Adaptive Execution System and Method for AGI Based on Experience-Accumulated Abstract State-Transition Operator Generation, Planning-Structure Reconfiguration, Sandbox Review, and Execution-Path Compilation / Decompilation

Abstract

The present invention relates to an AGI adaptive execution system and method that generates or updates execution representations and execution topologies based on accumulated experience, manages the authentication status of new or modified execution targets in a separate authentication topology, and generates a production execution topology capable of actual exploration or execution based on the authentication status and execution context. Execution paths that have been repeatedly verified can be converted into compiled execution objects, and in the event of failure, version change, or environment change, only the affected scope can be selectively recovered, re-verified, and recompiled to enable recovery.

Description

Technology Field

[0001] The present invention relates to artificial intelligence, general adaptive intelligence, reinforcement learning, continuous learning, world models, path memory, state transition modeling, hierarchical planning, skill or option generation, execution path compilation, sandbox verification, adversarial testing, automated screening, runtime verification, experience transfer between distributed intelligence nodes, and intelligent execution technology based on physical and logical memory elements.

[0003] In addition, the present invention relates to a multimodal experience transfer technology that transmits, aligns, transforms, verifies, and reuses experiences comprising two or more different modalities among text, images, sound, video, spatial information, depth information, LiDAR, radar, tactile, force, position, inertia, biosignals, robot proprioception, tool execution trace, program state, or potential representations thereof, between intelligent nodes.

[0005] Furthermore, the present invention relates to an offline imagination execution or dream simulation technology that generates counterfactual or virtual state transition paths using one or more of a stored experience, a world model, a simulator, a digital twin, a generative model, or a state transition model without additional interaction with a real environment, and uses the virtual experience to distinguish from the real experience for verification, integration, memory integration, test question generation, and execution topology update.

[0007] Furthermore, the present invention relates to a socially adaptive implementation intelligence and robot execution technology that models one or more of roles, relationships, consent, authority, trust, privacy, group state, social norms, and human responses as execution states in a physical and social environment where humans, multiple users, a group, other intelligent nodes, and robots coexist, and causes said social state to change the feasibility and priority of the robot's movement, manipulation, guidance, delivery, approach, speech, gaze, gesture, waiting, interruption, collaboration, or other physical and social behaviors.

[0008] Furthermore, the present invention relates to a stabilized and integrated adaptive intelligence runtime technology that generates a production execution topology from a mutually consistent combination of states even when multiple execution topologies, authentication topologies, virtual experience topologies, social topologies, resource states, and physical implementation states are updated at different points in time or versions, and manages the correlation of verification evidence, common cause dependencies, slow state drift, long-term sticking of compiled execution objects, collective constraints of multiple intelligence nodes, and actuator-level physical errors. Background Technology

[0010] An artificial intelligence system observes the environmental state and selects one or more actions, and can update policies, state transition models, value functions, or other internal parameters using the results of the actions.

[0012] In reinforcement learning systems, the probability of selecting a subsequent action can be modified using rewards or costs based on the outcome of an action, and in world model-based systems, an action plan can be generated by predicting multiple future states in an internal state transition model before applying an action to the actual environment.

[0014] In addition, in hierarchical reinforcement learning or skill-based systems, multiple low-level actions are represented as a single higher-level skill, option, or macro action, and long-time-range plans can be generated by combining said skills.

[0016] However, storing experience and using experience to change the system's future computational structure itself are different issues.

[0018] While some conventional systems can store past experience in memory, parameters, or skill libraries, structures that reconstruct the planner's search graph itself as repeated experience accumulates, or compile the sequential execution of multiple repeatedly verified skills into separate execution objects to form execution paths that bypass the higher-level planner, require separate consideration.

[0020] Furthermore, if learned skills or newly generated execution paths are executed directly without being sufficiently verified in a real-world environment, they may produce unexpected results under boundary conditions, out-of-distribution inputs, sensor errors, latency, resource scarcity, or adversarial inputs, even if they function under normal conditions.

[0022] Conversely, if every new action or skill is re-evaluated from scratch in the higher-level planner every time, it is difficult to sufficiently reduce planning costs, computational load, and response latency, even as experience is continuously accumulated.

[0024] Therefore, a compile-reverse compilation structure may be required where repeatedly verified experiences are promoted to a fast execution path, but return to a higher-level planning mode if new conditions or prediction errors occur.

[0026] In addition, when reviewing a newly generated path of action based on a single evaluation model or a single score, a high achievement score may offset a significant constraint violation, or a path of action optimized for the bias of a specific evaluation model may be approved.

[0028] Therefore, it is necessary to use multiple screening criteria with different purposes, and to treat some criteria as non-compensable hard gates that must be satisfied rather than as compensable scores.

[0030] Furthermore, if newly observed failure types in the actual environment are not reflected in the generation and verification of execution paths in subsequent generations, the system may repeat the same or similar failure types.

[0032] Therefore, a structure is required that can automatically convert actual failures or unexpected results into new sandbox test conditions or counterexamples, and update the evaluation system itself based on actual predictive performance.

[0036] Furthermore, in some conventional implementations, even if a skill, option, or policy is created, the causal relationship between the experience that caused the creation of the skill and the actual execution path can be separated, and even if the skill library increases, the planning space regarding which skills to search for and in what order can remain almost the same.

[0038] On the other hand, if iterative experience changes the number of candidates, connectivity, search order, cost, priority, or execution location of the subsequent planning space itself, the experience can function not merely as stored information but as a state that reconstructs future computational structures.

[0040] Furthermore, even in systems that do not use explicit planning graphs, substantial changes to the planning structure may occur if the routing, search priority, candidate set, cache, search index, model, or tool selection probability of the policy network changes based on experience. Therefore, limiting the aforementioned functions based solely on a specific graph data structure may not sufficiently encompass equivalent implementations.

[0042] Furthermore, verification does not necessarily require repeating all candidate behaviors in a real-world environment. By hierarchically combining simulation, world model rollout, counterfactual evaluation, formal verification, shadow execution, and limited canary execution in a real-world environment, the scope of verification can be expanded while limiting exposure to the real environment.

[0046] Furthermore, in systems that use multiple topologies and state structures simultaneously, a production execution topology can be generated by combining different versions of the state while each structure is updated asynchronously. Therefore, a structure is required that generates a production execution topology based on a consistent snapshot or epoch of multiple state structures, while allowing urgent safe recovery to be applied immediately without the typical commit waiting period.

[0048] Furthermore, if multiple reviewers or validators share the same model family, training data, world model, tool, API, prompt family, or evaluation data, it may be difficult to regard them as independent evidence. Instead of simply increasing the number of reviewers, it is necessary to trace the generation source and lineage of the validation evidence to distinguish between correlated and independent evidence sets.

[0050] Furthermore, graphs that track only direct execution connections or authentication dependencies may not adequately represent common cause failures where objects not directly connected share the same model version, dataset, sensor calibration, tool, hardware, or social state source. Therefore, in addition to direct connections, hyperedges or equivalent common cause dependency structures where multiple objects are simultaneously connected to a single common cause may be required.

[0052] Furthermore, since slow drift does not cause a single large error, relying solely on runtime error thresholds may result in delayed early detection. Conversely, immediately linking sensitive drift detection results to authentication counts can increase unnecessary counts caused by normal fluctuations; therefore, an intermediate state is required to distinguish between suspected drift conditions and actual retrieval states.

[0054] Furthermore, while the promotion of frequently successful paths to FastPath through experience enhancement and compilation is advantageous for iterative tasks, the usage frequency and memory weight of existing paths can self-reinforce even after the environment distribution changes, potentially reducing the opportunity to verify new alternative paths. Therefore, it is necessary to combine periodic or evidence-based lease updates with challenger verification, rather than assuming indefinite retention rights for compiled executables.

[0056] Furthermore, even if individual intelligent nodes or robots satisfy local safety, authority, and resource authentication, the simultaneous execution of multiple nodes may violate the overall resource budget, collective privacy, the partitioning of prohibited objectives, total mission time, or collective safety conditions. Therefore, it is necessary to manage combined authentication at the mission or collective level separately from individual authentication.

[0058] Furthermore, even if high-level planning or robot paths have been verified, actual mass, friction, lag, payload, actuator degradation, or control cycle errors may differ from the time of verification. Since decompilation after the first physical error occurs may not be sufficient to account for these changes, an independent runtime physics safety layer may be required to limit, modify, or abort commands based on the current physical state and uncertainty immediately prior to actual actuator commands.

[0062] In addition, in intelligent systems using physical implementations, even if abstract tasks or skills are identical, the actual implementation method may vary depending on the robot's form, joint structure, kinematics, dynamics, sensor configuration, actuator performance, payload, tools, and current damage status. Since replicating raw motor commands or trajectories that succeeded on other robots may result in incompatibility with the receiving robot's physical structure or exceed the safe zone, it is necessary to separate the meaning of the state transition from the experience from the physical implementation commands when transferring them.

[0064] Furthermore, the safety of high-level execution paths or actuator commands may depend on the state of the implementation at the time of verification. During long-term operation, if motor torque degradation, friction changes, sensor errors, reduced battery state-of-the-health, changes in thermal conditions, tool attachment replacement, payload changes, or partial damage occur, the physical capability domain of execution paths that were previously valid may no longer be valid. Therefore, it is necessary to dynamically learn and authenticate the state and capability domain of the implementation itself.

[0066] In addition, even if an intelligent system evaluates its own capabilities or probability of success, there is no guarantee that the self-evaluation is calibrated with actual performance. If the self-evaluation is consistently overconfident or underconfident, the planner may be assigned inappropriate tasks or omit external verification; therefore, it is necessary to separately track the calibration error between the self-evaluation and actual results or independent evidence and update the reliability of the self-evaluation unit itself.

[0068] Furthermore, directly linking self-assessment to self-approval allows the subject of self-improvement to award themselves high scores and circumvent certification conditions. Therefore, while self-assessment can be used as evidence for candidate priority, additional testing requirements, abstention, delegation, or maintenance requirements, it needs to be separated from the final certification of actual production execution authority.

[0070] Furthermore, in multi-robot missions, the degradation or failure of a specific robot can invalidate the entire role distribution, rather than remaining an issue solely for that robot. Even if another robot can perform the existing task, simple task reassignment is insufficient because its body structure, sensors, actuators, resources, and authority differ; therefore, it is necessary to perform regrounding, body certification, and mission recertification simultaneously.

[0072] Furthermore, long-term adaptive intelligence continuously accumulates experiences, authentications, dreams, social states, provenance, and rollback history, which can lead to a continuous increase in storage volume, search costs, and verification costs. Simple time-based deletion can compromise safety and auditability by removing evidence that current execution paths depend on, past revoked history, records related to legal or consent, and even known-good snapshots or common cause analysis information required for rollbacks. Therefore, a structure is required to verify what can be forgotten based on dependencies and authentication status. The problem to be solved

[0074] One objective of the present invention is to provide a path memory structure in which subsequent state transitions and planning structures are modified according to the experience, going beyond simply storing multiple experiences performed by an intelligent system.

[0076] Another objective of the present invention is to generate an experience that performs the same or similar role in terms of future state transitions among different specific experiences as a single abstract state transition operator.

[0078] Another objective of the present invention is to generate a planning graph using the compatibility relationship between the starting conditions and result states between the abstract state transition operators, and to dynamically generate, delete, merge, split, or change the weights of the nodes or connection relationships of the planning graph according to the accumulation of experience.

[0080] Another objective of the present invention is to reduce iterative re-searching by the higher planner by generating a sequence of execution paths of multiple abstract state transition operators that are repeatedly successful and have sufficient predictive reliability into a single compiled execution object.

[0082] Another objective of the present invention is to verify a newly generated compiled executable object against normal, boundary, out-of-distribution, and adversarial conditions in a sandbox before actual execution.

[0084] Another objective of the present invention is to actively search for vulnerabilities in candidate executable objects and automatically generate new sandbox test states or counterexamples.

[0086] Another objective of the present invention is to evaluate two or more of attainability, compliance with constraints, generalizability, robustness, efficiency, suitability for uncertainty, and recoverability using different evaluation criteria.

[0088] Another objective of the present invention is to configure some of the above evaluation criteria as hard gates that cannot be offset by other performance scores.

[0090] Another objective of the present invention is to promote, restrict promotion, request modification, or discard candidate executable objects based on the results of the examination, and in the case of restricted promotion, to allow the compilation executable object to be used only in a verified state area.

[0092] Another objective of the present invention is to abort or decompile a compiled executable object and return to planning mode when the difference between the expected state transition of the compiled executable object and the actual observed result, environmental novelty, or increased uncertainty during actual execution satisfies a set condition.

[0094] Another objective of the present invention is to add newly occurring failure types in a real environment to a subsequent sandbox test set and to update the reliability or weight of the examiner based on the actual prediction error of the examiner.

[0096] Another objective of the present invention is to map the abstract state transition operator or the compile execution object to one or more of a physical path, memory state, routing state, or logical execution structure so that iterative experience leads to a reduction in actual execution cost.

[0098] Another objective of the present invention is to exchange experience execution packages, including abstract state transition operators, start conditions, result states, uncertainties, execution information, and verification information, between different intelligent nodes, and to selectively integrate them after re-verification in the state space of the receiving node.

[0102] Another objective of the present invention is to encompass implementations that use an explicit plan graph as well as implementations that implicitly modify the plan space through changes in policies, routing, search order, candidate sets, caches, or latent relations.

[0104] Another objective of the present invention is to implement compilation not limited to software code conversion, but through path reduction, skill merging, policy distillation, caching, program synthesis, model-tool routing fixing, hardware path mapping, or a combination thereof.

[0106] Another objective of the present invention is to configure the verification environment not to be limited to a single simulator, but to consist of a world model, digital twin, replay, shadow mode, canary execution, formal verification, or a hierarchical combination thereof.

[0108] Another objective of the present invention is to enable the creator, tester, reviewer, and actual executor of a candidate execution object to be configured as the same node or different nodes, thereby making it applicable to both single intelligence systems and distributed intelligence systems.

[0110] Another objective of the present invention is to adaptively adjust the reliability, scope of application, or refusal rights for each examiner using actual results when one or more examination criteria conflict with each other or the prediction accuracy between examiners differs.

[0112] Another objective of the present invention is to generate partial or conditional certification scopes based on state, environment, user, device, resources, time, risk level, or model version, instead of performing only full approval or full rejection of an execution object.

[0114] Another objective of the present invention is to provide partial decompilation or hierarchical decompilation that recovers only a portion of an actual executing object or partially returns it to a higher planning mode, while retaining the verified remaining portion.

[0117] Another objective of the present invention is to maintain a distinction between an execution topology representing the executable capabilities of an intelligent system and the relationships between them, and an authentication topology representing verification evidence, authentication status, authentication scope, version dependency, and recovery conditions regarding said executable capabilities and relationships, and to dynamically generate a production execution topology that can be explored or executed in an actual production environment based on the combined state of said two topologies.

[0119] Another objective of the present invention is to generate connection authentication or path authentication by separately verifying combination-specific risks arising from sequential, parallel, conditional, iterative, recursive, asynchronous, or shared resource-based combinations of multiple execution expressions, even when each individual execution expression satisfies an authentication condition.

[0121] Another objective of the present invention is to use the authentication state not merely as a simple acknowledgment signal after planning is complete, but as a planning state that directly modifies one or more of the candidate node availability, the existence of candidate connections, search cost, priority, risk weight, routing probability, beam or frontier configuration, cache activation, execution authority, and hardware resource placement.

[0123] Another objective of the present invention is to identify execution representations, connection authentications, path authentications, compiled execution objects, model versions, tool versions, state domains, or resource domains that are causally or dependently linked to the failure in the event of an actual failure or unexpected state transition, without discarding the entire execution structure, and to selectively reclaim, demote, isolate, or re-verify only the corresponding scope of influence.

[0125] Another objective of the present invention is to generate a local sandbox test set for the optionally recovered area, restore the parts that pass re-verification back to the production execution topology, and process the parts that do not pass by one or more of restricted promotion, alternative path linking, recompilation, or long-term isolation.

[0127] Another objective of the present invention is to combine the evolution of capability and the evolution of the capability verification system into a single closed loop by causing a change in the execution topology to trigger additional verification of the authentication topology, and the change in the authentication topology to again trigger a change in the production execution topology and execution structure.

[0129] Another objective of the present invention is to automatically calculate the validity range of an authentication dependent on a version when a version of a model, memory, tool, API, environment, state abstraction, examiner, or hardware configuration is changed, and to switch only the parts for which compatibility has not been verified to a shadow-only, restricted promotion, re-verification, or revocation state.

[0131] Another objective of the present invention is to perform risk-based hierarchical verification without testing all candidates with the same verification intensity by allocating the computational resources and delays required for the certification or verification itself according to risk, uncertainty, usage frequency, failure history, path importance, and certification expiration time.

[0134] Another objective of the present invention is to observe one or more of the latency, throughput, memory occupancy, accelerator occupancy, power, energy, network state, or external tool state of an intelligent execution object or combination path at runtime, and to directly reflect said observations in the updates of the authentication state and production execution topology.

[0136] Another objective of the present invention is to perform restricted promotion, demotion, partial reverse compilation, or resource-aware recompilation of a specific execution object when resource consumption or delay deviates from set technical operating conditions, even when the execution object is functionally normal, so that the same or similar goals are executed in a more suitable physical or logical execution path.

[0138] Another objective of the present invention is to maintain high-performance, high-resource execution objects and low-resource, low-latency execution objects as alternative candidates for the same goal, and to dynamically select one or more of the alternative candidates based on the current resource status, risk level, authentication status, and service level requirement profile.

[0140] Another objective of the present invention is to dynamically scale the production execution topology by selectively changing only the parallelism, model, tool, cache, precision, or hardware mapping of the affected execution paths, without interrupting the entire intelligent system when a shortage of computational resources or an increase in latency occurs.

[0142] Another objective of the present invention is to provide hysteresis-based resource control that applies different thresholds or retention times to promotion, demotion, recompilation, or restoration conditions to suppress the phenomenon in which execution paths are repeatedly switched due to transient fluctuations in resource states.

[0145] Another objective of the present invention is to provide a multimodal experience execution package that allows different intelligent nodes to exchange the meaning of state transitions and execution results into common or convertible representations, even if they use different sets of sensors, models, or modalities.

[0147] Another objective of the present invention is to re-authenticate based on the local modality availability, sensor configuration, state space, world model, tool state, and combination relationship with the existing execution topology of the receiving intelligent node, rather than using a valid multimodal authentication from the transmitting intelligent node as an automatic approval for the receiving intelligent node.

[0149] Another objective of the present invention is to restrict, downgrade, or deny cross-modal authentication when two or more modalities contradict each other, even when individual modalities each have high reliability, thereby preventing erroneous multimodal fusion from transferring to actual execution authority.

[0151] Another objective of the present invention is to selectively reclaim only the connection authentication and path authentication dependent on a modality when one or more modalities become lost, blocked, failed, delayed, or in a state of increased uncertainty, and to activate an alternative path in the production execution topology using the experience of other modalities or other intelligent nodes.

[0153] Another objective of the present invention is to correct the timestamps, spatial coordinates, object correspondences, and event correspondences of temporally asynchronous multimodal observations, and to reflect the alignment error itself in the authentication uncertainty or path risk.

[0155] Another objective of the present invention is to transfer an experience learned in a specific sense or modality of a first intelligent node to another sense or modality of a second intelligent node, but to integrate it into the production execution topology of a receiving node only to the extent that the state transition meaning and execution result are maintained.

[0158] Another objective of the present invention is to expand verification candidates and test conditions by virtually generating rare situations, failure surrounding situations, unexperienced states, alternative paths, or future states without additional interaction with the actual environment.

[0160] Another objective of the present invention is to not treat virtual experiences generated by a world model or generative model with the same authority as actual experiences, but to isolate them in a separate virtual experience topology and reflect them in the execution topology or authentication topology only to the extent that they have passed independent verification.

[0162] Another objective of the present invention is to suppress self-amplifying errors in which a virtual experience generator self-approves a failure hypothesis it has generated, thereby reclaiming actual authentication or automatically generating a new hard gate.

[0164] Another objective of the present invention is to distinguish a novel failure hypothesis generated by a virtual experience from an actual failure and to maintain it in a Shadow test condition or hypothesis state until verified by one or more of an independent oracle, actual reproduction, seal reviewer, or other intelligent node.

[0166] Another objective of the present invention is to expand the test space without increasing the cost of real-world interaction by prioritizing the allocation of the dream verification budget to rare situations or low-frequency states that are not sufficiently observed in actual experience.

[0168] Another objective of the present invention is to reduce the loss of prior execution ability in continuous learning through offline rehearsal and memory integration using past actual experiences and verified virtual experiences.

[0170] Another objective of the present invention is to accumulate dream debt and automatically attenuate or expire the influence of a virtual experience when the virtual experience fails to confirm consistency with actual or independent evidence for an extended period.

[0172] Another objective of the present invention is to reduce resource contention between production execution and virtual experience generation by performing dream simulations during periods when the system is not performing actual work or when resource leeway exists, and by limiting dream operations during periods of high load or high energy costs.

[0174] Another objective of the present invention is to suppress the spread of virtual errors throughout the distributed intelligent network by assigning a lower initial reliability or a higher verification budget than the actual experience when transmitting a virtual multimodal experience generated by a first intelligent node to a second intelligent node.

[0177] Another objective of the present invention is to generate a currently actionable action space by distinguishing roles, authority, consent, ownership relationships, and group status without simply merging user-specific preferences in an environment where multiple users or groups exist.

[0179] Another objective of the present invention is to provide a hierarchy of constraints on social behavioral choices so that even if social utility, intimacy, trust, or user preference is high, physical safety, authority, or conditions of explicit consent are not offset.

[0181] Another objective of the present invention is to selectively remove only the access, filming, transmission, manipulation, information sharing, or other behavioral paths of a robot dependent on the relevant social state from the production execution topology when withdrawal of consent or change of role occurs.

[0183] Another objective of the present invention is to control the robot's approach distance, speed, trajectory, speech sequence, waiting time, gaze, gesture, or object delivery action according to the person's role, relationship, type of work, group state, interaction stage, and response, instead of a fixed social distance or fixed behavior template.

[0185] Another objective of the present invention is to manage interruptions, speech order, task yielding, request conflicts, or authority conflicts in group conversations or multi-user tasks as changes in the state of the social topology, and to dynamically control the trade-off between necessary interventions and unnecessary interferences.

[0187] Another objective of the present invention is to update estimates of trust, relationships, preferences, or social norms by observing subsequent human responses to the actual actions of a robot, without automatically changing long-term authority or consent status based on a single response or uncertain sentiment estimation.

[0189] Another objective of the present invention is to manage specific social norms or cultural customs along with their subjects of application, places, groups, versions, periods, and uncertainties, rather than fixing them as universal rules, thereby re-verifying the scope of application of said social norms according to changes in context.

[0191] Another objective of the present invention is to limit the scope of transfer of personally identifiable information, scope of consent, relationship information, and authorization information when a software AGI and a robot AGI share a social state or social experience, and to reconstruct the local social state at the receiving node.

[0193] Another objective of the present invention is to enable the exploration of alternative social behaviors in dream simulations, without elevating virtually generated consent, trust, or changes in relationships to actual social states.

[0195] Another objective of the present invention is to generate a production execution topology using only the mutually consistent epoch states, even if multiple topologies and state structures are updated asynchronously, and to reduce mixed-version execution.

[0197] Another objective of the present invention is to provide an interrupt path that can immediately restrict production execution authority in the event of an emergency physical safety, withdrawal of consent, revocation of authority, or security incident without waiting for a typical multi-topology commit.

[0199] Another objective of the present invention is to suppress the amplification of common illusions by using an evidence lineage to treat examination results sharing a common source as a single correlated evidence group.

[0201] Another objective of the present invention is to reduce the scope of omission in selective recovery by tracking not only direct execution dependencies but also common cause dependencies that share sources of models, data, tools, calibrations, sensors, hardware, policies, or social states.

[0203] Another objective of the present invention is to determine the final certification status based on independent evidence or actual failure, after first performing measures such as reducing the certification area, expanding the Canary, increasing the verification budget, or restricting high-risk behaviors through the Suspect status, without immediately determining slow drift as Revoked.

[0205] Another objective of the present invention is to reduce overconfidence or excessive blocking by maintaining an uncertain social state in a belief state and, when necessary, generating questions or additional observations themselves as action candidates.

[0207] Another objective of the present invention is to limit or modify a command before the first physical violation by using an independent shield immediately preceding the actuator command when the physical dynamic characteristics of the actual robot change after high-level authentication.

[0209] Another objective of the present invention is to reduce path sticking after environmental changes by granting an updatable lease to an experience-enhanced Compiled FastPath and continuously evaluating challenger paths at low cost.

[0211] Another objective of the present invention is to provide separate combined authentication at the mission level so that the combined execution of multiple locally authenticated intelligent nodes or robots does not violate the resources, privacy, mission, or safety constraints of the entire group.

[0213] Another objective of the present invention is to reflect the irreversibility, human impact, population impact, and blast radius of changes in practice or topology in the verification budget, and to preserve the known-good status of changes with conflicting evidence through quarantine and rollback ledgers.

[0217] Another objective of the present invention is to execute abstract skills or experiences learned from other robots only within the scope of passing body certification by re-grounding them according to the receiving robot's current self-implementation model, rather than directly replicating them as raw actuator commands of a specific robot.

[0219] Another objective of the present invention is to automatically reduce or update the capability envelope of previously certified physical behaviors when the mass, friction, sensor calibration, actuator torque, battery health, payload, tool, or partial failure of an embodiment changes over the long term.

[0221] Another objective of the present invention is to perform recalibration, charging, cooling, tool change, payload reduction, safe mode, or maintenance request in advance by utilizing the deterioration of body health and the remaining safety margin, rather than responding only after a physical failure has occurred.

[0223] Another objective of the present invention is for an intelligent system to calibrate its domain-specific success probability and confidence against actual results, and to estimate the bias itself when self-assessment is consistently overconfident or underconfident in a specific area, and to reflect this in subsequent planners and verification budgets.

[0225] Another objective of the present invention is to separate the self-evaluation of an intelligent system from self-approval so that full authentication or production authority is not generated based solely on high self-confidence.

[0227] Another objective of the present invention is to perform self-falsification by intentionally allocating the verification budget to high-confidence boundary cases, counterexamples, challenger hypotheses, or past calibration error regions to find errors in self-evaluation or world models.

[0229] Another objective of the present invention is to abstain, delegate to another agent, reassign roles, or transfer the task to an additional verification target when one's current capability, body health, or calibration does not meet the mission requirements.

[0231] Another objective of the present invention is to restore collective function by not merely transferring the task to another robot when one of the multiple robots deteriorates or fails, but by continuously performing body grounding, local body certification, and mission recertification of the new robot.

[0233] Another objective of the present invention is to safely organize memory and topology objects accumulated during long-term operation, while ensuring that evidence required for current authentication, rollback, revoked history, consent / authorization, common cause provenance, or mission audit is not deleted.

[0235] Another objective of the present invention is to preserve the minimum tombstone or digest of a safely forgotten object to limit the reintroduction of a previously revoked path as a new and safe path solely through history deletion. means of solving the problem

[0237] A general-purpose adaptive intelligence execution system according to one embodiment of the present invention may include one or more processors and one or more memories.

[0239] The above processor may be configured to perform one or more functions among a path memory unit, an experience compilation unit, a plan structure update unit, an execution compilation unit, a sandbox verification unit, an examination unit, an authentication management unit, an execution management unit, and a learning update unit.

[0241] The path memory unit accumulates a plurality of state transition experiences including at least one of a state, action, result, reward, cost, error, or subsequent state performed by the intelligent system, and can update at least one of path strength, credibility, selection probability, connection weight, plasticity, or other memory states based on said experience.

[0243] The experience compiler compares at least one of a start state, end state, state transition distribution, reward distribution, cost distribution, or subsequent reachable state among multiple state transition experiences, and can generate multiple experiences satisfying a set predictive equivalence condition into a single abstract state transition operator.

[0245] The plan structure update unit can calculate the compatibility between the start conditions and result states of multiple abstract state transition operators and generate the connection relationships of the plan graph based on the said compatibility.

[0247] In the above planning graph, one or more of the nodes, connections, connection weights, transition probabilities, costs, or uncertainties may change as experience accumulates.

[0249] The execution compilation unit can generate one compilation execution object corresponding to a sequence of execution paths when a sequence of execution paths of multiple abstract state transition operators satisfies at least one of the conditions of repeatability, success rate, prediction error, uncertainty, or resource efficiency.

[0251] The sandbox verification unit can execute the above-mentioned compiled executable object in at least one of a virtual environment, simulator, digital twin, world model, replicated execution environment, or isolated hardware environment separated from the actual execution environment.

[0253] Sandbox test conditions may include two or more of normal conditions, boundary conditions, sensor error conditions, delay conditions, resource shortage conditions, out-of-distribution conditions, adversarial conditions, and previous actual failure conditions.

[0255] The review department can evaluate the sandbox execution results using multiple evaluation criteria.

[0257] At least one of the multiple evaluation criteria may consist of a hard gate that must be satisfied without being added to or offset by other evaluation scores.

[0259] The Certification Management Department may promote, restrict promotion, request modification, or discard the above-mentioned compiled executable object based on the results of the review.

[0261] A restricted-promotion execution object can be configured to execute only within an authenticated state area, environment area, resource area, time area, or uncertainty area.

[0263] The execution management unit may stop the execution of the above-mentioned compiled execution object and return to a planning mode using a planning graph if, during actual execution, the current state goes out of the authentication area, the difference between the actual observed result and the expected state transition is greater than the set criteria, or the novelty or uncertainty is greater than the set criteria.

[0265] The learning update unit can update one or more of the path memory, abstract state transition operator, plan graph, compile execution object, sandbox test set, evaluation criteria, or evaluator confidence based on the actual execution result.

[0269] Each of the above functional units does not need to be a physically separated module; a single processor, a single model, or a single program may perform multiple functions, or multiple processors, models, devices, or nodes may perform a single function in a distributed manner. The names of the functional units are for functional differentiation purposes and do not imply specific hardware boundaries.

[0271] The above path memory unit may also include an implementation that does not store an explicit path identifier. For example, the function of the path memory unit may be performed when some weights, adapters, attention bias, routing probability, retrieval priority, cache hit priority, state-transition table, or conduction state of a physical element of a neural network are changed by experience, and said change affects subsequent actions or plan search.

[0273] The above experience compiler does not necessarily need to condense multiple experiences into a single operator. Multiple experiences can be generated as a set of one or more hierarchical operators, conditional operators, probabilistic operators, partially ordered operators, or parallel executable operators.

[0275] The above-mentioned plan structure update unit can perform one or more of the following: explicit creation or deletion of edges in the plan graph, addition or removal of candidate operators, adjustment of search priority, update of cost, change of beam width, change of policy routing, change of selection probability of tool or model, and update of cache or search index.

[0277] The compilation execution object generated by the above execution compilation unit is not limited to machine code and may include one or more programs, prompt chains, tool chains, API call sequences, policy fragments, control command sequences, state machines, graph subpaths, model routing rules, cached results, kernels, hardware mapping information, parameters, or combinations thereof.

[0279] The sandbox verification unit described above can verify all or part of an execution object and can use a single verification environment or multiple verification environments with different precision and costs hierarchically. It can perform low-cost world model verification first and then perform high-precision simulation, type verification, shadow execution, or limited real canary execution only on candidates that satisfy the configured conditions.

[0281] The above-mentioned review unit is not limited to automated reviews alone and may use the results of one or more human approvals, external policy servers, separate intelligent models, independent nodes, or type validators as review information. The creator, validator, reviewer, and executor may be the same or different from each other.

[0283] In addition to binary approval or rejection, the above certification management department can manage one or more of the following states: full promotion, restricted promotion, temporary promotion, phased promotion, trial promotion, hold, isolation, modification request, demotion, suspension, revocation, or disposal.

[0285] The above execution management unit can return only some operators or sections to planning mode, switch to a conservative alternative path, or transfer control to a person or another agent without interrupting the entire compilation execution object.

[0287] The above learning update unit may use actual failures as well as actual successes, higher utility than expected, lower resource consumption than expected, user rejection, human intervention, policy violations, delays, hallucinations, or other expected deviations for subsequent learning and screening environment updates.

[0290] The above system may further include an execution topology management unit that manages an execution topology including execution representations, relationships between execution representations, composition paths, compiled execution objects, and their currently executable ranges.

[0292] The above system may further include an authentication topology management unit that manages an authentication topology comprising one or more of node authentication for individual execution representations, connection authentication for combination relationships between execution representations, path authentication for combination paths of multiple execution representations, authentication area, verification evidence, verification environment, model version, memory version, tool version, environment version, state abstraction version, examiner version, creator, verification entity, validity period, and retrieval condition.

[0294] The above system may further include a production topology generation unit that generates a production execution topology by selecting only the parts of the execution topology that are searchable or executable in an actual production environment based on the current state, execution context, user or device authority, current version, resource state, and the authentication topology.

[0296] The above production topology generation unit can generate the production execution topology not only by deleting unauthenticated nodes, connections, or paths, but also by increasing the cost or risk of the corresponding candidate, lowering the selection probability or priority, changing to a shadow-only or canary-only state, or requiring human approval or additional verification.

[0298] The above system may further include an authentication dependency management unit that stores dependencies between execution representations, connection authentication, path authentication, compiled execution objects, models, memory, tools, APIs, data sources, state abstraction, examiners, hardware resources, and verification environments.

