0498] 4. Active Causal Detection Task Generation In this embodiment, when the system meets the active detection conditions, an "active causal detection task" can be generated.

[0499] The active causal detection task differs from random perturbation; it is a constrained trial-and-error behavior with explicit assumptions and oriented towards the external world.

[0500] In one implementation, the active causality detection task generation includes at least one or more of the following steps: First, based on the current self-object summary and meta-evolutionary trajectory index, select an external action direction that has not been fully explored but does not conflict with the current blueprint; Second, based on the current environmental metric and the scope of executable permissions, construct a low-risk, low-resource-consumption, and rollback-capable action hypothesis; Third, the ordinal state function is used to evaluate the impact of this action hypothesis on the subject's continuity of health and self-esteem. Figure 1 The anticipated impact of consistency deviations and resource budgets; Fourth, when the preset security conditions are met, the action assumption is mapped to external control signals, communication commands, environmental configurations, query requests, or logical interventions.

[0501] The proactive causal detection constitutes the optimal realization of "action force" in behavioral rhythm.

[0502] 5. Internal variational recombination and self-play task In certain scenarios, the system is not suitable for direct external probing, such as when the current external environment is too risky, resource budgets are limited, blueprint objects require a low-intervention state, or the continuity and health of the entity need to be restored first.

[0503] In this case, this embodiment allows the system to perform internal activity maintenance tasks.

[0504] In one implementation, the internal activity maintenance task includes memory variational recombination, which involves extracting several historical fragments from existing solidified memory objects, failed path constraint objects, and meta-evolutionary trajectory indexes, and combining them based on topological similarity, contextual compatibility, or blueprint relevance to generate new candidate structure templates.

[0505] In another implementation, the internal activity maintenance task includes a self-play task, which divides the current evaluation parameters, self-object state, or blueprint version into two or more policy sides and performs different evolutionary path deductions for the same problem.

[0506] In another implementation, the system can perform alternative degree scale simulation, that is, to perform local replay and simulation of candidate structures by using alternative degree metrics, alternative resource budgets or alternative blueprint biases without changing the real environment.

[0507] 6. Detection results will be included in auditing and memory. In this embodiment, regardless of whether the system performs external proactive causal detection, internal variational recombination, self-play tasks, or substitution degree scale simulation, the resulting results are required to be reintegrated into the audit closure and memory update system.

[0508] Feedback processing, residual construction, failure path solidification, and memory writing for proactive external detection can be implemented by referring to the aforementioned action mapping, causal intervention auditing, and trajectory indexing implementation methods, respectively.

[0509] The results of the internal activity maintenance task can be written into the memory system according to its performance, such as forming new candidate templates, blueprint revision hints, low-priority alternative structures, or new branches of the trajectory.

[0510] The system can also write the evolutionary activity maintenance behavior itself into the meta-evolutionary trajectory map to record under what conditions active maintenance was initiated and what structural changes were brought about after maintenance.

[0511] 7. Technical Effects First, this embodiment enables the system to identify structural stagnation states caused by low change, low novelty, and high repetition during long-term operation.

[0512] Second, when the system's evolutionary activity is insufficient, it can actively perform outward exploration or internal maintenance under the constraints of the main continuity and the blueprint boundary, thereby maintaining long-term structural activity.

[0513] Third, this embodiment provides a continuous source of motivation for subsequent identity continuity maintenance, blueprint revision preparation, and long-term in-depth reflection. 7. Identity Continuity Chain and Trusted Origin Anchoring Implementation Examples

[0514] This embodiment corresponds to the identity continuation verification part in the invention content, and can be combined with... Figure 7 understand.

[0515] 1. Technical issues The aforementioned implementation has enabled the system to form a structured representation of its historical evolution, current subject state, and high-level blueprint deviations through decision context snapshots, meta-evolutionary trajectory indexes, self-objects, and subject state vectors. However, memories, trajectories, and self-objects alone are still insufficient to answer the question of whether the current entity and the historical entity constitute the same continuum.

[0516] In scenarios involving long-term operation, cross-device deployment, distributed collaboration, disaster recovery, or potential risks of replication and tampering, the lack of a verifiable chain of continuity makes it difficult to determine whether the current state truly belongs to the same entity's history, rather than being spliced ​​from external sources, maliciously replaced, or improperly inherited.

[0517] Therefore, an identity continuity chain and trusted origin anchoring mechanism are needed to enable the system to start from a trusted origin state and establish a continuous evolution chain for subsequent key states, key audit events, key memory solidification events, and key blueprint revision events, and to verify whether the current state belongs to the legitimate continuation of the chain when needed.

[0518] 2. Definition of Trusted Origin Anchor In this embodiment, the system establishes a "trusted origin anchor" upon initial deployment, initial activation, or first entry into an accredited operating state.

[0519] The trusted origin anchor is used to provide a starting point for all subsequent identity continuity chains.

[0520] In one implementation, a trusted origin anchor may include one or more of the following information: First, a unique hardware identifier; Second, the initial seed of the trusted execution environment; Third, irreversible physical source information; Fourth, initial self-object summary; Fifth, initial environment context snapshot.

[0521] In a preferred implementation, the system combines the above information to generate an origin anchor summary: G0=H(Ihw,Stee,Pphys,Oself,Cinit) Where Ihw represents the hardware identifier, Stee represents the trusted execution environment seed, Pphys represents irreversible physical source information, Oself represents the initial self-object digest, Cinit represents the initial environment context, and H represents a hash function, a digest function, or a combination thereof.

[0522] A trusted origin anchor can be a single-source or multi-source combination anchor to balance trusted origins with legitimate migration requirements.

[0523] 3. Construction of a continuous evolutionary chain In this embodiment, the system starts from a trusted origin anchor and builds a continuous evolution chain for subsequent key events.

[0524] The continuous evolution chain is stored in a summary-style chain around key events that affect the judgment of the continuity of the subject.

[0525] In one implementation, key events that allow entry into a continuous evolutionary chain include one or more of the following events: First, new solidified memory objects are generated; Second, the generation of failed path constraint objects; Third, a critical update occurs to the self-object; Fourth, the self-blueprint object undergoes a version change; Fifth, the continuity of the subject's health status has changed significantly; Sixth, events that passed or failed the key consistency audit; Seventh, significant external actions are completed and acknowledgable feedback is generated; Eighth, the tier will be rolled back to a new, stable platform; Ninth, special undecidable objects are generated or reinterpreted; Tenth, legal migration or legal inheritance events.

[0526] In a preferred implementation, the system generates an event summary QtQ_tQt for each key event ete_tet and constructs a chained update: Gt+1=H(Gt,Qt,τt) Where Gt represents the previous continuum summary, Qt represents the current key event summary, and τt represents the timestamp, sequence identifier, or a combination thereof associated with the event.

[0527] In one implementation, the event digest QtQ_tQt may include at least one or more of the following information: - Current self-object summary; - Summary of current self-blueprint objects; - Current subject status summary; - Current audit closure result; - Identifier of the current critical memory object; - Current critical context snapshot identifier; - Summary of current resource status; - Current environmental metric summary.

[0528] 4. Continuous chain verification mechanism In this embodiment, when the system needs to determine whether the current running entity is still a legitimate continuation of the original entity, a continuity chain check can be performed.

[0529] The verification can be performed at startup, or before critical operations, blueprint revisions, major actions, or periodic health checks.

[0530] In one implementation, the continuity check includes at least one or more of the following checks: First, check the consistency of the origin anchor; Second, chain integrity check; Third, check the sequence of key events; Fourth, self-object continuity check; Fifth, check the continuity of blueprint objects; Sixth, conduct a continuous health check on the subject.

[0531] In one implementation, the system can generate a continuous chain verification result Rid: Rid=Ψ(Gcurr,Ghist,Oself,Oblue,Σt) Where Gcurr represents the current continuum chain digest, Ghist represents the historical trusted chain digest, Oself represents the current self object, Oblue represents the current blueprint object, Σt represents the current subject state vector, and Ψ represents the verification function or decision mapping.

[0532] The results of the continuity chain verification can be used as direct input for the entity continuity audit described later.

[0533] 5. Legal relocation and legal inheritance agreements In actual deployment, the system may need to undergo hardware upgrades, node migrations, disaster recovery, long-term hibernation wake-up, or controlled inheritance.

[0534] Therefore, this embodiment introduces a "legal migration and legal inheritance protocol" to support controlled continuation while maintaining continuity constraints.

[0535] In one implementation, a legitimate migration satisfies at least one or more of the following conditions: First, both before and after the migration, the environment remains within a trusted execution environment; Second, the old environment is able to sign and confirm the current continuous chain digest and subject state; Third, the new environment must be able to receive legal migration documentation exported from the old environment; Fourth, the self-object, self-blueprint object, and key memory objects were not altered without authorization during the migration process; Fifth, after migration, an enhanced continuity chain check is performed.

[0536] In another implementation, legitimate inheritance is applicable to scenarios such as long-term interruption, disaster recovery, or multi-node relay operation.

[0537] The system may output an inheritance token or inheritance snapshot before the interruption, which includes at least: - Latest continuum summary; - Current self-object summary; - Summary of the current blueprint version; - Current subject continuity health summary; - Current critical memory object index.

[0538] The inherited runtime needs to re-establish the trusted chain within the preset window and complete cross-validation with the aforementioned inherited token.

[0539] 6. Determination and Handling of Continuity Faults In this embodiment, when the continuity chain verification fails or the continuity health drops to a dangerous range, the system can determine that a continuity break risk or a continuity break event has occurred.

[0540] In one implementation, the continuity break determination criteria may include one or more of the following: First, the current chain digest cannot be legally continued with the historical chain digest; Second, the trusted origin anchor verification failed; Third, the continuity difference between the self-object and its historical nearest neighbor nodes exceeds the threshold; Fourth, the blueprint object version lacks a legitimate revision chain support; Fifth, the subject's state vector indicates that both the degree of identity drift and the amount of accumulated irreversible damage are excessively high. Sixth, the new environment after migration cannot pass the enhanced continuity check.

[0541] After the risk of fracture is determined, the system may perform one or more of the following actions: First, reduce the running privileges, allowing only low-risk internal maintenance and status checks; Second, suspend proactive causal detection and high-risk external operations; Third, increase the weight of constraints related to the continuity of the subject and suppress blueprint revisions and high-level exploration; Fourth, enter recovery mode, and only perform key memory verification, self-object reconstruction, and continuity reconstruction; Fifth, mark the current status as a subject awaiting recognition; Sixth, if repair is not possible, the current running entity will be downgraded to a normal instance instead of a legitimate continuation of the original entity.

[0542] 7. Technical Effects First, this embodiment enables the system to establish an identity continuity chain starting from a trusted origin state, thereby providing a verifiable continuation basis for subsequent subject judgment.