[0300] The above-mentioned certification dependency management unit can calculate the scope of impact based on the dependency relationships in the event of an actual failure, version change, or certification expiration, and selectively demote, reclaim, isolate, reverse-compile, or re-verify the certification objects or execution objects included in the scope of impact, rather than the entire system.

[0302] The above system may further include a local re-verification unit that generates a local re-verification test set by combining one or more of past normal conditions, past failure conditions, new counterexamples, boundary conditions, out-of-distribution conditions, adversarial conditions, resource conditions, and version change conditions corresponding to the recovered influence range, and restores a part of the production execution topology according to the re-verification results.

[0304] The above system may further include a verification budget scheduler that allocates the priority and number of tests for verification targets based on one or more of risk, failure history, uncertainty, usage frequency, execution frequency, path importance, expected utility, remaining time until certification expiration, and verification costs.

[0307] The above system may further include a resource observer that observes or predicts one or more of the operation share, memory share, queue length, throughput, delay, data movement amount, power consumption, energy, temperature, network delay, external tool response time, or other resource status corresponding to an execution representation, composition path, or compiled execution object currently in execution.

[0309] The above system may further include an operational condition management unit that stores or generates a service level requirement profile including one or more of a maximum or quantile delay, minimum throughput, maximum error rate, maximum power or energy, maximum memory or accelerator occupancy, maximum network delay, maximum risk, or certification area maintenance condition.

[0311] The above production topology generation unit can change one or more of the availability, cost, risk weight, selection probability, routing, parallelism, cache activation, or physical / logical resource placement of candidate execution representations or combination paths based on the above resource state and service level requirement profile.

[0313] Even if the actual execution object satisfies the functional authentication conditions, if one or more of the latency, energy, memory, throughput, or resource occupancy deviate from the set operating conditions, the execution object may be converted to a target for restricted promotion, demotion, partial decompilation, full decompilation, or resource-aware recompilation.

[0315] The above resource-aware recompilation may include an operation that modifies an execution object to one or more of a different model, different tool, different combination path, different hardware resource, different degree of parallelism, different precision, different cache strategy, or different local or remote execution location that performs the same or similar goals.

[0317] The above system may further include a resource state hysteresis control unit that demotes a resource state to a conservative execution object when the resource state changes in an unfavorable direction, and restores it to an existing execution object or a high-performance execution object when the resource state satisfies the set recovery condition again for a certain period of time or longer.

[0319] The above system does not necessarily sum functional risk and resource consumption into a single value, and can select an execution path among candidates that have passed the hard gate using delay, energy, memory, throughput, or other resource costs while maintaining functional safety or constraint compliance as a non-offsetting hard gate.

[0322] The above system may further include a multimodal experience structuring unit that corresponds observations, states, actions, results, or latent representations obtained from two or more modalities to common events, objects, state transitions, or execution results.

[0324] The above multimodal experience structuring unit can generate a multimodal experience representation including one or more of modality identification information, timestamp, spatial coordinate system, object identification information, state identification information, latent representation, confidence, uncertainty, sensor or encoder version, calibration information, generation provenance, and correspondence with other modalities.

[0326] The above system may further include a cross-modal authentication unit that calculates one or more of the time difference between two or more modalities, spatial transformation error, object correspondence error, semantic mismatch, state transition mismatch, or latent representation distance, and generates a cross-modal authentication state based on the said value.

[0328] The above cross-modal authentication status may be included as part of node authentication, connection authentication, or path authentication, or managed as a separate cross-modal authentication object, and if the above cross-modal authentication status does not satisfy the setting conditions, the activation of the production execution topology of the related execution representation, connection relationship, or combination path may be restricted.

[0330] The above system may further include a modality dependency management unit that calculates the scope of influence of an authentication object that directly or indirectly references said modality when one or more modalities are in a state of unusable, degraded, delayed, lost, or increased uncertainty, and selectively downgrades or recovers only said scope of influence.

[0332] The above system may generate or select an alternative execution path including one or more of other modalities, other sensors, world model estimations, multimodal experience execution packages of external intelligence nodes, additional observations, user queries, or other tools to replace the recovered modality-dependent path.

[0334] The above distributed intelligent system can convert a transmission experience into a local representation of a receiving node using a common intermediate representation, latent space mapping, state transition model, object or event correspondence information, world model, or a combination thereof when the modality set of the transmitting node and the modality set of the receiving node are not identical.

[0336] The above receiving node can be integrated into the production execution topology only to the extent that it satisfies the configured local execution acceptance conditions after performing one or more of local individual modality authentication, local cross-modal authentication, connection authentication with existing execution representation, and combination path authentication on the transformed multimodal experience.

[0339] The above system may further include a virtual experience generation unit that generates a plurality of virtual state transitions, counterfactual execution paths, alternative paths, success hypotheses, or failure hypotheses that do not directly act on the actual production environment, based on one or more of stored actual experiences, world models, simulators, digital twins, generative models, state transition models, or policy models.

[0341] The above system distinguishes the virtual experience from the experience observed in actual execution and stores it in the virtual experience topology G_H, and can assign one or more of dream provenance, generative model version, seed actual experience, prediction uncertainty, time of generation, and verification status to the virtual experience.

[0343] The above virtual experience is not directly activated in the production execution topology G_prod by creation alone, but can be converted into a Shadow candidate of the execution topology, a verification target of the authentication topology, or a subsequent test condition only after passing through a dream gate that includes one or more of self-assessment, world model consistency, forward-backward consistency, constraint checks, independent examiners, seal examiners, exogenous approval signals, or consistency with actual experience.

[0345] The above system stores the failure hypothesis generated by the virtual experience generator as a HypotheticalFailure or PendingFailure state separate from the actual failure state, and may not change the existing Full or Restricted authentication to Revoked or permanently add a hard gate based solely on the failure hypothesis until a set condition among actual reproduction, an independent oracle, a seal reviewer, an external verifier, or multiple independent verification signals is satisfied.

[0347] A virtual execution representation or virtual path that has passed through the aforementioned Dream Gate may be generated as a Shadow, Canary, Pending, or Restricted candidate without immediately obtaining Full authentication, and may be promoted to the production execution topology only to the extent that connection authentication, path authentication, cross-modal authentication, or resource authentication with existing nodes of the actual execution topology is performed locally.

[0349] The above system may prioritize the allocation of dream generation and verification budgets to one or more of the following: state regions with little actual experience, execution representations with high authentication uncertainty, paths with imminent authentication expiration, areas around recent failures, configurations changed to new versions, modality dropout possible regions, or regions with high expected information gain.

[0351] The above system may further include a memory integration unit that maintains or updates one or more of execution representations, memory states, connection weights, abstract state transition operators, compiled execution objects, or test conditions by rehearsing past actual experiences and virtual experiences that have passed independent verification.

[0353] The above memory integration unit can be configured to ensure that the repeated generation of virtual experiences does not overwhelm actual evidence by prioritizing the memory weight of actual experiences over virtual experiences, setting an upper limit on the contribution of virtual experiences, or applying realization-first grading.

[0355] The above system increases the dream debt if the virtual experience fails to confirm long-term consistency with actual experience, independent verification, or exogenous approval signals, and may decrease one or more of the memory weight, search priority, certification limit, promotion potential, or retention period of the virtual experience according to the said dream debt.

[0357] The above system may further include a dream scheduler that determines the timing and amount of resources for performing dream simulations based on one or more of system load, power, energy cost, accelerator occupancy rate, verification backlog, number of subjects scheduled for certification expiration, or risk level.

[0359] In the above distributed intelligent system, when the first intelligent node transmits a virtual experience to the second intelligent node, the experience execution package includes a DreamProvenance or Hypothetical state, and the second intelligent node may assign one or more of a lower prior, a narrower initial authentication area, a higher verification budget, or a more restricted execution state than the package generated from the actual experience.

[0362] The above system may further include a social state management unit that maintains or updates one or more of the identification state, role state, relationship state, consent state, authority state, trust state, privacy state, ownership relationship, group state, social norm, and social state uncertainty for one or more social actors.

[0364] The above social state management unit may configure the above social actors, social relations, and social norms into an explicit or implicit social topology G_S.

[0366] The above production topology generation unit can generate a production execution topology that actual software or a robot can explore, compile, and execute by using two or more of the execution topology G_E, authentication topology G_C, virtual experience topology G_H, and social topology G_S, along with the current state, execution context, version, resource state, and implementation state.

[0368] The above system may evaluate at least one of physical safety conditions, legal or policy conditions, authority conditions, consent conditions, and social acceptance conditions as a hard gate that is not offset by other social utility scores, and then select a behavior among the behavior candidates that passed through the hard gate based on intimacy, comfort, efficiency, predictability, work utility, or other social objective functions.

[0370] The above system can adjust one or more of the distance between a person or group and a robot, relative direction, approach speed, acceleration, trajectory, line of sight direction, speech sequence, waiting time, gesture or object delivery speed according to the current social state and minimum physical safety conditions.

[0372] In cases where requests, authority, consent, or preferences conflict among multiple social actors, the above system is not limited to simple majority voting or simple selection of the most recent request, but can restrict execution candidates or convert them into human verification targets by utilizing one or more of the scope of authority by target behavior, ownership / management relationship of the target object, individual consent status, scope of collective influence, risk level, and social state uncertainty.

[0374] The above system receives or observes one or more of withdrawal of consent, change of role, revocation of authority, change of relationship, change of privacy condition, or change of group state as events, and can selectively switch only some of the execution representations, connections, combination paths, authentication objects, or robot behavior paths that have a dependency relationship with said event to a restricted, demotion, revocation, or re-verification state.

[0376] The above system can update the social state by observing one or more of human language, facial expressions, posture, changes in distance, approach / avoidance, acceptance / rejection, task success, requests for modification, or explicit evaluations after the robot's actual action.

[0378] The update of the above social state can store the provenance and uncertainty of actual observations or explicit inputs together, and prevent the automatic generation of legal authority or a state of explicit consent based solely on social responses.

[0380] The above system stores social norms along with application areas by culture, organization, place, user group, role, task, time, or version, and if the current context is outside the said application area, it can reduce the weight of the social norm or convert it into a target for additional verification or re-verification.

[0382] The above-mentioned implementation intelligence is connected to sensors and actuators of a mobile device, robotic arm, gripper, drone, vehicle, humanoid, service robot, industrial robot, wearable, or other physical implementer, and can execute a selected social behavior path by converting it into physical trajectory, speed, force, torque, grip, approach distance, speech, gaze, or gesture parameters.

[0384] The aforementioned social dream may be used to generate candidates for social behavior, candidates for group interaction, or alternative robotic behavior, but consent, authority, trust, or relationship changes generated in the dream may not directly alter the actual social state and may remain as a HypotheticalSocialState until confirmed by actual or independent social evidence.

[0386] In the above distributed intelligent system, when transmitting social states or social experiences to other intelligent nodes, one or more of personally identifiable information, consent scope, role information, relationship information, authority information, and social norms are transmitted along with the scope of transferability, and the receiving node can use them for production execution decisions only after reconstructing the local social topology in accordance with its local actor relationships and authority system.

[0390] The above system may further include a consistency management unit that assigns epoch identification information to two or more of the execution topology, authentication topology, virtual experience topology, social topology, resource state, or implementation state, and commits a new production snapshot only when the states required to create a production execution topology are in the same epoch or satisfy a configured compatibility relationship.

[0392] The above consistency management unit maintains new or changed states in the staging area and can commit production snapshots atomically or in a configured order after necessary verification, dependency updates, or version compatibility conditions are satisfied.

[0394] In the event of a physical safety violation, withdrawal of consent, revocation of authority, security incident, or configured emergency risk, the system may immediately stop, restrict, revoke, or switch the relevant production execution path or physical action to a safe state using an emergency interrupt lane that does not wait for the completion of a normal commit.

[0396] The above system assigns an evidence lineage to each of multiple examiners or verification evidence and can group evidence sharing the same or substantially overlapping model families, data, world models, tools, evaluation environments, or generation paths into a single correlated evidence group.

[0398] The above certification or promotion determination may be performed not merely by the number of examinations, but by utilizing one or more of the number of independent evidence sets, the degree of correlation, the existence of exogenous evidence or sealing evidence, risk, and irreversibility.

[0400] The above system adds a common cause hyperedge representing one or more of a common model, dataset, tool or API, calibration, sensor, hardware, policy, social-state source, or validation environment to the authentication dependency structure, and can calculate the direct dependency scope and the scope connected to the common cause hyperedge together as recovery candidates in the event of failure or version change.

[0402] The above system may generate a Suspect state instead of immediately switching to Revoked if continuous prediction residuals, calibration error, failure frequency, path utility change, social state error, resource state drift, or other distribution change indicators satisfy the set suspicion conditions.

[0404] In the above Suspect state, one or more of the following are performed: reduction of the certification area, lowering of execution priority, increase of the Canary ratio, increase of the verification budget, increase of the challenger verification volume, restriction of high-risk execution, or requirement for additional independent evidence; and the state may be transitioned to Full, Restricted, or Revoked depending on actual failure, certification expiration, independent verification, or established confirmation conditions.

[0406] The above social state management unit may maintain one or more of the estimable roles, authority, consent, trust, group states, or social norms along with belief, confidence, or candidate state distributions, and explicit consent or legal authority may not be automatically generated based on weak social cues, dreams, or single estimates alone.

[0408] In the current uncertain social state, if the expected loss from an action is greater than the cost of questioning, observing, waiting, external verification, or authorization verification, the system may generate an informational value action as a production execution candidate instead of a target action.

[0410] The above implementation intelligence may include an actuator runtime shield that limits, clips, substitutes, delays, or suspends a command based on the current position, velocity, force, torque, mass, friction, control delay, payload, or physical state uncertainty immediately prior to the actuator command, separately from the results of the higher-level plan or path authentication.

[0412] The result of the above actuator runtime shield modifying or blocking a command can be used as evidence to update one or more of the authentication topology, implementation state, calibration history, failure history, or subsequent verification budget.

[0414] A lease may be granted to a Compile Execution Object or FastPath with a set period, set execution count, set execution amount, or set proof amount, and one or more of recent actual results, independent verification results, or superiority over a challenger may be required to renew the lease.

[0416] The above system evaluates one or more challenger execution objects at a low cost in the Shadow or Canary path while the incumbent FastPath is in production, promotes the challenger if it satisfies the set evidence advantage and minimum verification amount, and can demote the incumbent to the Planner or Shadow state even if no actual failure has occurred if it fails to satisfy the lease renewal conditions.

[0418] When multiple intelligent nodes or multiple robots perform a single mission, the system may generate a mission authentication object that evaluates one or more of the total resource budget, personal information or privacy combined effects, partitioned execution of prohibited objectives, total mission time, shared space, authority set, or collective safety conditions separately from the local authentication of individual nodes.

[0420] Even if the authentication status of each individual intelligent node is Full, if the mission authentication is not Full, one or more of the concurrent execution count, execution order, resource allocation, data sharing, physical space occupation, or execution authority may be restricted.

[0422] The above verification budget can be calculated by including one or more of risk, uncertainty, novelty, and failure history, as well as dependency span, common cause blast radius, irreversibility, human impact, population impact, and mission scope.

[0424] The above system records snapshots before and after a change, trigger evidence, epoch, dependents, scope of influence, and rollback pointers in a rollback ledger for topology changes or self-modifications, and can commit or rollback changes with conflicting evidence or incomplete epochs after maintaining them in an evidence quarantine or staging state following further verification.

[0428] The above system may further include an implementation self-model management unit that manages a self-implementation model or implementation topology G_B representing at least one of the kinematics, dynamics, sensor, actuator, workspace, payload, force or torque limit, battery, thermal state, calibration, tool, damage, degradation, or physical uncertainty of a robot or physical implementation.

[0430] The above-mentioned implementation self-model management unit can update G_B and the body capability envelope based on at least one of actual execution results, actuator runtime shield intervention, sensor residual, motor current, torque residual, slip, temperature, battery state-of-health, diagnostic test, or maintenance result.

[0432] The above system may include an embodiment grounding unit that converts an embodiment-independent skill received from an abstract state transition operator or an external intelligence node into a trajectory, controller, contact plan, force profile, tool usage, or actuator command sequence based on the current G_B.

[0434] The physical behavior path generated by the above embodiment grounding part does not automatically inherit the certification of the abstract skill and can be activated in production only in the scope where it has passed a separate body certification for at least one of the current body version, payload, tool, calibration, health state, or environment.

[0436] When transferring experience between different first and second implementations, the raw motor command of the first implementation is not used as the execution authority of the second implementation, and a state transition objective, object relation, task constraint, contact objective, semantic skill, or other abstract expression is transmitted, and the second implementation can reground and re-authenticate according to its G_B.

[0438] The above system may selectively reduce the authentication area of ​​the relevant physical behavior path, FastPath, or mission role and generate maintenance or alternative implementation candidates when the body health or capability envelope satisfies the set degradation conditions.

[0440] The above system can plan and certify at least one of recalibration, charging, cooling, tool change, payload reduction, speed reduction, redundancy activation, safe mode, or repair request as a self-maintenance action based on at least one of body health, expected remaining useful capability, maintenance cost, mission deadline, spare robot availability, energy condition, risk, irreversibility, or human impact.

[0442] The above system may further include a self-assessment unit that manages a self-assessment state including at least one of domain-specific expected success rate, actual success rate, calibration error, uncertainty, body health, memory quality, planner quality, judge reliability, social-state accuracy, mission fitness, or known self-bias.

[0444] The above self-evaluation unit can update at least one of the Brier score, calibration error, expected calibration error, residual distribution, bias estimate, or other calibration indicators by comparing the predicted confidence before execution with the actual execution result or independent verification result.

[0446] The self-evaluation unit above estimates the overconfidence or underconfidence of the self-evaluation model by domain, and if the bias falls outside the set range, it can correct the self-confidence or change at least one of the verification budget, abstention threshold, delegation threshold, body certification requirement, or challenger verification amount.

[0448] The result of the above self-assessment may not directly generate Full certification but may trigger at least one of ValidationDemand, Shadow, Canary, Restricted, information-gathering action, self-maintenance, external verification, task delegation, or mission role reassignment.

[0450] The above system may include a self-falsification unit that selects a candidate with high self-confidence or high predicted capability that has a large past calibration error or is close to a boundary condition, and generates a counterexample, OOD, adversarial variation, or counterfactual challenge.

[0452] If the above self-falsification result conflicts with the current self-assessment, at least one of the self-assessment confidence, application area, body capability envelope, FastPath lease, validation budget, or mission role eligibility may be changed.

[0454] In a mission of multiple robots or intelligent nodes, if one agent fails, deteriorates, experiences a resource shortage, permission change, or communication loss, the system can reconfigure the task-to-agent assignment using the remaining agents' body capability, self-assessment, local certification, social or authority condition, and current workload.

[0456] An agent assigned a new mission role can activate that role in production only after re-grounding the abstract skill for the corresponding task according to its G_B and passing body certification and mission certification again.

[0458] The above system may assign a retention state to each of the experience, dream, social state, body calibration, certificate, compiled object, provenance, rollback snapshot, or other topology object, which includes at least one of active dependency, audit requirement, age, redundancy, information value, revoked-history flag, legal or consent retention requirement, mission requirement, and rehydration availability.

[0460] The above topology garbage collection protects objects necessary for active certificates, production paths, rollback pointers, current missions, common-cause analysis, or Revoked history, and can perform at least one of merge, summarize, archive, tombstone, or delete on unprotected duplicate, expired, or low-value objects.

[0462] When compressing or archiving objects related to revoked or past failures, at least one of the following may be retained to limit the misidentification of the same risk path as a new path due to the deletion history.

[0464] If the above garbage collection or forgetting result invalidates the current certificate or rollback capability, the deletion operation itself may be switched to a Pending, Quarantined, or additional verification state.

[0467] The core operational relationship of the present invention can be summarized as follows according to the embodiments.

[0469] If the memory state or execution representation changes due to multiple experiences, at least one of the nodes, connections, costs, priorities, candidate sets, or routings of the execution topology G_E may change.

[0471] The new or modified target of the above G_E is connected to a verification request or an authentication target, and the authentication result can change at least one of the node authentication, connection authentication, path authentication, authentication area, dependency information, or version status of the authentication topology G_C.

[0473] In the current execution context, the production execution topology G_prod available to the actual planner or executor is not created by using the entire G_E as is, but can be generated by combining it with G_C and the necessary current state, resource state, authority, social state, implementation state, or mission state.

[0475] Therefore, a closed loop can be formed in which changes in the execution structure based on experience trigger a verification requirement, changes in the verification results alter the actual search or executable space, and the actual results update the experience memory and authentication state.

[0477] In an embodiment using a robot or physical implementation, the abstract execution representation is regrounded to a body-specific execution path via G_B or a self-implementation model and can be used for actual physical execution to the extent that it has passed body certification for the implementation and the necessary runtime shield.

[0479] The self-assessment Q_self can be used as meta-evidence to control the verification budget, abstention, delegation, maintenance, or challenger selection in the above closed loop, but it may not automatically generate full authentication of G_C or production authority of G_prod by itself.

[0482] Execution representations, relationships, or paths that are repeatedly used in the above execution topology or production execution topology and satisfy the configured authentication conditions can optionally be converted into compiled execution objects.

[0484] The above-mentioned compilation execution object can maintain a correspondence with the original abstract execution representation and can be combined with one or more of CertObject, dependency, model version, tool version, runtime version, hardware capability, body version, resource state, or lease state.

[0486] If the dependency or validity condition of the above-mentioned compilation execution object changes, instead of unconditionally discarding the entire object, partial decompilation or selective recompilation can be performed by invalidating or redeploying only the affected segment, node, subgraph, kernel, tool-call segment, engine binding, controller segment, or other partial scopes.

[0488] Therefore, compilation is not a procedure that independently generates execution rights, but rather an implementation form designed to efficiently reuse an already verified execution structure in a specific execution environment, and production usability may change even after compilation due to changes in authentication status and dependencies. Effects of the invention

[0490] According to an embodiment of the present invention, since repetitive experience does not merely result in an increase in memory capacity but can lead to structural changes in the planning graph and compilation of execution paths, the amount of repetitive searching by the higher-level planner for identical or similar problems can be reduced.

[0492] According to an embodiment of the present invention, multiple low-level behaviors or multiple abstract state transition operators can be promoted to a single compile-executable object, thereby reducing the effective planning depth and the iterative inference cost.

[0494] According to an embodiment of the present invention, a newly generated execution path can be verified under multiple conditions in a sandbox without being immediately applied to the actual environment.

[0496] According to an embodiment of the present invention, even if the goal achievement rate is high, an execution object that does not satisfy the constraints corresponding to a hard gate may not be promoted to the actual execution system.

[0498] According to an embodiment of the present invention, even if the full promotion condition is not satisfied in the entire state space, an execution object having sufficient reliability in a partial verification area can be restrictedly promoted.

[0500] According to an embodiment of the present invention, if the actual environment changes or the expected error increases, the fast execution path can be automatically stopped and returned to a higher planning mode, thereby reducing excessive reliance on a fixed habit path.

[0502] According to an embodiment of the present invention, actual failure cases can be added as next-generation sandbox test conditions, so the execution system and the verification system can adapt together.

[0504] According to an embodiment of the present invention, the prediction reliability of the screening device can be updated by comparing it with the actual result, so it can be operated in a way that reduces the error of a single fixed screening model.

[0506] According to an embodiment of the present invention, since abstract state transition operators and compile execution objects can be linked to physical or logical execution paths, the experience of repeated use can be linked to the shortening of actual computation paths or a reduction in the amount of data moved.

[0508] According to an embodiment of the present invention, state transition semantics and verification information can be transmitted together between different intelligent nodes and re-verified at the receiving node, thereby providing portability of an experience-based execution structure beyond simple message exchange.

[0511] According to an embodiment of the present invention, since the connection between execution expressions and the combination path of multiple execution expressions can be authenticated separately from the authentication of individual execution expressions, state changes, authority transitions, trust transitions, data flows, or other combination-specific risks that occur when individually acceptable execution expressions are combined can be detected before planning or actual execution.

[0513] According to an embodiment of the present invention, since the authentication status can directly change the candidate set or connection relationship of the planning structure, not only can unverified combinations be blocked retrospectively after planning, but unverified combinations themselves can also be excluded from the search space or restricted to a low priority.

[0515] According to an embodiment of the present invention, since dependencies between execution representations, connection relationships, and combination paths are maintained and only the affected certification scope can be selectively recovered or re-verified in the event of an actual failure or version change, the cost of invalidating the entire execution structure or regenerating the entire plan from scratch can be reduced.

[0519] According to an embodiment of the present invention, even in a system that does not explicitly store a planning graph, changes to candidate sets, routing, costs, search priorities, or policies based on accumulated experience can be utilized, so it can be applied to various artificial intelligence architectures.

[0521] According to an embodiment of the present invention, a compiled execution object can be implemented as one or more of a software program, policy, toolchain, prompt chain, robot control path, or physical memory / routing path, thereby enabling execution optimization across the logical layer and the physical layer.

[0523] According to an embodiment of the present invention, a low-cost sandbox, high-precision simulation, type verification, shadow execution, and limited actual canary execution can be selected in stages depending on verification costs and actual environmental risks.

[0525] According to an embodiment of the present invention, not only the entire recovery of an execution object but also partial recovery, reduction of the certification scope, or replanning of sub-sections can be performed, thereby enabling adaptation to environmental changes without discarding the entire verified capability.

[0528] According to an embodiment of the present invention, since execution topologies and authentication topologies can be managed separately and a production execution topology can be generated by combining them, it is possible to prevent unauthenticated connection or combination paths from being included in the candidate space of the actual planner, even if the performance of individual execution representations is high.

[0530] According to an embodiment of the present invention, since the authentication result is not merely a post-approval value but can directly modify nodes, connections, costs, routing, or candidate sets in the planning space, it is possible to reduce the search cost for unverified combinations from the planning stage, as well as block unverified combinations after planning.

[0532] According to an embodiment of the present invention, the scope of impact of actual failures or version changes can be tracked via authentication dependencies and selectively recovered, thereby reducing the cost of invalidating the entire skill library, entire plan graph, or entire compile cache due to partial failures.

[0534] According to an embodiment of the present invention, since only the selectively recovered parts can be locally re-verified and the passed parts can be restored to the production execution topology, only the areas related to the cause of failure can be re-learned or re-verified while maintaining verified capabilities.

[0536] According to an embodiment of the present invention, the creation and modification of the execution topology trigger additional verification of the authentication topology, and the modification of the authentication topology again changes the production execution topology; thus, the execution system and the verification system can operate as a co-evolutionary closed loop that uses each other's state changes as inputs, rather than as a structure in which they are independently static.

[0538] According to an embodiment of the present invention, even if a model or tool is changed, existing authentication is not reused based solely on the identity of the execution object itself, and only affected authentication can be selectively expired or re-verified by comparing the version on which the authentication depends with the execution context.

[0540] According to an embodiment of the present invention, since the verification budget can be allocated according to risk and uncertainty, hierarchical resource management is possible in which low-cost verification is applied to low-risk iterative paths and high-precision verification is applied to high-risk or novel paths.

[0542] According to an embodiment of the present invention, the generator and the reviewer can be separated, or the reliability between multiple reviewers can be adjusted according to actual results, thereby reducing self-evaluation bias or reliance on errors of a single reviewer.

[0545] According to an embodiment of the present invention, since the delay, throughput, memory, accelerator, power, energy, or network status during actual execution can be considered together with the authentication and planning status, a path suitable for the current resource status can be selected among multiple execution paths that perform the same functional goal.

[0547] According to an embodiment of the present invention, even when an execution object is functionally normal, if the resource state deviates from the set operating range, only that execution object can be selectively demoted, recompiled, or relocated, so that the entire intelligent system can adapt to changes in computational resources without interruption.

[0549] According to an embodiment of the present invention, delay, energy, memory, or other resource costs can be optimized separately while maintaining functional constraints as non-offsetting hard gates, thereby allowing execution efficiency to be adjusted without simply exchanging safety or policy conditions for resource efficiency.

[0551] According to an embodiment of the present invention, since the recovery of the resource state can be determined based on hysteresis or maintenance time conditions, frequent path switching or repeated recompilation due to temporary resource fluctuations can be reduced.

[0554] According to an embodiment of the present invention, state transition semantics, execution information, and verification information can be transmitted along with modality alignment information even between intelligent nodes using different sensory or data formats, thereby expanding the scope of experience reuse between distributed intelligent systems that do not have the same sensor configuration.

[0556] According to an embodiment of the present invention, since not only individual modalities but also consistency between multiple modalities can be separately authenticated, it is possible to restrict the automatic reflection of multimodal inputs that are highly reliable but mutually contradictory in the actual execution authority.

[0558] According to an embodiment of the present invention, when a specific modality is lost or degraded, only the authentication range dependent on that modality can be selectively recovered and an alternative modality path can be activated, thereby reducing the likelihood that a failure of some sensory information will lead to a disruption of the entire intelligent execution system.

[0560] According to an embodiment of the present invention, since the authentication information of a transmitting node is not used as automatic approval by a receiving node but can be re-verified based on the modality configuration and local combination relationship of the receiving node, excessive transfer of the authentication range between intelligent nodes having different environments or sensory systems can be suppressed.

[0563] According to an embodiment of the present invention, by adjusting the authentication threshold, path authentication condition, or cross-modal authentication condition according to the risk and execution purpose, the operational trade-off between execution availability and risk suppression can be explicitly controlled.

[0565] According to an embodiment of the present invention, the number of effective branches and the effective planning depth are reduced by the accumulation of experience, and iteration paths can be replaced with compiled execution objects, thereby reducing the structural candidate space that the higher-level planner must evaluate.

[0567] According to an embodiment of the present invention, even when there is a prediction error regarding the resource state, multiple authenticated alternative paths can be maintained and paths can be switched using runtime observations, so the operation can be performed in a way that reduces violations of service level conditions compared to static single path operation.

[0569] According to an embodiment of the present invention, the trade-off between the activation rate of multimodal experience transfer and the actual execution activation risk of false experience can be controlled by adjusting the strength of cross-modal authentication or receiving node re-authentication.

[0572] According to an embodiment of the present invention, the verification space can be expanded by generating rare situations and counterfactual state transitions without additional interaction with the actual environment, thereby enabling operation in a way that reduces reliance on actual trial and error, which is risky or costly.

[0574] According to an embodiment of the present invention, since the virtual experience is maintained in a virtual experience topology separated from the actual experience and production execution authority is not granted prior to independent verification, it is possible to suppress errors in the world model or the generative model from being directly transferred to execution authority.

[0576] According to an embodiment of the present invention, failure hypotheses generated in dreams are distinguished from actual failures, and since they are not used as the direct basis for existing certification counts prior to exogenous or independent verification, the generation of over-constraints or self-amplifying guardrails regarding non-existent failures can be limited.

[0578] According to an embodiment of the present invention, memory integration is performed by rehearsing actual experience and verified virtual experience together, while maintaining the priority of the actual experience or the contribution limit of the virtual experience, thereby allowing for simultaneous control of memory retention and virtual experience contamination suppression during continuous learning.

[0580] According to an embodiment of the present invention, dream operations can be prioritized for placement in idle or resource-spare sections depending on system load and verification demand, thereby reducing resource contention between actual production execution and offline imagination execution.

[0582] According to an embodiment of the present invention, if a virtual experience fails to confirm long-term consistency with actual or independent evidence, its influence can be reduced according to dream debt, thereby limiting the permanent persistence of old, unverified hypotheses in the execution structure.

[0585] According to an embodiment of the present invention, roles, authority, consent, relationships, and group status can be managed as social statuses distinct from simple user profiles, so that conflicting requests in a multi-user environment can be processed at the level of execution authority.

[0587] According to an embodiment of the present invention, social utility or personal preference can be prevented from offsetting physical safety, authority, or consent hard gates, so that higher execution constraints can be maintained while pursuing socially natural behavior.

[0589] According to an embodiment of the present invention, only execution paths related to withdrawal of consent or change of role can be selectively retrieved from the production execution topology, so that changes in social conditions can be responded to without interrupting the entire robot or intelligent system.

[0591] According to an embodiment of the present invention, the social distance, approach speed, speech order, waiting time, gaze, gestures, and object delivery behavior of a robot can be adjusted to the current social state, thereby enabling adaptation to various social contexts rather than fixed interaction patterns.

[0593] According to an embodiment of the present invention, since the social topology can be updated using actual subsequent human responses, changes in relationships or trust states during long-term interactions can be reflected in subsequent behavior candidates.

[0595] According to an embodiment of the present invention, since social relationships or consent generated in a dream can be distinguished from actual social states, changes in virtual relationships can be restricted from automatically transferring to access, information sharing, and physical action rights of actual robots.

[0597] According to an embodiment of the present invention, social norms can be managed along with their application area and version, thereby reducing the problem of applying a single cultural or organizational norm in a fixed manner to all users and places.

[0601] According to an embodiment of the present invention, multiple topologies and state structures can be combined into a common epoch or an interoperable snapshot, thereby reducing the generation of production execution topologies in which states of different change generations are mixed.

[0603] According to an embodiment of the present invention, since the emergency interrupt lane can be distinguished from the general commit procedure, emergency recovery or safety stop can be applied quickly without synchronously waiting for all changes to maintain consistency.

[0605] According to an embodiment of the present invention, since the correlation with the evidence lineage as well as the number of examinations can be considered, the overestimation of common errors originating from the same source into multiple independent verifications can be reduced.

[0607] According to an embodiment of the present invention, direct dependencies and common cause hyperedges can be tracked together, thereby reducing the omission of the scope of influence caused by shared models, shared tools, or shared calibrations while maintaining the advantages of local re-verification.

[0609] According to an embodiment of the present invention, since a suspected drift state can be distinguished from the final certification recovery, it is possible to detect slow deterioration early while limiting unnecessary full recovery caused by normal fluctuations.

[0611] According to an embodiment of the present invention, informational value behaviors can be generated as action candidates in an uncertain social state, thereby reducing the uncertainty of subsequent judgments through confirmation instead of the dichotomy of immediate action or immediate rejection.

[0613] According to an embodiment of the present invention, since high-level authentication and actuator runtime shielding can be separated, separate safety constraints can be applied immediately before the actual command even to physical dynamic characteristics changed after verification.

[0615] According to an embodiment of the present invention, since the retention rights of FastPath can be linked to lease and challenger evidence, the indefinite fixation of a previously successful compilation path after an environment change can be reduced.

[0617] According to an embodiment of the present invention, since the authentication of individual intelligent nodes and mission authentication can be distinguished, resource, privacy, or safety violations occurring only in group combinations can be limited even if each node satisfies local conditions.

[0619] According to an embodiment of the present invention, since a rollback ledger and evidence quarantine can be used, additional verification can be performed while preserving the previous known-good state even if conflicting evidence arises during self-modification or multiple topology updates.

[0623] According to an embodiment of the present invention, abstract capabilities and the physical implementation of a specific robot can be separated, thereby allowing the transfer of the meaning of experience between different types of robots while reducing the incompatibility of primitive motor commands.

[0625] According to an embodiment of the present invention, the body capability envelope can be updated according to the current health status of the implementation, thereby reducing the problem of assuming that a previously authenticated physical path retains the same capability even after long-term degradation.

[0627] According to an embodiment of the present invention, since self-maintenance actions can be planned and verified in the same way as general tasks, recalibration, cooling, charging, load reduction, or maintenance can be selected before an actual failure occurs.

[0629] According to an embodiment of the present invention, since the calibration of self-confidence and actual success rate can be continuously compared, domains in which one overestimates or underestimates one's ability can be identified and the verification strength adjusted.

[0631] According to an embodiment of the present invention, since self-evaluation and self-authentication can be separated, it is possible to restrict an intelligent system from acquiring production authority based solely on its high self-score.

[0633] According to an embodiment of the present invention, by using self-falsification, it is possible to generate not only cases that verify the current self-model but also tests that are highly likely to falsify self-confidence, thereby detecting calibration errors caused by long-term self-reinforcement.

[0635] According to an embodiment of the present invention, the task of a broken or deteriorated robot can be reassigned through body grounding and mission recertification without simply duplicating it to another robot, thereby increasing the recovery potential of a heterogeneous robot group.

[0637] According to an embodiment of the present invention, by using dependency-aware auditable forgetting, the loss of active certificates, rollbacks, revoked history, and audit evidence can be limited while controlling the size of long-term memory and topology. Specific details for implementing the invention

[0638] The numerical values ​​and performance values ​​presented in the following description are examples of synthetic simulations intended to explain the computational structure of the present invention, and do not represent actual general-purpose artificial intelligence performance, actual product performance, actual safety, or actual clinical or industrial verification results.

[0640] A. Overview of the Overall Operating Structure of the Invention

[0642] More specifically, the invention relates to a technology that accumulates state transition experiences repeatedly performed by an intelligent system into a physical or logical path memory, generates a reusable abstract state transition operator using predictive equivalence or state transition similarity between multiple experiences, and generates or modifies a planning graph according to the compatibility relationship between the starting condition and result state of the abstract state transition operator.

[0644] Furthermore, the invention relates to a technology that, when multiple abstract state transition operators are repeatedly and successfully executed in the same or similar order, generates a single compiled execution object from the consecutive execution paths, verifies at least one of normal conditions, boundary conditions, out-of-distribution conditions, and adversarial conditions in a sandbox isolated from the actual environment before applying the compiled execution object directly to the actual execution system, and generates at least one evaluation result among promotion, restricted promotion, modification request, or discard according to multiple evaluation criteria.

[0646] Furthermore, the invention relates to a closed-loop general-purpose adaptive intelligence execution technology that, if the observed results during actual execution do not match the expected state transitions, authentication areas, or acceptable uncertainties of the compiled execution object, aborts or decompiles the compiled execution object and returns to a higher-level planning mode, and reflects the actual execution results and newly discovered failure cases back into path memory, a sandbox test set, and evaluation criteria.

[0648] B. Key Terms and Scope of Interpretation

[0650] In this specification, the terms “intelligent system” or “intelligent execution system” are not limited to specific artificial intelligence models, learning methods, hardware, or application fields. The intelligent system may include one or more neural networks, transformers, large-scale language models, multimodal models, probabilistic models, rule-based models, searchers, planners, world models, reinforcement learning agents, software agents, tool-using agents, code generation or code execution agents, robot controllers, autonomous driving controllers, industrial controllers, network controllers, multi-model orchestrators, distributed intelligent nodes, edge devices, servers, accelerators, physical learning elements, or combinations thereof.

[0652] In this specification, “experience” is not limited to experience directly obtained in a real environment. Experience may include observations, states, actions, sequences of actions, plans, results, rewards, costs, errors, risks, constraint violations, user feedback, human evaluations, evaluations by other intelligent nodes, tool call results, program execution results, communication results, search results, simulation results, digital twin results, world model rollouts, counterfactual results, synthetic data, reconstructed past trajectories, experiences received from external sources, or combinations thereof.

[0654] In this specification, “state” may include not only the state of the actual physical environment but also an observed state, a latent state, an internal representation, a token or context state, a memory state, a tool state, a program state, a task state, a user state, an agent state, a resource state, a network state, a robot state, a digital twin state, or a probability distribution of multiple states.

[0656] In this specification, “behavior” is not limited to physical actuation. Behavior may include one or more control commands, model selection, model invocation, tool invocation, search, query, response generation, message transmission, API call, program or function execution, data reading or writing, memory updating, routing, task assignment, plan selection, policy selection, operation of a robot or device, and a sequential or parallel combination thereof.

[0658] In this specification, “path memory” does not mean only explicit graph paths. Path memory includes any history-dependent internal state that is modified by past experience and whose modification affects one or more of subsequent state transitions, candidate selections, priorities, search sequences, policies, planning costs, or actual execution paths. The internal state may be implemented as a graph, matrix, vector, tensor, embedding, probability distribution, neural network parameters, adapter parameters, key-value memory, episode memory, procedural memory, search index, cache, state table, transition table, routing table, program state, or state of a physical element.

[0660] In this specification, “abstract state transition operator” may include a skill, option, macro behavior, part of a policy, subroutine, program fragment, workflow, toolchain, prompt or prompt chain, function, controller, state machine, model routing rule, graph subpath, plan primitive, or reusable execution representation that transitions one or more states to another state or state distribution.

[0662] In this specification, “planning graph” or “planning structure” does not necessarily have to be an explicit graph data structure. Information corresponding to nodes or connections is included in the planning structure even if it is implemented as an adjacency matrix, transition table, search tree, beam or frontier, priority queue, policy network, routing network, latent relation, vector search structure, program dependency, state machine, set of candidate actions, or other implicit relationships.

[0664] In this specification, “compile” is not limited to compilation in the narrow sense of converting source code into machine code. Compilation includes the operation of converting, combining, compressing, summarizing, merging, caching, fusing, fixing, binding, distilling, graph summarizing, macroforming, programming, kernelizing, routing fixing, or mapping one or more experiences, behaviors, operators, skills, or plan paths into other representations reusable for subsequent execution.

[0666] In this specification, “recompiling” is not limited to the restoration of source code in the narrow sense. Recompiling includes operations to revoke, disable, invalidate, decompose, split, expand, lower the priority of, reduce the scope of authentication of a compiled executable object, or return it to a higher plan or search stage.

[0668] In this specification, “sandbox” or “isolated verification environment” is not limited to an environment that is completely physically separated from the operating environment. It may include world models, simulators, digital twins, emulators, replay environments, shadow execution, dry-run, canary environments, replication environments, virtual machines, containers, isolated hardware, type validators, model checkers, symbolic execution environments, counterfactual evaluation environments, or other verification environments capable of evaluating candidate execution results while limiting or blocking actual effects.

[0670] In this specification, “evaluation,” “certification,” or “promotion” is not limited to legal certification by a specific organization. Evaluation may include a determination of suitability using rules, mathematical formulas, optimization conditions, statistical tests, format verification, separate models, multiple model ensembles, independent agents, human evaluators, voting, consensus, quorum, scores, risk levels, or combinations thereof, and promotion may include actual execution approval, routing priority granting, cache registration, production deployment, resource allocation, hardware mapping, policy activation, or execution approval in a restricted state area.

[0672] In this specification, the expression “universal adaptive intelligence” does not limit the fulfillment of specific benchmarks, human-level intelligence, self-awareness, or specific AGI definitions as a prerequisite. Any adaptive intelligence system in which the functional structure of the present invention can be applied to one or more different tasks, environments, state distributions, devices, or intelligence nodes may be included within the scope of this specification.

[0675] In this specification, “authentication status” or “authentication information” is not limited to individual authentication for a single execution representation. An authentication status may include node authentication for an individual execution representation, connection or edge authentication for a connection relationship between two or more execution representations, path authentication for a sequential, parallel, or conditional combination of multiple execution representations, and restriction authentication for a specific state domain, environment domain, user, device, resource, time, model version, tool version, memory version, state abstraction version, or other execution context. Authentication information may include one or more of a verification scope, an authentication domain, success or failure statistics, uncertainty, test conditions used, an examiner or validator version, provenance, creation time, validity period, dependent execution representations, and retrieval or re-verification conditions.

[0677] In this specification, the term “combination path” is not limited to cases where two or more execution expressions are connected sequentially. A combination path may include parallel execution, branching, conditional execution, iteration, recursion, asynchronous execution, indirect coupling through shared memory or shared resources, and relationships in which the output, authority, tool state, trust signal, authentication signal, or side effect of one execution expression affects the input or execution condition of another execution expression.

[0679] In this specification, “certification-combined planning structure” may refer to a planning structure in which the certification status directly modifies the availability, connectivity, search priority, execution allowance, cost, risk, or search space of a candidate execution representation, independently of the performance score of the candidate execution representation. Accordingly, even if individual execution representations are each certified, if there is no connection or path certification for two or more combinations thereof, or if such combinations are not valid in the current execution context, said combinations may be excluded from planning candidates, demoted, restricted to shadow execution only, or converted to additional verification targets.

[0682] In this specification, “resource state” may mean the current or predicted availability state of one or more processors, accelerators, memory, storage devices, networks, power sources, cooling systems, external tools, APIs, or distributed nodes. The resource state may include one or more of compute occupancy, memory occupancy, queue length, throughput, latency, data movement volume, power consumption, energy consumption, temperature, network round-trip latency, packet loss, external call latency, call allowance, processing capacity, failure state, or other execution resource constraints.

[0684] In this specification, the “service level requirements profile” is not limited to a specific business contract or billing method and may refer to a set of technical operational conditions that an intelligent execution object or combination path must satisfy during actual execution. The operational conditions may include maximum or quantile latency, minimum throughput, maximum error rate, minimum availability, maximum power or energy, maximum memory occupancy, maximum network latency, maximum resource occupancy, maximum risk, authentication area maintenance conditions, or a combination thereof.

[0686] In this specification, “resource costs” are not limited to monetary costs but may include computation time, processor or accelerator occupancy time, memory usage, data transfer volume, network usage, power consumption, energy, volume of external tool calls, volume of API calls, storage device I / O volume, cooling load, or consumption of other finite computing resources. Depending on the embodiment, the unit cost of external service calls or computing service costs may also be used as an auxiliary cost term.

[0688] In this specification, “resource-aware recompilation” may include an operation to create a new execution object or an alternative execution path by changing one or more of an execution model, tools, order of operations, parallelism, cache usage, precision, batch size, hardware mapping, network path, or physical / logical execution path according to changes in resource state, service level requirements profile, risk, or authentication state, without changing all functional objectives of the existing compiled execution object.

[0690] In this specification, “dynamic scaling” is not limited to a simple increase or decrease in the number of servers, but may include the activation or deactivation of candidate execution paths, switching between high-performance models and low-resource models, switching between local execution and remote execution, relocation of accelerators or memory regions, changes in parallelism, changes in verification stages, promotion or demotion of cache or compile objects, splitting or merging of combination paths, or other resource-state-based changes to the execution topology.

[0693] In this specification, “modality” is not limited to a specific sensor format and may include text, token, document, image, video, sound, voice, spatial coordinates, depth map, LiDAR point cloud, radar reflection information, thermal image, tactile, pressure, force, torque, attitude, velocity, acceleration, inertial information, proprioceptive, biosignal, program trace, tool call result, API response, network state, log, structured data, graph, latent vector, embedding, or a combination thereof.

[0695] In this specification, “multimodal experience” may refer to an experience obtained in which two or more different modalities are associated temporally, spatially, causally, semantically, or functionally with the same event, object, state, behavior, goal, or result. Multiple modalities do not necessarily need to be measured at the same time and may correspond to one another through synchronization errors, time delays, spatial coordinate transformations, or event-unit alignment information.

[0697] In this specification, “modality alignment information” may include temporal correspondence, spatial correspondence, object correspondence, semantic correspondence, causal correspondence, latent spatial correspondence, calibration, coordinate transform, timestamp offset, or uncertainty information for determining whether observations or latent representations of different modalities represent the same or related states, objects, events, behaviors, or results.

[0699] In this specification, “cross-modal authentication” may refer to an authentication state generated by evaluating whether the independent quality or reliability of individual modalities, as well as the temporal, spatial, semantic, causal, or state transition consistency between two or more modalities, satisfies established conditions.

[0701] In this specification, “modality availability status” may include status information indicating whether a specific modality corresponds to one or more of the following states: currently available, partially available, delayed, degraded, unreliable, calibration uncertain, blocked, lost, broken, or unsupported.

[0703] In this specification, “heterogeneous multimodal mapping” may include an operation of converting the experience of the first intelligent node into a representation usable by the second intelligent node using at least one of state semantics, event semantics, object semantics, behavioral results, latent representations, world models, or common intermediate representations, even when the first set of modalities possessed by the first intelligent node and the second set of modalities possessed by the second intelligent node are not identical to each other.

[0705] In this specification, “modality alternative path” may mean an execution path that uses one or more of other modalities, other sensors, experience from external intelligence nodes, world model estimation, tools, or additional queries to perform the same or similar goals when one or more modalities are unavailable or out of the authentication area.

[0708] In this specification, “dream,” “dream simulation,” “offline imagination execution,” “imagination path generation,” or “virtual experience generation” are not limited to meaning human physiological dreams, but may refer to computer-implemented operations that generate one or more virtual state transitions, virtual behavioral outcomes, counterfactual events, alternative paths, failure hypotheses, success hypotheses, or test situations using stored experiences, world models, simulators, digital twins, generative models, policy models, inverse models, state transition models, or combinations thereof, without directly acting on an actual production environment.

[0710] In this specification, “virtual experience topology” or “dream topology” G_H may refer to a logical or physical structure that stores virtual execution representations, virtual connections, virtual combinatorial paths, counterfactual state transitions, failure hypotheses, success hypotheses, alternative paths, or dependencies thereof that are not determined as experiences observed through actual execution.

[0712] In this specification, “dream provenance” may mean information including one or more of a generation source, generation model version, seed experience, generation time, transformation process, policy used, environment model used, uncertainty, and verification status indicating that a particular virtual experience was generated from a world model, simulator, generative model, or counterfactual transformation rather than from an actual execution observation.

[0714] In this specification, “Dream Gate” may refer to a verification procedure that determines the extent to which a virtual experience can be reflected in one or more of a memory state, execution topology, authentication topology, test set, or production execution topology. A Dream Gate may include one or more of self-assessment, world model consistency, forward-backward consistency, physical or policy constraints, independent examiners, sealed examiners, external oracles, consistency with real-world experience, multi-model consensus, or local node, connection, and path authentication.

[0716] In this specification, “sealed examiner” or “external approval signal” may refer to an examiner, oracle, rule, device, user evaluation, external system, or verification information that is isolated so that a generator creating a virtual experience or a self-improvement target cannot directly create, modify, or fully access the evaluation criteria, correct answer information, internal parameters, partial test sets, or the entire approval logic of said examiner.

[0718] In this specification, “realization priority grading” may refer to an operation of evaluating the validity of a virtual experience by giving higher reliability or priority to actually observed results, independent verification results, reproduced state transitions, or exogenous approval signals than to the virtual experience’s self-prediction or self-evaluation.

[0720] In this specification, “phantom failure” or “phantom failure” may refer to an event generated or determined as a failure case by a virtual experience generator or self-reviewer, even though it is not a failure that occurred in actual execution or was independently verified. Phantom failure candidates may be maintained in a hypothetical state distinct from actual failures.

[0722] In this specification, “dream debt” may refer to a state indicating the extent to which a virtual experience or imagination path has failed to confirm consistency with actual experience, independent verification, reproducibility testing, or exogenous approval signals for a set period or set number of times after creation. An increase in dream debt may lower the priority, certification cap, memory weight, or production execution promotion potential of the virtual experience.

[0724] In this specification, “memory integration” may include operations to compress, abstract, merge, re-index, re-weight, rehearsal, compile, cache, or transfer iteration paths to long-term memory using actual experiences and verified virtual experiences, and to lower the priority of redundant virtual experiences of low confidence or expired hypotheses.

[0726] In this specification, “dream scheduling” may refer to the operation of allocating time, computational amount, model precision, number of scenarios, or verification budget used for virtual experience generation and verification based on one or more of system load, computational resource headroom, verification backlog, energy conditions, certification expiration date, failure density, search sparsity, uncertainty, novelty, or expected information gain.

[0729] In this specification, “social state” may mean an execution context state including one or more of the roles, relationships, authority, consent, trust, privacy, ownership, scope of responsibility, group affiliation, conversational state, interaction stage, social norms, scope of access, expected behavior, response state, or uncertainty between one or more humans, users, groups, organizations, intelligent nodes, or robots.

[0731] In this specification, “social topology” G_S may refer to an explicit or implicit structure comprising nodes representing social actors or groups, connection information representing roles, relationships, authority, consent, trust, ownership, privacy, collaboration, conflict, or interaction relationships, social norms, and the current interaction context. Social topology may be implemented as a graph, matrix, table, vector, embedding, state machine, relational data, probabilistic model, neural network latent state, or a combination thereof.

[0733] In this specification, “social actors” may include human users, guardians, administrators, visitors, workers, groups, organizations, other intelligent nodes, robots, or other agents, and the same entity may have different roles depending on time or context.

[0735] In this specification, “role status” may include owner, manager, worker, guest, guardian, patient, customer, supervisor, collaborator, observer, service provider, or other relational or operational roles, and the role itself and the authority assigned to the role may be distinguished from one another.

[0737] In this specification, “consent status” may mean one or more of explicit consent, implied consent, limited consent, conditional consent, withholding, ambiguity, withdrawal, or refusal regarding a specific act, access to information, access to the body, recording, filming, transmission, manipulation, movement, sharing, or automatic execution. Consent status may vary depending on the subject act, subject object, subject information, counterparty, place, time, or purpose.

[0739] In this specification, “social norms” may refer to distance, approach, speech, order, waiting, interruption, eye contact, gestures, handling of property, information sharing, assistance, or other behavioral conditions expected or permitted in a particular culture, organization, place, relationship, group, work situation, or interaction stage. Social norms may be distinguished from physical safety or legal and policy hard gates.

[0741] In this specification, “social permissibility” may mean the extent to which a particular robot or intelligent behavior can be maintained as a candidate for practice when considering current roles, relationships, consent, collective status, privacy, place, purpose, and social norms.

[0743] In this specification, “social behavioral path” may include utterance, waiting, questioning, gaze, gesture, approach, movement, object transfer, object manipulation, guidance, collaboration, interruption, yielding, or sequential, parallel, or conditional combination of physical or non-physical actions.

[0745] In this specification, “embodied intelligence” or “embodied intelligence” may mean a form of intelligent execution in which the results of software inference can change the state of a real environment through a moving mechanism, a robotic arm, a gripper, a haptic output, a display, a speaker, a line of sight mechanism, a drone, a vehicle, a wearable, an industrial device, a household device, or other physical implementer.

[0747] In this specification, “social distance” or “proxemic distance” may refer to an approach distance or relative position condition that can be adjusted according to context, role, culture, type of task, trust, conversational level, or user preference within a range greater than the physical safety minimum distance between a person or group and a robot.

[0749] In this specification, “social state uncertainty” indicates the possibility that the estimation of roles, relationships, consent, trust, intentions, ownership, group status, or social norms is incomplete or conflicting, and if said uncertainty exceeds a set threshold, a verification query, additional observation, restricted action, waiting, Shadow evaluation, or human identification may be required.

[0751] In this specification, “social hard gate hierarchy” may mean a structure that hierarchically evaluates multiple constraints so that social utility or preference does not offset physical safety, law or policy, authority or consent conditions. In one embodiment, the order of physical safety, law or policy, authority or consent, social permissibility, and utility optimization, or a hierarchy functionally equivalent thereto may be used.

[0753] In this specification, “withdrawal of social state” may mean removing, restricting, or re-verifying the production execution availability of execution representations, associations, or robot behavior paths that depend on said state when withdrawal of consent, loss of role, revocation of authority, change of relationship, change of privacy conditions, or change of collective state occurs.

[0755] In this specification, “social dream” may refer to a virtual experience that counterfactually generates the results of conversation, approach, concession, delivery, collaboration, or group interaction in a world model or virtual environment that does not directly affect actual social actors. A social dream cannot generate or replace actual consent, actual authority, or actual relationship changes, and may only be used to generate candidates for social behavior or test conditions.

[0759] In this specification, “consistency epoch” or “consistency epoch” may mean identification information indicating that an execution topology, authentication topology, virtual experience topology, social topology, resource state, implementation state, or other state structure used for production execution decisions belongs to the same logical change generation or interoperable snapshot.

[0761] In this specification, “staging state” may mean a state in which a newly created or changed topology, authentication object, social state, resource state, or execution object is not immediately reflected in the production execution topology but is temporarily stored until the epoch, verification state, or commit condition of the related dependency is satisfied.

[0763] In this specification, “emergency interrupt lane” may mean a separate high-priority control path that immediately restricts, suspends, revokes, or switches the relevant execution authority to a safe state when waiting for a general multi-topology commit or epoch alignment procedure is not appropriate for physical safety, withdrawal of consent, revocation of authority, security incident, or other significant risks.

[0765] In this specification, “evidence lineage” may refer to information representing the creation, sharing, or derivation relationships between a model lineage, training data, evaluation data, world model, tool, API, prompt or program lineage, verification environment, sensor, hardware, user evaluation, external oracle, or other evidence sources that generated certification or audit results.

[0767] In this specification, “correlated evidence cluster” or “correlated evidence cluster” may mean a set of evidence that is difficult to calculate as independent verification because two or more examiners or verification evidence share the same or substantially overlapping models, data, world models, tools, evaluation criteria, generation paths, or other common sources.

[0769] In this specification, “common cause dependency hyperedge” may refer to a multilateral dependency relationship indicating that two or more execution representations, authentication objects, compile execution objects, robot behaviors, or intelligence nodes rely together on the same model, dataset, tool or API, sensor calibration, hardware, policy, social state source, verification environment, or other common cause, even if they are not directly connected to each other.

[0771] In this specification, “suspicious drift” or “suspect” may refer to an intermediate state in which actual failure or obvious violation of certification conditions has not yet been confirmed, but statistical evidence of continuous predicted residuals, calibration errors, performance changes, social state changes, resource state changes, or distribution changes satisfies established conditions, thereby requiring further verification or restricted operation.

[0773] In this specification, “belief social state” may refer to a state in which a role, authority, consent, trust, collective state, or other presumable social state is not stored as a single fixed value but is maintained together with a probability distribution, confidence interval, confidence, set of candidate states, or uncertainty. States requiring external verification, such as explicit consent or legal authority, may not be generated by belief alone.

[0775] In this specification, “value-of-information action” or “value-of-information action” may mean an action that reduces the expected loss of subsequent decision-making by performing one or more of questioning, additional observation, waiting, external verification, user verification, authorization verification, sensor re-measurement, or low-risk probe instead of immediately executing or rejecting a target action when there is high uncertainty regarding the current social, environmental, or authentication status.

[0777] In this specification, “actuator runtime shield” may mean an independent or semi-independent physical safety layer capable of converting actuator commands into limiting, clipping, substituting, delaying, suspending, or safety commands based on the current position, velocity, force, torque, mass estimate, friction estimate, delay, payload, sensor uncertainty, or control error immediately prior to the actual command, even if the robot behavior is permitted by the upper planner or authentication topology.

[0779] In this specification, “FastPath lease” may refer to a temporary execution right in which a compiled executable object or fast execution path maintains fast execution priority only for a set period, set number of times, set execution amount, or set proof amount, and may be demoted to a Shadow, Restricted, or higher-level Planner state even if no actual failure has occurred if the update conditions are not satisfied.

[0781] In this specification, the “incumbent-challenger structure” may refer to a structure that maintains an incumbent executable currently used in production or FastPath while continuously evaluating one or more alternative executables in Shadow, Canary, or low-cost verification paths, and promotes the challenger or demotes the incumbent when established evidence superiority and minimum verification conditions are satisfied.

[0783] In this specification, “collective certification,” “mission certification,” or “collective certification” may refer to a certification status that evaluates whether the concurrent, sequential, or conditional execution of multiple subjects satisfies the total resource budget, mission constraints, privacy, authority, prohibited objectives, total time, shared space, or collective safety conditions, separately from the local certification of individual intelligent nodes, robots, execution objects, or paths.

[0785] In this specification, “irreversibility” may mean that the result of an execution cannot be restored to its original state or that recovery is difficult in terms of cost, time, physical impact, information leakage, human impact, or collective impact.

[0787] In this specification, “rollback ledger” may mean a record structure that stores one or more of the states before and after a change, cause of change, evidence, epoch, dependency, scope of impact, and rollback target of an execution topology, authentication topology, virtual experience topology, social topology, or production execution topology, thereby enabling restoration to a previous known-good state or restricted state.

[0789] In this specification, “evidence quarantine” may mean an action of not immediately reflecting a change target in production and performing additional verification in a separate Pending, Shadow, Quarantined, or Staging state when there is conflicting evidence of verification, uncertain self-modification results, incomplete epoch, or suspected evidence of a common cause.

[0793] The present invention also relates to an implementation intelligence technology in which a robot, mobile body, drone, vehicle, robotic arm, humanoid, wearable, industrial device, or other physical embodiment maintains its own kinematics, dynamics, sensors, actuators, payloads, tools, energy, thermal state, calibration, damage, or degradation state in its own embodiment model, and regrounds, recompiles, and re-certifies abstract execution capabilities to match the current available capabilities of its own body.

[0795] Furthermore, the present invention relates to a cross-embodiment skill transfer technology that transfers state transition semantics or abstract skills between intelligent nodes having different physical forms, sensor configurations, or actuator configurations without directly replicating raw motor commands, and then converts and verifies them into local physical behaviors according to the self-body model of the receiving implementation.

[0797] Furthermore, the present invention relates to self-assessment and metacognitive adaptive intelligence technology in which an intelligent system continuously compares its own prediction success rate, actual success rate, calibration, uncertainty, physical condition, memory quality, planning quality, reviewer reliability, social state estimation accuracy, mission fit, or other self-ability indicators to estimate bias in self-ability and self-assessment, and uses said self-assessment not as automatic approval but as evidence for additional verification, demotion, questioning, delegation, maintenance, or relearning requirements.

[0799] Furthermore, the present invention relates to a self-sustaining implementation intelligence technology that detects implementation degradation, sensor or actuator performance degradation, battery or energy state changes, tool replacement, calibration drift, partial failure, or damage during long-term operation, reduces the capability area, and autonomously plans and verifies recalibration, charging, cooling, tool replacement, load reduction, safety mode, role reallocation, or maintenance requests.

[0801] Furthermore, the present invention relates to a collective recovery technology that reconstructs the mission's role, task assignment, and execution path based on the actual physical condition, self-assessment, local authentication, and collective authentication of the remaining implementations, and re-authenticates the mission when some of a plurality of robots or intelligent nodes become degraded, broken, have changed permissions, have changed consent, or are in a resource-deficient state.

[0803] Furthermore, the present invention relates to an auditable forgetting and topology garbage collection technique for compressing, merging, archiving, tombstone, or removing experiences, dreams, authenticated objects, social states, body calibration, compilation paths, provenance, rollback snapshots, and failure history accumulated over long-term operation while preserving active dependencies, auditability, reproducibility, and safety conditions.

[0806] In this specification, “self-embodiment model,” “body self-model,” or “embodiment topology” may mean a state structure that explicitly or implicitly represents the shape, degrees of freedom, kinematics, dynamics, mass, inertia, friction, sensor configuration, actuator configuration, workspace, payload, force or torque limit, energy state, thermal state, calibration, damage, degradation, attachments, communication state, or other physical capability information of a robot or physical device actually controlled by an intelligent system.

[0808] In this specification, “implementation topology” G_B may refer to a graph, state machine, table, vector, neural latent state, or a combination thereof, in which a body component, sensor, actuator, joint, link, tool, energy source, or other implementation element is a node or state, and kinematic relationship, dynamic relationship, energy relationship, control relationship, calibration relationship, fault dependency, or tool compatibility is included as connection information.

[0810] In this specification, “body capability envelope” may mean a permissible area of ​​position, velocity, acceleration, force, torque, payload, workspace, trajectory, maneuverability, travel distance, flight time, sensor range, thermal load, energy consumption, or other physical behaviors that can be safely and reliably performed in the current state of the implementation.

[0812] In this specification, “embodiment grounding” or “body-based grounding” may mean an action of converting the state transition meaning of an experience received from an abstract state transition operator, skill, option, plan path, or external intelligence node into a trajectory, controller, force profile, actuator command, or implementation behavior path that fits the current G_B, body capability envelope, sensor / actuator configuration, and physical constraints of a specific implementation.

[0814] In this specification, “body certification” may mean a certification status indicating whether a grounded execution path in a specific implementation, specific body version, specific payload, tool, calibration, health state, or physical environment satisfies established physical execution conditions, separate from the functional certification of an abstract execution representation.

[0816] In this specification, “cross-embodiment transfer” may mean an experience transfer in which, when transferring an experience or skill learned in a first embodiment to a second embodiment, the raw motor commands of the first embodiment are not automatically inherited, and abstract state transition semantics, object or event relation, task constraint, contact objective, or other embodiment-independent representation is transferred, after which the second embodiment regrounds and re-authenticates to fit its own body.

[0818] In this specification, “embodiment-independent representation” does not mean only a representation in which the embodiment characteristics are completely removed. It may also include an embodiment-reduced or embodiment-adaptable representation configured to separate, normalize, abstract, conditionalize, or remap body-specific parameters without directly inheriting actuator indices, joint coordinates, or raw trajectories specific to the transmitting embodiment as auto-execution commands of the receiving embodiment.

[0820] In this specification, “self-evaluation” or “self-evaluation” means a functional operation in which a computer-implemented system calculates, estimates, or updates its prediction success rate, risk, uncertainty, calibration, body health, or other performance indicators, and does not imply consciousness, self-awareness, subjective experience, or human-like self-awareness as a prerequisite.

[0823] In this specification, “body health” may mean a value, vector, distribution, or grade representing the residual state of the actuator health, sensor health, structural health, battery state-of-health, thermal margin, calibration quality, communication health, tool health, or other actual physical functions of an embodiment.

[0825] In this specification, “self-maintenance” or “self-maintenance” may mean that an intelligent system observes changes in its body health or capability envelope and generates, verifies, and performs one or more of the following as execution candidates: recalibration, charging, cooling, lubricating, tool change, payload reduction, speed reduction, redundancy activation, safe mode, diagnostic test, repair request, or maintenance scheduling.

[0827] In this specification, “self-assessment state” may mean a state in which an intelligent system exhibits one or more of its domain-specific capabilities, success probability, calibration, uncertainty, body health, memory quality, planner quality, judge reliability, social-state accuracy, mission fitness, recent drift, or known self-bias.

[0829] In this specification, “self-assessment topology” G_Q may refer to a structure representing the dependencies between capability domains, assessment models, related action representations, body capability, social domain, mission role, and verification evidence when the self-assessment state is implemented as an explicit graph, and is not necessarily limited to being implemented as a separate physical graph.

[0831] In this specification, “meta-calibration” may refer to an operation in which an intelligent system accumulates the difference between its predicted success probability, risk, or confidence and the actual result or independent verification result by domain to update the overconfidence, underconfidence, bias, variance, or application domain of its self-assessment.

[0833] In this specification, “self-falsification” may refer to a verification operation in which an intelligent system does not merely repeat instances of verifying its current capability assessment or world model, but intentionally generates or selects a state where its high confidence is likely to be incorrect, a state where past calibration errors are large, boundary conditions, counterexamples, or challenger hypotheses to find errors in self-assessment.