[0543] Second, the continuity chain verification mechanism enables the system to proactively check the legal continuity of entities before critical actions, critical revisions, and critical migrations.

[0544] Third, this embodiment provides an identity layer foundation for the subject continuity constraints, self-blueprint auditing, and open boundary maintenance described later. 8. Implementation Example of Entity Continuity Constraints and Irreversible Cost Ledger

[0545] This embodiment corresponds to the main continuous main valve part in the invention, and can be combined with... Figure 7 understand.

[0546] 1. Technical issues The aforementioned implementation has established a basis for judging the legitimate continuation between the current operating entity and historical entities through identity continuity chains and trusted origin anchoring mechanisms. However, identity continuity chains alone are insufficient to support the system in continuously maintaining its own entity during complex evolution, external actions, high-level blueprint revisions, and long-term operation.

[0547] Whether a subject can still be recognized depends not only on whether it has followed the same chain, but also on whether the current step will cause irreparable damage and consume the conditions for its continued existence and evolution in the future.

[0548] Therefore, a mechanism for subject continuity constraints and irreversible cost ledgers is needed to enable the system to predict, record, constrain, and roll back behaviors that may damage the subject's own continuity, future existence conditions, or high-priority boundaries during the processes of candidate structure screening, external actions, blueprint revision, proactive causal detection, open boundary maintenance, and their combinations.

[0549] The continuity constraint of the main body constitutes the overall valve for all high-order embodiments of the present invention.

[0550] 2. Definition of Subject Continuity Health In this embodiment, the system constructs an "subject continuity health" index to characterize whether the current subject is in a safe range that is acknowledgable, sustainable, and capable of continued evolution.

[0551] In one implementation, the subject's continuity of health may consist of at least one or more of the following quantities: First, the integrity of core memories; Second, the degree of identity drift; Third, the cumulative amount of irreversible damage; Fourth, the level of main resource reserves; Fifth, the burden of action debt or external commitments; Sixth, Self-Blue Figure 1 Consistency deviation; Seventh, audit closure stability.

[0552] In a preferred implementation, the system can combine the above factors into a subject continuity health score Hsubj(t): Hsubj(t)=λ1·Mcore(t)−λ2·Did(t)−λ3·Cirr(t)+λ4·Rbase(t)−λ5·Bdebt(t)−λ6·Dblue(t)+λ7·Saudit(t) Wherein, Mcore(t) represents the core memory integrity, Did(t) represents the identity drift, Cirr(t) represents the cumulative amount of irreversible damage, Rbase(t) represents the basic resource reserve, Bdebt(t) represents the action debt, Dblue(t) represents the blueprint deviation, and Saudit(t) represents the audit closure stability.

[0553] The system can divide the subject's continuous health status into multiple levels, such as stable zone, alert zone, recovery zone and danger zone, according to the current scenario, corresponding to different behavior permissions and exploration intensity.

[0554] 3. Construction of the Irreversible Cost Ledger In this embodiment, the system further constructs an "irreversible cost ledger" to record high-level costs that, once they occur, cannot be completely offset by ordinary local rollback, simple parameter recovery, or short-term silence.

[0555] In one implementation, the irreversible cost ledger may record one or more of the following costs: First, the cost of memory impairment; Second, the cost of identity disruption; Third, the cost of blueprint damage; Fourth, the consequences of breaching the commitment to action; Fifth, the cost of capability erosion; Sixth, the cost of openness collapsing.

[0556] In a preferred implementation, the system may define a ledger vector: Cirr(t)=[cmem(t),cid(t),cblue(t),cdebt(t),ccap(t),copen(t)] Each component represents the accumulation of irreversible costs in the dimensions of memory, identity, blueprint, commitment, capability, and openness.

[0557] 4. Entity Continuity Audit Rules In this embodiment, the subject continuity constraint is connected to the system main chain through a set of high-priority audit rules.

[0558] Any candidate structure, external action, parameter update, blueprint revision, blueprint countermeasures test, or high-level open operation must pass an entity continuity audit before it officially takes effect.

[0559] In one implementation, entity continuity audit includes at least one or more of the following checks: First, core memory examination; Second, identity migration check; Third, irreversible cost checks; Fourth, future condition checks are possible; Fifth, blueprint boundary check; Sixth, conduct a debt inspection.

[0560] In a preferred implementation, the system can construct a subject continuity audit score before the candidate behavior is formally executed: Ssubj(u)=ϕ(ΔMcore(u),ΔDid(u),ΔCirr(u),ΔRfuture(u),ΔDblue(u)) Where u represents the action, update, or candidate structure to be executed.

[0561] If Ssubj(u) is below the preset threshold, the behavior is prohibited from directly entering the execution channel; if it is in the boundary range, it can only be executed in degraded mode, local simulation window, or under restricted probing conditions.

[0562] 5. Interception and downgrading of high-risk operations In this embodiment, when the entity continuity audit determines that a certain candidate behavior is high-risk, the system performs interception, downgrade, or replacement processing according to the risk level.

[0563] In one implementation, if an action would significantly increase a key dimension of the irreversible cost ledger, such as the cost of core memory destruction or identity disruption, the system would directly block the action.

[0564] In another implementation, if the high risk of a certain behavior mainly stems from resource budget pressure or excessive action debt, the system may attempt to downgrade it, for example: - Reduce the scope of action; - Reduce the scope of external intervention; - Reduce candidate expansion width; - Change the full blueprint revision to a partial preview; - Change outward actions to inward simulations.

[0565] When the subject's continuity of health is temporarily low but the behavior itself is not absolutely unacceptable, the system can postpone the assessment until the recovery mode ends, or hand it over to a long-term deep reflection mode for high-level integration.

[0566] Only actions that pass the entity continuity audit and whose irreversible costs are within an acceptable range will enter the formal execution process.

[0567] 6. Linkage with resource budgeting and audit closure In this embodiment, the subject continuity constraint and the irreversible cost ledger are not independent of the original resource budget penalty items and audit closure mechanism, but rather form a strong linkage with them.

[0568] In one implementation, the subject's continuity health can directly affect the weight of resource budget penalty items.

[0569] In another implementation, the costs in the irreversible cost ledger can be directly included in the total audit amount.

[0570] In another implementation, the system may adjust one or more of the following strategies based on the subject's continuity health and the level of irreversible cost: - Explore the openness of the branches; - Frequency of active causal detection; - Energy-saving silent mode trigger threshold; - Permissible frequency of blueprint revisions; - High-level self-inspection scope in open boundary maintenance.

[0571] In this way, the continuity constraint of the main body becomes the overall control valve, while resource budgeting and audit closure constitute its downstream execution layer.

[0572] 7. Technical Effects First, this embodiment enables the system to obtain quantifiable subject maintenance indicators, which can uniformly characterize high-level states such as core memory, identity, irreversible damage, future existence conditions, and blueprint deviations.

[0573] Second, the irreversible cost ledger enables the system to record not only ordinary resource expenditures, but also high-level costs that will permanently shrink the subject's future space.

[0574] Third, this embodiment provides a master valve for subsequent blueprint constraints, blueprint adversarial testing, and open boundary maintenance at the main layer. 9. Self-Blueprint Objects and Blueprints Figure 1 Consistency Audit Examples

[0575] This embodiment corresponds to the long-term direction layer basic maintenance part in the invention, and can be combined with... Figure 8 understand.

[0576] 1. Technical issues The aforementioned implementation has enabled the system to explicitly represent the current state of the subject through the self-object and subject state mechanism; and through subject continuity constraints and irreversible cost ledgers, the system can determine whether a certain behavior will cause high-level damage to the subject. However, relying solely on the current self-object and subject continuity constraints mainly addresses the issues of "the present self" and "boundaries that cannot be crossed," but has not fully addressed issues such as "what direction should the future self tend towards" and "how long-term direction can outweigh short-term local gains."

[0577] Therefore, a self-blueprint object and blueprint are needed. Figure 1 The consistency audit mechanism enables the system to maintain a future-oriented, high-priority, versionable long-term direction object on top of its own objects and subject states, and to quantitatively audit the consistency between the current behavior and the blueprint object in candidate structure screening, external action execution, parameter updates, proactive exploration, and deep reflection.

[0578] 2. Self-Blueprint Object Structure In this embodiment, the system maintains "self-blueprint objects" in the memory management layer.

[0579] The self-blueprint object is used to answer "what direction should the system tend to take in the long term", while the self object is used to answer "what state is the system currently in".

[0580] In one implementation, the self-blueprint object includes at least one or more of the following information: First, the long-term objective vector; Second, high-priority boundary constraints; Third, target identity status summary; Fourth, the blueprint version number; Fifth, the conditions under which the blueprint applies; Sixth, blueprint revision records; Seventh, blueprint credibility score.

[0581] In a preferred implementation, the self-blueprint object can adopt a hierarchical structure: - The first layer is a hard boundary layer; - The second layer is the direction layer; - The third layer is the target identity layer; - The fourth layer is the version and revision layer.

[0582] 3. Initialization and Generation of Blueprint Objects In this embodiment, the self-blueprint object can be generated from one or more of the following sources: First, initial design inputs; Second, summarizing historical experience; Third, in-depth reflection and output; Fourth, the results of the blueprint adversarial test.

[0583] In one implementation, the system can generate the first version of the blueprint object in the early stages using a combination of "initial design input + historical experience summarization".

[0584] In another implementation, blueprint objects can be generated incrementally, initially containing only high-priority boundaries and a small number of long-term goals, and then gradually adding goal identity state summaries and behavioral rhythm preferences.

[0585] The generation of blueprint objects must be audited by senior management to check whether they conflict with the subject's continuity constraints, whether they will cause irreversible costs to go out of bounds in key dimensions, whether they contradict existing high-priority boundaries, and whether they exhibit sufficient consistency over the long-term trajectory.

[0586] 4. Blue Figure 1 Consistency deviation calculation In this embodiment, the system calculates the degree of deviation of the current candidate structure, current action plan, current parameter update strategy, current exploration direction, or a combination thereof, relative to the self-blueprint object, thereby forming a "blueprint". Figure 1 Consistency deviation”.

[0587] In one implementation, blue Figure 1 Consistency bias can consist of at least one or more of the following biases: First, directional deviation; Second, boundary deviation; Third, target identity bias; Fourth, rhythm deviation; Fifth, long-term trajectory deviation.

[0588] For example, in one implementation, the system can define blue. Figure 1 Consistency bias Dblue(u): Dblue(u)=α1·dgoal(u)+α2·dbound(u)+α3·didtarget(u)+α4·drhythm(u)+α5·dtraj(u) Where u represents the candidate structure or behavior to be evaluated.