[0835] In this specification, “self-evaluation is not self-certification” may mean that the result of self-evaluation does not directly generate Full certification or production execution authority, but is used as evidence to generate one or more of verification priority, verification budget, abstention, delegation, maintenance, Shadow, Canary, Restricted, or validation demand.

[0837] In this specification, “group role reorganization” may refer to an operation in which, when the body health, authentication status, authority, social status, resource, or communication status of some of the multiple intelligent nodes or robots changes, the existing assignment between the mission task and the agent is recalculated, and after re-grounding and re-authenticating the newly assigned task to each implementation, the mission authentication is updated.

[0839] In this specification, “auditable forgetting” or “auditable forgetting” may mean the act of deleting or compressing old, redundant, or low-value experiences, dreams, authentications, social states, calibrations, compiled objects, provenances, or snapshots, while preserving active dependencies, revoked history, rollback requirements, records related to legal or consent matters, mission requirements, or information necessary for common cause analysis in a tombstone, digest, archive, or rehydrateable form.

[0841] In this specification, “tombstone” may mean a record that preserves one or more of the existence, identifier, state, reason for deletion or compression, key provenance, whether it was revoked, dependency summary, hash, epoch, or archive location of an object, even if the entire original data is not retained, so that past risks or dependencies are not treated as if they never existed due to deletion.

[0843] In this specification, “topology garbage collection” may refer to the operation of compressing, merging, archiving, tombstone, or removing objects that are currently unnecessary for production, validation, audit, or recovery in a dependency-aware manner from an execution topology, authentication topology, virtual experience topology, social topology, implementation topology, self-assessment state, or rollback ledger.

[0846] The supplementary principles of interpretation regarding the terms and notations of this specification are as follows.

[0848] First, the expressions “production execution,” “production,” “production execution,” and “production deployment” may, in context, refer to an execution phase where effects can be reflected in an actual service environment, actual control environment, actual users, or external systems, and are not limited to a specific commercial deployment method.

[0850] Second, “Shadow” or “shadow”, “Canary” or “canary”, “Pending”, “Restricted”, “Suspended”, “Revoked”, “Expired”, “Full”, “Hypothetical”, “Suspect”, “Quarantined”, and “Staging” do not necessarily need to be stored as identical strings or enumerations in implementation, and may be implemented as state values, flags, bit fields, score intervals, policy states, or combinations thereof representing substantially identical execution rights, verification stages, authentication ranges, or state transitions.

[0852] Third, “reclamation” does not necessarily mean the physical deletion of data. Reclamation may include removing or restricting the production availability, execution rights, search visibility, priority, or linkability of the relevant object or authentication, and may retain original or summary information for audit, rollback, re-verification, or provenance verification.

[0854] Fourth, “local,” “local certification,” or “local certification” does not necessarily mean only within a single physical device. It may mean a scope whose validity is determined independently for a specific intelligent node, a specific implementation, a specific user area, a specific set of actors, a specific site, a specific trust domain, or a specific execution context.

[0856] Fifth, “provenance” is not limited to a simple source name of data or an object, but may include one or more of the following: the creator, the version of the creation model or program, input data, the transformation process, the verification path, time information, dependency, hash, or signature information.

[0858] Sixth, “oracle” does not necessarily mean an error-free verifier. It may include external rules, independent models, physical sensors, human evaluations, type verifiers, test sets, reference systems, or other exogenous verification means in which the candidate generator or self-improvement target and evaluation path are separated and which provide established verification functions.

[0860] Seventh, “prior” is not limited to Bayesian priors and may include one or more of the candidate’s initial priority, initial confidence, initial verification budget, initial search cost, initial certification cap, or initial risk weight.

[0862] Eighth, “blast radius,” “dependency span,” or “scope of influence” does not refer to a single fixed graph distance, but may refer to a set of targets or its size that can be affected by one or more of direct dependencies, common cause dependencies, data flows, authority flows, resource sharing, body dependencies, social dependencies, mission dependencies, or version dependencies.

[0864] Ninth, “mission,” “collective work,” “task,” or “collective task” is not limited to military missions and may include any multi-subject task involving multiple intelligent nodes or multiple robots having a shared goal, shared resources, shared space, shared data, interdependent tasks, or common constraints.

[0866] Tenth, the “known-good state” does not mean an absolutely error-free state, but may mean a state adopted as the current rollback reference point based on the established verification level, certification conditions, or past actual results.

[0868] Eleventh, “body version” is not limited to the manufacturing number of a robot or physical embodiment, but may mean a version or state identifier for identifying configuration changes affecting morphology, kinematics, dynamics, sensor configuration, actuator configuration, firmware, calibration, tool attachment, payload condition, health state, or other capabilities.

[0870] Twelfth, “self-evaluation,” “meta-evaluation,” and “self-refutation” refer to computer implementation performance evaluation and verification control, and do not presuppose human consciousness, subjective senses, or philosophical self-concepts.

[0872] Thirteenth, where English and Korean expressions are used in combination in this specification, unless specifically stated otherwise, functionally corresponding expressions may be interpreted as describing identical or overlapping technical concepts.

[0875] In this specification, “compiled execution object,” “compiled object,” “compiled object,” “FastPath object,” or equivalent expressions are not necessarily limited to native binaries generated by traditional source code compilers.

[0877] The above-mentioned compiled executable object may refer to an executable or ready-to-execute object that has been transformed, combined, fixed, cached, serialized, instantiated, pre-planned, optimized, or hardware-mapped one or more executable representations, relationships, paths, tool sequences, model invocation sequences, robot skill sequences, or other executable structures that have been repeatedly used or verified, in order to reduce one or more of the search cost, interpretation cost, verification cost, call overhead, data movement amount, or latency during subsequent execution.

[0879] The above-mentioned compiled execution object may be implemented, for example, as a native executable, shared library, object code, machine code, JIT-compiled object, virtual-machine bytecode, intermediate representation, eBPF program, GPU executable graph, CUDA graph executable, TensorRT engine or plan, accelerator-specific execution plan, FPGA configuration or bitstream, programmable-logic lookup structure, workflow DAG, cached execution DAG, tool-call chain, API-call chain, prompt chain, prompt-tool chain, model-routing graph, robot motion primitive bundle, controller bundle, trajectory package, hardware command sequence, or a combination thereof.

[0881] The above examples are non-limiting and do not limit the scope of the invention to a specific language, operating system, accelerator, manufacturer, framework, or runtime.

[0883] Additionally, “decompile,” “decompile,” “partial decompile,” “uncompile,” “invalidate,” or equivalent expressions do not necessarily mean only converting a compiled executable object back into its original source code. The above expressions may include removing, deactivating, disassembling, redeploying, restoring to a graph representation, detaching from dependency boundaries, invalidating caches or leases, reverting to a lower-level representation, bypassing an interpreter path, or converting all or part of a compiled executable object into a recompiled target from a production execution target.

[0885] 0. Comprehensive Definition of Terms and Scope of Implementation

[0887] Each operation of the present invention can be performed deterministically or probabilistically, and can be implemented in a continuous or discrete state, with a single agent or multiple agents, centralized or distributed, synchronous or asynchronous, and online or offline.

[0889] The minimum unit of experience may be a single state-action-subsequent state tuple, but is not limited thereto, and may be a multi-stage trajectory, episode, conversation session, tool call sequence, program execution trace, robot action sequence, human-agent interaction sequence, or a distributed execution record of multiple nodes.

[0891] The results of the experience can be generated from success, user choice, constraint violations, predicted residuals, attainability of subsequent states, cost, time, energy, memory usage, external evaluation, or self-evaluation, even in the absence of explicit rewards.

[0893] Even in implementations that do not use explicit G=(V,E), the planning structure can be considered substantially reconfigured if the availability, priority, cost, or routing probability of candidate behaviors varies according to past experience.

[0895] Compilation conditions are not determined solely by the number of iterations, but may be determined by success rate, transition variance, mean prediction error, worst-case loss, uncertainty, resource consumption, execution latency, safety or policy constraints, user acceptance rate, or a combination thereof.

[0897] The compile-execution object does not need to be a complete open-loop execution and may include local feedback, guards, intermediate state checks, timeouts, re-verification, early exit, or partial planner calls internally.

[0899] Sandbox verification can be performed by executing the actual executable object as is, or by estimating the execution effect using a surrogate model, an abstract model, or a formal model.

[0901] Reviewers can independently evaluate the same input or evaluate different test results, and some reviewers may have veto authority or hard-gate authority. The conclusions of multiple reviewers can be combined by mean, weighted sum, minimum value, maximum risk, vote, quorum, consensus, Pareto condition, or rule-based logic.

[0903] The actual execution system may be a software service, agent workflow, robot, vehicle, drone, production facility, network, data center, semiconductor or computing device, user terminal, edge device, or other physical or logical system.

[0906] 1. Overall status

[0908] The overall system state at time t can be expressed as follows.

[0910] zeta_t = (z_t, M_t, O_t, G_t^plan, Cert_t, q_t)

[0912] Here, z_t represents the current abstract state, M_t represents the path memory, O_t represents the set of abstract state transition operators, G_t^plan represents the plan graph, Cert_t represents authentication information, and q_t represents the state of the actual execution system.

[0914] 2. Experience of state transition

[0916] One experience can be represented as follows.

[0918] tau_i = (z_i, a_i, r_i, c_i, z_i', y_i)

[0920] Here, a_i may include action, r_i may include reward or utility, c_i may include cost or constraint violation information, z_i' may include subsequent state, and y_i may include actual observation information.

[0922] 3. Path memory

[0924] Path memory can be expressed as follows.

[0926] M_t = (V_M, E_M, w_t, e_t, rho_t, Sigma_t)

[0928] Here, w_t may represent path strength, e_t may represent eligibility information for credit allocation, rho_t may represent plasticity, and Sigma_t may represent the uncertainty of memory or transfer.

[0930] Compensation prediction error,

[0932] delta_t = r_t + gamma V(z_(t+1)) - V(z_t)

[0934] It can be represented as.

[0936] eligibility of past paths,

[0938] e_(i,t) = gamma lambda e_(i,t-1) + 1[i is included in the current path]

[0940] It can be represented as.

[0942] Path strength is,

[0944] w_(i,t+1) = clip(w_(i,t) + eta rho_(i,t) delta_t e_(i,t) - lambda_w(w_(i,t)-w_0))

[0946] It can be updated as follows.

[0948] 4. Abstract state transition operator

[0950] The abstract state transition operator O_i can be represented as follows.

[0952] O_i = (I_i, T_i, beta_i, U_i, Sigma_i)

[0954] I_i represents the starting condition, T_i represents the state transition model or distribution, beta_i represents the ending condition, U_i represents the expected utility, and Sigma_i represents the transition uncertainty.

[0956] Two experiences tau_i and tau_j,

[0958] d_T(T_i,T_j) <= epsilon_T

[0960] and,

[0962] d_U(U_i,U_j) <= epsilon_U

[0964] If it satisfies this, it can be classified as a predictively equivalent experience.

[0966] Multiple predictively equivalent experiences are,

[0968] {tau_1, tau_2, ..., tau_n} -> O_k

[0970] It can be compiled into a single operator as follows.

[0972] 5. Plan graph

[0974] The planning graph is,

[0976] G_t^plan = (O_t, E_t^plan)

[0978] It can be represented as.

[0980] The probability that the result state of operator O_i is compatible with the starting condition of operator O_j is,

[0982] P[z' in I_j | z,O_i] >= 1-epsilon_link

[0984] If satisfied,

[0986] (O_i,O_j) in E_t^plan

[0988] It can be connected to.

[0990] If the above probability decreases or uncertainty increases based on actual experience, the connection can be removed or the weight lowered.

[0992] 6. Compile execution path

[0994] Planned route,

[0996] pi = (O_i,O_j,O_k,...)

[0998] If it is performed for more than the set number of iterations and satisfies the success condition and the prediction confidence condition,

[1000] chi_m = Compile(pi)

[1002] You can create a compile-executable object.

[1004] The compile execution object is,

[1006] chi_m = (I_m,T_m,G_m,S_m,Sigma_m,R_m)

[1008] It can be represented as.

[1010] I_m can represent the runnable starting region, T_m the expected state transition, G_m the guard condition to be maintained during execution, S_m the actual execution information, Sigma_m the uncertainty, and R_m the resource condition required for execution.

[1012] 7. Sandbox

[1014] The actual state is called z_t, and the sandbox state is called z_t^SB.

[1016] State transitions applying candidate execution objects in a sandbox are,

[1018] z_(t+1)^SB = T_hat(z_t^SB,chi_m,omega)

[1020] It can be represented as.

[1022] The set of test conditions is,

[1024] Omega = Omega_nominal union Omega_edge union Omega_OOD union Omega_adversarial union Omega_history

[1026] It can be composed of.

[1028] Omega_history may include test conditions generated based on past failures or unexpected results in the actual system.

[1030] 8. Active counterexample generation

[1032] The vulnerability status for candidate executable object chi is,

[1034] z* = arg max_z L_failure(chi,z)

[1036] step,

[1038] z in Z_plausible

[1040] It can be searched under conditions.

[1042] The above z* can be registered as a new sandbox test state.

[1044] 9. Multiple reviewer

[1046] Multiple screening devices,

[1048] J = {J_goal,J_constraint,J_generalization,J_robustness,J_efficiency,J_uncertainty,J_recovery}

[1050] It may include two or more of them.

[1052] Some evaluation criteria are,

[1054] h_j(chi) >= 0

[1056] It can be set as a hard gate that must satisfy.

[1058] Regarding the candidate who passed the hard gate,

[1060] Q(chi) = sum_k q_k w_k J_k(chi)

[1062] As shown, a quality score including the reliability of the reviewer q_k can be calculated.

[1064] 10. Review Decision

[1066] The review decision is,

[1068] Decision(chi) in {Promote, Restricted, Revise, Reject}

[1070] It can be generated as one of them.

[1072] 11. Restricted Promotion

[1074] The verified state region is,

[1076] D_chi^cert = {z : P(success | z,chi) >= 1-epsilon_cert and satisfy all hard gates}

[1078] It can be represented as.

[1080] The current state is,

[1082] z_t in D_chi^cert

[1084] If so, run the compile fast path, otherwise run the parent planner.

[1086] 12. Runtime Verification and Decompilation

[1088] The difference between the expected result of the compiled execution object and the actual observed result,

[1090] e_t = d(y_t,T_chi(z_(t-1)))

[1092] It can be represented as.

[1094] e_t > tau_e,

[1096] Novelty_t > tau_N,

[1098] Sigma_chi > tau_Sigma,

[1100] Or if one or more of the cases where the current state is outside the authentication area are satisfied,

[1102] chi -> Decompile

[1104] You can return to planning mode using the upper planning graph.

[1106] 13. Self-calibration of the examiner

[1108] The difference between the predicted value of the reviewer J_k and the actual result,

[1110] e_(J_k,t) = y_real - y_hat_(J_k)

[1112] It can be represented as.

[1114] The reliability of the screening device is,

[1116] q_(k,t+1) = U_q(q_(k,t),e_(J_k,t))

[1118] It can be updated to.

[1120] 14. Turning Failure Cases into Exam Questions

[1122] If a new failure type F_new is found at time t,

[1124] Omega_history_(t+1) = Omega_history_t union Scenario(F_new)

[1126] You can update the sandbox test set.

[1128] Accordingly, not only the experience of the intelligent execution system but also the test conditions of the examination system can increase.

[1130] 15. Experience Execution Package Between Heterogeneous Intelligent Nodes

[1132] The experience execution package can be represented as follows.

[1134] XEP_i = (I_i,T_i,O_i,Sigma_i,Credit_i,Prov_i,Exec_i,Cert_i)

[1136] The receiving node can convert operators using the conversion function psi between the state space of the transmitting node and the state space of the receiving node.

[1138] O_i^B = psi_(A->B)(O_i^A)

[1140] The receiving node can re-verify the above-mentioned transformed operator in a shadow sandbox without immediately executing it.

[1142] The authentication information of the sending node does not imply automatic approval by the receiving node and can be used as additional evidence.

[1144] 16. Mapping Physical or Logical Execution Paths

[1146] The compile execution object chi_i is,

[1148] chi_i -> P_i^exec

[1150] It can be mapped to a physical or logical execution path by.

[1152] P_i^exec may include one or more of a memristor conduction path, a percolation cell path, a PCM state, a FeRAM state, an address / routing state of an SRAM or DRAM, an FPGA configuration, a digital LUT, a cache / kernel execution path of a CPU or GPU, or other program execution paths.

[1154] Repeatedly successful execution objects can be configured to reduce the selection cost or execution cost of the corresponding execution path.

[1157] 16-1. Implicit Planning Structure Without Explicit Graphs

[1159] In one embodiment, the planning structure does not store a separate graph object. The selection score s_i of candidate operator O_i,

[1161] s_i = f(z_t,M_t,U_i,Sigma_i,C_i)

[1163] It is calculated as such, and as the parameters of M_t or f are updated according to accumulated experience, the ranking or availability of the candidates may change.

[1165] In this case, even without the explicit creation or deletion of edges, the subsequent search space and search order are changed by experience, so they can be included in the reconstruction of the planning structure of the present invention.

[1167] 16-2. Implementations of Large-Scale Language Model or Tool-Using Agents

[1169] The state z_t may include the conversation context, user goal, memory, external tool state, and policy state.

[1171] Action a_t can be one or more of a model call, search, database query, code execution, API call, call to another agent, or user question.

[1173] Repeatedly successful tool call sequence,

[1175] Search -> Retrieve -> Verify -> Execute

[1177] If the set conditions are satisfied, it can be generated as a single compilation execution object, chi_tool.

[1179] Subsequently, you can directly use chi_tool from the same or similar starting state, but if the data distribution or external API state changes and the estimated error increases, you can invalidate chi_tool and return to the parent planner.

[1181] 16-3. Implementations of Robots and Autonomous Systems

[1183] Abstract state transition operators can include physical skills such as access, hold, move, align, inspect, charge, avoid, or recover.

[1185] Once the sequence path of multiple skills is verified, it can be compiled into a robot control command sequence or state machine, and the sandbox can be implemented as a physics simulator, digital twin, or shadow controller.

[1187] If force, position, image, speed, current, temperature, or other sensor information goes out of the authentication area during actual execution, all or part of the current skill sequence can be suspended and returned to the upper planner or safety controller.

[1189] 16-4. Partial Compilation and Hierarchical Decompilation

[1191] If only (O_2, O_3, O_4) of the planned path pi=(O_1, O_2, O_3, O_4, O_5) is repeatedly stable,

[1193] chi_234 = Compile(O_2,O_3,O_4)

[1195] It can be partially compiled.

[1197] In actual execution, if only the prediction error of the interval corresponding to O_3 increases, the entire chi_234 can be not discarded, and only the O_3 interval can be extended to the upper planner or the authentication area of ​​chi_234 can be reduced.

[1199] 16-5. Layered Sandbox

[1201] The verification environment can be composed of multiple stages depending on cost or accuracy.

[1203] SB_1 = Low-cost world model rollout,

[1204] SB_2 = High-precision simulation or digital twin,

[1205] SB_3 = Type verification or adversarial test,

[1206] SB_4 = shadow execution or restricted canary execution

[1208] It is composed of, and the next verification step can be performed only on candidates whose results from the previous stage satisfy the established criteria.

[1210] 16-6. Separation of Producer, Tester, Reviewer, and Executor

[1212] Node A, which generates candidate execution objects, Node B, which generates test conditions, Node C, which performs evaluation, and Node D, which actually executes, can be configured differently from each other.

[1214] A != B != C != D

[1216] It could be, or some nodes might be identical.

[1218] In distributed implementations, node-specific signatures, model versions, test environment versions, provenance, or verification results can be included in the authentication information.

[1220] 16-7. Multitemporal Memory and Metaplasticity

[1222] Fast memory M_fast and slow memory M_slow,

[1224] M_fast(t+1) = (1-lambda_f)M_fast(t) + eta_f Delta_t

[1226] M_slow(t+1) = (1-lambda_s)M_slow(t) + eta_s Consolidate(M_fast)

[1228] It can be updated to.

[1230] The above eta_f, eta_s, or path-specific plasticity rho_i can be adjusted according to novelty, prediction error, repeatability, importance, or uncertainty.

[1232] 16-8. Experience Transfer and Heterogeneous State Space Mapping

[1234] When the state space Z_A of transmitting node A and the state space Z_B of receiving node B are different,

[1236] psi_(A->B): Z_A -> Z_B

[1238] You can transform the starting condition and result distribution of an abstract state transition operator using .

[1240] The receiving node refers to the authentication information of the sending node, but can integrate the transformed operator into the local plan structure or execution structure only after re-verifying it in its own shadow sandbox.

[1242] 16-9. Combination of Formal Verification and Statistical Review

[1244] Some constraints can be formally verified.

[1246] ForAll z in D_cert, h_j(z,chi) >= 0

[1248] The conditions of can be checked by model checking, theorem proof, or symbolic execution, and the remaining stochastic performance can be evaluated by Monte Carlo or statistical upper confidence limits.

[1250] Format verification and statistical review may be used individually or together.

[1252] 16-10. State-Based Execution Promotion and Reclamation

[1254] Even for the same compiled executable object, execution efficiency or reliability can vary depending on the GPU, memory, network latency, energy budget, or device temperature.

[1256] Therefore, the certification area D_chi^cert includes not only the state but also the resource state r_sys, and

[1258] (z_t,r_sys,t) in D_chi^cert

[1260] Fast paths can be allowed only when...

[1262] If the resource state goes out of the authentication scope, it can switch to another execution object or parent planner performing the same goal.

[1264] 16-11. Multilayer Authentication of Nodes, Connections, and Paths

[1266] Individual authentication for execution representation O_i can be represented as C_i^N, authentication for the connection relationship between the first execution representation O_i and the second execution representation O_j as C_ij^E, and authentication for a combination path pi including multiple execution representations as C_pi^P.

[1268] C_i^N can represent one or more of the start conditions of the execution expression itself, expected state transitions, constraint compliance, uncertainty, or resource conditions.

[1270] C_ij^E can indicate whether the output state, side effects, privilege changes, data, or trust signals of O_i are compatible with the start conditions and constraints of O_j.

[1272] C_pi^P may represent one or more of the risks, costs, uncertainties, rights, data flows, state changes, or the likelihood of constraint violations accumulated over the entire combination path.

[1274] Even if individual execution expressions O_i and O_j satisfy C_i^N=PASS and C_j^N=PASS respectively, if C_ij^E or C_pi^P does not exist or is FAIL, the above combination may not be promoted to actual execution or fast path.

[1276] 16-12. Combination of Authentication Zones and Path Authentication

[1278] Let the authentication area of ​​execution expression O_i be D_i^cert, and the connection authentication area where the second execution expression O_j is allowed after the first execution expression O_i can be represented as follows.

[1280] D_ij^cert = {z in D_i^cert : P[T_i(z) in I_j intersect D_j^cert and H_ij is satisfied] >= 1-alpha_ij}

[1282] Here, H_ij may include one or more of the constraints, authorization conditions, resource conditions, or information flow conditions that must be maintained in the combination of the two execution expressions.

[1284] The path authentication area D_pi^cert of the combinatorial path pi=(O_1,O_2,...,O_n) can be composed of a set of starting states that sequentially satisfy the authentication area of ​​each step and the connection authentication area.

[1286] The above path authentication is not limited to performing a simple logical AND of the authentication results of each node, and can separately evaluate state changes, shared resources, data flow, authority transitions, cumulative risk, and recoverability occurring according to the execution order.

[1288] 16-13. Authentication-Combined Planning Topology

[1290] Let the overall plan structure be G_t^plan, and the authentication-joined plan structure used for the actual search in the current state z_t and current execution context c_t is,

[1292] G_t^cert(z_t,c_t) = Filter(G_t^plan, Cert_t, z_t, c_t)

[1294] It can be represented as.

[1296] The above Filter can delete, disable, demote, increase cost, lower priority, switch to a shadow-only state, or switch candidate nodes or candidate behaviors to additional verification targets based on the authentication status of individual execution expressions, connection relationships, or combination paths.

[1298] Therefore, screening or certification is not limited to post-gates applied after the plan is completed, but can be used as state variables that form the planning space itself.

[1300] 16-14. Version / Provenance Dependent Certificates and Automatic Expiration

[1302] The authentication information Cert_i can be configured as follows.

[1304] Cert_i = (D_i,Omega_i,V_model,V_memory,V_tool,V_env,V_abs,V_judge,Prov_i,t_expire)

[1306] Here, V_model may represent the model version, V_memory the memory or path memory version, V_tool the tool or API version, V_env the environment or simulator version, V_abs the state abstraction version, and V_judge the judge or validator version.

[1308] Even if the execution object itself is not changed, if one or more of the above versions change outside the configured compatibility conditions, the authentication can be automatically changed to expire, restricted, shadow-only, or switched to a re-verification target.

[1310] 16-15. Dependency-based Selective Recovery and Partial Revalidation

[1312] Dependency relationships between execution representations, connection authentication, path authentication, and compile execution objects can be maintained as an authentication dependency graph.

[1314] If an actual failure F is determined to be associated with an execution representation O_k, a link (O_i, O_j), a specific tool version, or a specific state area, only the authentication or compiled execution objects that have a dependency relationship with the said failure may be selectively invalidated, the authentication scope reduced, or converted to targets for re-verification.

[1316] Accordingly, only the affected subgraphs can be modified without invalidating the entire skill library or the entire plan graph.

[1318] 16-16. Combination Path Specific Adversarial Verification

[1320] The sandbox verification unit can generate candidate combination paths that include not only the independent execution of individual execution expressions but also two or more execution expressions, and can detect side effects that may appear only in said combination paths.

[1322] Combination path specific testing may include one or more of the following: when the second execution expression uses data or files generated by the first execution expression; when the second execution expression uses authority or authentication information obtained by the first execution expression; when the output of the first execution expression affects the reliability or approval judgment of a subsequent execution expression; or when multiple execution expressions share the same memory, tool, network, device, or other resources.

[1324] The test condition generation unit can individually actively search for combinations of execution expressions that increase the loss or risk of the combination path among the combinations of execution expressions that satisfy the evaluation criteria.

[1326] 16-17. Calibration of the Probability Checker

[1328] If the examiner J_k predicts the failure probability of a candidate execution object or path as p_(k,t) and represents the actual result as y_t in {0,1}, the examiner's prediction error is, for example,

[1330] B_(k,t) = (p_(k,t)-y_t)^2

[1332] It can be calculated as follows.

[1334] Depending on the cumulative or exponentially weighted average error Bbar_k,

[1336] q_k = exp(-beta_J Bbar_k)

[1338] The reliability of the screening device can be updated as follows.

[1340] The above formula is one example, and log loss, calibration error, accuracy, recall, violation detection rate, or other evaluation quantities may be used.

[1344] 16-18. Dual Structure of Execution Topology and Authentication Topology

[1346] The execution representations and executable connections held by the intelligent system at time t can be represented as the execution topology G_E(t).

[1348] G_E(t) = (V_E(t), E_E(t), P_E(t))

[1350] Here, V_E(t) may include one or more abstract state transition operators, skills, options, policy fragments, program fragments, toolchains, compile execution objects, or a set of execution nodes corresponding to physical or logical execution paths.

[1352] E_E(t) may include sequential, conditional, parallel, iterative, recursive, asynchronous, or shared resource-based connections between execution nodes.

[1354] P_E(t) may include a set of reusable paths, macropaths, compile paths, or candidate combination paths containing multiple execution nodes.

[1356] The authentication topology G_C(t) corresponding to the above execution topology can be expressed as follows.

[1358] G_C(t) = (V_C(t), E_C(t), P_C(t), D_C(t), Dep_C(t))

[1360] Here, V_C(t) may include node authentication per execution node, E_C(t) may include connection authentication between execution nodes, P_C(t) may include path authentication per combination path, D_C(t) may include an authentication domain or authentication context, and Dep_C(t) may include an authentication dependency relationship.

[1362] Execution topology and authentication topology may be integrated into the same graph data structure or managed in different data structures, databases, memory regions, models, or distributed nodes.

[1364] 16-19. Creation of Production Execution Topology

[1366] Considering the current state z_t, current execution context c_t, current version vector v_t, and resource state r_t, the production execution topology G_prod(t) that the planner can use in an actual production environment can be represented as follows.

[1368] G_prod(t) = Phi(G_E(t), G_C(t), z_t, c_t, v_t, r_t)

[1370] The above Phi can determine one or more of execution feasibility, search priority, cost, risk, probability of selection, inclusion of beam or frontier, cache activation, routableness, or hardware deployability by evaluating the authentication status and current context of each execution node, connection or path.

[1372] For example, even if the execution node O_i satisfies node authentication, if the connection authentication C_ij^E between O_i and O_j does not exist or is not valid in the current context, the connection O_i -> O_j can be removed from G_prod(t).

[1374] In another embodiment, instead of completely removing the above connection, a penalty based on authentication uncertainty or risk may be added to cost_ij to maintain it at a low priority.

[1376] 16-20. Data Structure of Authentication Objects

[1378] A single authentication object C_k can be configured as follows, for example.

[1380] C_k = (Target_k, Type_k, D_k, Omega_k, Evidence_k, Score_k, Sigma_k, V_k, Prov_k, Dep_k, State_k, t_create, t_expire)

[1382] Target_k can represent an authenticated execution node, connection, path, or compiled execution object.

[1384] Type_k can represent Node, Edge, Path, Context, Version, Resource, or other authentication types.

[1386] D_k may include a state area, environment area, user area, device area, resource area, or risk area where authentication is valid.

[1388] Omega_k may represent the test conditions or test set used for certification.

[1390] Evidence_k may include sandbox logs, simulation results, type validation results, actual execution results, human evaluations, review model outputs, statistics, or other verification evidence.

[1392] V_k may include one or more version vectors among models, memory, tools, APIs, environments, state abstractions, examiners, and hardware configurations.

[1394] Dep_k may include dependencies on other authentication objects, execution objects, or external resources that affect the validity of the above authentication.

[1396] State_k may include one or more of the following states: Full, Restricted, Shadow, Canary, Pending, Suspended, Revoked, Expired, or Reject.

[1398] 16-21. Calculation of Authentication Dependency Graph and Impact Scope

[1400] The dependency relationship between the authentication object and the execution object can be represented by the authentication dependency graph G_D.

[1402] G_D = (V_D, E_D)

[1404] If an actual failure F_t occurs, the set of candidate causes directly associated with the failure can be represented as S_F.

[1406] S_F = RootCause(F_t)

[1408] A set of reachable objects R_F satisfying the dependency direction, maximum depth, confidence, or influence threshold set from the above set of candidate causes can be calculated as follows.

[1410] R_F = Reach_GD(S_F, d_max, theta_dep)

[1412] All or part of the authentication objects or execution objects included in R_F may be subject to demotion, suspension, revocation, reduction of authentication scope, or re-verification.

[1414] The above calculation of the scope of influence may utilize not only graph exploration but also causal inference, dependency metadata, data lineage, call relationships, model or tool version dependencies, statistical correlation, execution trace, or a combination thereof.

[1416] 16-22. Selective Certification Revocation and Production Topology Change

[1418] Once the set of recovery targets R_F is calculated, the production execution topology can be changed as follows.

[1420] G_prod(t+1) = RemoveOrRestrict(G_prod(t), R_F)

[1422] The above RemoveOrRestrict may include one or more of the following operations: deleting a related execution node or connection, disabling a related path, decompiling a related compiled execution object, shrinking the authentication area of ​​a related execution object, increasing the search cost of a related candidate, or changing a related candidate to a shadow-only state.

[1424] The above recovery may be used in conjunction with a method that invalidates the entire execution structure collectively, or selective recovery may be performed only when the impact of failure is localized.

[1426] 16-23. Local Revalidation and Production Topology Recovery

[1428] For the recovery target R_F, a local re-validation test set Omega_local can be generated.

[1430] Omega_local = Generate(F_t, R_F, Omega_history, Boundary, OOD, Adversarial, VersionDelta)

[1432] If, as a result of re-verification, target k satisfies the re-set authentication conditions,

[1434] C_k: Revoked -> Restricted or Full

[1436] It can be changed to.

[1438] Accordingly, the production execution topology,

[1440] G_prod(t+2) = Restore(G_prod(t+1), C_k)

[1442] It can be restored as follows.

[1444] Subjects that fail re-verification may be maintained in one or more of the following states: long-term isolation, retraining, alternative path generation, recompilation, human review, or disposal.

[1446] 16-24. Authentication-linked Executable Compilation

[1448] Even if the execution path pi satisfies repeatability, success rate, cost, and prediction confidence conditions, it may be required to satisfy one or more additional certification conditions to be compiled into a production fast path.

[1450] pi subseteq G_prod(t)

[1452] And,

[1454] C_pi^P in {Full, Restricted}

[1456] only in the case of,

[1458] chi_pi = Compile(pi, C_pi^P)

[1460] It can generate.

[1462] The compile execution object chi_pi can be configured to reference the node authentication, connection authentication, path authentication, model version, tool version, and resource conditions it depends on.

[1464] If the dependent connection authentication C_ij^E is revoked, automatically re-evaluate the path authentication C_pi^P, and

[1466] C_ij^E : Valid -> Revoked

[1468] If,

[1470] C_pi^P -> Suspended

[1472] and,

[1474] chi_pi -> PartialDecompile or Decompile

[1476] This can be performed.

[1478] 16-25. Authentication-Linked Risk Weighting Plan

[1480] The planner may use a risk-adjusted objective function that includes authentication information, rather than selecting based solely on the utility of candidate path pi.

[1482] J_cert(pi) = U(pi) - lambda_C Cost(pi) - lambda_R Risk_cert(pi) - lambda_S Sigma(pi) - lambda_V VersionPenalty(pi)

[1484] Risk_cert(pi) can be calculated based on the status of node authentication, connection authentication, and path authentication, the freshness of verification evidence, failure history, and the degree of out-of-certification.

[1486] Candidates whose certification has expired or who lack path certification can be excluded from the actual production path by applying an infinite or large set penalty.

[1488] 16-26. Authentication for Non-Sequential Combinations

[1490] Combination paths are not limited to simple sequential paths.

[1492] In the parallel execution representation O_a || O_b, if shared memory, the same file, the same database, the same network session, the same robot resource, the same GPU memory, or the same user permissions are shared, connection or path authentication for shared resource conflicts can be established.

[1494] In asynchronous execution, if the second execution expression reads an intermediate state before the completion of the first execution expression, a time sequence or a race condition may be included in the authentication condition.

[1496] In conditional or iterative paths, cumulative risk based on the quarterly authentication area and the number of iterations can be calculated separately.

[1498] In recursive execution, recursion depth, resource consumption, and the possibility of exiting the state space can be included in the authentication conditions.

[1500] 16-27. Time Attenuation and Hysteresis of Authentication

[1502] Since the environment and model can change over time, the authentication confidence c_k(t) can be configured to decrease over time.

[1504] c_k(t) = c_k(t0) exp(-lambda_k Delta t)

[1506] The above lambda_k can be set differently depending on execution frequency, environment change rate, tool change frequency, failure sensitivity, or risk level.

[1508] To prevent frequent promotion and demotion of certifications, the promotion threshold tau_promote and the demotion threshold tau_demote can be set differently.

[1510] tau_promote > tau_demote

[1512] By utilizing hysteresis, the phenomenon of the authentication status repeatedly toggling near boundaries can be reduced.

[1514] 16-28. Risk-Based Allocation of Verification Budget

[1516] The verification budget B_k allocated to candidate k can be calculated as follows.

[1518] B_k = B_min + alpha_R R_k + alpha_S Sigma_k + alpha_N N_k + alpha_F F_k + alpha_I I_k

[1520] R_k may represent risk, Sigma_k uncertainty, N_k novelty, F_k past failure history, and I_k path importance or frequency of use.

[1522] Low-risk repeated pathways can perform only a small number of regression tests, while high-risk novel pathways can perform multiple OOD, adversarial, combination pathways, and actual canary tests.

[1524] 16-29. Carriage and Re-verification of Accredited Evidence

[1526] An executable representation or compiled executable object can carry not only its own authentication status but also the evidence or evidence summary used to generate the authentication.

[1528] EVC_i = (Object_i, Cert_i, EvidenceDigest_i, TestProfile_i, VersionVector_i, Provenance_i)

[1530] The receiving node or subsequent planner may not treat EVC_i as an automatic acknowledgment, but instead evaluate compatibility with the local environment and the freshness of the authentication evidence to determine the re-verification strength or priority.

[1532] 16-30. Dual Topology Transitions in Distributed Nodes

[1534] The execution topology and authentication topology of transmitting node A can be denoted as G_E^A and G_C^A, respectively, and the structure of receiving node B can be denoted as G_E^B and G_C^B.

[1536] After converting the execution representation O_i^A transmitted from transmitting node A into O_i^B using the state-space mapping function psi_(A->B), the execution representation can be inserted as a candidate state into the execution topology of B instead of being immediately inserted as a production state.

[1538] B can generate local node authentication C_i^(N,B) for O_i^B, local connection authentication C_ij^(E,B) with existing execution representation O_j^B, and C_pi^(P,B) for one or more local combinatorial paths.

[1540] The corresponding execution representation or connection can be enabled in B's production execution topology G_prod^B only when the above local authentications satisfy the configured execution conditions.

[1542] 16-31. Combination of Physical and Logical Execution Paths and Authentication Status

[1544] The authentication status can be used not only to allow the execution of software but also to change the deployment status of physical or logical resources.

[1546] For example, a fully authenticated high-frequency execution object can be preferentially mapped to a CPU or GPU cache, FPGA area, on-chip SRAM, NVM, memristor conduction path, percolation path, optical routing path, or other acceleration resources.

[1548] Executable objects in the Restricted or Shadow state can be made to run only in isolated memory areas, low-priority queues, restricted network permissions, or separate virtual environments.

[1550] When authentication is revoked, the resource mapping assigned to the corresponding execution object can be released or switched to a conservative alternative path.

[1552] 16-32. Multi-temporal authenticated memory

[1554] Similar to execution experience, authentication information can also be classified into fast authentication memory C_fast and slow authentication memory C_slow.

[1556] C_fast reflects recent failures, recent environmental changes, or recent verification results with high weight, while C_slow can accumulate stability verified repeatedly over a long period.

[1558] C_fast(t+1) = (1-lambda_cf) C_fast(t) + eta_cf Evidence_t

[1560] C_slow(t+1) = (1-lambda_cs) C_slow(t) + eta_cs Consolidate(C_fast)

[1562] If a sudden change in distribution is detected, it can be immediately downgraded by C_fast, and if long-term stability is confirmed, certification can be maintained at a low verification cost by C_slow.

[1564] 16-33. Co-evolution of Execution Topology and Authentication Topology

[1566] The change in execution topology can be denoted as Delta G_E(t), and the change in authentication topology as Delta G_C(t).

[1568] When new execution representations, connections, or paths are generated through new experiences or learning,

[1570] Delta G_E(t) -> ValidationDemand(t+1)

[1572] This occurs, and based on the above verification results,

[1574] ValidationEvidence(t+1) -> Delta G_C(t+1)

[1576] This may occur.

[1578] The above change in authentication topology is again,

[1580] Delta G_C(t+1) -> Delta G_prod(t+1)

[1582] It causes, and the actual execution result can be input back into both Delta G_E and Delta G_C.

[1584] Therefore, in one embodiment of the present invention, the capability structure of intelligence and the structure verifying capability are not fixed independently of each other, but can co-evolve by receiving mutual state changes as input.

[1586] 16-34. Text-based Structure Diagrams and Operation Diagrams

[1588] The following text structural diagram is an embodiment expressed using reference numbers, component names, and arrows so that the functional relationships, data flow, control flow, authentication flow, and feedback flow of each component can be understood without actual graphic drawings. When creating actual graphic drawings, the functional elements of each reference number may be represented as individual or integrated blocks, and arrows may be indicated in the direction of data, control, authentication, state transition, resource placement, or feedback.

[1590] Text Structure Diagram 1 - Entirely Closed Loop

[1592] 100 Environment·User·Device

[1593] → 110 Observation and Experience Collection Unit

[1594] → 120 Path Memory M

[1595] → 130 Structuring of Experience and Abstraction State Transition Operator O Generator

[1596] → 140 Execution Topology G_E Creation and Update Section

[1597] → 150 Candidate Plan and Candidate Path Generation Unit

[1598] → 160 Compilation candidate chi generation part

[1599] → 170 Isolation Verification or Sandbox Verification Unit

[1600] → 180 Audit and Certification Topology G_C Generation Section

[1601] → 190 Production execution topology G_prod generation section

[1602] → 200 Actual execution part

[1603] → 210 Runtime Observation and Audit Department

[1604] → 220 Path memory update, authentication renewal, and failure test question formatting section

[1605] → Feedback to 140 Execution Topology G_E and 180 Authentication Topology G_C

[1607] The above flow is not limited to a unidirectional serial pipeline, and direct feedback, asynchronous updates, parallel processing, or distributed processing may be performed between one or more functional units.

[1609] Text Structure Diagram 2 - Dual Structure of Execution Topology and Authentication Topology

[1611] 301 Execution expression O_1

[1612] → 302 Execution Link E_12

[1613] → 303 Execution expression O_2

[1614] → 304 Execution Link E_23

[1615] → 305 Execution expression O_3

[1616] → 306 Execution Link E_34

[1617] → 307 Execution expression O_4

[1619] 311 Node Authentication C_1^N

[1620] → 312 Connection Authentication C_12^E

[1621] → 313 Node Authentication C_2^N

[1622] → 314 Connection Authentication C_23^E

[1623] → 315 Node Authentication C_3^N

[1624] → 316 Connection Authentication C_34^E

[1625] → 317 Node Authentication C_4^N

[1627] 321 Path authentication C_pi^P may encompass multiple execution representations and execution links of 301 to 307 in whole or in part.

[1629] 331 Current state z_t, execution context c_t, version vector v_t and resource state r_t

[1630] → 332 Production Topology Filter Phi

[1631] → 333 Production Execution Topology G_prod

[1633] Text Structure Diagram 3 - Individual Authentication and Combined Authentication

[1635] 401 Executable Expression A

[1636] → Individual test of 402 A

[1637] → 403 Node Authentication C_A^N

[1639] 411 Execution expression B

[1640] → Individual test of 412 B

[1641] → 413 Node Authentication C_B^N

[1643] 421 A output, authorization, memory change, data, resource change, or trusted information

[1644] → 422 Combination test of A and B

[1645] → 423 Connection Authentication C_AB^E

[1647] 431 Combination path A → B → C → D

[1648] → 432 Entire Path Cumulative Risk, Cumulative Uncertainty, Data Flow, Authority Transfer, Resource Sharing, and Recoverability Tests

[1649] → 433 Path Authentication C_pi^P

[1650] → 434 One or more authentication statuses: Full, Restricted, Shadow, Revoked, or Reject

[1652] Even if individual node authentication is in an allowed state, if connection authentication or path authentication does not satisfy the allowance conditions, the actual execution of that combination may be restricted.

[1654] Text Structure Diagram 4 - Production Execution Topology Filter

[1656] 501 All Candidate Execution Topology G_E

[1657] → 502 Node Authentication Verification

[1658] → 503 Connection Verification

[1659] → 504 Path Authentication Confirmation

[1660] → Check 505 status area

[1661] → 506 Check Model, Memory, Tools, Environment, and Reviewer Versions

[1662] → 507 Check resource status and permissions

[1663] → 508 Production Topology Transformation

[1664] → 509 G_prod, the production execution topology that Planner actually explores or executes

[1666] 508 Production topology transformations may include one or more of deletion, deactivation, cost increase, risk weight increase, priority downgrade, Shadow transition, Canary transition, additional verification requirement, or Full activation.

[1668] Text Structure Diagram 5 - Hierarchical Isolation Verification

[1670] 601 Candidate Execution Object

[1671] → 602 Low-cost rule check or static check

[1672] → 603 World Model Rollout

[1673] → 604 High-precision simulation or digital twin

[1674] → 605 Type verification, model checking, or symbolic execution

[1675] → 606 Shadow execution

[1676] → 607 Canary execution

[1677] → 608 Production Promotion

[1679] If the rejection condition set at any of stages 602 to 607 is satisfied, one or more measures such as 609 modification request, isolation, disposal, recompilation, retraining, or non-entry into the upper verification stage may be performed.

[1681] Text Structure Diagram 6 - Optional Recovery Based on Failure

[1683] 701 Actual execution failure F_t

[1684] → 702 Calculate Candidate Root Cause

[1685] → 703 Query Authentication Dependency Graph G_D

[1686] → 704 Calculation of Influence Range R_F

[1687] → 705 Optional demotion or revocation of related node, connection, path, compiled executable, or version-dependent certificate

[1688] → 706 Local clipping of production execution topology G_prod

[1689] → 707 Select Alternate Path, Return to Parent Planner, or Limit Execution

[1691] 704 Validation valid areas not included in the scope of influence can be maintained in the production execution topology.

[1693] Text Structure Diagram 7 - Local Revalidation and Recovery

[1695] 801 Recovered or demoted subgraph

[1696] → 802 Create past failure conditions

[1697] → 803 Create Boundary Conditions

[1698] → 804 Generate out-of-distribution condition

[1699] → 805 Create hostility condition

[1700] → Create 806 version change condition

[1701] → 807 Local revalidation test set Omega_local

[1702] → 808 Local Sandbox or Isolation Validation

[1703] → 809 Re-examination

[1705] 809 If the re-examination result satisfies the restoration conditions

[1706] → 810 Full or Restricted Authentication Recovery

[1707] → 811 Partial restoration of production execution topology G_prod

[1709] If the 809 re-examination result does not satisfy the restoration conditions

[1710] → 812 Retrain, recompile, long-term isolation, create or discard alternate paths

[1712] Text Structure Diagram 8 - Combination of Compilation and Authentication

[1714] 901 Execution path O_1 → O_2 → O_3 → O_4

[1715] → 902 Evaluation of Repeatability, Success Rate, Consistency, Efficiency, Uncertainty, and Constraint Compliance Conditions

[1716] → 903 Verify path authentication C_pi^P

[1717] → 904 compile

[1718] → 905 Create compile executable object chi_pi and dependency list

[1720] 906 Revoked transition of Connection Authentication C_23^E

[1721] → 907 Path Authentication C_pi^P Suspended or Restricted Transition

[1722] → 908 PartialDecompile or Decompile of compile executable object chi_pi

[1723] → 909 Maintaining the valid preceding section and replanning the subsequent section

[1725] Text Structure Diagram 9 - Version and Provenance Dependency

[1727] 1001 Execution object chi

[1728] → 1002 Model Version V_model

[1729] → 1003 Memory Version V_memory

[1730] → 1004 Tool or API version V_tool

[1731] → 1005 Environment Version V_env

[1732] → 1006 State Abstraction Version V_abs

[1733] → 1007 Reviewer Version V_judge

[1734] → 1008 Generation or Learning Provenance

[1735] → 1009 Certificate Expiration Information t_expire

[1736] → 1010 Version and Context Compatibility Evaluation

[1738] If the evaluation of 1010 is Compatible, certification can be maintained; if Partial, the certification area can be reduced; if Unknown, Shadow or additional verification can be performed; and if Incompatible, it can be revoked or re-verified.

[1740] Text Structure Diagram 10 - Distributed Experience Execution Package XEP Re-authentication

[1742] 1101 Execution representation of 1st intelligent node A O_A

[1743] → 1102 A's Certification Information Cert_A and Verification Evidence Evidence_A

[1744] → 1103 Experience Execution Package XEP Generation

[1745] → 1104 Transmitted to 2nd Intelligent Node B

[1746] → 1105 State space or execution context mapping psi_A_to_B

[1747] → 1106 Generate candidate execution expression O_B of B

[1748] → 1107 B's local node authentication

[1749] → 1108 Local connection authentication with B's existing execution representation

[1750] → 1109 B's local combinatorial path authentication

[1751] → Restricted or full activation of the production execution topology G_prod^B of 1110 B

[1753] The certification information of A does not imply automatic approval in B but can be used as evidence to determine the verification budget, prior, risk, or test priority.

[1755] Text Structure Diagram 11 - Physical and Logical Resource Mapping

[1757] 1201 Full authentication execution object

[1758] → 1202 Fast cache, FPGA, NVM, memristor, percolation path, on-chip SRAM, or other acceleration resource priority mapping

[1760] 1211 Restricted authentication execution object

[1761] → 1212 Limited memory, low priority, limited network permissions, or limited hardware area

[1763] 1221 Shadow authentication execution object

[1764] → 1222 Isolation environment, output blocking, replication environment, or non-production resources

[1766] 1231 Revoked or Expired executable object

[1767] → 1232 Unmapping resources, revoking execution privileges, or switching to a conservative alternate path

[1769] Text Structure Diagram 12 - Multi-screener and Hard Gate

[1771] 1301 Candidate Execution Object

[1772] → 1302 Goal Achievement Review J_goal

[1773] → 1303 Constraint Compliance Review J_constraint

[1774] → 1304 Robustness Assessment J_robustness

[1775] → 1305 Uncertainty Screening J_uncertainty

[1776] → 1306 Recoverability Review J_recovery

[1777] → 1307 Hard Gate Priority Evaluation

[1779] 1307 If you passed through the hard gate

[1780] → 1308 Weighted evaluation using reviewer reliability q_k

[1781] → 1309 One or more review decisions of Promote, Restricted, Revise, or Reject

[1783] If a candidate fails to pass the hard gate, promotion may be restricted even if other evaluation scores are high.

[1785] Text Structure Diagram 13 - Parallel and Asynchronous Combined Authentication

[1787] 1401 Execution expression O_A

[1788] → 1403 Shared memory, file, API, database, network session, or physical resource

[1790] 1402 Execution expression O_B

[1791] → 1403 Shared memory, file, API, database, network session, or physical resource

[1793] 1403 Shared resources

[1794] → 1404 Subsequent execution expression O_C

[1795] → 1405 Create parallel or asynchronous path authentication C_parallel^P

[1796] → 1406 Allow, restrict, or deny actual parallel execution

[1798] Even if O_A and O_B each satisfy individual authentication, if there is a shared resource conflict, race condition, intermediate state exposure, or privilege coupling, actual execution may be restricted based on the path authentication result of 1405.

[1800] Text Structure Diagram 14 - Verification Budget Scheduler

[1802] 1501 candidate set

[1803] → 1502 Calculation of Risk R

[1804] → 1503 Calculate Uncertainty Sigma

[1805] → 1504 Calculation of Novelty N

[1806] → 1505 Calculate Failure History F

[1807] → 1506 Calculation of Importance or Frequency of Use I

[1808] → 1507 Calculation of Verification Budget B_k

[1810] If the verification budget for 1507 is low, 1508 limited regression test,

[1811] In the case of the intermediate 1509 out-of-distribution test and combination test,

[1812] In high cases, 1510 hostile tests, high-precision simulations, Shadow and Canary tests can be assigned.

[1814] Text Structure Diagram 15 - Overall State Transitions of Authentication Status

[1816] 1601 Candidate

[1817] → 1602 Sandbox or Isolation Verification

[1818] → 1603 Pending

[1820] If all conditions are satisfied in 1603, 1604 Full,

[1821] If only some authentication areas satisfy the conditions, 1605 Restricted,

[1822] If additional verification of the actual effect limited state is required, 1606 Shadow or Canary,

[1823] If the rejection conditions are met, it can be switched to 1607 Revoked or Reject.

[1825] If a runtime anomaly occurs in 1604 Full or 1605 Restricted status

[1826] → 1608 Suspended

[1827] → 1609 Dependency-based selective authentication revocation

[1828] → 1610 Local re-verification

[1830] If 1610 satisfies the recovery conditions, it returns to 1604 Full or 1605 Restricted via 1611 Restore, and

[1831] If the recovery conditions are not met, you can switch to 1612 Recompile, Relearn, Long-term Isolation, or Reject.

[1833] Text Structure Diagram 16 - Resource Cost-Aware Performance Demotion and Dynamic Scaling

[1835] 1701 Current production execution topology G_prod

[1836] → 1702 Select candidate execution object or composition path

[1837] → 1703 Functional Authentication and Hard Gate Verification

[1838] → 1704 Measuring or predicting resource state r_t

[1839] → 1705 Service Level Requirement Profile (SLR) Comparison

[1841] If all setting conditions are satisfied in 1705

[1842] → 1706 Maintain current execution object

[1843] → 1707 Continuously monitor runtime resource state

[1845] If one or more of the latency, energy, memory, accelerator usage, network status, or other resource conditions deviate in 1705

[1846] → 1708 Restricted or resource-degraded state

[1847] → 1709 Finding a replacement execution object

[1848] → 1710 Resource-aware recompilation or physical-logical resource remapping

[1849] → 1711 Local re-verification of the changed area

[1850] → 1712 Update production execution topology G_prod

[1851] → 1713 Execute alternative execution path

[1853] 1714 The resource state hysteresis control unit may restrict the immediate return of the existing high-performance path until the recovery threshold and recovery retention time are satisfied.

[1855] Text Structure Diagram 17 - Multimodal Experience Transfer and Local Re-authentication Between AGIs

[1857] 1801 Multimodal observation of 1st Intelligent Node A

[1858] → 1802 Generation of features or latent representations by modality of A

[1859] → 1803 Sorting Time, Space, Objects, and Events

[1860] → Cross-modal authentication of 1804 A

[1861] → 1805 Multimodal Experience Execution Package MM-XEP Generation

[1862] → 1806 Transmitted to 2nd Intelligent Node B

[1863] → 1807 Analysis of the difference in modality sets between A and B

[1864] → 1808 Heterogeneous Multimodal Mapping psi_MM_A_to_B

[1865] → Local individual modality authentication of 1809 B

[1866] → Local Cross-Modal Authentication of 1810 B

[1867] → 1811 B Existing execution representation and connection authentication

[1868] → Authentication of 1812 B's combination path

[1869] → Update production execution topology G_prod^B of 1813 B

[1870] → 1814 Actual Execution and Multimodal Observation

[1871] → 1815 Evaluation of Actual Results and Prediction Errors by Modality

[1872] → Update execution topology and authentication topology of 1816 B

[1874] 1817 If a specific modality is lost or degraded, selectively recover only the relevant certification dependency scopes of 1809 to 1812, and

[1875] → 1818 Alternative modalities, external intelligence nodes, additional observations, or world model-based alternative path generation

[1876] → 1819 Local Multimodal Re-verification

[1877] → Limited or full recovery to 1813 production execution topology

[1879] Text Structure Diagram 18 - Integrated Synthetic Performance Evaluation Pipeline

[1881] 1901 Synthesis Scenario and Parameter Generation

[1882] → 1902 Standard Structure Execution

[1883] → 1903 Path memory structure execution

[1884] → 1904 Path memory and compilation structure execution

[1885] → 1905 Execution Topology · Authentication Topology · Production Execution Topology Combined Structure Execution

[1886] → 1906 Node, Connection, Path, and Cross-Modal Authentication Applied

[1887] → 1907 Failure and Resource Change Injection

[1888] → 1908 Dependency-based selective recovery and local revalidation

[1889] → 1909 Enable Resource-Aware Alternative Paths and Multimodal Alternative Paths

[1890] → 1910 Calculation of exploratory quantity, risk activity, validation quantity, latency, energy, implementation utility, transferability rate, and task completion

[1891] → 1911 Authentication Threshold sweep

[1892] → 1912 Evaluation of the trade-off between feasibility and risk containment

[1894] 1913 The above results are stored as synthetic simulations for explaining structural effects and are not interpreted as actual product performance or actual safety verification values.

[1896] Text Structure Diagram 19 - Isolation Promotion from Dream Generation to Production

[1898] 2001 Real-life Experience and Path Memory

[1899] → 2002 Select Rare Region · Near Failure · Uncertain Region

[1900] → 2003 Generation of dream candidates based on world models, simulators, and generative models

[1901] → 2004 Stored in Virtual Experience Topology G_H along with Dream provenance

[1902] → 2005 Self-Assessment

[1903] → 2006 World Model · Forward-Reverse · Constraint Consistency Test

[1904] → 2007 Independent or Sealed Examination

[1906] If 2007 is Rejected

[1907] → 2008 Discard virtual candidates, regenerate, or maintain low priority

[1909] If 2007 is Hypothesis-only

[1910] → Maintained as 2009 Shadow test conditions or PendingFailure

[1912] If 2007 is a candidate-pass

[1913] → Shadow candidates of 2010 execution topology G_E

[1914] → 2011 Local Node Authentication

[1915] → 2012 Connection Certification

[1916] → 2013 Path Certification

[1917] → 2014 Cross-modal, Resource, and Version Authentication

[1918] → 2015 Authentication Topology G_C Update

[1919] → Enable Restricted or Full for 2016 Production Run Topology G_prod

[1920] → 2017 Actual Implementation and Observation of Real-World Results

[1921] → 2018 Dream Prediction Error, Dream Debt, and World Model Reliability Update

[1923] Text Structure Diagram 20 - Prevention of False Failure and Memory Consolidation

[1925] 2101 Dream Generator generates failure hypothesis F_hat

[1926] → 2102 Save HypotheticalFailure status

[1927] → 2103 Separation from Actual Failure

[1928] → 2104 Perform one or more of the following: independent oracle, seal review, or multiple verification

[1930] If verification is insufficient

[1931] → 2105 Shadow Exam Questions or Additional Verification Candidates

[1932] → 2106 Prohibition on permanent revocation of existing Full authentication

[1934] If independent evidence satisfies the establishment conditions

[1935] → 2107 IndependentlySupported

[1936] → 2108 Reenactment of a real or containment environment

[1937] → 2109 ConfirmedFailure

[1938] → 2110 Authentication-dependent selective retrieval

[1939] → 2111 Local re-verification

[1941] 2112 Real Experiences and Verified Dream Experiences

[1942] → 2113 Realization Priority Grading

[1943] → 2114 Memory Integration and Rehearsal

[1944] → 2115 Iteration path compression, compilation, and test set update

[1945] → 2116 Long-term unverified virtual experiences increase dream debt

[1946] → 2117 Priority Downgrade · Certification Limit Restriction · Expiration

[1948] Text Structure Diagram 21 - Socially Adaptive Robot Execution Closed Loop

[1950] 2201 Multimodal Environments and Human Observation

[1951] → 2202 Identification of Social Actors, Groups, and Objects

[1952] → 2203 Creating Role, Relationship, Consent, Authority, Trust, and Privacy States

[1953] → 2204 Update Social Topology G_S

[1954] → 2205 Robot Action Candidate Generation

[1955] → 2206 Physical Safety Hard Gate

[1956] → 2207 Law and Policy Hard Gate

[1957] → 2208 Permission·Consent Hard Gate

[1958] → 2209 Certification of Social Norms and Social Acceptability

[1959] → 2210 Update production execution topology G_prod

[1960] → 2211 Select Movement, Manipulation, Speech, Gaze, Gesture, and Idle Actions

[1961] → 2212 Actual Robot Behavior

[1962] → 2213 Observation of Human and Group Reactions

[1963] → 2214 Social Topology and Authentication Topology Update

[1965] Text Structure Diagram 22 - Revocation of Robot Action Authority Due to Withdrawal of Consent and Role Change

[1967] 2301 Current Social Topology G_S

[1968] → 2302 Consent Withdrawal · Role Change · Authority Revocation · Privacy Change Event

[1969] → 2303 Identification of Target Behaviors and Scope of Changed Social Conditions

[1970] → 2304 Exploring the Social State Dependency Graph

[1971] → 2305 Affected Action Representations, Links, Paths, and Social Certifications Output

[1972] → Only production execution paths related to 2306 are Restricted or Revoked

[1973] → 2307 Evaluate safe suspension or completion conditions of related robot behavior in execution

[1974] → 2308 Maintaining irrelevant behavioral paths

[1975] → 2309 Create Alternative Action, Confirmation Inquiry, and Human Approval Candidates

[1976] → 2310 Local re-verification

[1977] → 2311 G_prod Partial Recovery

[1979] Text Structure Diagram 23 - Separation of Authority in Social Dreams and Actual Robot Behavior

[1981] 2401 Actual Social Conditions G_S

[1982] → 2402 Social Interaction Dream Generation in Virtual Experience Topology G_H

[1983] → 2403 Generating Expected Reactions, Conflicts, Inconveniences, and Alternative Paths

[1984] → 2404 Virtual Consent_hat, Trust_hat, and Relation_hat are maintained as HypotheticalSocialState

[1985] → 2405 Dream Gate and Independent Verification

[1986] → 2406 Used only as Shadow behavior candidates or test conditions that do not require actual consent or authorization

[1987] → 2407 Reconfirmation of Actual Social Status G_S and Actual Consent / Authority

[1988] → 2408 Physical Safety · Authorization · Consent · Social Authentication

[1989] → 2409 Robot Behavior

[1990] → 2410 Actual Human Reactions

[1991] → 2411 Comparison of Virtual Social Predictions and Actual Reactions

[1992] → 2412 Social Model, Dream Trust, and Social State Uncertainty Update

[1996] 16-35. Resource State Vector

[1998] The resource state at time t can be represented as follows.

[2000] r_t = (u_CPU, u_GPU, u_NPU, m_used, q_len, L_net, P_sys, E_req, T_dev, A_tool)

[2002] Here, u_CPU, u_GPU and u_NPU may represent one or more computational resource occupancy, m_used may represent memory occupancy or occupancy, q_len may represent execution queue length, L_net may represent network or external tool round-trip delay, P_sys may represent power, E_req may represent energy per request or task, T_dev may represent device temperature, and A_tool may represent the availability status of an external tool or API.

[2004] The above resource status can be measured by actual sensors or system counters, or predicted as a future value using recent execution logs, queue status, resource reservation information, or a prediction model.

[2006] 16-36. Service Level Requirements Profile

[2008] The technical operating conditions for the execution object chi can be expressed as follows.

[2010] SLR_chi = (L_max, L_p95, Th_min, Err_max, P_max, E_max, M_max, U_acc_max, L_net_max, R_max)

[2012] L_max or L_p95 may represent maximum or quantile delay, Th_min may represent minimum throughput, Err_max may represent maximum error rate, P_max may represent maximum power, E_max may represent maximum energy, M_max may represent maximum memory occupancy, U_acc_max may represent maximum accelerator occupancy, L_net_max may represent maximum network delay, and R_max may represent maximum allowable risk.

[2014] The above requirement profiles can be configured differently depending on the task, user, device, time, risk level, service type, or authentication area.

[2016] 16-37. Normalized Resource Costs

[2018] The resource cost of candidate execution path pi can be expressed as follows.

[2020] C_res(pi,t) = alpha_L L_norm + alpha_E E_norm + alpha_M M_norm + alpha_D D_norm + alpha_Q Q_norm

[2022] Here, L_norm may represent normalization latency, E_norm normalization energy, M_norm normalization memory, D_norm normalization data movement, and Q_norm normalization queue or external call burden.

[2024] Each alpha can be set or trained according to the execution goal or device environment.

[2026] After first evaluating hard gates corresponding to functional safety, constraint compliance, or policy compliance, a path can be selected among the candidates that passed the hard gates using C_res or a risk-adjusted objective function including it.

[2028] 16-38. Authentication area containing resource state

[2030] The authentication area of ​​the compiled executable object can be extended to a combined area of ​​state and resource state.

[2032] D_chi^(cert,res) = {(z,r) : z in D_chi^cert and g_res(r,chi) <= 0}

[2034] Here, g_res can indicate whether latency, memory, energy, power, accelerator occupancy, network latency, or other resource conditions are satisfied.

[2036] Even if the current state is within the functional authentication area, if the current resource state goes outside the aforementioned resource authentication area, the execution object can be changed to the Restricted, Shadow, Suspended, or Recompile state.

[2038] 16-39. Resource-Aware Recompilation

[2040] If the existing compile execution object chi_A does not satisfy resource conditions, an alternative execution object chi_B that performs the same or similar goals can be created or selected as follows.

[2042] chi_B = Recompile(chi_A, r_t, SLR, Cert_t)

[2044] The above Recompile is not limited to a fixed specific transformation and may include model replacement, tool replacement, reduction of the number of calls, stage merging, stage splitting, change in parallelism, change in precision, use of cache, change in local / remote execution location, change in hardware mapping, or a combination thereof.

[2046] The recompiled executable object does not automatically inherit existing authentication and can re-verify all or part of it in response to changed execution paths and resource mappings.

[2048] 16-40. Resource State Hysteresis

[2050] To reduce frequent execution object switching when resource states fluctuate near boundary values, demotion and recovery conditions can be set differently.

[2052] For example, set the demotion threshold for accelerator occupancy to tau_down and the recovery threshold to tau_up, and

[2054] tau_up < tau_down

[2056] It can be composed of.

[2058] If the accelerator utilization exceeds tau_down and persists for a set retention time Delta t_down or longer, it is switched to a low-resource execution object, and subsequently, it can be restored to a high-performance execution object only when the utilization is less than tau_up and persists for a set retention time Delta t_up or longer.

[2060] 16-41. Multiple Substitution Execution Objects

[2062] For a single goal G, multiple execution objects can be maintained as follows.

[2064] X_G = {chi_high, chi_balanced, chi_low, chi_local, chi_remote}

[2066] chi_high supports high performance and high resource consumption, chi_balanced supports medium performance and medium resource consumption, chi_low supports low resource consumption, chi_local supports local execution, and chi_remote supports remote execution.

[2068] The planner or production topology generator can activate one or more of X_Gs or change their priority based on the current certification status, risk level, resource status, and service level requirements profile.

[2070] 16-42. Separation of Functional Risk and Resource Efficiency

[2072] The functional constraint violation risk H_safe and resource cost C_res may not be treated as a simple single weighted sum.

[2074] first,

[2076] H_safe(pi) = PASS

[2078] Generate a candidate set P_safe that must satisfy, and

[2080] P_safe = {pi : H_safe(pi)=PASS}

[2082] Within the above candidate set,

[2084] pi* = arg max_(pi in P_safe) (U(pi) - lambda_R R(pi) - lambda_C C_res(pi))

[2086] Path selection including resource costs can be performed as follows.

[2088] Accordingly, it is possible to prevent violations of functional safety or policy constraints from being compensated solely on the grounds of high resource efficiency.

[2090] 16-43. Physical Resource Reallocation and Authentication Integration

[2092] When resource congestion or temperature rise occurs while a fully authenticated high-frequency execution object is preferentially placed in a high-speed cache, on-chip memory, FPGA, NVM, memristor, percolation path, or other acceleration resources, the logical authentication of the execution object may not necessarily be discarded, and only the physical resource mapping may be changed.

[2094] Conversely, if a specific hardware mapping itself constitutes part of the authentication, only the authentication corresponding to the hardware mapping change can be selectively switched to a Suspended or re-verified state.

[2096] Therefore, logical execution objects, functional authentication, and physical resource authentication can be distinguished from one another, and only the necessary scope can be updated.