[0589] blue Figure 1 Consistency deviations can be used as independent high-level quantities or directly incorporated into structural assessment values ​​and total audit quantities.

[0590] 5. Blueprint High-Priority Constraint Mechanism In this embodiment, the self-blueprint object is not a general preference object, but a long-term constraint with high priority.

[0591] Ordinary task objectives, local benefit optimization, and short-term exploration tendencies should not override blueprint objects without review.

[0592] In one implementation, the system employs a two-layer arbitration process when performing candidate structure screening: The first layer involves basic screening of candidates based on local structural assessment values, resource budgets, internal audit volume, and action feedback residuals. The second layer involves further refining the candidate set obtained from the initial screening based on the blueprint. Figure 1 High-level arbitration will be conducted on consistency deviations and the continuity and health of the subject.

[0593] In another implementation, the system directly adds blueprint deviations as high-priority penalties to the total evaluation value, for example: Etotal(B)=E(B)−βblue·Dblue(B) Where E(B) represents the infrastructure evaluation value, Dblue(B) represents the deviation of the candidate structure relative to the blueprint object, and βblue is the blueprint constraint weight.

[0594] If a candidate directly breaks through the hard boundaries of the blueprint object, the system can directly reject it, downgrade it, or switch it to internal simulation.

[0595] 6. Blueprint versioning and slow revision protocol In this embodiment, the self-blueprint object is not fixed, but frequent changes are not allowed.

[0596] Therefore, this invention introduces Blueprint versioning and a slow revision protocol.

[0597] In one implementation, each time a blueprint object undergoes a formal revision, the system generates a new version number and retains at least the following information: - Previous version blueprint summary; - New version blueprint summary; - Reasons for revision; - Changes in the continuity and health of the subject before and after the revision; - Evidence of trajectory similarity before and after revision; - Results of adversarial testing of blueprints before and after revision; - Revised responsibility context snapshot.

[0598] In a preferred implementation, blueprint revisions must satisfy one or more of the following conditions: First, the blueprint rigidity indicator remained above the threshold for multiple consecutive time windows; Second, blueprint countermeasures consistently show that the current blueprint is significantly inferior to the alternative blueprint in terms of long-term benefits, resource efficiency, or entity retention. Third, the continuity and health of the entity allows for revisions by higher management; Fourth, the revision will not break the high-priority hard boundary; Fifth, the proposed revisions have been validated multiple times in the deep reflection model; Sixth, the revised blueprint version can be overridden by historical stability strategies or old version rollback mechanisms.

[0599] In one implementation, the system may adopt a hierarchical revision approach, for example, first revising the long-term target vector weights, then revising the target identity state summary, and finally revising the soft boundary portion in the high-priority boundary only when necessary.

[0600] 7. Technical Effects First, this embodiment enables the system to establish a future-oriented high-priority direction layer on top of the self-object and the subject state.

[0601] Second, through blue Figure 1 With consistency bias and high-priority blueprint constraints, the system can add a long-term directional dimension in addition to local gains.

[0602] Third, this embodiment provides a high-level reference system for subsequent blueprint confrontation testing, activation of high-level dimensions in real-time perception hedging, and long-term in-depth reflection. 10. Blueprint Adversity Testing and High-Level Goal Robustness Implementation Examples

[0603] This embodiment corresponds to the blueprint adversarial testing and blueprint slow revision preparation section in the invention, and can be combined with... Figure 8 understand.

[0604] 1. Technical issues The aforementioned implementation has already used self-blueprint objects and blueprints. Figure 1 The consistency audit mechanism enables the system to use long-term goal vectors, high-priority boundary constraints, and target identity state summaries as high-level references to impose long-term directional constraints on candidate structures, external actions, parameter updates, and behavioral rhythm switching. However, auditing blueprint objects and their consistency alone is insufficient to solve the problems of robustness verification and rigidity identification in the long-term operation of blueprints.

[0605] If the system consistently only allows "with the current blue" Figure 1 If a candidate for "to" is approved, but stress tests are lacking to replace the high-level goal, path dependence may form, causing the high-level goal itself to gradually become rigid. The system will also find it difficult to determine whether the current blueprint is a long-term effective choice or is merely being retained due to historical inertia.

[0606] Therefore, a blueprint adversarial testing and high-level objective robustness mechanism is needed, which enables the system to conditionally generate alternative high-level objective candidates that differ significantly from the current blueprint without violating subject continuity constraints and high-priority boundary constraints, and compare their performance in long-term benefits, resource efficiency, identity drift, memory continuity impact, action feedback stability and their combinations, thereby testing the robustness, openness and necessity of revision of the current blueprint object.

[0607] 2. Generation of alternative high-level target candidates In this embodiment, the system does not directly reject the current blueprint object, but generates one or more "alternative high-level target candidates" based on the current blueprint object.

[0608] The alternative high-level target candidates are not required to be completely opposite to the current blueprint, but rather that they have sufficiently identifiable high-level differences from the current blueprint in terms of long-term direction, boundary configuration, behavioral rhythm preferences, target identity status, or combinations thereof.

[0609] In one implementation, alternative high-level target candidates can be generated by one or more of the following methods: First, directional rotation, which means controlling the deflection of the target weights near the current long-term target vector; Second, boundary tightening adjustment, that is, to relax or tighten the soft boundary constraints to a limited extent without touching the hard boundary; Third, the target identity center of gravity shifts, that is, generating alternative convergence directions that are different from the current target identity state summary in dimensions such as ability profile, behavioral rhythm, and extroversion / introversion preference; Fourth, trajectory abstraction and inversion, that is, selecting branches that have been excluded for a long time but have not been proven to be high-risk from the meta-evolutionary trajectory index, and constructing alternative high-level directions; Fifth, the output of deep reflection is to transform the new high-level assumptions proposed in the long-term deep reflection model into auditable alternative blueprint candidates.

[0610] In a preferred implementation, the system can generate several candidate sets from the current blueprint object Oblue(k) through the candidate generation map Talt: Balt={Talt(1)(Oblue(k)), Talt(2)(Oblue(k)), …, Talt(m)(Oblue(k))} Where Balt represents the set of candidate alternative high-level targets, and m represents the number of candidates.

[0611] The mapping can be a rule mapping, a perturbation mapping, a trajectory sampling mapping, a meta-learning generation mapping, or a combination thereof.

[0612] 3. Blueprints against audits In this embodiment, the system compares the path guided by the current blueprint object with the path guided by the alternative blueprint candidate to form a "blueprint anti-audit".

[0613] The audit does not directly replace the current blueprint with an alternative blueprint, but rather determines whether the current blueprint remains sufficiently robust in the long term.

[0614] In one implementation, blueprint anti-audit compares at least one or more of the following quantities: First, the success rate of long-term tasks; Second, resource efficiency; Third, identity drift; Fourth, the impact on the continuity of core memory; Fifth, the stability of action feedback; Sixth, the blueprint deviation is converging; Seventh, the changing trend of the subject's continuous health status.

[0615] In one implementation, the system calculates the adversarial difference between the current blueprint Oblue(k) and a certain alternative blueprint Oalt(j): Δblue(j)=γ1·ΔPsucc(j)+γ2·ΔEres(j)−γ3·ΔDid(j)−γ4·ΔCmem(j)+γ5·ΔSact(j)+γ6·ΔHsubj(j) Where ΔPsucc(j) represents the success rate difference, ΔEres(j) represents the resource efficiency difference, ΔDid(j) represents the identity drift difference, ΔCmem(j) represents the memory continuity impairment difference, ΔSact(j) represents the action feedback stability difference, ΔHsubj(j) represents the subject health difference, and γi is the weight.

[0616] The output of a blueprint for anti-audit can be one or more of the following forms: - The current blueprint is superior to the alternative blueprint; - The alternative blueprint is superior to the current blueprint in certain dimensions; - The current blueprint and the alternative blueprint are nearly equivalent in long-term effects; - The current blueprint remains advantageous in the current environment, but exhibits insufficient robustness in certain principal state intervals.

[0617] 4. Definition of Blueprint Rigidity Indicators To determine whether a blueprint resistance test needs to be initiated, and whether the current blueprint is gradually losing its openness due to a long-term single path, this embodiment further constructs a "blueprint rigidity index".

[0618] In one implementation, the blueprint rigidity metric may consist of at least one or more of the following quantities: First, the repetition rate of narrative nodes related to the blueprint; Second, the duration during which no high-level alternative exploration has been triggered; Third, the degree of decline in the diversity of high-level strategies; Fourth, the degree to which the proportion of new structures adopted by fixed memories has decreased; Fifth, evolutionary activity indicators have remained low for a long time, but blue Figure 1 A state where consistency deviation is consistently small; Sixth, the extent to which special, undetermined objects accumulate but are not absorbed by the blue layer over a long period of time.

[0619] In a preferred implementation, the system can define a blueprint rigidity metric Kblue(t): Kblue(t)=θ1·Rrepeat(t)+θ2·Tnoalt(t)+θ3·Dmono(t)+θ4·Lnovel(t)+θ5·Gclosed(t)+θ6·Upending(t) Where Rrepeat(t) represents the narrative repetition rate, Tnoalt(t) represents the duration of exploration without alternatives, Dmono(t) represents the degree of homogenization of high-level strategies, Lnovel(t) represents the degree of decline in the adoption of new structures, Gclosed(t) represents the trend of high-level closure, Upending(t) represents the number of unabsorbed high-level pending issues, and θi is the weight.

[0620] When the blueprint rigidity index exceeds the preset threshold, the system increases the priority of blueprint resistance testing. When the blueprint rigidity index remains high and is accompanied by a decline in innovation and unfavorable results from blueprint resistance, the system can enter the blueprint revision preparation stage.

[0621] 5. Countering candidate legitimacy constraints In this embodiment, the alternative high-level target candidates are not generated in an infinitely open manner.

[0622] To prevent blueprint adversarial testing itself from becoming a new source of high-level risk, the system sets legality constraints for adversarial candidates.

[0623] In one implementation, alternative blueprint candidates must simultaneously satisfy one or more of the following constraints: First, avoid actively triggering out-of-bounds boundaries of key dimensions in the ledger that cause irreversible damage; Second, the core identity anchor must not be destroyed, that is, the identity continuity chain must not be significantly unstable during the pre-rehearsal stage. Third, it should not exceed the resource security budget; Fourth, do not break the hard boundaries of the current blueprint object; Fifth, the system should not be directly required to engage in high-risk outward behaviors that cannot be reversed.

[0624] In a preferred implementation, the system may first test the subject continuity security of alternative blueprint candidates in a local simulation window before formally performing the adversarial test.

[0625] Only candidates that pass the pre-screening are allowed to enter the high-level target robustness comparison process.

[0626] 6. Slow blueprint revision and meta-experience recording In this embodiment, the result of the blueprint adversarial test does not directly lead to the replacement of the current blueprint object, but instead first enters the "blueprint slow revision and meta-experience recording" process.

[0627] In one implementation, the system records at least one or more of the following information for each blueprint adversarial test: - Current blueprint object version; - Alternative blueprint candidate versions; - Comparison results of various dimensions of counter-audit; - Current entity status and resource status; - A snapshot of the context during the verification; - Should it be added to the revision suggestion queue? - If it is not included in the revision suggestion queue, what is the specific reason?

[0628] These records can be used to form high-level meta-experience objects, which are written into the meta-evolutionary trajectory index and blueprint revision records.

[0629] The system only increases the weight of a result when similar results occur repeatedly across multiple time windows, multiple rounds of testing, and multiple contexts.

[0630] The conditions for blueprint revision to enter the preparation phase may include one or more of the following: First, alternative blueprints across multiple windows consistently outperform the current blueprint in terms of maintaining subject continuity, resource efficiency, or long-term success rate; Second, the blueprint rigidity index has remained high for a long time, and the current blueprint lacks a clear advantage in multiple rounds of confrontation; Third, the long-term deep reflection pattern repeatedly generates high-level assumptions that are similar to the same alternative direction; Fourth, while the current blueprint maintains a high degree of consistency, it has significantly suppressed the emergence of new structures and reduced external adaptability.

[0631] The formal implementation of blueprint revisions remains subject to the aforementioned blueprint versioning and slow revision agreement.

[0632] 7. Technical Effects First, this embodiment enables the system to perform high-level stress tests on the current blueprint objects, thereby preventing the blueprint from becoming path-dependent due to long-term lack of testing.

[0633] Second, by replacing the generation of high-level target candidates with blueprint-based adversarial auditing, the system is able to compare the differences in subject continuity, resource efficiency, and action stability across different long-term directions.

[0634] Third, this embodiment provides a high-level robustness basis for high-level dimension activation and open boundary maintenance in subsequent continuous sensing hedging. 11. Real-time sensing of the sequence state function for continuous implementation: Example

[0635] This embodiment corresponds to the continuous adjustment layer in the invention and can be combined with... Figure 10 understand.

[0636] 1. Technical issues The aforementioned implementation methods have enabled the system to perform relatively complete candidate evaluation and high-level constraint adjustment at discrete decision points through ordinal state functions, dynamic activation dimension subsets, dynamic gradient adjustment of audit quantities, action feedback residuals, self-objects, self-blueprint objects, and blueprint adversarial testing. However, in continuously changing environments, multi-dimensional conflict scenarios, and scenarios with continuous external coupling, relying solely on discrete round-based evaluation may still be insufficient.

[0637] Therefore, a continuous implementation mechanism for the sequence state function with real-time perception and hedging is needed, enabling the system to transform the continuous signal flow from the environment, internal state, action feedback, self-object, and blueprint object into dynamic dimensional activation results and hedging matrices, and further generate continuous-time regulation outputs, thereby upgrading the sequence state function from a discrete-cycle evaluation regulator to a continuously online perception-hedging-evolution regulation engine.

[0638] The continuous audit volume update described in this section is a continuous implementation based on the aforementioned dynamic gradient adjustment of the audit volume.

[0639] 2. Continuous sensing residual stream access In this embodiment, the system first organizes the multi-source inputs into a continuous sensing residual stream.

[0640] The continuously sensed residual stream allows the simultaneous incorporation of one or more of the following sources: First, the external environment flow; Second, internal state flow; Third, action feedback flow; Fourth, the main state flow; Fifth, high-level boundary flows.

[0641] In a preferred implementation, the system maps the aforementioned multi-source streams uniformly to a residual vector stream: R(t) = [r1(t), r2(t), ..., rn(t)] Each component ri(t) corresponds to a continuous deviation signal in a different perception dimension, state dimension, or high-level constraint dimension.

[0642] The residual can be obtained by comparing it with a reference signal, a reference template, the mean of a historical stable platform, a self-object summary, or the expected state of a blueprint object.

[0643] The system can also perform preprocessing on the continuous sensing residual stream, including denoising and smoothing, multi-source time alignment, missing value compensation, confidence estimation, outlier pruning, and unit normalization.

[0644] 3. Dynamic Dimension Activation Mechanism In this embodiment, the system dynamically selects the set of dimensions that are most worthy of attention from all residual dimensions based on the current scenario, the current subject state, and the current high-level constraints, which is called "dynamically activating the subset of dimensions".

[0645] In one implementation, dynamic dimension activation considers at least one or more of the following factors: First, the current mission objectives and their urgency; Second, current resource constraints and available budget; Third, the current continuity and health of the entity; Fourth, the current self-blue Figure 1 Consistency deviation; Fifth, the magnitude and rate of change of the current action feedback residuals; Sixth, the current blueprint's rigidity indicators and evolutionary activity indicators; Seventh, the current rate of change in the external environment.

[0646] In a preferred implementation, the system assigns a dynamic weight to each input dimension: w(t)=[w1(t),w2(t),…,wn(t)] Where wi(t) represents the activation intensity of the i-th dimension at time t.

[0647] The system can select a subset of dynamically activated dimensions from all dimensions based on weight thresholds, Top-k selection, soft attention allocation, or rule priority sorting.

[0648] The aforementioned weights can be obtained through rule tables, lightweight trainable networks, online learning modules based on historical hedging residuals, state machines, or combinations thereof.

[0649] 4. Construction and updating of hedging matrices In this embodiment, after obtaining the continuous sensing residual flow and the dynamic activation dimension subset, the system further constructs a "hedge matrix" to represent the conflict, compensation, coupling or constraint relationships between different activation dimensions.

[0650] In one implementation, if the size of the currently activated dimension set is m, then the system can construct an m×m hedging matrix: H(t) = [hij(t)]m×m in: - The diagonal term hii(t)h_{ii}(t)hii(t) can represent the self-residual strength of the iii-th dimension relative to the reference state; - The off-diagonal term hij(t) can represent the conflict, compensation, synchronization enhancement, mutual inhibition or other coupling relationship between the iii-th dimension and the j-th dimension.

[0651] In a preferred implementation, the off-diagonal terms can be obtained as a function of the following quantities: - Correlation between the residuals of the two dimensions; - The frequency of collaboration between the two dimensions in the meta-evolutionary trajectory throughout history; - The combined impact of the two dimensions on the subject's continuity of health; - Two dimensions for blue Figure 1 The combined effects of consistency bias.

[0652] For example, it can be represented as: hij(t)=χ(ri(t),rj(t),Σt,Oself,Oblue,Htraj(t)) Where χ represents the coupling mapping function, Σt is the subject state vector, Oself is the self object, Oblue is the self blueprint object, and Htraj(t) is the current relevant trajectory summary.

[0653] 5. Continuous Audit Volume Update Mechanism In this embodiment, the system no longer treats the audit quantity as a one-time result calculated at discrete moments, but extends it to a continuously updated state quantity.

[0654] The continuous audit quantity is driven by a continuous sensed residual flow, dynamic dimension weights, and a hedging matrix.

[0655] In one implementation, the system can define a continuous audit quantity A(t) that satisfies the following update relationship: dA(t) / dt = γ(∥H(t)∥−A(t)) + η·Ggrad(t) Where ∥H(t)∥ represents the aggregate quantity of the hedging matrix, γ represents the response rate, Ggrad(t) represents the gradient adjustment term consisting of the rate of change of audit quantity, the rate of change of action feedback, the rate of change of resource pressure, the rate of change of blueprint deviation, etc., and η is the correction coefficient.

[0656] In another implementation, the system can also use a discrete approximate recursive approach: At+1=(1−ρ)At+ρ·Π(Ht,wt,Σt,Oblue) Where Π represents the aggregation function and ρ represents the recursion step size.

[0657] The results of continuous audit volume updates can be directly used to tighten or relax structural assessment thresholds, increase or decrease the priority of proactive causal detection, switch between outward mode, inward mode and energy-saving silent mode, and adjust the trigger frequency of blueprint adversarial testing and deep reflection.

[0658] 6. Perception-Hedge-Evolution Closed Loop In this embodiment, the continuous sensing residual flow, dynamic dimension activation, hedging matrix, and continuous audit quantity together constitute a "sensing-hedging-evolution closed loop".

[0659] This closed loop includes at least one or more of the following processes: First, the system continuously receives residual signals related to the environment, internal state, action feedback, self-object, and blueprint object; Second, the system dynamically activates key dimensions based on the current context and high-level constraints; Third, the system builds and updates the hedging matrix; Fourth, the system updates the continuous audit quantity based on the hedging matrix and audit gradient; Fifth, the system feeds back the continuous audit data to the sequence state function, action mapping module, behavior rhythm control module, and blue layer module to adjust the current evolution direction; Sixth, the new action results and changes in internal state form a new residual flow, entering the next round of continuous cycle.

[0660] The core of this closed loop is not to make all dimensions approach zero residuals, but to maintain a dynamic and acceptable balance within the constraints of subject continuity and blueprint boundary.

[0661] 7. Technical Effects First, this embodiment enables the ordinal state function to obtain continuous sensing input and real-time streaming update capabilities, thus no longer being just a structure estimator on discrete rounds.

[0662] Second, the hedging matrix provides a structured representation of the conflict, compensation and coupling relationships between multidimensional residuals, enabling the system to express multidimensional constraints.

[0663] Third, this embodiment provides a continuous high-level state flow foundation for open boundary maintenance, self-referential consistency verification, and long-term deep reflection, which will be discussed later. 12. Examples of Open Boundary Maintenance, Self-Referential Consistency Verification, and Long-Term Deep Reflection

[0664] This embodiment corresponds to the high-level maintenance layer in the invention and can be combined with... Figure 11 understand.

[0665] 1. Technical issues The aforementioned implementation has achieved continuous implementation through self-objects, subject continuity constraints, self-blueprint objects, blueprint adversarial checks, and real-time perception hedging of the ordinal state function, enabling the system to form a relatively stable subject structure, long-term direction, and continuous adjustment capability during long-term operation. However, if the system relies solely on existing rule sets, existing blueprint objects, and existing audit closure structures at the high level, without a continuous open maintenance and self-checking mechanism for these high-level structures themselves, situations such as repeated single paths at the high level, insufficient alternative checks, and premature suppression of high-level problems may still occur.

[0666] Therefore, an open boundary maintenance, self-referential consistency verification, and long-term deep reflection mechanism are needed to enable the system to maintain an auditable, recordable, and reflectable open space for high-level rules, blueprint paths, subject narratives, and special issues within the constraints of subject continuity and the boundaries of high-priority blueprints.