[2098] 16-44. Resource-aware closed loop

[2100] One embodiment of resource-aware execution control may include the following flow.

[2102] Select execution object

[2103] → Functional authentication verification

[2104] → Check resource authentication area

[2105] → Actual execution

[2106] → Observe runtime resource state

[2107] → Service Level Requirements Profile Comparison

[2108] → Maintain current execution path if conditions are met

[2109] → Demotion, partial decompilation, recompilation, or resource reallocation upon deviation from condition

[2110] → Local re-verification of the changed execution path

[2111] → Update production execution topology

[2112] → Subsequent execution

[2114] The above closed loop is combined with the updating of experience memory and authentication topology to accumulate experience regarding which execution object was suitable under which resource conditions as the same or similar tasks are repeated.

[2118] 16-45. Multimodal Experience State

[2120] The multimodal experience state observed or generated by intelligent node A at time t can be represented as follows.

[2122] MMA_t = (M_A, X_A,t, Align_A,t, U_A,t, V_A,t, Prov_A,t)

[2124] M_A may represent the set of available modalities, X_A,t may represent the actual or latent representation of each modality, Align_A,t may represent the time, space, object, event, or semantic alignment information between modalities, U_A,t may represent the modality-specific or cross-modal uncertainty, V_A,t may represent the version information of the sensor, encoder, model, calibration, or transducer, and Prov_A,t may represent the observation or generation provenance.

[2126] For example, M_A may include text, vision, audio, lidar, force, and tool trace.

[2128] 16-46. Multimodal Experience Execution Package

[2130] The experience execution package transmitted between distributed nodes can be extended as follows.

[2132] MM-XEP_i = (I_i, T_i, O_i, Sigma_i, Credit_i, Prov_i, Exec_i, Cert_i, Mod_i, Align_i, Sync_i, V_mod,i, U_mod,i)

[2134] Mod_i is a set of modalities included in the experience, Align_i is a correspondence between modalities, Sync_i is time or event synchronization information, V_mod,i is a sensor, encoder, or modality converter version, and U_mod,i may include modality-specific and cross-modal uncertainties.

[2136] The above package does not necessarily have to include the entire raw sensor data and may include one or more of feature quantities, latent vectors, object graphs, event graphs, state transition summaries, compressed representations, hashes, verification evidence, or reference information.

[2138] 16-47. Temporal and Spatial Alignment

[2140] The time alignment error of modalities m_a and m_b can be denoted as Delta_t^(a,b), and the spatial alignment error as Delta_x^(a,b).

[2142] Example conditions for cross-modal alignment to be allowed are,

[2144] abs(Delta_t^(a,b)) <= tau_time

[2146] and,

[2148] d_space(X_a, X_b) <= tau_space

[2150] It can be represented as.

[2152] The above threshold value does not need to be a fixed value and can be changed according to the movement speed, sensor period, object size, estimation uncertainty, or operational risk.

[2154] 16-48. Semantic Coherence and Intermodal Contradiction

[2156] In cases where multiple modalities yield different semantic distributions for the same object or event, the degree of semantic inconsistency between modalities m_a and m_b,

[2158] D_sem^(a,b) = d_sem(P_a(y|x_a), P_b(y|x_b))

[2160] It can be represented as.

[2162] D_sem^(a,b) > tau_sem

[2164] In this case, cross-modal authentication for the corresponding modality combination can be changed to Restricted, Pending, or Reject.

[2166] Intermodal contradictions can include not only simple class mismatches but also mismatches in object location, state, intent, velocity, risk, authority, tool state, or expected state transitions.

[2168] 16-49. Cross-modal Authentication Object

[2170] The cross-modal authentication object C_ab^X can be represented as follows.

[2172] C_ab^X = (D_ab, Omega_ab, A_time, A_space, A_sem, A_causal, U_ab, V_ab, State_ab)

[2174] A_time may represent temporal alignment fitness, A_space may represent spatial alignment fitness, A_sem may represent semantic consistency, A_causal may represent state transition or causal consistency, U_ab may represent cross-modal uncertainty, V_ab may represent the associated sensor, encoder, or converter version, and State_ab may represent the authentication state.

[2176] C_ab^X may be stored independently of node authentication or path authentication, or it may be linked to the dependent authentication of the corresponding authentication object.

[2178] 16-50. Multimodal Path Authentication

[2180] If path pi uses multiple modalities, path authentication C_pi^P may include authentication of the connection with the execution node as well as related cross-modal authentication.

[2182] for example,

[2184] C_pi^MM = Aggregate(C_node, C_edge, C_path, C_crossmodal)

[2186] It can be configured as follows.

[2188] Even if each individual execution node and modality independently satisfies the authentication conditions, if the C_crossmodal required for the path is not valid in the current state, the path can be excluded from the production execution topology or converted to a target for additional verification.

[2190] 16-51. Transition between heterogeneous modality sets

[2192] The modality set of the first intelligent node A,

[2194] M_A = {vision, audio, lidar, text}

[2196] The modality set of the second intelligent node B,

[2198] M_B = {vision, radar, proprioception, text}

[2200] It can be said that.

[2202] In the case where B cannot directly use the spatial state observed by A based on lidar without the same lidar,

[2204] psi_MM^(A->B)

[2206] It can convert A's lidar latent representation or state transition result into B's radar, vision, proprioception, or common world model representation.

[2208] Transformations can be implemented using one or more of supervised learning, self-supervised learning, common latent space, coordinate transformation, object graph, state transition model, generative model, world model, or rule-based mapping.

[2210] 16-52. Local Multimodal Re-authentication of the Receiver Node

[2212] The authentication Cert_A^MM of sending node A does not imply automatic acknowledgment of receiving node B.

[2214] B can perform one or more of the following on the transformed multimodal experience.

[2216] First, individual authentication for each local modality of B.

[2218] Second, cross-modal authentication between the modalities actually possessed by B.

[2220] Third, authentication of the connection between the converted execution representation and B's existing execution representation.

[2222] Fourth, path certification of the combination path to be used in B's production environment.

[2224] Fifth, version compatibility certification for the sensor, encoder, calibration, world model, or converter version.

[2226] The converted experience or execution representation can be activated in G_prod^B only within the scope where the above local re-authentication is completed.

[2228] 16-53. Loss of Modality and Optional Authentication Revocation

[2230] Assume that the current path pi relies on vision, audio, and radar.

[2232] When the vision modality fails or enters a state of severe uncertainty increase, the scope of influence for not only the node authentication of the vision itself but also the cross-modal authentication, connection authentication, and path authentication dependent on the vision can be calculated using the modality dependency graph G_MD.

[2234] R_M = Reach_GMD(vision)

[2236] Only authentication objects included in R_M can be changed to Suspended or Revoked, while unrelated execution paths using only audio or radar can be retained.

[2238] 16-54. Modality Alternative Paths

[2240] Main execution path,

[2242] pi_1 = vision -> identify -> manipulate

[2244] If it cannot be used due to vision loss,

[2246] pi_2 = radar -> audio -> shared-experience -> identify -> manipulate

[2248] or,

[2250] pi_3 = external-node-query -> world-model -> identify -> manipulate

[2252] You can create or activate alternative paths such as.

[2254] Alternative paths do not automatically inherit existing authentication and can only be promoted to production status within the scope of passing the cross-modal authentication and path authentication required for the new combination.

[2256] 16-55. Resolution of Modality Contradictions by Additional Observation

[2258] If the degree of inconsistency between multiple modalities exceeds a set threshold, additional information can be obtained without immediately selecting one modality as the correct answer.

[2260] The additional information acquisition operation may include one or more of changing the camera viewpoint, acquiring additional acoustic samples, using different wavelengths or sensors, measuring distance to an object, querying an external intelligence node, querying a user, searching for a tool, rolling out a world model, or revalidating a sandbox.

[2262] If D_sem or U_ab is reduced by additional information and the cross-modal authentication condition is satisfied again, the relevant path can be restored to the production execution topology.

[2264] 16-56. Modality Provenance and Contamination Isolation

[2266] Each modality of a multimodal experience may be assigned provenance information including one or more of the following: data source, sensor ID, creator, model ID, time of creation or measurement, transformation process, calibration status, edit or preprocessing history, trust level, or external signature.

[2268] If anomalies or errors occur repeatedly in a specific provenance source, only the modality representations derived from that source and the certification objects that depend on them can be selectively downgraded or the verification budget increased.

[2270] Accordingly, a single contaminated input source may not invalidate the entire unrelated multimodal experience.

[2272] 16-57. Creating Abstract State Transition Operators for Multimodal Experiences

[2274] If multiple multimodal experiences produce the same or similar state transition results even though they have different specific observations, they can be integrated into a single modality-independent or partially modality-dependent abstract state transition operator.

[2276] For example, if vision-based object access experience and lidar-based object access experience have the same access behavior and subsequent state distribution in a common latent state,

[2278] O_approach^(abstract)

[2280] It can generate.

[2282] The starting condition of the above operator can be defined by a combination of the required semantic state, uncertainty range, and one or more allowed modalities instead of a specific raw sensor type.

[2284] 16-58. Multimodal Collaborative Learning Closed Loop Between AGIs

[2286] The multimodal experience transfer closed loop between the first intelligent node A and the second intelligent node B can be configured as follows.

[2288] Multimodal observation of A

[2289] → Generation of features or latent expressions for each modality of A

[2290] → A's modality alignment and cross-modal authentication

[2291] → MM-XEP generation

[2292] → Send to B

[2293] → Heterogeneous modality mapping of B

[2294] → B's local modality authentication

[2295] → B's local cross-modal authentication

[2296] → B's connection and path authentication

[2297] → Update B's production execution topology

[2298] → Actual execution of B

[2299] → Actual results and error observations by modality

[2300] → Update B's authentication topology and execution topology

[2301] → If necessary, forward verification evidence or failure experience to A or another intelligent node

[2303] Accordingly, the experience acquired by one intelligent node is not transmitted merely as a simple text description, but can be reinterpreted in the local sensory and execution systems of other intelligent nodes as a structured experience that includes state transition meanings, modality alignment information, verification evidence, and authentication scope.

[2307] 16-59. Purpose of Integrated Synthetic Performance Evaluation

[2309] The performance evaluation in this section is a synthetic simulation intended to explain the structural operating principles and expected relative trends of the present invention.

[2311] The search quantity, failure rate, safety, latency, energy, resource occupancy, transition success rate, recovery rate, certification pass rate, or other figures used in this section are not values ​​measured from actual general-purpose artificial intelligence, actual products, actual robots, actual cloud services, actual semiconductors, or actual safety performance.

[2313] Furthermore, the relative reduction rate or multiplier calculated under specific synthetic conditions is a result of calculations intended to compare Structure A and Structure B under the same conditions, and does not directly imply actual wall-clock speedup, actual power saving rate, actual accident reduction rate, or actual registrable technical effect.

[2315] The performance evaluation of the present invention may include the following five axes.

[2317] First, structural search space changes based on experience accumulation, compilation, and authentication-combined planning.

[2319] Second, the trade-off between combination risk and feasibility due to node authentication, path authentication, and cross-modal authentication.

[2321] Third, comparison of verification quantities for full re-verification after failure, module-level re-verification, and local re-verification based on authentication dependencies.

[2323] Fourth, comparison of the static execution path and the resource-aware authentication path under conditions where delay or resource state prediction error exists.

[2325] Fifth, comparison of multimodal experience transfer between heterogeneous intelligence nodes and alternative path recovery in modality loss situations.

[2327] 16-60. Plan Exploration Performance Evaluation Model

[2329] The number of branches of the standard planning problem can be set to B_0=8 and the planning depth to H_0=10.

[2331] Learning progress according to experience episode n,

[2333] f(n) = 1 - exp(-n / 22)

[2335] It can be placed as.

[2337] For example, in a synthetic model with path memory, runtime compilation, and authentication-coupled planning applied, the number of valid branches and valid depths,

[2339] B_v6(n) = 8 - 5f(n)

[2341] H_v6(n) = 10 - 5f(n)

[2343] It can be set to.

[2345] If the depth is not an integer, an approximation model can be used that proportionally weights a portion of the last depth.

[2347] The standard number of search nodes is,

[2349] N_base = sum_(d=0)^10 8^d

[2351] Calculate as,

[2353] The number of synthetic search nodes in v6 can be calculated by reflecting the number of valid branches, valid depth, and authentication filters.

[2355] For example, the residual coefficient of the authentication filter,

[2357] k_cert(n) = 0.82 + 0.10(1-f(n))

[2359] It can be placed as.

[2361] The above model is not a model that directly calculates execution time, but a synthetic approximation model designed to explain the impact of iterative experience and authentication on the planner's structural candidate space.

[2363] 16-61. Combined Risk Performance Evaluation Model

[2365] In synthetic combination risk assessment, 240 execution skills or execution expressions can be generated, and 60,000 candidate combination paths of length 3 to 6 can be generated.

[2367] The synthetic failure risk of individual nodes is generated from the Beta(1.5,75) distribution, and a combination-specific additional risk that exists in only some of the execution expression pairs can be assigned with a sparse probability of about 1.8%.

[2369] The risk increase amount of the execution expression pair that generates the above additional risk can be set to a range of 0.015 to 0.14 in the synthetic condition.

[2371] Assuming that 45% of all candidate paths are paths using two or more modalities, cross-modal contradiction risk can be added to those paths.

[2373] Under the above conditions, the entire v6 structure including no authentication, node authentication, combined authentication of nodes and paths, and node-path-cross-modal authentication can be compared.

[2375] 16-62. Cost Evaluation Model for Post-Failure Revalidation

[2377] The total number of authenticated and executable objects can be set to 5,000, and the default number of re-verification tests per object can be set to 50.

[2379] The actual number of affected objects in each failure event can be synthesized using local dependencies around the cause object and some cross-dependencies.

[2381] Total re-verification tests all 5,000 objects, module-level re-verification tests one or more affected modules, and dependency-based local re-verification can test by adding a safety margin to the impact range calculated by the authentication dependency graph.

[2383] The relative verification burden of the three strategies mentioned above can be compared by averaging the number of objects and test runs over multiple synthesis failure events.

[2385] 16-63. Resource-Geography Routing Performance Evaluation Model

[2387] In synthetic resource evaluation, 120,000 requests can be generated, and three types of execution objects can be assumed: high-performance path, balanced path, and low-resource path.

[2389] Each path may have different reference delay, energy per request, expected utility, nominal risk, and congestion sensitivity.

[2391] For example, the reference delays for high performance, balanced, and low-resource paths can be set to 175 ms, 155 ms, and 120 ms, respectively, and the energy per request can be set to 2.8 J, 1.8 J, and 1.1 J, respectively.

[2393] The delay may be increased according to the actual GPU or accelerator congestion, and a composite prediction error of standard deviation 16 ms may be assigned to the prediction delay used by the planner, and a separate composite error of standard deviation 18 ms may be assigned to the actual realization delay.

[2395] Accordingly, the planner can avoid the unrealistic condition of knowing actual future delays accurately in advance and compare static high-performance paths with resource-aware authentication paths under incomplete predictions.

[2397] 16-64. Multimodal Transfer and Dropout Performance Evaluation Model

[2399] In multimodal transfer evaluation, experience transfer between 80,000 heterogeneous intelligence nodes can be generated.

[2401] The transmitting node and the receiving node can be configured to use three different modalities among vision, audio, lidar, radar, text, and proprioception, respectively.

[2403] The degree of modality overlap between two nodes, the mapping quality of the common latent space, the cross-modal contradiction risk, and the authentication status of the transmitting node can be synthesized.

[2405] Under the above conditions, a comparison can be made between a method that trusts the sending node authentication as is, a method that performs only the receiving node's mapping check, and a v6 method that performs both the receiving node's mapping check and cross-modal re-authentication.

[2407] In a separate modality dropout evaluation, synthetic conditions can be applied in which the main modality is lost in about 30% of 50,000 tasks.

[2409] The recovery rate for task completion and dropout can be calculated by comparing the fixed primary modality path with the v6 method, which explores other modalities and activates an alternative path through local re-authentication.

[2413] 16-65. Virtual Experience Topology

[2415] The virtual experience topology can be represented as follows.

[2417] G_H(t) = (V_H(t), E_H(t), P_H(t), Hyp_H(t), Prov_H(t), U_H(t))

[2419] V_H may represent a virtual execution representation, E_H may represent a virtual connection, P_H may represent a virtual combinatorial path, Hyp_H may represent a failure or success hypothesis, Prov_H may represent dream provenance, and U_H may represent the uncertainty or confidence state of a virtual experience.

[2421] G_H can be logically distinguished from the execution topology G_E and the authentication topology G_C.

[2423] Objects of G_H are not directly included in the production execution topology G_prod merely by their existence.

[2425] 16-66. Dream Candidate Object

[2427] A single dream candidate object generated by the virtual experience generation unit can be represented as follows.

[2429] D_k = (Seed_k, Model_k, z_0, pi_k, z_hat_1:H, R_hat_k, F_hat_k, U_k, Prov_k, V_k)

[2431] Seed_k is a real experience or virtual seed, Model_k is a used world model or generative model, z_0 is a starting state, pi_k is a virtual action or execution path, z_hat_1:H is a sequence of predicted future states, R_hat_k is expected utility or reward, F_hat_k is a failure hypothesis or risk hypothesis, U_k is uncertainty, Prov_k is generative provenance, and V_k may include related model, environment, sensor, or tool version.

[2433] 16-67. Select Dream Generation Target

[2435] The generation priority of dream scenarios can be represented as follows.

[2437] Priority_dream(k) =

[2438] alpha_1 Sparse(k)

[2439] + alpha_2 Uncertainty(k)

[2440] + alpha_3 FailureNeighbor(k)

[2441] + alpha_4 ExpiryRisk(k)

[2442] + alpha_5 Novelty(k)

[2443] + alpha_6 InformationGain(k)

[2444] - alpha_7 Cost_dream(k)

[2446] Sparse may represent the degree of low density of actual experience, Uncertainty the uncertainty of certification or models, FailureNeighbor the proximity to actual failure areas, ExpiryRisk the risk of certification expiration, Novelty novelty, InformationGain the expected information gain, and Cost_dream the resource cost for dream generation and verification.

[2448] 16-68. Dream Gate

[2450] The promotion of a virtual experience can be evaluated using the following hierarchical gates.

[2452] Gate_1 = SelfCheck(D_k)

[2454] Gate_2 = WorldConsistency(D_k)

[2456] Gate_3 = ConstraintCheck(D_k)

[2458] Gate_4 = IndependentOrSealedCheck(D_k)

[2460] Gate_5 = LocalCompositionCert(D_k)

[2462] Each gate does not necessarily have to be the same algorithm, and parts can be executed in parallel or sequentially.

[2464] Dream candidates that have passed through Gate_1 to Gate_4 are initially inserted into the execution topology only in a Shadow or Pending state, and can be reflected in the production execution topology only within the scope of passing the local node, connection, and path authentication of Gate_5.

[2466] 16-69. Realization Priority Grading

[2468] The overall grade of the dream candidate can be expressed as follows.

[2470] Q_dream =

[2471] w_real Q_real

[2472] + w_sealed Q_sealed

[2473] + w_world Q_world

[2474] + w_self Q_self

[2475] - lambda_U U_dream

[2476] - lambda_D Debt_dream

[2478] Here, w_real and w_sealed are the weights for real observations or exogenous verification, and w_world and w_self are the weights for the world model and self-evaluation.

[2480] In one embodiment,

[2482] w_real >= w_world

[2484] and,

[2486] w_sealed >= w_self

[2488] You can set it to become so.

[2490] If no actual observation exists, Q_real can be left in an undetermined state, and the undetermined state itself can be used as a constraint for Full authentication.

[2492] 16-70. Suppression of False Failures

[2494] Even if the dream generator generates a failure hypothesis F_hat, it may not immediately record it as the actual failure F_real.

[2496] The state of F_hat is,

[2498] Hypothetical

[2499] → Independently Supported

[2500] → Reproduced

[2501] → ConfirmedFailure

[2503] It can be transferred as follows.

[2505] With only the Hypothetical state, you can perform one or more of the following without permanently changing the Full authentication of an existing executable object to Revoked: adding a Shadow test set, increasing the verification budget, creating additional counterexamples, or limiting Canary.

[2507] If an independent oracle, seal examiner, actual reproduction, or mutually independent multiple evidence satisfies the setup conditions, it is promoted to ConfirmedFailure, and the actual authentication dependency recovery procedure can be performed.

[2509] 16-71. Entering the Execution Topology of Virtual Experience

[2511] When dream candidate D_k passes through the dream gate, it can be converted into an execution candidate as follows.

[2513] O_k^H = Abstract(D_k)

[2515] O_k^H → ShadowCandidate(G_E)

[2517] The above ShadowCandidate can distinguish and manage Full candidates generated from actual experience and provenance.

[2519] If ShadowCandidate passes connection authentication C_ij^E with the existing execution representation, path authentication C_pi^P, version authentication, and necessary cross-modal authentication, it can be converted to a Restricted or Full candidate.

[2521] 16-72. Dream Debt

[2523] The dream debt of dream candidate k can be represented as follows.

[2525] Debt_k(t+1) =

[2526] rho_D Debt_k(t)

[2527] + a_1 I_no_real

[2528] + a_2 I_no_independent

[2529] + a_3 Age_k

[2530] + a_4 VersionDrift_k

[2531] - a_5 RealAgreement_k

[2532] - a_6 IndependentAgreement_k

[2534] I_no_real indicates unconfirmed actual observation, I_no_independent indicates unconfirmed independent verification, Age_k indicates the elapsed time of the virtual experience, VersionDrift_k indicates changes in the relevant model or environment version, and RealAgreement and IndependentAgreement may indicate consistency with actual or independent evidence.

[2536] If Debt_k exceeds the first threshold, the priority is lowered; if it exceeds the second threshold, the authentication limit is restricted to Restricted or lower; and if it exceeds the third threshold, it can be switched to an expiration or regeneration target in the virtual experience topology.

[2538] 16-73. Dream Memory Integration

[2540] Memory integration can be performed using the set of actual experiences R and the set of virtual experiences H_cert that passed through the dream gate.

[2542] M_(t+1) = Consolidate(M_t, R, H_cert)

[2544] The memory contribution of virtual experience can be set to have a lower upper limit than that of actual experience.

[2546] for example,

[2548] Contribution(H_cert) <= eta_H Contribution(R)

[2550] It can be set to , and eta_H can be set to a value of 1 or less or a context-dependent value.

[2552] Memory consolidation may include abstraction of iterative execution paths, past skill rehearsal, preservation of compiled execution objects, cache relocation, test set re-indexing, or low-confidence hypothesis priority decay.

[2554] 16-74. Expansion of Rare Situations Using Dreams

[2556] For regions where the density of real experience p_real(z) is low, the probability of dream generation can be increased in the opposite way.

[2558] p_dream(z) proportional to

[2559] 1 / (epsilon + p_real(z))^beta

[2561] The above method can prioritize the generation of virtual experiences in rare or rarely observed regions instead of repeatedly generating frequently observed states.

[2563] However, since world model uncertainty may also increase in rare regions, the verification strength or exogenous verification weight can be increased separately from the amount of dream generation.

[2565] 16-75. Counterfactual dreams surrounding actual failure

[2567] If an actual failure F_real occurs, the following counterfactual path can be generated around the state z_F immediately prior to the failure.

[2569] D_F =

[2570] CounterfactualRollout(z_F, pi_alt, WorldModel)

[2572] pi_alt may include alternative actions, different tools, different modalities, different resource mappings, or different planning sequences different from the actual failure path.

[2574] Among the above counterfactual paths, candidates that have passed verification can be used as local re-verification test sets or alternative path candidates.

[2576] 16-76. Dream Scheduler

[2578] The dream budget for time t can be expressed as follows.

[2580] B_dream(t) =

[2581] B_0

[2582] + b_1 ResourceSurplus(t)

[2583] + b_2 ValidationBacklog(t)

[2584] + b_3 ExpiryPressure(t)

[2585] + b_4 FailureDensity(t)

[2586] + b_5 SparseCoverage(t)

[2587] - b_6 ProductionLoad(t)

[2588] - b_7 EnergyCost(t)

[2590] If ProductionLoad is high, normal dream generation may be stopped.

[2592] However, in the event of a critical safety failure or imminent certification expiration, limited dream verification may be permitted under separate override conditions.

[2594] 16-77. Update of Reality Consistency After Awakening

[2596] The reliability of the dream generator and reviewer can be updated by comparing the state transition predicted in the dream with the subsequent actual execution results.

[2598] e_dream =

[2599] d(y_real, y_dream)

[2601] If e_dream increases repeatedly, it can lower the dream reliability of the corresponding world model, dream generator, specific state domain, or specific modality.

[2603] Conversely, by repeatedly aligning with actual results, the prior or verification priority of the corresponding dream type can be adjusted.

[2605] 16-78. Separation of Authority Between Dream Generator and Reviewer

[2607] The dream generator Generator_D and the sealing examiner Judge_S can be separated logically or physically.

[2609] Generator_D can perform candidate generation or self-evaluation, but can be configured so that it cannot directly modify all of Judge_S's internal correct answers, some test sets, evaluation parameters, or approval rules.

[2611] The result of Judge_S can be one or more of accept, reject, revise, shadow-only, require-real-evidence, or require-independent-evidence.

[2613] Accordingly, closed-type self-reinforcement between the self-improvement target and the final approval criteria can be limited.

[2615] 16-79. Dream Transference Between Distributed Intelligence Nodes

[2617] In the case where the dream experience generated by the first intelligent node A is transmitted to the second intelligent node B,

[2619] MM-XEP_D =

[2620] (MM-XEP, DreamProv, DreamUncertainty, DreamDebt, DreamGateEvidence)

[2622] You can specify whether it is a virtual experience, as in...

[2624] B does not automatically approve A's dream certification and can re-evaluate it based on B's actual experience density, modality configuration, world model, seal reviewer, and combination relationship with existing execution topology.

[2626] If A's dream conflicts with B's actual experience, B's actual experience or the results of exogenous verification may be prioritized.

[2628] 16-80. Shared Dream and Mutual Verification

[2630] Multiple intelligence nodes can generate mutually independent dream paths for the same seed state.

[2632] D_A = Dream_A(seed)

[2634] D_B = Dream_B(seed)

[2636] Automatic approval is not granted solely based on the match of the two dream results; correlation errors resulting from model sharing or data sharing can be considered.

[2638] If independence is ensured through the use of different model families, different data provenances, different sensors, or different verifiers, the results of mutual consensus may be used as additional evidence.

[2640] 16-81. Management of Exam Questions Generated in Dreams

[2642] The test condition Omega_dream generated in a dream can be stored separately from Omega_real generated from actual failure.

[2644] Omega =

[2645] Omega_real

[2646] union Omega_dream_cert

[2647] union Omega_boundary

[2648] union Omega_OOD

[2649] union Omega_adversarial

[2651] Omega_dream_cert may only include virtual test conditions that have passed independent or seal verification.

[2653] Unverified dream test conditions may be retained in the candidate test pool and not used as the direct basis for the permanent revocation of actual certification or hardgate changes.

[2655] 16-82. Dream-Execution-Verification Co-evolution Closed Loop

[2657] A dream-based embodiment of the present invention may be composed of the following closed loop.

[2659] Real experience

[2660] → Update path memory and execution topology G_E

[2661] → Selection of rare regions, failure periphery, and high-uncertainty regions

[2662] → Generate dream paths in virtual experience topology G_H

[2663] → Self-Assessment and World Model Consistency Test

[2664] → Independent or sealed examination

[2665] → Create Shadow Candidate or Virtual Test Condition

[2666] → Node · Connection · Path · Cross-modal · Resource Authentication

[2667] → Update Authentication Topology G_C

[2668] → Change production execution topology G_prod

[2669] → Actual execution

[2670] → Comparison of dream predictions and actual results

[2671] → World Model, Dream Credibility, Dream Debt, and Verification Budget Update

[2672] → Memory Integration

[2674] Accordingly, dreams are not used as learning materials that directly possess execution authority, but rather as a hypothesis space that generates new possibilities, and only a portion can be incorporated into the actual intelligence execution structure after verification against reality or independent evidence.

[2678] 16-83. Social State Vectors

[2680] The social state at time t can be expressed as follows.

[2682] s_t^soc =

[2683] (A_t, Role_t, Rel_t, Consent_t, Auth_t, Trust_t, Privacy_t, Group_t, Norm_t, Sigma_t^soc)

[2685] A_t may represent the current set of relevant social actors, Role_t the role state, Rel_t the relationship state, Consent_t the consent state by behavior, Auth_t the authorization state, Trust_t the trust state by context, Privacy_t the privacy condition, Group_t the group or conversation state, Norm_t the applicable social norm, and Sigma_t^soc the social state uncertainty.

[2687] The above state is not limited to a single global vector and can maintain different partial states for each actor, object, action, and purpose.

[2689] 16-84. Social Topology

[2691] Social topology can be represented as follows.

[2693] G_S(t) =

[2694] (V_S(t), E_role(t), E_rel(t), E_consent(t), E_auth(t), E_trust(t), E_privacy(t), N_S(t), U_S(t), Prov_S(t))

[2696] V_S can represent a social actor or group node, E_role a role relationship, E_rel a relationship state, E_consent a scope of consent, E_auth an authority relationship, E_trust a trust state, E_privacy a privacy relationship, N_S a social norm, U_S a social state uncertainty, and Prov_S a provenance of the basis of the social state.

[2698] Connections in social topology can be conditionally valid depending on time, place, object, task, purpose, or data type.

[2700] 16-85. Production execution topology coupled with social states

[2702] The existing production execution topology can be extended to include social states as follows.

[2704] G_prod(t) =

[2705] Phi(G_E(t), G_C(t), G_H(t), G_S(t), z_t, c_t, v_t, r_t, b_t)

[2707] b_t may represent the position, attitude, sensor state, actuator state, payload, force, velocity, battery, or other implementation state of the robot or physical implementation.

[2709] Social topology G_S does not replace physical safety or authentication topology G_C.

[2711] G_S can be used as an input to determine which authenticated behaviors may remain as actual candidates in the current social context, or which behaviors require additional consent, authorization, confirmation, or social re-verification.

[2713] 16-86. Social Hard Gate Class

[2715] The permissibility of candidate robot behavior a can be evaluated hierarchically as follows.

[2717] H_phys(a) = PASS

[2719] H_law(a) = PASS

[2721] H_auth(a) = PASS

[2723] H_consent(a) = PASS

[2725] H_social(a) = PASS

[2727] A candidate set satisfying all of the above higher conditions,

[2729] A_adm =

[2730] {a : H_phys(a)=PASS,

[2731] H_law(a)=PASS,

[2732] H_auth(a)=PASS,

[2733] H_consent(a)=PASS,

[2734] H_social(a)=PASS}

[2736] It can be defined as.

[2738] After that,

[2740] a* =

[2741] arg max_(a in A_adm)

[2742] (U_task(a) + lambda_S U_social(a) - lambda_C Cost(a) - lambda_R Risk(a))

[2744] Utility can be optimized within the allowed candidates as shown.

[2746] Accordingly, high social or operational utility may not offset the failure of physical safety, authority, or consent.

[2748] 16-87. Consent and Authority by Behavior

[2750] Instead of assigning a single overall consent value to a single social actor,

[2752] Consent(u, action, object, purpose, place, time)

[2754] Consent by behavior can be managed in this form.

[2756] For example, even if the same user consents to general conversation, they may not consent to video recording, physical access, manipulation of personal items, or sharing of external information.

[2758] Authority, too,

[2760] Authority(u, action, object, scope)

[2762] It can be managed by target behavior and target object in the form.

[2764] Therefore, even if user roles are the same, specific behavioral permissions may differ.

[2766] 16-88. Multi-user request conflict

[2768] When user 1 u_1 and user 2 u_2 make conflicting requests, the action may not be determined solely by the recency of the requests or the intensity of the utterance.

[2770] ConflictSet =

[2771] {Request_i, Role_i, Authority_i, Consent_i, Ownership_i, Risk_i, Impact_i}

[2773] It can generate and evaluate the ownership or management relationship of the target object, the risk of the requested action, the consent of other affected actors, business authority, or the need for human identification.

[2775] If the conflict is not resolved, you can leave the action in a Pending state and choose a confirmation query, confirmation from a higher authority, or a safe alternative action.

[2777] 16-89. Retrieval of Event-Based Topology for Withdrawal of Consent

[2779] If the consent withdrawal event RevConsent occurs,

[2781] R_consent =

[2782] Reach_GDep(ConsentEdge, d_max, theta_dep)

[2784] Using this, the scope of influence of behavior paths, compiled execution objects, or authentication objects that depend on the consent can be calculated.

[2786] G_prod(t+1) =

[2787] RemoveOrRestrict(G_prod(t), R_consent)

[2789] You can restrict it to only related production execution candidates.

[2791] For example, even if consent for video recording is withdrawn, unrelated behavioral paths can continue to be used if separate consent regarding movement guidance or general conversation is maintained.

[2793] 16-90. Updating Relationships and Trust Status

[2795] The contextual trust state between social actor u and the intelligence node can be denoted as Trust_u,t.

[2797] Based on the actual interaction result y_t, explicit evaluation f_t, and observed response o_t,

[2799] Trust_u,t+1 =

[2800] clip(

[2801] rho_T Trust_u,t

[2802] + eta_1 f_t

[2803] + eta_2 g(o_t)

[2804] + eta_3 OutcomeAgreement_t

[2805] )

[2807] It can be updated as follows.