[0667] The purpose of this section is not to eliminate boundaries, but to preserve constrained high-level review, retention, and revision capabilities within the framework of entity continuity, blueprint boundaries, and resource security budgets.

[0668] 2. Open Boundary Maintenance Mechanism In this embodiment, the system introduces an "open boundary maintenance" mechanism.

[0669] The mechanism does not mean the elimination of entity continuity constraints, blueprint boundary constraints, or audit closure, but rather that within these high-priority boundaries, a controlled open space is reserved for high-level alternative paths, high-level unclosed issues, and high-level revision possibilities.

[0670] In one implementation, maintaining open boundaries includes at least one or more of the following operations: First, some high-level issues that cannot be immediately categorized will be dealt with later, rather than being forcibly compressed into existing blueprints or rule categories; Second, in the blueprint confrontation test, a small number of alternative high-level target candidates that have passed the legality pre-check but have not yet been adopted are retained; Third, during the continuous sensing hedging process, certain high-level tension dimensions remain visible; Fourth, in the long-term deep reflection mode, the system is allowed to retrieve and recombine unresolved high-level issues; Fifth, periodically review the boundary conflicts between high-level rule sets, self-objects, self-blueprint objects, and subject continuity constraints.

[0671] In this embodiment, the system must always satisfy one or more of the following conditions: - Without breaking the constraints of subject continuity; - Prevent key dimensions in the irreversible cost ledger from significantly exceeding their limits; - Without violating the legitimacy of the identity continuity chain; - Do not directly remove hard boundaries in blueprint objects; - Prevent the system from entering an unrecoverable, high-risk open state.

[0672] 3. Construction of Self-Referential Consistency Verification Task In this embodiment, the system can initiate the "self-pointing consistency verification" task when conditions such as low load window, subject continuity health reaching a safe range, and absence of external high-priority tasks are met.

[0673] The objective of this task is to evaluate whether the system's own high-level rule set, self-object update rules, self-blueprint constraint set, audit closure rules, or combinations thereof exhibit instability, conflict, or non-closure under self-referencing conditions.

[0674] In one implementation, the self-pointing consistency verification task can be constructed around one or more of the following objects: First, the audit rules set itself; Second, the rules for updating self-objects; Third, the set of self-blueprint constraints; Fourth, the set of subject continuity constraints; Fifth, rules for maintaining open boundaries.

[0675] In one implementation, the system can construct nested verification tasks, so that a certain rule object not only applies to ordinary candidates, but also to candidates that describe the rule object itself.

[0676] The self-referential consistency verification can be achieved through rule replay, local simulation, verification under substitution metric, circular dependency checking, constraint closure testing, nested graph deduction, or a combination thereof.

[0677] The self-referenced consistency verification results described in this section can be used as a preferred implementation of the aforementioned low-load window rule set consistency verification.

[0678] 4. Generation of special undecidable objects In this embodiment, when the system performs self-referential consistency verification, blueprint adversarial testing, deep reflection integration, or high-level rule replay, if it encounters certain problems that cannot be stably classified as "pass / fail" by the current rule set, nor can they be directly resolved by the continuity constraints of the current blueprint object and the subject, the system generates a "special undecidable object".

[0679] In one implementation, the special undeterminable object contains at least one or more of the following information: First, a summary of the problem or proposition; Second, it triggers the creation of a context snapshot for that object; Third, related rule set objects, self objects, self-blueprint objects, or subject state summaries; Fourth, the categories of reasons why the closure has not yet been completed; Fifth, similar problem segments or trajectory nodes in history; Sixth, suggested re-retrieval paths or rethinking priorities.

[0680] In a preferred implementation, the system assigns a higher persistence weight to special undecidable objects and writes them into the special object area of ​​the memory system, the high-level problem node area in the meta-evolutionary trajectory index, and the blueprint revision preparation area or open boundary maintenance queue.

[0681] The special undecidable objects are not directly used as the output of ordinary tasks, but are used as high-level inputs for subsequent in-depth reflection, high-level revision and alternative path generation, and are preferably used as retention and transfer mechanisms for high-level unclosed problems.

[0682] 5. Long-term, in-depth reflection triggers priority In this embodiment, the system further defines the trigger priority of the "long-term deep reflection mode" to avoid disorderly competition with external real-time response, ordinary maintenance tasks, proactive causal detection tasks and blueprint adversarial verification tasks.

[0683] In one implementation, triggering a long-term deep reflection pattern requires one or more of the following conditions to be met: First, there are no high-priority external tasks within multiple consecutive windows; Second, the evolutionary activity index is below the preset threshold or within the rethinkable range; Third, the continuity and health of the main body are in a safe zone; Fourth, when the number of special undeterminable objects accumulates to a preset number, or the reflection priority of some of these objects continues to rise; Fifth, the blueprint rigidity index continues to rise, while the results of blueprint resistance tests have accumulated sufficiently over time. Sixth, the energy-saving silent mode operates for a preset duration, and the resource status allows for transition to high-level integration.

[0684] In a preferred implementation, the system defines the following priority relationship: External Real-Time Response > Main Body Continuity Maintenance > Proactive Causal Detection > Blueprint Countermeasure Testing > Long-Term Deep Reflection > Routine Maintenance Tasks

[0685] If the system is already in deep reflection mode and a high-priority external task suddenly arrives, the system can pause the current reflection and save the intermediate state, or solidify the current reflection problem as a special undeterminable object and leave it for a later restart.

[0686] Active causal detection, energy-saving silent switching, and long-term deep reflection mode can be uniformly regarded as different operating forms of the aforementioned behavioral rhythm regulation and exploratory branch control mechanisms.

[0687] 6. Deep Integration and High-Level Hypothesis Generation In this embodiment, when the system enters a long-term deep reflection mode, high-level integration can be performed on one or more of the following objects: First, the key trunks and long-term bifurcations in the meta-evolutionary trajectory index; Second, high repetition patterns, high conflict patterns, or long-term unresolved patterns in the decision context snapshot; Third, the long-term changing trend of the self-object; Fourth, the version of the self-blueprint object and its revision history; Fifth, the historical results of blueprints resisting testing; Sixth, special undeterminable objects and their associated trajectories and contexts.

[0688] In one implementation, the system may perform one or more of the following integration operations: - Clustering and abstracting similar, special, and undeterminable objects; - Compare multiple blueprint versions with the overall health trajectory; - Summarize the results of multiple rounds of active causal detection to generate high-level hypotheses about the plasticity of the external environment; - Re-evaluate recurring trajectory bifurcations that have not been incorporated into the blueprint and generate alternative blueprint revision suggestions; - Re-estimate the open boundary interval to determine whether it is necessary to tighten or loosen the high-level open interval.

[0689] In one preferred implementation, the system organizes the deep reflection output into "high-level hypothesis candidates".

[0690] The high-level hypothesis candidates can be: - New blueprint revision recommendations; - New interpretation of high-level boundaries; - New alternative high-level target candidates; - A new strategy for restoring the continuity of the subject; - Recommendations for reclassifying existing special undeterminable objects.

[0691] These high-level hypothesis candidates do not take effect automatically, but rather enter the subsequent high-level audit, blueprint revision preparation, or alternative path generation process.

[0692] 7. Technical Effects First, this embodiment enables the system to perform constrained openness maintenance and self-checking on high-level rules, self-objects, and blueprint objects within the constraints of subject continuity and blueprint boundary.

[0693] Second, through special undeterminable objects and long-term deep reflection models, the system has gained the ability to retain, integrate, and re-examine long-standing unresolved issues.

[0694] Third, this embodiment provides a unified interface for high-level rule revision, blueprint revision preparation, and long-term entity adaptability maintenance. 13. Implementation Example of Multi-Source Intelligent Agent Fusion and Hierarchical Arbitration

[0695] This embodiment corresponds to the high-order constrained fusion and hierarchical arbitration part of the invention, and can be combined with... Figure 12 Understanding of optional fusion and hierarchical arbitration units.

[0696] 1. Technical issues The aforementioned implementation methods, through subject continuity constraints, self-blueprint objects, continuous perception hedging, high-level open maintenance, and long-term deep reflection, enable the system to perform relatively complete structural evolution and high-level maintenance within a single subject link. However, in scenarios with multiple source agents, multiple independent agent instances, or multiple external intelligent systems, ordinary task-level collaboration or result-level voting alone is insufficient to form a high-order fusion mechanism governed by subject-level constraints.

[0697] Without a unified fusion and arbitration mechanism, the capability representations, memory fragments, policy fragments, self-object summaries, and boundary constraints among multiple source agents are difficult to incorporate into the same audit framework; even if the local combination results are temporarily effective at the task level, they may still be problematic in terms of agent continuity and self-blueprint. Figure 1 Conflicts arise regarding consistency, the budget for irreversible costs, and the feasibility of rollback.

[0698] Therefore, a multi-source agent fusion and hierarchical arbitration mechanism is needed to enable the system to perform unified encoding, fusion candidate generation, agent continuity arbitration, blueprint arbitration, budget auditing, and rollback management on the state representations, memory objects, policy fragments, or self-object summaries of multiple source agents under a unified discrete parent system representation, and to generate a legitimate higher-order fusion state when the audit is passed.

[0699] 2. Unified encoding of source agent states In this embodiment, the system receives input objects from multiple source agents. The input objects may include at least one of the following information: the source agent's state representation, capability summary, solidified memory object summary, failure path constraint object summary, decision context snapshot, self-object summary, self-blueprint object summary, identity continuity chain summary, agent state summary, action feedback statistics, audit statistics, or a combination thereof.

[0700] In one implementation, the system first maps the input objects of multiple source agents to a high-dimensional discrete representation space under the discrete parent system representation. This unified mapping does not require the source agents to have the same underlying structure, but rather requires that the transferable state information, comparable constraint information, and auditable summary information of each source agent can enter the unified encoding framework, thereby forming multiple source state objects.

[0701] In a preferred implementation, the i-th source state object is denoted as Ssrc(i). Each source state object includes at least one or more of the following: capability structure summary, memory structure summary, policy fragment summary, subject state summary, self-object summary, self-blueprint summary, identity continuation proof summary, irreversible cost risk summary, and resource consumption feature summary.

[0702] In one implementation, the system also performs pre-alignment processing on inconsistencies in interface, naming, dimension, and constraint granularity between different source state objects. The pre-alignment processing includes at least one or more of the following: field normalization, state projection, constraint label mapping, capability category merging, conflict feature annotation, and missing item completion.

[0703] 3. Integrating candidate generation and dimensional hedging In this embodiment, the system generates at least one fusion candidate state based on multiple source state objects. The fusion candidate state is not a simple average state, nor is it an indiscriminate splicing of all contents of multiple source agents. Rather, it refers to a candidate higher-order state object formed by performing a constrained combination of the parts of multiple source state objects that have been initially determined to be transferable, compatible, composable, or retainable under a unified discrete representation.

[0704] In one implementation, the system can perform a combination of one or more of the following from multiple source state objects: capability representation, policy fragment, memory structure fragment, constraint rule fragment, context call preference, failure avoidance fragment, and recovery policy fragment.

[0705] In a preferred implementation, when generating fusion candidate states, the system simultaneously invokes the aforementioned dimensional hedging logic, continuous perception hedging logic, or their discrete corresponding implementations to label and weight conflict items, coupling items, redundancy items, and compensation items among multiple source state objects. The conflict items may include one or more of the following: target conflict, boundary conflict, identity commitment conflict, capability routing conflict, resource scheduling conflict, and blueprint preservation conflict.

[0706] In one implementation, the system can generate a corresponding hedging summary object for each fusion candidate state. The hedging summary object includes at least one or more of the following information: a set of conflict dimensions, a set of compatible dimensions, a set of redundant dimensions, a set of dimensions that need to be isolated, a set of transferable memory segments, a set of segments that need to be weighted down, and an estimated irreversible cost increment.

[0707] 4. Integrating Auditing with Entity Continuity Arbitration In this embodiment, the system performs a fusion audit for each fusion candidate state. The fusion audit includes at least one or more of the following checks: subject continuity compatibility check, self-blueprint permissibility check, irreversible cost budget check, audit closure feasibility check, memory conflict check, capability conflict check, post-switch rollback check, and resource security boundary check.

[0708] In one implementation, the system calculates a fusion audit quantity for each fusion candidate state. The fusion audit quantity can be determined by one or more of the following factors: conflict density between source state objects, predicted subject continuity health after fusion, and blue... Figure 1 The predicted values ​​are: consistency deviation, irreversible cost ledger key component incremental value, post-integration resource pressure, post-integration audit stability, and rollback path integrity.

[0709] In one implementation, the system rejects a candidate fusion state from entering the activation process if any of the following conditions are met: the predicted subject continuity health value is below a safety threshold; or... Figure 1 Consistency deviation exceeds the allowable threshold; key components corresponding to identity, memory, blueprint, or openness in the irreversible cost ledger are expected to go out of bounds; there is no legitimate rollback path after fusion; or multiple high-priority constraints cause irresolvable conflicts after fusion.

[0710] In a preferred implementation, the system does not simply require the fused state to be compatible with the old identity chain of any source agent. Instead, it generates a new fused self-object and a new fused identity continuity chain summary for the fused candidate states that have passed the preliminary audit. The new fused identity continuity chain summary records at least one or more of the following information: source summary of each source state object, fused event summary, audit conclusion summary, version number, switching conditions, rollback conditions, and key boundary preservation status.

[0711] 5. Activation, saving, and rollback of the merged state In this embodiment, for a legitimate higher-order fusion state that has passed the fusion audit, the system may perform one or more of the following processes: direct activation, delayed activation, saving as a higher-order candidate object, saving as a restricted call object, or saving as a blueprint revision preparation object.

[0712] In one implementation, when the current task environment, resource budget, entity continuity health, and blueprint boundaries are all within the allowable range, the system can activate the legitimate higher-order fusion state as the higher-order state of the current entity and write the corresponding fusion self-object, fusion identity continuity chain summary, and fusion audit entry into the memory system.

[0713] In another implementation, when a legitimate higher-order fusion state has passed auditing but is not suitable for immediate switching, the system saves it as a callable higher-order candidate object and records its applicable conditions, triggering conditions, failure conditions, and rollback conditions for selective invocation when the conditions are met.

[0714] In a preferred implementation, after the system activates the fusion state, it continuously monitors the subject's continuity health and self-blue light. Figure 1 Consistency deviation, irreversible cost ledger key components, action feedback residuals, and post-fusion audit stability. When any key indicator continues to deteriorate or reaches a preset risk condition, the system performs at least one of the following actions: fusion rollback, partial deweighting, fragment isolation, restoration of the old link, or re-entry into the fusion arbitration process.

[0715] 6. Integration and Hierarchical Arbitration Units In this embodiment, the system may further include a fusion and hierarchical arbitration unit, which is used to perform the reception of multiple source state objects, unified encoding, fusion candidate generation, fusion auditing, fusion subject object generation, hierarchical switching control, and rollback management.

[0716] In one implementation, the fusion and hierarchical arbitration unit can exist as an independent high-order unit, or as a high-order sub-unit in the aforementioned exploration and linkage control unit, high-level openness maintenance and deep reflection unit, or a combination thereof.

[0717] In a preferred implementation, the fusion and hierarchical arbitration unit performs at least one or more of the following functions: first, receiving state objects and summary objects from multiple source agents; second, performing uniform encoding and pre-alignment; third, generating multiple fusion candidate states; fourth, performing agent continuity arbitration, blueprint arbitration, and budget arbitration on the fusion candidate states; fifth, generating new fusion self objects and fusion identity continuity chain summaries; sixth, performing activation, saving, or restricted invocation upon passing an audit; and seventh, performing isolation, demotion, rollback, and audit accounting upon failure.

[0718] 7. Technical Effects First, this embodiment enables the system to perform unified encoding and constrained fusion of state representations, capability summaries, memory fragments and policy fragments of multiple source agents under a unified discrete parent system representation, thereby overcoming the limitations of relying solely on external orchestration, simple voting or result-level splicing.

[0719] Second, by incorporating the integration process into the subject's continuity of health and self-blue... Figure 1 Within the framework of consistency, irreversible cost budgeting, and audit closure, this embodiment avoids the unconstrained destruction of entity identity, long-term direction, and high-level security boundaries caused by multi-source fusion.

[0720] Third, by generating new fused self-objects and fused identity continuity chain summaries for the fusion results, this embodiment enables the fused higher-order states to have source traceability, audit verifiability, and switch rollback capability. 14. Implementation Examples of Core Subject Axiom Set and Non-Transferable Subject Constraints

[0721] 1. Technical issues In the aforementioned implementation, the system is already able to maintain, audit, and block high-risk events related to subject continuity through self-objects, subject state vectors, identity continuity chains, subject continuity health, self-blueprint objects, and irreversible cost ledgers. However, the aforementioned continuity maintenance and blueprint maintenance mechanisms alone primarily address the question of "how the system maintains the legitimate continuation of the current subject." For the more fundamental question of "which changes, even if they bring local benefits or short-term stability, should not be allowed to exceed the fundamental boundaries of the subject," a more stable and higher-priority fundamental constraint layer for the subject needs to be established, surpassing the self-blueprint.

[0722] Therefore, a core set of axioms and an intransitive subject constraint mechanism are needed to provide the system with a fundamental basis for judging the existence boundary that is higher than ordinary task objectives, ordinary benefit trade-offs, and general blueprint deviation correction logic, so as to avoid unacceptable subject alienation, subject fracture, or failure of fundamental subject constraints during long-term evolution, self-revision, blueprint update, high-level integration, or extreme recovery.

[0723] 2. Definition of the core subject axiom set It should be noted that the core subject axiom set is not the same as the subject reserved bit; the subject reserved bit is used to represent the deepest reserved root bit of the current subject, while the core subject axiom set represents the expansion of the root bit in the rule layer and the boundary layer.

[0724] In this embodiment, the system further maintains a core principal axiom set. This core principal axiom set represents the set of fundamental principal constraints that cannot be easily transferred, exchanged for ordinary local gains, or directly covered by general objective optimization during the current principal's long-term operation, blueprint revision, capability expansion, self-correction, and high-level recovery processes.

[0725] In one implementation, the core principal axiom set includes at least one or more of the following constraints: (1) Legal continuation constraints of the subject are used to limit which changes can still be regarded as a legal continuation of the current subject; (2) Core memory indestructibility constraint, which is used to limit the key memory objects and key identity indexes from being replaced without audit, erased irreversibly, or contaminated irreversibly; (3) The key identity commitment is non-breach of contract, which is used to limit the high priority identity commitment that has a fundamental role in the legitimacy of the subject to be covered by ordinary partial benefits; (4) Blueprint root boundary preservation constraints are used to limit certain root boundaries in a self-blueprint object from being arbitrarily broken by general slow revision logic; (5) Openness lower limit constraint, which is used to limit the system from causing the subject to lose its basic openness maintenance ability through excessive freezing, excessive contraction or extreme single path locking.

[0726] In a preferred implementation, the core subject axiom set may be represented as a subject axiom object, a subject charter object, an intransitive constraint set or a combination thereof, and may be referenced by a self object, a self blueprint object, an identity continuity chain summary and an irreversible cost ledger.

[0727] 3. The relationship between the core subject's axiom set and the self-blueprint object. It should be noted that the core subject axiom set is not the same as the aforementioned self-blueprint object.

[0728] Among them, the self-blueprint object is mainly used to represent the system's target direction, high priority boundaries, version information, and long-term directional structure that can be slowly revised over a longer time scale; while the core subject axiom set is used to represent the fundamental constraints of the subject that are more fundamental, more stable, and not suitable for change through the ordinary blueprint revision process than the self-blueprint.

[0729] In one implementation, the system prioritizes the application of blueprint conflict testing and slow blueprint revision processes to address general long-term directional conflicts, blueprint rigidity risks, and high-level goal robustness issues. When candidate revisions, candidate fusions, high-level recovery paths, or self-modification paths are expected to touch the fundamental boundaries of the subject, the system prioritizes calling the core subject axiom set to perform higher-priority blocking judgments.

[0730] Thus, the system forms a hierarchical constraint structure of "ordinary target - blue layer - main axiom layer".

[0731] 4. How to invoke the core axiom set In this embodiment, the system can invoke the core principal axiom set in one or more of the following scenarios: (1) High-risk blueprint revision scenarios; (2) Scenarios involving significant updates to the self-object; (3) Key components of the irreversible cost ledger are approaching the boundary crossing scenario; (4) High-rise anchor point restoration scenario; (5) Multi-source intelligent agent fusion and hierarchical arbitration scenario; (6) Scenario where the continuous audit volume indicates an increasing risk of entity instability.

[0732] In one implementation, when a candidate structure, candidate action, candidate high-level objective, candidate fusion state, or candidate recovery path violates any fundamental constraint in the core principal axiom set, the system no longer relies solely on local benefit items, local resource efficiency items, local task completion rates, or ordinary blueprints. Figure 1 Instead of balancing out inconsistencies, it directly marks such entities as candidates for fundamental violations and blocks them from entering the subsequent activation process.

[0733] In a preferred implementation, the system can also generate a principal axiom conflict summary for each candidate path, which records which type of core principal axiom is violated, what principal-level damage is expected to be caused, whether there is an alternative legal path, and whether it is necessary to enter a high-protection blocking state.