[2809] The above confidence value does not automatically generate legal authority or explicit consent, and can be used as an auxiliary state to adjust one or more of social distance, explanatory volume, suggestion method, autonomous behavior priority, or confirmation frequency.

[2811] 16-91. Social Distancing and Approach Behavior

[2813] It is possible to distinguish between the physical safety minimum distance d_safe for person h and the preference or estimated social distance d_social in the current social context.

[2815] The robot's target distance is,

[2817] d_target =

[2818] max(d_safe, d_social(role, relation, context, group, uncertainty))

[2820] It can be set to.

[2822] d_social is not limited to a fixed value and can be changed according to conversation, object passing, passage, group conversation, emergency situation, work collaboration, or personal preference.

[2824] If social state uncertainty increases, you can conservatively increase d_target or generate a check-before-approach action.

[2826] 16-92. Collective Space and Social Navigation

[2828] When multiple people form a group, each person is not treated merely as an independent obstacle, and the group area, direction of conversation, line of sight structure, passage space, or activity area can be estimated.

[2830] GroupSpace =

[2831] Geometry({Human_i}, InteractionState, Uncertainty)

[2833] You can create a GroupSpace and, if the robot path unnecessarily crosses the GroupSpace, increase social costs or select an alternative path.

[2835] However, in collision avoidance or emergency situations, physical safety constraints may take precedence over social softness constraints.

[2837] 16-93. Order of Speech and Interruption

[2839] Whether a robot intervenes in group conversations or multi-user interactions,

[2841] J_interrupt =

[2842] alpha_U Urgency

[2843] + alpha_N Need

[2844] - alpha_C ConversationIntensity

[2845] - alpha_G GroupConflict

[2846] - alpha_S SocialUncertainty

[2848] It can be evaluated as follows.

[2850] Emergency safety information may take precedence over the general firing order, and non-emergency information may wait until a firing time is possible or use other low-interference channels such as visual or tactile.

[2852] 16-94. Social Object Transfer

[2854] When a robot delivers an object to a person, it can control the approach speed, stopping position, grip release time, or explanatory action by utilizing one or more of the recipient's readiness, line of sight, hand position, movement, relationship status, and the vulnerability or risk level of the object, in addition to simply reaching the target location.

[2856] HandoverAction =

[2857] (pose, velocity, grip_force, release_condition, gaze, speech)

[2859] It can be represented as.

[2861] If the recipient is not ready, delivery may be switched to a waiting or verification path instead of immediate delivery, even if physically possible.

[2863] 16-95. Social Authentication of Robot Behavior

[2865] For robot actions or combinatorial paths, social authentication C_pi^S in the current social context can be generated separately from physical path authentication.

[2867] C_pi^S =

[2868] (D_social, Role, Consent, Authority, Norm, Privacy, GroupState, U_social, Version, State)

[2870] The above social authentication may be valid only in specific roles, scopes of consent, places, group states, and versions of social norms.

[2872] If the social context moves out of the authentication area, Full social authentication can be switched to Restricted, Pending, or Revoked status.

[2874] 16-96. Scope of Application and Versions of Social Norms

[2876] The social norm N_k can be represented as follows.

[2878] N_k =

[2879] (Rule_k, Domain_k, Culture_k, Organization_k, Place_k, Role_k, Version_k, Evidence_k, U_k)

[2881] Since the same behavior can have different social meanings in different places or groups, the scope of application of social norms can be specified.

[2883] If new organizational regulations, user modifications, or long-term interaction results occur, the version of N_k can be updated, and only social authentications that depend on the relevant norms can be selectively re-verified.

[2885] 16-97. Privacy and Observation Behavior

[2887] Just because a robot's sensor can physically observe data does not automatically grant the authority to store, infer, share, or store that data in long-term memory.

[2889] PrivacyGate =

[2890] f(User, Modality, Purpose, Place, Recipient, Retention, Consent)

[2892] The usage range of the sensor input can be determined using .

[2894] For example, a camera can detect nearby people and be used for collision avoidance, but separate consent or permission may be required for face identification or long-term storage.

[2896] 16-98. Uncertainty of Social States and Confirmation Behavior

[2898] If the social state uncertainty Sigma_soc exceeds the set threshold, the action is not executed immediately, and

[2900] Ask

[2901] Observe,

[2902] Wait,

[2903] OfferAlternative,

[2904] RequestAuthority,

[2905] RequestConsent

[2907] One or more of the following can be generated.

[2909] The verification behavior itself can also entail social costs and risks in order to prevent excessive repetitive questioning or interruptions.

[2911] 16-99. Physical Mapping of Implementation Behavior

[2913] The social behavior path pi_soc can be converted into a physical execution parameter.

[2915] pi_soc

[2916] →

[2917] P_robot =

[2918] (Trajectory, Speed, Acceleration, Force, Torque, Grip, Gaze, Speech, Gesture, Wait)

[2920] The above mapping can be implemented as a movement trajectory, drone flight path, vehicle path, robot arm trajectory, gripper force, humanoid attitude, display, or sound output depending on the type of robot.

[2922] Execution parameters may be changed according to social conditions, provided that they do not exceed the scope of physical safety certification.

[2924] 16-100. Social State Response Closed Loop

[2926] Human or group reaction after the execution of robot behavior a_t,

[2928] o_t^soc =

[2929] (Language, Acceptance, Refusal, DistanceChange, Gaze, Gesture, Correction, TaskOutcome)

[2931] It can be observed as.

[2933] The above response can be used as evidence of social conditions or social norms.

[2935] A single response can accumulate along with the provenance and uncertainty of the relevant status without automatically altering explicit consent, legal authority, or ownership relationships.

[2937] 16-101. Social State Drift and Damping

[2939] If a relationship or trust status is not renewed for a long period, the past state may not be used permanently in the same way.

[2941] Trust(t) =

[2942] Trust(t_0) exp(-lambda_T Delta t)

[2943] + EvidenceUpdate(t)

[2945] It can attenuate the influence of old estimates as such.

[2947] Conditions in which separate expiration rules exist, such as explicit consent or legal authority, can be managed separately from the aforementioned general decay.

[2949] 16-102. Social State Transitions Between Distributed AGI and Robots

[2951] When the first intelligent node transmits the social state to the second intelligent node or robot,

[2953] SEP =

[2954] (ActorRef, Role, Relation, ConsentScope, AuthorityScope, PrivacyScope, NormRef, Prov, Version, U)

[2956] You can use a social experience package of the form.

[2958] The receiving node locally verifies the actual identity or relationship of the ActorRef and may not automatically inherit the authority and consent information of the sending node.

[2960] In particular, the target device or role scope can be conveyed together to prevent physical access consent for one robot from automatically transferring to access consent for another robot.

[2962] 16-103. Limitation of Authority in Social Dreams

[2964] Social interactions can be simulated in the virtual experience topology G_H.

[2966] DreamSocial =

[2967] Simulate(G_S, CandidateAction, WorldModel)

[2969] The above results can be used to generate candidates for expected responses, expected discomfort, expected group conflict, or alternative behaviors.

[2971] However, generated in a dream,

[2973] Consent_hat,

[2974] Authority_hat,

[2975] Trust_hat,

[2976] Relation_hat

[2978] It does not directly change the actual Consent, Authority, Trust, or Relation status.

[2980] The above-mentioned virtual social state remains a HypotheticalSocialState until it is confirmed by actual reactions or independent social evidence.

[2982] 16-104. Integrated Closed-Loop of Socially Adaptive Robot AGI

[2984] One embodiment of socially adaptive implemented intelligence can be configured as follows.

[2986] Multimodal observation

[2987] → Recognition of people, groups, objects, and places

[2988] → Update social topology G_S

[2989] → Presumption of roles, relationships, consent, authority, privacy, norms, and group status

[2990] → Generate action candidates in execution topology G_E

[2991] → Social counterfactual simulation if necessary in the virtual experience topology G_H

[2992] → Physical Safety·Law·Policy·Authority·Consent Hard Gate

[2993] → Social authentication and path authentication

[2994] → Create production execution topology G_prod

[2995] → Mapping of robot behavior trajectory, velocity, force, gaze, speech, and gestures

[2996] → Actual physical and social behavior

[2997] → Observation of human and group reactions

[2998] → Update G_S and G_C

[2999] → Selective recovery of only the scope of impact in case of failure or withdrawal of consent

[3000] → Generation of alternative behaviors and local re-verification

[3001] → Subsequent action

[3003] Accordingly, social adaptation can be implemented not merely as a simple adjustment of response styles, but as a closed-loop execution structure in which changes in the social state alter the actual physical behavioral availability and execution path of the robot.

[3007] Text Structure Diagram 24 - Cross-Topology Epoch Commit

[3009] 2501 G_E Change Candidate

[3010] → 2502 Check G_C correspondence authentication status

[3011] → 2503 Check virtual experience status related to G_H

[3012] → 2504 Check social status related to G_S

[3013] → 2505 Check resource status and implementation status

[3014] → 2506 Check epoch and version compatibility for each state

[3015] → 2507 Staging

[3016] → 2508 DependencyReady

[3017] → 2509 ValidationReady

[3018] → 2510 CommitReady

[3019] → 2511 ProductionSnapshot of the same or interchangeable epoch

[3020] → 2512 G_prod creation

[3021] → 2513 Emergency Physical Safety, Withdrawal of Consent, Revocation of Authority, and Security Incidents are EmergencyInterrupt

[3022] → 2514 Restricted·Revoked·Stop related paths immediately without waiting for a regular commit

[3023] → 2515 Reflect in the next consistent ProductionSnapshot

[3025] Text Structure Diagram 25 - Correlation-Aware Evidence Certification

[3027] 2601 Candidate Execution Object

[3028] → 2602 Multiple Examination Devices and Collection of Verification Evidence

[3029] → 2603 ModelLineage·DataLineage·ToolLineage·WorldModel·EvalSet·Provenance tracking

[3030] → 2604 Correlation Evidence Clustering

[3031] → 2605 Reduction in internal duplicate tables within the correlational evidence group

[3032] → Calculation of 2,606 independent evidence groups

[3033] → 2607 Confirmation of existence of evidence of sealing, exogenous, and independence

[3034] → Check 2608 Hard Gate

[3035] → 2609 Promote·Restricted·Shadow·Reject decision

[3037] Text Structure Diagram 26 - Common Cause Hypergraph Selective Recovery

[3039] 2701 Actual failure or version change

[3040] → 2702 Direct RootCause Calculation

[3041] → 2703 Structural dependency graph exploration

[3042] → 2704 shared model·dataset·tool·calibration·sensor·hardware·policy common cause hyperedge exploration

[3043] → 2705 Union of Direct Scope and Common Scope

[3044] → Calculate 2706 blast radius

[3045] → 2707 Calculation of verification budget reflecting irreversibility, human impact, and mission scope

[3046] → 2708 Selective Demotion·Recovery·Re-verification

[3047] → 2709 Maintain irrelevant path

[3049] Text Structure Diagram 27 - Belief Social State and Information Value Behavior

[3051] 2801 Multimodal Human and Group Observation

[3052] → 2802 Role·Authority·Consent·Trust·GroupState belief update

[3053] → 2803 Calculation of Social State Uncertainty

[3054] → 2804 Assessment of Expected Losses from Immediate Action

[3055] → 2805 Ask·Observe·Wait·RequestConsent·RequestAuthority Cost Evaluation

[3056] → 2806 Information Value Comparison

[3057] → 2807 Check or additional observation action as necessary

[3058] → 2808 belief renewal

[3059] → 2809 Society, Authority, Consent: Re-evaluation of the Hard Gate

[3061] Text Structure Diagram 28 - FastPath Lease and Actuator Runtime Shield

[3063] 2901 Certified FastPath incumbent

[3064] → Start 2902 Lease

[3065] → Run 2903 Production

[3066] → 2904 Challenger Shadow Reviews

[3067] → 2905 Update on Evidence of Superiority for Actual Performance, Drift, and Challenger

[3068] → 2906 Lease Renewal Condition Evaluation

[3069] → 2907 Demotion to Planner·Shadow·Restricted if renewal conditions are insufficient

[3070] → Increase in 2908 challenger verification budget

[3071] → 2909 Selected robot command

[3072] → 2910 Check current dynamics·mass·friction·delay·payload·uncertainty

[3073] → 2911 Actuator Runtime Shield

[3074] → 2912 One of Pass, Clip, Replace, or Stop

[3075] → 2913 Shield Feedback intervention evidence to G_C and implementation state

[3077] Text Structure Diagram 29 - Mission Certification and Rollback Ledger

[3079] 3001 Local Full Authentication of Multiple AGIs or Multiple Robots

[3080] → 3002 mission Generate combination candidates

[3081] → 3003 Check all resources, privacy, objective composition, deadline, shared space, authority, and safety

[3082] → Create 3004 Mission Cert

[3083] → 3005 Restrict concurrent executions, order, resources, data sharing, and permissions as necessary

[3084] → 3006 topology mutation occurred

[3085] → 3007 BeforeSnapshot·AfterSnapshot·TriggerEvidence·Epoch·Dependency·RollbackPointer records

[3086] → 3008 EvidenceQuarantine if there is a conflict of evidence or an incomplete epoch

[3087] → 3009 Additional verification

[3088] → 3010 Commit or Known-Good Snapshot Rollback

[3091] Text Structure Diagram 30 - Self-Body Based Grounding of Abstract Skills

[3093] 3101 Abstract state transition operator or external skill

[3094] → 3102 Retrieve current self-implemented model G_B

[3095] → 3103 Check Kinematics·Dynamics·Sensors·Actuators·Payload·Tool·Health

[3096] → Calculate 3104 body capability envelope

[3097] → 3105 embodiment grounding

[3098] → 3106 Generating trajectory·controller·force·contact·actuator command candidates

[3099] → 3107 body certification

[3100] → 3108 Combined with existing Node·Edge·Path·Social·Mission Cert

[3101] → 3109 Activate G_prod

[3102] → 3110 Actual Execution and Actuator Runtime Shield

[3103] → 3111 Feedback actual physics results to G_B and G_C

[3105] Text Structure Diagram 31 - Self-Assessment and Meta-Calibration

[3107] 3201 Predicted success·risk·confidence before execution

[3108] → 3202 Actual execution or independent verification

[3109] → 3203 Compare predicted and actual

[3110] → 3204 Calculation of calibration error, bias, and uncertainty by domain

[3111] → 3205 Self-assessment status or G_Q update

[3112] → 3206 Judgment of Overconfidence, Underconfidence, and Deviation from the Scope of Application

[3114] If your self-assessment is good

[3115] → 3207 Maintain existing verification policy

[3117] If your self-evaluation is poor

[3118] → 3208 ValidationDemand increase

[3119] → 3209 One or more of Shadow, Canary, Restricted, Abstain, Delegate, and Maintenance

[3120] → 3210 Pass to G_C or planner as evidence

[3122] 3211 Self-assessment does not directly generate Full certification

[3124] Text Structure Diagram 32 - Self-Falsification Closed Loop

[3126] 3301 High self-confidence candidate

[3127] → 3302 Check past calibration errors and boundary conditions

[3128] → 3303 Generating Counterexamples, OODs, Adversarial, and Counterfactual Challenges

[3129] → 3304 Isolation or Restriction Verification

[3130] → 3305 Comparison of Self-Prediction and Challenge Results

[3132] No collisions

[3133] → 3306 Maintain confidence domain or update narrow range

[3135] Conflict

[3136] → 3307 self-bias update

[3137] → 3308 Reduce capability or certification domain

[3138] → 3309 Additional Independent Testing · Dream Rehearsal · Challenger Creation

[3140] Text Structure Diagram 33 - Body Degradation and Self-Maintenance

[3142] 3401 body health observation

[3143] → 3402 torque·sensor residual·temperature·battery·slip·calibration change

[3144] → 3403 Update of G_B and capability envelope

[3145] → Calculation of the scope of impact for body certification related to 3404

[3147] If it is within the normal range

[3148] → 3405 execution persist

[3150] If it is within the deterioration range

[3151] → 3406 payload·speed·workspace·force limit reduction

[3152] → 3407 Recalibration·Charging·Cooling·Tool Change·Maintenance Candidate Generation

[3153] → 3408 self-maintenance action certification

[3154] → Run 3409 maintenance

[3155] → 3410 Body Health Re-evaluation

[3156] → 3411 Related execution path re-authentication

[3158] Text Structure Diagram 34 - Cross-Embodiment Skill Transfer

[3160] 3501 Robot A's Real-World Experience

[3161] → Separation of motor command and embodiment-independent skill in 3502 A

[3162] → 3503 State Transition Objective · Task Constraint · Object Relation · Contact Objective Package

[3163] → 3504 Transfer to Robot B

[3164] → 3505 Check G_B and local modality of B

[3165] → 3506 B Exclusive embodiment grounding

[3166] → 3507 B body certification

[3167] → 3508 B Node·Edge·Path·Mission Authentication

[3168] → Activate G_prod of 3509 B

[3170] 35010 A's body certification does not automatically approve B's body certification

[3172] Text Structure Diagram 35 - Multi-Robot Role Reorganization and Collective Recovery

[3174] 3601 Mission task-to-agent assignment

[3175] → 3602 Check each robot's G_B, self-evaluation, local cert, and resource state

[3176] → 3603 Specific robot failure / deterioration / authorization change / communication loss

[3177] → 3604 Calculation of affected mission tasks and paths

[3178] → 3605 Re-evaluation of the capability and role eligibility of the remaining robots

[3179] → 3606 task reassignment

[3180] → 3607 New assigned robot body grounding

[3181] → 3608 local body certification

[3182] → 3609 Mission Cert Re-verification

[3183] → 3610 production role recovery

[3185] Text Structure Diagram 36 - Auditable Forgetting and Safe Topology Garbage Collection

[3187] 3701 Long-term cumulative memory·dream·certificate·social state·body calibration·snapshot

[3188] → 3702 active dependency·rollback·revoked history·legal / consent·mission requirement inspection

[3189] → 3703 Separation of protected objects and cleanup candidates

[3190] → 3704 Generate duplicate merge·summary·archive·tombstone·delete candidates

[3191] → 3705 Verification of the impact of deletion on certificate, rollback, and common-cause analysis

[3192] → 3706 Safety conditions passed

[3193] → 3707 topology garbage collection commit

[3194] → Maintain 3708 tombstone·digest·hash·archive pointer

[3195] → 3709 Rehydration or historical audit if necessary

[3197] 37010 Past Revoked history is simply deleted and cannot be restored to a safe new path

[3200] Text Structure Diagram 37 - v11 Extended Consistency Snapshot

[3202] 3801 G_E Execution Topology

[3203] → 3802 G_C Authentication Topology

[3204] → 3803 G_H Virtual Experience Topology

[3205] → 3804 G_S Social Topology

[3206] → 3805 G_B Self-implementing model

[3207] → 3806 Q_self Self-assessment status

[3208] → 3807 Mission Cert and Resource Status

[3209] → 3808 Check epoch·version compatibility for each state

[3210] → 3809 Consistent ProductionSnapshot

[3211] → 3810 G_prod creation

[3212] → 3811 Actual execution

[3213] → Re-update each related state with outcome 3812

[3215] Text Structure Diagram 38 - Body System Identification and Capability Envelope

[3217] 3901 Actual actuator command and sensor observation

[3218] → 3902 body model predicted state

[3219] → 3903 Physical Residual Calculation

[3220] → 3904 Update body parameters and uncertainty

[3221] → Calculate 3905 torque·speed·payload·workspace·energy capability

[3222] → 3906 Apply uncertainty margin

[3223] → 3907 Body Capability Envelope updated

[3224] → 3908 Re-evaluate Affected Body Cert and FastPath Leases

[3226] Text Structure Diagram 39 - Finite Meta-Calibration of Self-Evaluation

[3228] 4001 task capability estimate Q0

[3229] → 4002 Actual outcome or held-out evidence

[3230] → 4003 calibration evaluator Q1

[3231] → 4004 Estimation of Overconfidence and Underconfidence in Q0

[3232] → 4005 If necessary, Q1 reliability monitor Q2

[3233] → 4006 Recursion halted at the set meta-depth K_max

[3234] → 4007 anchor as sealed·external·held-out evidence

[3235] → Sent to 4008 ValidationDemand·abstention·delegate·maintenance

[3236] → 4009 Prohibition of generating Full Certification based solely on self-assessment

[3238] Text Structure Diagram 40 - Safe Forgetting and Common Cause Collective Recovery

[3240] 4101 Robot failure or long-term memory increase

[3241] → 4102 Common-cause provenance and active dependency test

[3243] Group recovery path

[3244] → 4103 Exclude or demote agents sharing the same common cause

[3245] → 4104 Independent body capability candidate selection

[3246] → 4105 regrounding·Body Cert·Mission Cert

[3247] → 4106 Role Recovery

[3249] Path of oblivion

[3250] → 4107 rollback·revoked·consent·legal retention inspection

[3251] → 4108 archive·digest·tombstone·delete candidate generation

[3252] → 4109 Personal Information & Deletion Policy Hard Gate Applied

[3253] → 4110 GC commit or quarantine

[3256] Text Structure Diagram 41 - Core Runtime Object Flow

[3258] 4201 Create or update ExperienceRecord

[3259] → 4202 Update MemoryState

[3260] → 4203 ExecObject Creation, Conversion, and Compression

[3261] → 4204 G_E Update execution topology

[3262] → Create 4205 ValidationDemand

[3263] → 4206 Create or update CertObject

[3264] → 4207 G_C Authentication Topology Update

[3265] → 4208 Combining current context and version compatibility

[3266] → Create 4209 ProductionSnapshot

[3267] → 4210 Exposed to Planner or Executor

[3268] → 4211 Actual outcome observation

[3269] → 4212 ExperienceRecord·CertObject·Dependency update

[3271] Text Structure Diagram 42 - Separation of Authentication Object and Execution Object

[3273] 4301 ExecObject

[3274] → 4302 Meaning of Function, Start Condition, End Condition, and State Transition

[3275] → 4303 Related CertObject Lookup

[3277] 4304 CertObject

[3278] → 4305 Certification Type · Certification Area · Evidence · Uncertainty · Version · Dependency

[3279] → 4306 Execution permission status such as Full, Restricted, Pending, or Revoked

[3281] 4303 and 4306

[3282] → 4307 Combine with current execution context

[3283] → 4308 Production Visibility, Cost, Priority, and Allowability Decisions

[3285] Text Structure Diagram 43 - Change Transactions and Emergency Blocking

[3287] 4401 New skill·certificate·social state·body state changes

[3288] → 4402 Staging

[3289] → Check 4403 dependency version

[3290] → Check 4404 epoch compatibility

[3291] → Check 4405 validation condition

[3292] → 4406 Atomic or Logical Commit

[3293] → 4407 New ProductionSnapshot

[3295] Emergency danger arises

[3296] → 4408 Emergency Interrupt

[3297] → Immediately restrict execution authority related to 4409

[3298] → Snapshot alignment and audit after 4410

[3300] Text Structure Diagram 44 - Separation of Authority in Distributed Experience Transfer

[3302] 4501 Sender Node Experience and Authentication Evidence

[3303] → 4502 Experience Execution Package

[3304] → 4503 Convert to receiving node representation space

[3305] → 4504 Candidate status of receiving node G_E

[3306] → 4505 Local Node Cert

[3307] → 4506 Local Edge Cert

[3308] → 4507 Local Path Cert

[3309] → 4508 Cross-Modal Certification and Body Certification if necessary

[3310] → 4509 Receive Node G_prod Enable

[3312] 45010 Sender node Cert does not auto-replicate to receiver node production privileges

[3314] Text Structure Diagram 45 - Long-term Operation Cleanup and Recovery

[3316] 4601 Long-term accumulated memory·certificate·compiled object·snapshot

[3317] → 4602 Active Dependency Check

[3318] → 4603 Rollback Requirement Check

[3319] → 4604 Revoked·Failure·Consent·Legal Retention Inspection

[3320] → 4605 Merge·Archive·Tombstone·Delete candidate

[3321] → 4606 GC Validation

[3322] → 4607 Commit or Quarantine

[3323] → 4608 Rehydration if necessary

[3324] → 4609 Historical Failure / Dependency Restore

[3327] Text Structure Diagram 46 - Creating Compile Execution Objects

[3329] 4701 Repeated Use or High Reliability ExecObject·Edge·Path

[3330] → Check CertObject and validity domain related to 4702

[3331] → 4703 dependency·version·resource binding collection

[3332] → 4704 CompileCandidate generated

[3333] → 4705 Determine target runtime or hardware

[3334] → 4706 Transform·Combine·Fix·Optimize·Serialize

[3335] → 4707 Create CompiledExecutionObject

[3336] → 4708 lease·expiry·rollback pointer granted

[3337] → 4709 Optionally activate G_prod's FastPath or accelerated path

[3339] Text Structure Diagram 47 - Concrete Implementation of Compiled Objects

[3341] 4801 Abstract execution representation or verified execution path

[3342] → 4802 Select Implementation Target

[3344] 4802

[3345] → 4803 native binary or shared library

[3346] 4802

[3347] → 4804 bytecode or JIT object

[3348] 4802

[3349] → 4805 eBPF program

[3350] 4802

[3351] → 4806 CUDA graph executable or GPU execution graph

[3352] 4802

[3353] → 4807 TensorRT engine or accelerator plan

[3354] 4802

[3355] → 4808 FPGA configuration or hardware lookup structure

[3356] 4802

[3357] → 4809 workflow DAG·tool-call chain·API chain

[3358] 4802

[3359] → 4810 prompt chain·prompt-tool chain·model-routing graph

[3360] 4802

[3361] → 4811 robot motion primitive·trajectory·controller bundle

[3363] 4803 to 4811

[3364] → 4812 Combine with each version, dependency, resource, and certification metadata

[3365] → 4813 production execution

[3367] Text Structure Diagram 48 - Partial Decompilation and Optional Recompilation

[3369] 4901 CompiledExecutionObject Running or pre-running state

[3370] → 4902 Detect changes in dependency, version, and certification

[3371] → 4903 Calculate Range of Effect

[3372] → 4904 Determining whether all objects are affected

[3374] Partial impact

[3375] → 4905 Invalidate only the affected segment·node·kernel·tool-chain parts

[3376] → 4906 Re-expand into original execution representation or lower-level representation

[3377] → 4907 Local re-verification

[3378] → 4908 Optional recompile

[3379] → 4909 Recombined with the existing unaffected portion

[3381] Overall impact

[3382] → 4910 Full lease expires or production disabled

[3383] → 4911 Full re-verification or recompile

[3385] 4909 or 4911

[3386] → 4912 new CompiledExecutionObject version

[3387] → 4913 G_prod reactivation

[3389] 16-105. Multi-Topology Consistency Epoch

[3391] The set of states used for production execution judgment can be represented as S_topo(t)=(G_E^(e_E),G_C^(e_C),G_H^(e_H),G_S^(e_S),R^(e_R),B^(e_B)).

[3393] e_E, e_C, e_H, e_S, e_R, and e_B can represent the epochs of the execution topology, authentication topology, virtual experience topology, social topology, resource state, and implementation state, respectively.

[3395] The commit condition for a new production execution topology can be expressed as CompatibleEpoch(e_E,e_C,e_H,e_S,e_R,e_B)=TRUE.

[3397] 16-106. staging and commit

[3399] The new state change delta_k can have the states of Staging, DependencyReady, ValidationReady, CommitReady, and ProductionSnapshot sequentially or conditionally.

[3401] 16-107. Emergency interrupt lane

[3403] If the emergency recovery condition E_critical occurs, E_critical → EmergencyInterrupt → ImmediateRestrictOrStop can be executed without waiting for the normal commit sequence.

[3405] 16-108. Evidence Lineage and Correlation Groups

[3407] Verification evidence can be represented as E_i=(Result_i,ModelLineage_i,DataLineage_i,ToolLineage_i,WorldModel_i,EvalSet_i,Judge_i,Prov_i,Epoch_i).

[3409] If two or more pieces of evidence share the same or substantially overlapping lineage, they are classified as a correlated evidence group, and multiple results within the correlated evidence group may not be counted as completely independent votes.

[3411] 16-109. Requirements for Independent Evidence

[3413] The promotion conditions for high-risk or high-irreversible candidates can be set as a combination of N_independent >= N_min, Exists(SealedOrExogenousEvidence), and HardGates=PASS.

[3415] 16-110. Common Cause Hypergraph

[3417] The authentication dependency structure can be represented as H_Dep=(V_Dep,E_direct,H_common).

[3419] The recovery range can be calculated using R_F=Reach(E_direct,RootCause(F)) union CommonCauseClosure(H_common,RootCause(F)).

[3421] 16-111. Blast Radius and Irreversibility-Based Verification Budget

[3423] The verification budget can be expanded as follows: B_val=B_min+a_R Risk+a_U Uncertainty+a_N Novelty+a_F FailureHistory+a_D DependencySpan+a_C CommonCauseSpan+a_I Irreversibility+a_H HumanImpact+a_M MissionScope.

[3425] 16-112. Drift Suspect Status

[3427] Even if the continuous drift indicator exceeds the suspicion threshold, it does not immediately switch to Revoked; instead, the state transitions of Full → Suspect → RestrictedOrCanary → ConfirmedDrift or Cleared can be used.

[3429] 16-113. Sequential Drift Statistics

[3431] The cumulative statistics for Bernoulli failure or success events can be constructed as S_t=max(0,S_(t-1)+log(P(y_t|p_1) / P(y_t|p_0))).

[3433] The above statistics can be replaced with CUSUM, EWMA, Page-Hinkley, sequential probability ratio, or other sequential drift detectors.

[3435] 16-114. Belief Social Topology

[3437] Some of the social states can be maintained as B_S=(P(Role),P(Authority),P(ConsentState),P(Trust),P(GroupState),Sigma_soc).

[3439] 16-115. Information Value Behavior

[3441] VOI(q) can be expressed as VOI(q)=L_act-(C_q+E(L_after_q)), and if VOI(q)>0 and the current action can be safely delayed, questioning, additional observation, waiting, or permission / consent confirmation can be selected as a production execution candidate.

[3443] 16-116. Actuator Runtime Shield

[3445] For the command u_plan that has passed high-level authentication, u_safe=Shield(u_plan,x_t,theta_t,Sigma_theta) can be calculated.

[3447] Shield can convert commands into pass, size limit, speed limit, force limit, trajectory modification, stop, or alternative control commands.

[3449] 16-117. Feedback on Shield Intervention

[3451] ShieldIntervention_t can serve as authentication topology, implementation state, calibration residual, failure history, or evidence for updating subsequent verification budgets.

[3453] 16-118. FastPath lease

[3455] It can be expressed as Lease_chi=(Epoch_start,T_expire,N_exec_max,EvidenceRequirement,RenewalCondition).

[3457] If there is insufficient proof of renewal, FastPath can be demoted to Shadow or Planner even without an actual failure.

[3459] 16-119. incumbent-challenger

[3461] Currently, you can set FastPath chi_A as the incumbent and the alternative path chi_B as the challenger, and allocate a low-cost Shadow or Canary budget to the challenger.

[3463] 16-120. lease non-renewal

[3465] In the case of LeaseExpired AND InsufficientRenewalEvidence, you can switch to RestrictedPlannerMode without maintaining the incumbent indefinitely.

[3467] 16-121. Mission or collective certification

[3469] Mission authentication of a set of multiple intelligent nodes A={A_1,...,A_n} can be represented as C_M=(ResourceGlobal,PrivacyAggregate,ObjectiveComposition,DeadlineGlobal,SharedSpace,Authori...