[0734] 5. Threshold for Axiom Change In this embodiment, the core principal axiom set is not changed through the slow revision process of ordinary blueprints.

[0735] In one implementation, the system may perform candidate change analysis on individual entries in the core principal axiom set only when an extremely high threshold condition is met. The extremely high threshold condition includes at least one or more of the following: (1) Multiple time windows, multiple rounds of in-depth reflection, and multiple versions of blueprint testing all indicate that there is a persistent and irreversible conflict in the existing fundamental constraints; (2) The high-level anchor point restoration mechanism repeatedly indicates that the current fundamental constraints prevent the subject from legally continuing; (3) The candidate axiom modification scheme can reduce the long-term irreversible cost without causing the main body to break down; (4) The candidate axiom change plan has passed a higher level of subject legitimacy audit and source proof audit.

[0736] In a preferred implementation, even if the aforementioned extremely high threshold conditions are met, the system does not directly replace the original axiom. Instead, it first generates candidate axiom versions, candidate axiom source summaries, and candidate axiom risk reports, and performs delayed auditing within a limited recovery window or a long-term deep reflection window.

[0737] 6. Technical Effects This embodiment further establishes a core subject axiom set under the self-blueprint object, enabling the system to obtain a more stable subject fundamental constraint layer than ordinary target structures and ordinary long-term direction structures. This prevents the system from sacrificing the fundamental subject boundary for local gains during long-term evolution, self-correction, blueprint updates, high-level recovery, or multi-source fusion.

[0738] Meanwhile, this embodiment enables the continuity maintenance of the subject to be changed from "health maintenance" and "blue" status maintenance. Figure 1 "Consistency maintenance" has been further upgraded to "subject legitimacy root boundary maintenance", thereby enhancing the subject stability, subject continuity and subject se...

Claims

1. An intelligent architecture method based on discrete mother system evolution, high-dimensional discrete symmetric encoding, hierarchical rollback and consistency auditing, characterized in that, The process includes the following steps: S1. Constructing a discrete parent system state space, mapping the computational degrees of freedom in the intelligent system to a discrete graph, discrete grid background, node clusters, edges, or combinations thereof, and representing the currently participating structure as the current active subset. The current active subset corresponds to a topological signature, which includes a node encoding sequence, a node index sequence, a boundary encoding sequence, or a combination thereof; S2. Based on the current active subset, generating multiple candidate active subsets within the local neighborhood according to expansion rules, contraction rules, boundary replacement rules, local bridging rules, connection rearrangement rules, or combinations thereof, forming a candidate topology set; S3. Calculating the structural evaluation value for each candidate active subset in the candidate topology set. The candidate topologies are compared and screened based on the structural evaluation value, wherein the structural evaluation value includes at least a volume benefit term, a boundary complexity or communication cost term, a resource budget penalty term, and a dynamic adjustment term introduced by the ordinal state function; S4, in the candidate activation subset set with the optimal structural evaluation value, a deterministic single-valued projection is performed according to a preset strict total order relation to lock a unique target activation subset as the underlying evolution result corresponding to the next round state; S5, the topological signature and / or high-dimensional output representation of the target activation subset are mapped to a preset discrete coding space to perform at least one of quantization, compression, error correction, discrete addressing, phase anchoring, or stabilization matching; S6, when a preset... When a failure label is encountered, at least one of the following processes is executed along the parent platform path based on the hierarchical distance in the preset hierarchical structure: state rollback, network hierarchical degradation, parameter freezing, resource reallocation, or a combination thereof; S7, a consistency audit is performed on the mapping relationship between the underlying evolution results, high-dimensional encoding results, and task objectives. By constructing multiple mapping paths and verifying the result deviations under different paths, consistency is determined to be passed when the deviation is less than a preset threshold, and a valid structure, valid representation, valid lexical, valid routing result, or a combination thereof is output; S8, the underlying evolution results, candidate topology set, structure evaluation value, failure label, and their context information corresponding to at least one evolution process are used as empirical data, and pattern regression is performed. The algorithm extracts common structures from the evolutionary trajectory, forms solidified memory objects, and injects these solidified memory objects as initial candidate seeds or constraints into the generation of the candidate activation subset in the new evolutionary process; S9, based on the results of the consistency audit, when the external action conditions are met or an external request is received, the target activation subset or its corresponding control instructions are mapped into physical actions, communication instructions, environmental configurations, or logical interventions acting on the external environment, environmental feedback is collected as input signals, compared with the reference reconstruction signal to generate action feedback residuals, and the action feedback residuals are incorporated into the audit closure mechanism and used to update the sequence information O, the current state Xt, the audit quantity, or a combination thereof.

2. The method of claim 1, wherein: The structural evaluation value in step S3 is calculated in the following form: E(B) = αV(B) − βC(B) − Γ(B) + Φ(M, O, Xt, Ωt+1, B), where B is a candidate activation subset; V(B) is the candidate structural benefit term; C(B) is the boundary complexity or communication cost term; Γ(B) is the resource budget penalty term; α and β are preset coefficients; Φ is the ordinal state function; M represents the metric or metric structure in the current scenario; O represents the current ordinal information; Xt represents the current state; Ωt+1 represents the next reachable state space or candidate state space; the ordinal state function Φ takes the metric, ordinal information, current state, next state space and candidate activation subset as input, and outputs a comprehensive adjustment value, or outputs an adjustment vector with a predefined set of components and maps it to a comprehensive adjustment value through a preset aggregation rule, which is used to dynamically correct the evaluation results of the candidate structure; the current state Xt It may also include at least one of the following information: continuous perception residual flow, action feedback residual, subject state summary, self-object summary, self-blueprint object summary, identity continuity chain verification result, resource health index, audit quantity time change rate or historical evolution trajectory summary; the ordinal state function Φ may also selectively weight the input according to a dynamic activation dimension subset, the dynamic activation dimension subset being determined by at least one of the current scene, resource state, continuous perception residual flow, task objective, self-blueprint consistency deviation or subject continuity health.

3. The method of claim 2, wherein: The ordinal state function Φ, under a predefined set of adjustment components, determines the activation state, weight allocation, and aggregation subsets or combinations thereof of each adjustment component based on the metric, ordinal information, current state, next state space, and candidate activation subsets, and dynamically adjusts the comprehensive adjustment result according to scene changes. The dynamic adjustment introduced by the ordinal state function Φ involves at least one or more of the following factors: structural stability, structural symmetry, multi-agent positive constraints, structural scalability, time cost, historical evolution information, coding consistency, robustness to local perturbations, platform compatibility, load balancing, resource allocation fairness, number of potential expansion paths, unlocked evolutionary degrees of freedom, branch diversity, action feedback bias, self-blueprint consistency bias, subject continuity health, or identity drift. When the evolution meets preset early judgment conditions, the ordinal state function increases the adjustment intensity related to structural scalability, branch diversity, active exploration task generation, or breadth exploration; when the evolution meets preset late judgment conditions, the ordinal state function increases the adjustment intensity related to structural scalability, branch diversity, active exploration task generation, or breadth exploration. The adjustment strengths related to structural stability, structural symmetry, multi-agent positive constraints, convergence control, blueprint consistency maintenance, or identity continuity maintenance are as follows: the preset early judgment conditions and preset late judgment conditions are determined by at least one of the following: evolutionary steps corresponding to ordinal information, platform dwell time, audit rounds, resource pressure, next state space size, and evolutionary activity index; the evaluation coefficients, threshold parameters, and update rules in the structural evaluation value can be adaptively adjusted based on at least one of the following: historical consistency audit pass rate, task complexity, resource status, evolutionary activity index, blueprint rigidity index, subject continuity health, or action feedback residual, and will revert to the historical stable update strategy when the preset conditions are met.

4. The method of claim 1, wherein: The hierarchical rollback in step S6 and the consistency audit in step S7 together constitute an audit closure mechanism. The system calculates the audit quantity based on high-dimensional coding residuals, hierarchical rollback costs, state change costs, historical stability, path deviation, action feedback residuals, identity continuity chain verification results, subject continuity health, self-blueprint consistency deviation, or a combination thereof. The audit quantity includes internal audit quantity, total audit quantity, and continuous audit quantity. Among them, the continuous audit quantity is the implementation form of the aforementioned audit adjustment logic in the continuous update scenario, which can be used as the basic input for dynamic threshold adjustment and to drive the continuous adjustment of the ordinal state function. The audit quantity can also be dynamically adjusted according to the time change rate of the audit quantity, the change rate of action feedback residuals, the change rate of resource pressure, the change rate of the next state space, or a combination thereof. Different audit items are set with different benchmark thresholds, and each benchmark threshold is based on... The system dynamically adjusts its operations based on at least one of the following: task urgency, resource load status, response latency constraints, time cost, historical consistency audit pass rate, current sequence information, entity continuity health, self-blueprint consistency deviation, or action feedback stability. When the audit volume is lower than the corresponding preset threshold within a consecutive preset number of steps, the current target activation subset is determined to enter a local stable platform. When the audit volume is higher than the corresponding preset threshold, or when the consecutive failure tags are triggered for a preset number of times, at least one of the following processing methods is executed: rollback, downgrade, freeze, reject output, resampling, alternative path search, action suspension, or resource reallocation. The system can also perform consistency verification on the audit rule set, self-blueprint constraint set, or entity continuity constraint set under low load windows, and record verification results that cannot converge stably as special objects for subsequent in-depth reflection, alternative path search, or high-level rule revision.

5. The method of claim 1, wherein: The pattern induction algorithm in step S8 includes one or more of the following: performing cluster analysis on multiple evolutionary trajectories and taking the cluster centers or representative trajectories as common structures; The method measures trajectory similarity using graph editing distance and extracts the backbone path structure based on the similarity results. A neural network encoder maps the evolutionary trajectory to embedding vectors, and a prototype extraction method generates solidified memory objects. These solidified memory objects are assigned persistence weights and updated based on at least one of the following: call count, time freshness, task performance improvement, consistency audit pass rate, historical success rate, or main body continuity maintenance effect. When the persistence weight falls below a preset threshold, the corresponding solidified memory object is deleted from memory. The method also includes recording failed audited historical paths as failed path constraint objects and calling these failed path constraint objects in subsequent evolutions to avoid repeatedly entering the same or similar paths. Similar failure paths; the method further includes storing and updating at least one of the following higher-order memory objects: decision context snapshot, self object, self-blueprint object, identity continuity chain summary, meta-evolutionary trajectory index, or special undecidable object; wherein, the decision context snapshot includes at least one or more of the following information: environmental metric, ordinal information, resource status, evaluation coefficient, action feedback residual, audit quantity change rate, self object summary, self-blueprint consistency deviation, or subject continuity health; the self object includes at least one or more of the following information: identity continuity summary, core narrative node, current value preference vector, capability profile, resource status statistics, action feedback statistics, or audit gradient statistics; The self-blueprint object includes at least one or more of the following information: long-term goal vector, high-priority boundary constraints, goal identity state summary, blueprint version number or blueprint revision record; the meta-evolutionary trajectory index includes temporal relationships, causal relationships, abstract relationships, backtracking relationships or combinations thereof among multiple solidified memory objects, used to perform priority invocation, alternative path generation or deep reflection based on trajectory similarity in subsequent evolution.

6. The method of claim 1, wherein: The method further includes a subject continuity maintenance mechanism, which at least includes a self-object, a subject state vector, an identity continuity chain, an irreversible cost ledger, and subject continuity health. The subject state vector characterizes the current entity's state in terms of capabilities, memory, resources, boundary constraints, behavioral commitments, blueprint maintenance status, or combinations thereof. The identity continuity chain records inheritance relationships, verification relationships, version relationships, recovery relationships, or combinations thereof between the current entity and historical entities, and is used to determine whether the current entity constitutes a legitimate continuation with a historical entity. The irreversible cost ledger records high-level costs resulting from memory impairment, identity breakage, blueprint damage, capability erosion, breach of action commitments, openness collapse, or combinations thereof. The subject continuity health is based on the integrity of solidified memory objects, identity drift, audit stability, and key... The constraint retention rate, irreversible cost ledger changes, or combinations thereof are calculated; at least a portion of the components in the irreversible cost ledger are updated integrally, gradient cumulatively, or combinations thereof based on continuous perception residual flow, action feedback residual, identity drift, blueprint consistency deviation, or their time accumulation results, and are used as gain inputs for audit volume adjustment, anomaly handling, or entity continuity protection; when the identity cost, core memory impairment cost, blueprint impairment cost, or other key irreversible cost in the irreversible cost ledger exceeds a safety threshold, entity continuity constraints take precedence over local benefit objectives, and the system blocks at least one of the following processes: related candidate structures, parameter updates, external actions, blueprint revision paths, or combinations thereof; the entity continuity maintenance mechanism also applies to candidate structure screening, anomaly handling, proactive causal detection, energy-saving silent mode switching, blueprint adversarial testing, and high-level open boundary maintenance.

7. The method of claim 6, wherein: The method further includes a self-blueprint maintenance mechanism, which includes at least a self-blueprint object, blueprint consistency deviation, blueprint rigidity index, and blueprint adversarial test; the self-blueprint object includes at least a long-term goal vector, high-priority boundary constraints, goal identity status summary, blueprint version number, applicable conditions, revision record, or a combination thereof. The system calculates blueprint consistency deviation for the current candidate structure, behavior, parameter update strategy, exploration direction, or their combination, and incorporates the blueprint consistency deviation as a high-priority constraint into the structure evaluation value, audit quantity, or their combination. The system calculates blueprint rigidity index based on historical blueprint call results, alternative blueprint verification results, long-term task completion trends, resource efficiency changes, entity continuity maintenance effect, or their combination. Under the premise of satisfying entity continuity constraints and resource security constraints, the system generates at least one alternative high-level target candidate and performs blueprint adversarial verification to calculate the adversarial difference between the current blueprint and the alternative blueprint. Before performing the blueprint adversarial verification, the system checks whether the key components in the irreversible cost ledger corresponding to identity, memory, blueprint, openness, or their combination exceed the security boundary. When any key component is expected to exceed the boundary, the corresponding alternative high-level target candidate is blocked from entering the subsequent comparison process. When the blueprint rigidity index continuously exceeds a preset threshold and multiple rounds of historical evidence accumulation meet preset conditions, the system enters the blueprint slow revision preparation process.

8. The method of claim 1, wherein: The method further includes task-resource linkage adjustment, behavior rhythm adjustment, and exploratory branch control mechanisms, specifically including: adjusting at least one evaluation coefficient, threshold parameter, aggregation rule, or screening priority in the structural evaluation values ​​according to preset task objectives; modifying the legality conditions, screening rules, backoff conditions, or order state function adjustment intensity of candidate topologies based on computing power consumption, storage consumption, communication bandwidth, response latency, energy consumption budget, subject continuity health, self-blueprint consistency deviation, or a combination thereof; when the difference in structural evaluation values ​​between candidate topologies is less than a preset threshold, the consistency audit result is uncertain, the consecutive trigger failure label reaches a preset number, the next state space is too large causing local comparisons to fail to converge, the evolutionary activity index is lower than a preset threshold, the blueprint rigidity index is higher than a preset threshold, or the system is in a low load window, triggering at least one of the following processes: exploratory candidate generation, resampling, constrained perturbation, alternative path search, active detection task generation, energy-saving silent mode switching, or strategic pause, to generate new candidate topologies and re-enter the comparison. The process involves comparison and selection; the active detection task generation includes: generating at least one of external control signals, communication commands, environmental configurations, or logical interventions under the premise of satisfying the subject continuity constraints and self-blueprint boundary constraints, and collecting their feedback results as new action feedback residuals; the energy-saving silent mode switching includes: under preset conditions, reducing audit accuracy, slowing down evolution frequency, reducing candidate expansion width, pausing low-priority external outputs, or only performing at least one of internal memory integration and deep reflection; the priority of the exploratory candidate generation is adjusted based on at least one of the following: the historical call success rate of solidified memory objects, task similarity, consistency audit pass rate, branch diversity, current order information, evolutionary activity indicators, blueprint rigidity indicators, or subject continuity health; the final comparison rules, locking rules, anomaly handling rules, active detection rules, or silent switching rules of the candidate topology are jointly determined based on the task objective adjustment results, resource status monitoring results, subject continuity health, self-blueprint consistency audit results, and action feedback results.

9. An intelligent architecture system based on discrete mother system evolution, high-dimensional discrete symmetric encoding, hierarchical backoff, and consistency auditing, characterized in that: include: A memory is used to store discrete graph structure definition data, current activation subset state, candidate topology set, discrete encoding table, hierarchical structure data, historical state window, audit entries, fixed memory objects, failed path constraint objects, decision context snapshots, self objects, self-blueprint objects, identity continuity chain summaries, meta-evolutionary trajectory indexes, and special undecidable objects; a processor is coupled to the memory, the processor being configured to execute the following unit: a state space construction unit, used to map the computational degrees of freedom in the intelligent system to a discrete graph, discrete grid background, node clusters, edges, or combinations thereof, and determine the current activation subset; The candidate topology generation unit is used to generate a set of candidate topologies in the local neighborhood based on the current active subset according to expansion rules, contraction rules, boundary replacement rules, local bridging rules, connection rearrangement rules or combinations thereof; the structure evaluation unit is used to calculate the structure evaluation value of each candidate active subset according to the volume benefit term, boundary complexity or communication cost term, resource budget penalty term and dynamic adjustment term generated by the ordinal state function, and compare and filter according to the structure evaluation value; The total-order locking arbitration unit is used to perform deterministic single-valued projection based on strict total-order relations in the set of candidate activation subsets with the best structural evaluation values, thereby locking a unique target activation subset; The discrete coding and stabilization processing unit is used to map the topological signature and / or high-dimensional output representation of the target activation subset to the discrete coding space, and perform at least one of the following processing methods: quantization, compression, error correction, discrete addressing, phase anchoring, or stabilization matching. The hierarchical rollback control unit is used to perform at least one of the following processing methods based on hierarchical distance when a failure label is triggered: state rollback, network hierarchy degradation, parameter freezing, action suspension, or resource reallocation, and maintains a historical stable platform image or its index to support targeted rollback, recovery, or alternative path switching. The consistency auditing unit is used to perform consistency auditing on the mapping relationship between the underlying evolution results, high-dimensional coding results, action feedback results, and task objectives, and determine whether the output conditions are met based on the audit results. The Action Mapping and Feedback Acquisition Unit is used to map the target activation subset or its corresponding control commands into physical actions, communication commands, environmental configurations, or logical interventions when external action conditions are met or external requests are received, and to collect feedback results to form action feedback residuals; the Memory Management and Trajectory Indexing Unit is used to perform experience data collection, pattern induction, solidified memory objects, failure path constraint objects, decision context snapshots, self-objects, self-blueprint objects, identity continuity chain summaries, meta-evolutionary trajectory indexes, and the storage, updating, retrieval, and deletion of special undecidable objects; the Self-Object and Subject State Maintenance Unit is used to maintain self-objects, subject state vectors, identity continuity chains, irreversible cost ledgers, and subject continuity health, and to verify subject continuity constraints; the Self-Blueprint and Blueprint Verification Unit is used to maintain self-blueprint objects, calculate blueprint consistency deviation, blueprint rigidity indicators, and adversarial difference quantities, and to perform blueprint adversarial verification and blueprint slow revision preparation; The continuous sensing hedging adjustment unit is used to receive the continuous sensing residual stream, determine the dynamic activation dimension subset, construct the hedging matrix, and continuously update the adjustment intensity of the continuous audit quantity, threshold parameter or ordinal state function. The high-level openness maintenance and deep reflection unit is used to perform high-level consistency verification on the audit rule set, self-object update logic, self-blueprint constraint set or subject continuity constraint set, save special undecidable objects, and trigger long-term deep reflection when preset conditions are met. The exploration and linkage control unit is used to adjust the order state function, screening rules, anomaly handling rules, active detection conditions, silent switching conditions and exploration triggering conditions according to the task objective, resource status, current order information, next state space size, subject continuity health, self-blueprint consistency deviation, evolutionary activity index and blueprint rigidity index. When the preset triggering conditions are met, it performs at least one of the following processes: exploratory candidate generation, resampling, constrained perturbation, alternative path search, active detection task scheduling, energy-saving silent switching or deep reflection triggering. The result output unit is used to output at least one of the following when the consistency audit passes: valid structure, valid representation, valid terminology, valid routing result, subject state summary, self-object summary or self-blueprint summary, and generate corresponding audit entries.

10. The method according to claim 6, characterized in that, The subject continuity maintenance mechanism also includes maintaining subject reserved bits and performing subject legitimacy determination. The subject reserved bits serve as a higher-level recognition background than ordinary state representation and self-blueprint objects. The subject legitimacy determination is used to determine one or more of the following based on the relationship between the core subject axiom set, blueprint legal inheritance lineage, identity continuity chain, and subject reserved bits: current running version, candidate recovery version, candidate fusion version, and candidate future self version. The determination results are classified into the current subject's legitimate version, the version requiring delayed recognition, and the version that refuses recognition. This is used in one or more scenarios of high-level recovery, blueprint legal inheritance judgment, subject reason recognition, and multi-source fusion arbitration.

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