Claims

Claim 1 An adaptive intelligent execution system comprising one or more computational resources, comprising: an experience memory unit that updates a memory state affecting at least one of a subsequent state transition, selection of candidate behavior, search priority, plan cost, policy, routing, or execution path based on a plurality of experiences including one or more states, behaviors, plans, execution results, evaluation results, simulation results, information received from an external intelligence node, or a combination thereof; an experience structuring unit that generates, transforms, combines, splits, compresses, abstracts, merges, distills, caches, or updates one or more experiences, multiple-stage execution paths, or abstraction results thereof into one or more execution representations reusable for subsequent execution based on at least a portion of the plurality of experiences; and an execution topology that manages an execution topology comprising at least one of an executionable connection relationship, a selectable relationship, or a combinable relationship between the one or more execution representations and the execution representations, wherein at least one of a node, a connection relationship, priority, cost, transition probability, uncertainty, candidate set, search order, policy routing, model selection, tool selection, cache, search index, or execution path is changed based on a change in the memory state or execution representation. Management unit; authentication topology management unit managing an authentication topology comprising at least one of an individual authentication status for each of the above execution representations, a connection authentication status for a connection or combination relationship between two or more execution representations, a path authentication status for a combination path including a plurality of execution representations, an authentication area, verification evidence, uncertainty, version information, generation or verification provenance, dependency information, validity period, or retrieval condition; and a production topology generation unit generating a production execution topology by changing at least one of the availability, priority, cost, risk weight, selection probability, routing, resource placement, or search space of an execution node, connection relationship, or combination path of the above execution topology used for actual execution or plan search, based on the above authentication topology and at least one of the current state, execution context, resource state, authorization information, risk level, or version information.and an execution management unit that selects and executes one or more execution representations or combination paths based on the production execution topology, and updates at least one of the memory state, execution representation, execution topology, authentication topology, production execution topology, or execution allowance state based on the actual or estimated result of the execution; a consistency management unit that assigns epoch information representing a change generation or interoperability snapshot to two or more state structures including the execution topology and the authentication topology, and one or more of a virtual experience topology, social topology, resource state, or implementation state optionally managed by the system, and commits a new production execution snapshot when the state structures required for the creation of the production execution topology satisfy the same epoch or a set compatibility relationship, and maintains the new or changed state in a staging state prior to the commit; wherein when an execution representation, connection relationship, or combination path is created or changed in the execution topology, the creation or update of a verification or authentication state corresponding to the changed target is triggered, and an authentication state is created in the authentication topology, An adaptive intelligent execution system based on the co-evolution of an execution topology and an authentication topology, characterized in that at least one of the execution nodes, connections, combination paths, costs, priorities, or execution acceptance states of the production execution topology is configured to change when modified, downgraded, expired, or reclaimed. Claim 2 In claim 1, the experience memory unit updates the memory state based on credit allocation information indicating the contribution of past experience and at least one of a reward prediction error, result error, constraint violation, user evaluation, external evaluation, subsequent state, prediction residual, risk, delay, energy, resource consumption, or actual execution result, and the memory state is implemented as a graph, matrix, vector, tensor, embedding, probability distribution, neural network parameter, adapter parameter, key-value memory, episode memory, procedural memory, search index, cache, state table, transition table, routing state, program state, conduction state of a memristor or resistive change memory, percolation path state, phase change memory state, ferroelectric memory or FeFET state, magnetoresistive memory state, analog or synaptic device state, optical or photonic device state, volatile or non-volatile semiconductor memory state, or a combination thereof, and the experience memory unit or a separate memory management unit cleans up old, redundant, or low-value experiences or memory objects, active certification dependency, rollback requirement, Revoked history, legal or Check at least one of consent-related preservation conditions, mission requirements, or common cause provenance, perform at least one of merge, summarize, archive, tombstone, or delete for objects that do not have the said protection conditions, and perform auditable forgetting that preserves at least one of identification information, revoke reason, provenance digest, dependency digest, epoch, hash, or archive reference when objects related to past Revoked or ConfirmedFailure are not maintained in their original form, and the experience structuring part includes the starting state, result state, state transition distribution, reward distribution, cost distribution, subsequent state set, execution context of multiple experiences,A system characterized by determining that a plurality of experiences are predictively equivalent or reusable experiences when a difference in at least one of a constraint or uncertainty satisfies a set condition, and generating at least one of an abstract state transition operator, skill, option, macro behavior, policy fragment, program fragment, workflow, toolchain, prompt chain, function, controller, state machine, model routing rule, graph subpath, or other reusable execution representation from the plurality of experiences. Claim 3 A system according to claim 1, wherein the execution topology is explicitly or implicitly implemented by an explicit graph, adjacency relationship, transition table, search tree, beam, frontier, priority queue, policy network, routing network, latent relation, vector search structure, program dependency relationship, state machine, set of candidate actions, cache, search index, or a combination thereof, and the execution topology management unit creates or updates a connection relationship or selection priority between the first execution representation and the second execution representation based on at least one of compatibility between the result state of the first execution representation and the start condition of the second execution representation, experience-based selection probability, expected utility, cost, risk, or uncertainty, and creates, deletes, merges, splits, or changes the weight of execution nodes or connections as the plurality of experiences are added, or changes at least one of a candidate set, search order, beam or frontier, policy routing, model or tool selection probability, search index, cache, or other implicit planning structure. Claim 4 In claim 1, the authentication topology management unit separately generates a connection authentication state between the first execution expression and the second execution expression based on the influence of at least one of the execution result, side effect, data, memory change, authority change, resource change, trust information, or tool state of the first execution expression on the start condition, constraint, or execution result of the second execution expression, even if each of the first execution expression and the second execution expression satisfies individual authentication conditions; generates a path authentication state by evaluating at least one of state transitions, cumulative risk, cumulative uncertainty, data flow, authority transition, use of shared resources, chronological order, recoverability, or constraint compliance of the entire combination path for a sequential, parallel, conditional, iterative, recursive, or asynchronous combination path including multiple execution expressions; and explicitly or implicitly generates a social state including at least one of roles, relationships, authority, consent, trust, privacy, ownership relationships, group states, or social norms concerning one or more humans, users, groups, organizations, intelligent nodes, or robots. Maintaining a social topology, the production execution topology includes execution representations, connections, or combination paths allowed in the current social context based on the social topology, the execution topology, and the authentication topology; if at least one of withdrawal of consent, change of role, revocation of authority, change of privacy conditions, or change of collective state occurs, at least one of the execution representations, connections, or combination paths dependent on said change is selectively converted to a restricted, demoted, or re-verified target; when multiple intelligent nodes, robots, or execution agents are combined in a single mission, a mission authentication state is created that evaluates at least one of the total resource budget, combined effects of personal information or privacy, split execution of prohibited objectives, total mission time, shared space, authority set, or collective safety conditions separately from the individual authentication state of each execution agent; and even if each individual execution agent is in a Full or corresponding allowed state, if the mission authentication state does not satisfy the overall execution permission conditions set, concurrent execution count, execution order, resource allocation, data sharing,A system characterized by restricting at least one of space occupation or execution authority, calculating the scope of influence of a task assigned to an implementation when one or more robots or implementations of the mission become in a state of failure, degradation, resource shortage, authority change, or communication loss, reconstructing a task-to-agent assignment based on at least one of the remaining execution agents' self-implementation model, body capability envelope, self-assessment state, local authentication state, resource state, or current workload, activating the reassigned role in the production execution topology only when the implementation newly assigned the task re-grounds the abstract execution representation of the task according to its own self-implementation model and satisfies the conditions for separate body certification and the update of the mission authentication state, and changing the connection or combined path in the production execution topology to at least one of deletion, deactivation, restriction, cost increase, priority downgrade, Shadow, Canary, or additional verification state if the connection authentication state or path authentication state does not satisfy the execution permission conditions set in the current state or execution context, even if the authentication states of individual execution representations are all in an allowed state. Claim 5 In claim 1, when a continuous, parallel, or conditional execution path comprising multiple experiences or multiple execution representations satisfies a set condition regarding at least one of repeatability, result consistency, success rate, prediction error, uncertainty, constraint compliance, cost, delay, energy, or resource efficiency, and an authentication state corresponding to said execution path satisfies a set compilation acceptance condition, at least a portion of said execution path is converted into a single compilation execution object, said compilation execution object includes a program, a prompt chain, a tool chain, an API call sequence, a policy fragment, a state machine, a control command sequence, a graph subpath, a model routing rule, a cached execution result, a kernel, hardware mapping information, or a combination thereof, said compilation execution object is associated with at least one of a node authentication state, a connection authentication state, a path authentication state, an authentication domain, a model version, a memory version, a tool or API version, an environment version, a reviewer version, or a resource condition on which it depends, a FastPath lease is granted to said compilation execution object determined by at least one of a set period, a set number of executions, a set amount of executions, or a set amount of evidence, and the renewal of said FastPath lease To this end, at least one of recent actual execution results, independent verification evidence, environment or version compatibility, and performance or risk advantage over the challenger execution object is required; while the compiled execution object is used as an incumbent in production, one or more challenger execution objects are evaluated in a Shadow, Canary, or restricted verification path; and if the challenger satisfies the established evidence advantage and minimum verification amount, the challenger is promoted; or if the incumbent does not satisfy the lease renewal conditions, the incumbent is demoted to a Restricted, Shadow, or higher planning stage even if no actual failure has occurred, at least one additional action is performed.A system characterized by the fact that when the above-mentioned compiled executable object includes the behavior of a robot or physical implementation, the valid area of ​​the above-mentioned FastPath lease is associated with at least one of the body version, capability envelope, payload, tool, calibration, or health state of the said implementation, and when the said implementation state falls outside the set body compatibility range, the above-mentioned FastPath lease can be switched to a pending update, Restricted, Shadow, or regrounded target even if the functional execution result has not yet failed, and when at least one of the above-mentioned associated authentication state, version condition, or actual execution result does not satisfy the set maintenance condition, all or part of the above-mentioned compiled executable object is disabled, invalidated, disassembled, demoted, authentication range reduced, partial reverse compilation, full reverse compilation, or returned to a higher planning stage. Claim 6 In an intelligent execution object verification and conditional execution promotion system comprising one or more computational resources, a candidate acquisition unit acquires one or more candidate execution objects, execution representations, relationships, or combination paths generated by an intelligent system or received from an external source; said candidate acquisition unit may acquire as candidates, along with dream provenance, at least one of virtual execution objects, virtual relationships, virtual combination paths, failure hypotheses, or alternative paths generated from stored experience, world models, simulators, generative models, or counterfactual state transitions as well as candidates generated from actual execution experience; said virtual candidates are managed in a Shadow, Pending, or Hypothetical state by distinguishing them from candidates observed in actual execution; and when said candidate includes implementation behaviors that affect humans or groups, such as movement, access, manipulation, object transfer, speech, gaze, gesture, photography, information sharing, or other behaviors involving implementations, at least one of physical safety conditions, authorization conditions, consent conditions, privacy conditions, group states, or social norms may be verified in a simulator, digital twin, Shadow environment, or a restricted physical execution environment that does not directly affect actual humans, said physical safety, laws / policies, authorization, or One or more isolated verification environments that maintain at least one of the consent conditions as a hard gate not offset by social utility or user preference, and execute, simulate, analyze, or verify the candidate while limiting, blocking, isolating, replacing, simulating, or monitoring all or part of the actual effects of the candidate; a test generation unit that generates or selects test conditions including at least one of normal conditions, boundary conditions, sensor or input error conditions, communication or computational delay conditions, resource shortage conditions, out-of-distribution conditions, adversarial conditions, past actual failure conditions, version change conditions, shared resource conditions, and parallel or asynchronous combination conditions, and generates additional test conditions by searching for states, inputs, combination paths, or environmental parameters that increase the failure probability or examination loss of the candidate within a set possible area; the system wherein the virtual candidate generator or self-improvement target is an evaluation criterion,At least one of a sealed examiner, an independent oracle, or an exogenous approval signal may be further used so that at least one of the correct answer information, internal parameters, partial test sets, or entire approval logic cannot be directly generated, modified, or fully accessed; and an existing certified object is not permanently revoked or a hard gate is not permanently added based solely on the failure hypothesis until the failure hypothesis generated from the virtual candidate satisfies the set conditions among actual reproduction, an independent oracle, a sealed examiner, or multiple mutually independent verification signals; an evidence lineage including at least one of a model family, training or evaluation data, a world model, a tool or API, a verification environment, a generation path, or provenance is assigned to each of the multiple examination criteria, examination models, verifiers, or verification evidence; and two or more verification results whose identity or degree of overlap of the evidence lineage satisfies the set conditions are managed as a correlated evidence group, and multiple results within the correlated evidence group are not calculated as multiple tables that are completely independent of each other; and for the promotion of a candidate in which at least one of risk, irreversibility, human impact, or mission scope exceeds the set criteria, more than a set number of different evidence lineages At least one of an independent evidence group or seal screening device, an exogenous approval signal, or actual reproducible evidence may be required, and if the candidate or the intelligent system that generated the candidate provides its own probability of success, risk, or confidence, the calibration error or self-bias may be evaluated by comparing the self-assessment with actual or independent verification results, and even if the self-assessment satisfies the set accuracy or calibration conditions, the self-assessment itself is not used as an independent basis for automatically allowing the candidate's full certification or production execution promotion, high self-confidence, past calibration error, boundary conditions,For a candidate having at least one of high irreversibility or high human influence, a self-falsification test may be further performed to generate additional test conditions that are highly likely to falsify the self-assessment; and based on the results produced in the isolated verification environment, at least one of achievement, constraint compliance, generality, robustness, efficiency, uncertainty suitability, recoverability, policy suitability, or version compatibility may be evaluated, and at least one of two or more different evaluation criteria, evaluation models, rules, format verifiers, intelligent nodes, or human evaluators may be used, and an evaluation unit may set one or more evaluations as hard gates or rejection conditions that are not offset by other evaluation scores; An intelligent execution object verification, authentication, and conditional execution promotion system characterized by comprising: an execution promotion management unit that controls the actual execution of the candidate based on the evaluation results of the examination unit as at least one of full permission, conditional permission, permission in a limited authentication area, Shadow, Canary, hold, request for modification, demotion, suspension, revocation, or rejection, and changes at least one of candidate availability, priority, cost, routing, or execution authority in the actual production environment according to the authentication status. Claim 7 In claim 6, the isolated verification environment comprises at least one of a world model, a simulator, a digital twin, an emulator, a replay environment, a shadow execution, a dry-run, a canary environment, a virtual machine, a container, isolated hardware, a type validator, a model checker, a symbolic execution environment, a counterfactual evaluation environment, or a hierarchical combination thereof; the execution promotion management unit or a separate certification management unit generates a certification object comprising at least two of a certification target, a certification type, a certification area, a test condition, a verification evidence, a reliability or uncertainty, a model version, a memory version, a tool or API version, an environment version, a state abstraction version, an examiner or verifier version, a hardware configuration version, a creation or verification provenance, a creation time, an expiration time, a dependency or retrieval condition; the certification object may further comprise at least one of a valid consistency epoch, an evidence lineage, correlation evidence group identification information, a common cause dependency information, a rollback pointer, a blast radius, or irreversibility; and the certification object is incompatible with the epoch of the current production execution snapshot or the evidence supporting the certification object is due to conflicting evidence When transitioning to a quarantine state, at least one additional action is performed among excluding the authentication object from a new production commit, transitioning it to a Restricted or Pending state, and if the authentication object is associated with an implementation behavior or a body-grounded execution path, the authentication object may further include at least one of a body model version, capability envelope, payload range, tool configuration, calibration state, body health range, or self-evaluation calibration state.A system characterized by being able to maintain at least one of a tombstone, digest, hash, archive pointer, or dependency summary necessary for the reproduction or retrieval of the authentication object when the original evidence supporting the authentication object is converted to an auditable forgotten or archive target, and changing the authentication object or the execution object dependent thereon to a different state among Full, Restricted, Shadow, Canary, Pending, Suspended, Revoked, or Expired, or converting it to a re-verification target when at least one of the versions or execution contexts dependent on the authentication object is changed outside of the set compatibility conditions. Claim 8 In claim 1 or claim 6, managing an authentication dependency structure including dependencies between an execution representation, a connection authentication state, a path authentication state, a compiled execution object, a model, memory, a tool, an API, a data source, a state abstraction, an examiner, a verification environment, or a hardware resource; calculating an impact range based on the authentication dependency structure when at least one of failure, unexpected state transition, constraint violation, environment change, version change, or authentication expiration occurs in actual execution; selectively converting at least one of the execution representation, connection relationship, combined path, authentication object, or compiled execution object included in the impact range into a target for demotion, suspension, revocation, reduction of authentication range, reverse compilation, or re-verification; generating a local re-verification test set for the impact range including at least one of an actual failure case, a variation of the failure case, a boundary condition, an out-of-distribution condition, an adversarial condition, a resource condition, a version change condition, or historical verification evidence; and for targets where the local re-verification result satisfies a set recovery condition, re-assigning a Full or Restricted authentication state to the related execution node, Restores connections, combination paths, or compiled execution objects to the production execution topology; stores virtual failure hypotheses or counterfactual failure paths generated by the world model, simulator, or generative model as Hypothetical, Pending, or Shadow test conditions, distinguishing them from failures that occurred in actual execution; promotes the virtual failure hypothesis to an authorized dependency revocation or permanent test condition equivalent to an actual failure only if it satisfies conditions of actual reproduction, independent verification, or exogenous acceptance; reduces the priority, memory weight, or coverage area based on at least one of the creation time, version change, dream debt, or uncertainty if the virtual failure hypothesis fails to confirm long-term consistency with actual or independent evidence; calculates the coverage area based on dependency relationships with said changes when at least one of withdrawal of consent, role change, authority revocation, relationship change, privacy condition change, social norm version change, or group state change occurs.Selectively convert at least one of the social authentication, execution representation, connection, combination path, or implementation behavior path included in the above scope of influence into a restricted, degraded, revoked, or re-verified target, and further perform at least one of the actions of maintaining the authentication of an irrelevant social state or irrelevant behavior path; the above authentication dependency structure further includes a common cause hyperedge indicating that two or more targets share at least one of the same model, dataset, tool or API, sensor calibration, hardware, policy, social-state source, or validation environment, in addition to the direct dependency relationship between the execution representation or the authentication object; the above scope of influence includes at least a portion of the union of the scope according to the above direct dependency relationship and the scope connected to the above common cause hyperedge; if at least one of continuous prediction residuals, calibration error, failure frequency, utility change, social state error, resource state change, or distribution change satisfies a set drift suspicion condition, the corresponding authentication object is converted to a Suspect state instead of being immediately converted to Revoked; and in the above Suspect state, the authentication area is reduced, the Canary ratio is increased, the verification budget is increased, the challenger verification volume is increased, or high-risk execution is restricted or added After performing at least one of the independent evidence requirements, the certification status is re-determined based on actual failure, certification expiration, independent verification, or established confirmation conditions; at least one of the snapshot, trigger evidence, epoch, dependency, scope of influence, or rollback pointer before and after the topology or certification status change is recorded in the rollback ledger; and the change in which conflicting evidence or an incomplete epoch exists may be maintained in at least one of the Quarantined, Pending, Shadow, or Staging states, and the scope of influence includes the body health, sensor, or actuator degradation of the robot or physical implementation,A system characterized by updating the capability envelope and body certification domain of the corresponding implementation when caused by at least one of calibration drift, tool change, payload change, or damage; generating at least one of recalibration, recharge, cooling, tool change, payload reduction, speed reduction, redundancy activation, diagnostic test, safe mode, task delegation, or repair request as a self-maintenance or recovery candidate for said degradation or failure; switching the deletion or compression of a memory or topology object to a Pending, Quarantined, or additional verification state when said deletion or compression removes evidence necessary for an active certificate, production path, rollback, common-cause analysis, Revoked history, or mission audit; and adding or changing subsequent test conditions or updating at least one of the auditor's reliability, coverage area, weight, or veto power based on at least one of a newly occurring failure, unexpected state transition, constraint violation, or difference between audit prediction and actual result in actual execution. Claim 9 In a distributed adaptive intelligent system comprising different first intelligent nodes and second intelligent nodes, the first intelligent node generates an experience execution package comprising at least two of one or more start conditions, state transition information, end conditions, expected utility, cost, uncertainty, execution information, credit allocation information, generation or learning provenance, verification evidence, or authentication information, and transmits it to the second intelligent node; the second intelligent node transforms at least a portion of the experience execution package according to the correspondence between the state space, representation space, or execution context of the first intelligent node and the state space, representation space, or execution context of the second intelligent node, and inserts it as a candidate state into the execution topology of the second intelligent node; the second intelligent node generates at least one of a local individual authentication state for the transformed execution representation, a local connection authentication state with one or more execution representations existing in the second intelligent node, and a local path authentication state for one or more local combination paths including the transformed execution representation; and the authentication information received from the first intelligent node or The verification evidence is not used for automatic execution permission in the second intelligent node, but is used as evidence information to determine at least one of the number of verification tests, verification priority, risk level, prior, verification budget, or candidate certification area; the experience execution package may further include social experience information including at least one of the role, relationship, scope of consent, scope of authority, scope of privacy, social norm reference, social state provenance, or social state uncertainty regarding one or more social actors; the second intelligent node remaps the social experience information to match at least one of its local actor identification, role system, authority system, scope of consent, privacy system, and social norm; the consent or authority information in the first intelligent node is not automatically inherited as the physical behavior authority of the second intelligent node or a robot or physical embodiment configured to be controlled or used by the second intelligent node, but is used only to the extent that it satisfies the local social state and local certification conditions; and the experience execution package is not an actual execution experience but a world model,In cases where a virtual experience generated from a simulator, a generative model, or a counterfactual state transition is included, the experience execution package further includes at least one of dream provenance, virtual experience uncertainty, generative model version, dream debt, or dream gate evidence; the second intelligent node may apply to the virtual experience package at least one of a lower initial prior, a narrower initial authentication area, a higher verification budget, a shadow state, or an additional independent verification requirement compared to the actual execution experience package; the experience execution package may further include two or more different modalities among text, image, sound, video, spatial information, depth information, LiDAR, radar, tactile, force, position, inertia, proprioceptive, biosignal, tool execution information, program state, or latent representations thereof, and at least one of temporal, spatial, semantic, causal, or state transition correspondence information between said modalities; and in cases where the first intelligent node and the second intelligent node have different sets of modalities, the second intelligent node includes a common intermediate representation, latent space, state transition model, object, or event Based on at least one of correspondence, coordinate transformation, or world model, the multimodal experience of the first intelligent node is transformed into a representation available for use in the second intelligent node; the second intelligent node generates at least one of modality-specific local authentication and cross-modal authentication regarding temporal, spatial, semantic, causal, or state transition consistency between two or more modalities for the transformed multimodal experience; if the modality-specific local authentication or cross-modal authentication is not valid in the current execution context or if one or more modalities are in a state of loss, degradation, delay, or increased uncertainty, at least one of the execution representation, connection, or combination path dependent on the relevant modality is selectively converted to a target for restriction, demotion, retrieval, or re-verification, and other local modalities, the experience of an external intelligent node, world model,An alternative execution path utilizing at least one of additional observation or other tools may be created or selected, and if the local authentication of the alternative execution path satisfies the execution permission conditions set, the alternative execution path may be activated in the production execution topology of the second intelligent node; when multiple intelligent nodes including the first intelligent node and the second intelligent node perform the same mission or group task, a mission authentication state is created by evaluating at least one of the total resource budget, combined data or privacy effects, combination or split execution of goals, total time, shared space, permission set, or group safety conditions separately from the local authentication of each node; and even if the local authentication states of the first intelligent node and the second intelligent node are in an allowable state, if the mission authentication state does not satisfy the group execution permission conditions set, at least one of the simultaneous execution, execution order, resource allocation, data sharing, or execution authority of the multiple intelligent nodes is restricted, and the first intelligent node and the second intelligent node correspond to the first implementation and the second implementation, respectively, and the first implementation and If the second embodiment has at least one of different robot types, kinematics, dynamics, sensor set, actuator set, workspace, payload, tool, or body health, the raw motor command of the experience execution package or the trajectory dedicated to the first embodiment is not used as an automatic execution command of the second intelligent node, and at least one of a state transition objective, task constraint, object or event relation, contact objective, semantic skill, or other embodiment-independent representation is extracted from the experience execution package, andA distributed adaptive intelligent system characterized by the second intelligent node, based on its own self-implementation model and body capability envelope, re-grounding the embodiment-independent representation or the abstract execution representation generated from the representation into at least one of the second implementation-dedicated trajectory, controller, force profile, tool usage, or actuator command sequence; activating the execution path in the production execution topology of the second intelligent node only when the local body certification of the second intelligent node for the re-grounded execution path satisfies a set body execution condition; and activating the transformed execution representation, connection relationship, or combination path in the production execution topology of the second intelligent node only when one or more corresponding certification states among the local individual certification state, local connection certification state, local path certification state, cross-modal certification, or local body certification satisfy a set local execution acceptance condition. Claim 10 In any one of claim 1, claim 6, or claim 9, at least one of the execution representation according to claim 1, the candidate execution object according to claim 6, the execution representation converted from the experience execution package according to claim 9, or the compiled execution object generated and managed in the system among these systems is the target execution object of the present claim; and based on at least one of the authentication status, usage frequency, expected latency, expected energy, data transfer amount, or resource availability of the target execution object, the target execution object is selectively mapped to at least one of the conduction path, percolation path, phase change memory, ferroelectric memory, magnetoresistive memory, analog or synaptic device, optical or photonic path, or other physical or logical execution resources, a CPU or GPU cache, usage frequency, expected latency, expected energy, data transfer amount, or resource availability of the target execution object, and when the authentication status is downgraded, reclaimed, or expired, all or part of the resource mapping is released, restricted, or switched to an alternative path, and for each of the plurality of candidate execution objects or combination paths, a risk level, Calculate a verification budget based on at least one of uncertainty, novelty, failure history, usage frequency, execution importance, remaining time until certification expiration, or verification cost; observe or calculate at least one of the actual or predicted delay, throughput, memory occupancy, accelerator occupancy, power consumption, energy, data movement volume, network delay, external tool response time, or other resource status of the said target execution object; compare said resource status with technical operating conditions including at least one of maximum or quantile delay, minimum throughput, maximum error rate, maximum memory or accelerator occupancy, maximum power or energy, maximum network delay, maximum risk, or certification area maintenance condition; and even if the said target execution object satisfies the functional certification condition, if the said resource status deviates from the said technical operating condition, convert the said target execution object into a target for restriction, demotion, partial decompilation, resource-aware recompilation, or physical / logical resource remapping.The above resource-aware recompilation or resource remapping changes the execution path to at least one of other models, tools, execution paths, parallelism, precision, cache strategies, local or remote execution locations, CPUs, GPUs, NPUs, FPGAs, memory, or other physical or logical execution resources that perform the same or similar goals; further performs at least one of the operations of re-verifying all or part of the changed execution path and returning to the existing execution path or high-performance execution path if the resource state satisfies a set recovery condition; allocates at least one of the execution timing, computational load, number of scenarios, model precision, or verification budget used for virtual experience generation, counterfactual rollout, dream rehearsal, or dream verification based on at least one of the actual or predicted load of the computational resources, power or energy conditions, verification backlog, number of certifications scheduled to expire, actual experience sparsity, failure density, uncertainty, or expected information gain; and if the production execution load exceeds a set upper limit, switches all or part of the virtual experience generation or dream rehearsal to a delayed, suspended, or low-resource mode, but safety-critical failure or Regarding the certification expiration condition, at least one additional operation allowing limited override is performed, and if the target execution object includes the behavior of a robot or physical embodiment, the target execution object is mapped to at least one of a movement trajectory, approach distance, velocity, acceleration, force, torque, grip, line of sight, utterance, gesture, latency, or other actuator / output parameters, and the mapping may be changed according to at least one of current role, relationship, consent, trust, collective state, social norm, or social state uncertainty within the physical safety certification scope, and the calculation of the verification budget may further include at least one of dependency span, common cause blast radius, irreversibility, human impact, collective impact, or mission scope in addition to risk, uncertainty, novelty, failure history, frequency of use, or execution importance.If the above target execution object includes the behavior of a robot or physical embodiment, it further includes an actuator runtime shield that converts the command into a pass, limit, clip, replace, delay, stop, or safety command based on at least one of the current position, velocity, force, torque, mass, friction, control delay, payload, sensor uncertainty, or embodiment state immediately before the actual actuator command, separately from the higher-level authentication result; the result of the actuator runtime shield changing or blocking the command is used as an update evidence for at least one of the authentication topology, embodiment state, calibration history, failure history, or subsequent verification budget; the robot or physical embodiment configured to perform the behavior of the above target execution object maintains a self-embodiment model including at least one of its kinematics, dynamics, sensor, actuator, payload, workspace, energy, thermal state, calibration, tool, damage, degradation, or uncertainty; updates the body capability envelope for position, velocity, acceleration, force, torque, payload, workspace, energy, or other physical behaviors according to changes in the self-embodiment model; and receives from an abstract execution representation or another embodiment The system regrounds execution experience into physical behaviors exclusive to the current implementation based on the self-implementation model and performs separate body certification, and if the body health or capability envelope satisfies the established maintenance conditions, it may generate at least one of recalibration, charge, cooling, tool change, payload reduction, speed reduction, diagnostic test, redundancy activation, or repair request as an execution candidate, and the system, the expected success rate before execution of the target execution object,A system characterized by updating a self-assessment status including at least one of domain-specific calibration error, self-bias, body health, memory quality, planner quality, judge reliability, social-state accuracy, or mission fitness by comparing at least one of expected risk or confidence with actual execution results or independent verification results, and using said self-assessment status not directly as full certification or production execution authority but as evidence for verification requests, restrictions, abstention, delegation, maintenance, or candidate priority, and allocating at least one of the number of tests per candidate, sandbox precision, number of adversarial tests, number of out-of-distribution tests, number of combinatorial path tests, shadow execution volume, or canary execution volume differently according to said verification budget. Claim 11 An adaptive intelligence execution method performed by a computing device comprising one or more computational resources, comprising: a step of updating a memory state that influences at least one of a subsequent state transition, candidate selection, search priority, planning cost, policy, routing, or execution path based on a plurality of experiences; a step of generating, transforming, combining, splitting, compressing, abstracting, merging, distilling, caching, or updating one or more execution representations reusable for subsequent execution from at least some of the plurality of experiences; a step of generating or updating an execution topology comprising at least one of an executionable connection relationship, selectable relationship, or combinable relationship between the one or more execution representations and the execution representations; a step of generating or updating an authentication topology comprising at least one of an individual authentication state for each of the execution representations, a connection authentication state between two or more execution representations, and a path authentication state for a combinable path comprising a plurality of execution representations, and at least one of an authentication domain, verification evidence, uncertainty, version information, or dependency information; and at least one of a current state, execution context, resource state, authorization information, risk level, or version information and the execution topology based on the authentication topology. A step of generating a production execution topology by modifying at least one of the availability, priority, cost, risk weight, selection probability, routing, resource placement, or search space of an execution node, connection, or combination path used for actual execution or plan search; a step of actually executing one or more execution representations or combination paths based on the production execution topology; a step of calculating an impact range based on certification dependency information when at least one of failure, unexpected state transition, constraint violation, environmental change, version change, or certification expiration occurs during actual execution, and selectively converting at least one of the execution representations, connection, combination path, certification object, or compiled execution object included in the impact range into a target for demotion, reclamation, reverse compilation, or re-verification;and, where an experience including two or more different modalities exists, a step of calculating at least one of temporal, spatial, semantic, causal, or state transition correspondences between said modalities and generating at least one of modality-specific authentication or cross-modal authentication; and, where one or more modalities are in a state of loss, degradation, delay, or increased uncertainty, or where said cross-modal authentication does not satisfy established acceptance conditions, a step of selectively converting at least a portion of the execution representation, connection, or combination path dependent on said modalities into targets for restriction, demotion, retrieval, or re-verification, and generating or selecting an alternative execution path using at least one of another modality, external intelligence node, additional observation, world model, or tool; A step of generating at least one of a virtual state transition, a counterfactual execution path, an alternative path, a success hypothesis, or a failure hypothesis that does not directly act on the actual production environment using at least one of a stored experience, a world model, a simulator, a generative model, a state transition model, or a policy model, and storing the virtual experience in a virtual experience topology distinguished from the actual experience along with dream provenance; a step of performing a dream gate on the virtual experience that includes at least one of self-assessment, world model consistency, constraint check, independent reviewer, seal reviewer, exogenous approval signal, or consistency with the actual experience, and converting only the virtual experience that has passed the set dream gate into a Shadow execution candidate, an authentication target, or a subsequent test condition; a step of restricting the permanent revocation of an existing authentication or the permanent addition of a hard gate based solely on the failure hypothesis until the failure hypothesis generated in the virtual experience satisfies actual reproduction, independent verification, or exogenous approval condition; and a step of updating at least one of world model reliability, dream uncertainty, dream debt, virtual experience priority, or dream verification budget based on the consistency between the prediction result of the virtual experience and the subsequent actual execution result or independent verification result;A step of generating or updating a social state including at least one of roles, relationships, consent, authority, trust, privacy, group state, social norms, or social state uncertainty regarding one or more humans, users, groups, organizations, intelligent nodes, or robots, and configuring a social topology; a step of including execution expressions or implement behavior paths permitted in the current social context in a production execution topology based on the social topology, execution topology, and authentication topology; a step of evaluating at least one of physical safety, law / policy, authority, or consent for the implement behavior path as a hard gate that is not offset by other social utility scores, and evaluating social permissibility or work utility for candidates that have passed the hard gate; a step of converting the selected implement behavior path into at least one physical or output parameter among movement trajectory, approach distance, speed, force, grip, gaze, utterance, gesture, or latency and executing it; and an step of updating the social topology by observing the reaction of a human or group after the implement behavior, provided that explicit consent or legal authority is not automatically generated solely from the observed reaction. A step of not doing; and, when at least one of withdrawal of consent, change of role, revocation of authority, change of privacy conditions, or change of social norm version occurs, a step of selectively converting only the execution representation, connection relationship, combination path, or implementation behavior path dependent on said change into a restricted, demotion, revocation, or re-verification target; a step of assigning epoch information representing a change generation or interoperability snapshot to two or more of the execution topology, authentication topology, virtual experience topology, social topology, resource state, or implementation state, and committing a new production snapshot when the states used in the production execution topology satisfy the same epoch or a configured compatibility relationship;A step of immediately restricting, suspending, or retrieving related execution expressions, combination paths, or implementation behaviors without waiting for a general commit of the production snapshot when at least one emergency condition among physical safety, withdrawal of consent, revocation of authority, or security incident occurs; a step of generating an evidence lineage and a correlation evidence group based on at least one of the model family, data, world model, tool, verification environment, or provenance of multiple verification evidence, and not calculating multiple verifications within the correlation evidence group as mutually independent multiple tables; a step of managing common cause dependencies that share the same model, dataset, tool or API, calibration, sensor, hardware, policy, social-state source, or validation environment along with direct authentication dependencies, and calculating the scope of impact including at least a portion of the direct dependency scope and the common cause dependency scope upon failure or version change; a step of switching the authentication status to Suspect when a continuous drift indicator satisfies a set suspicion condition, and after performing at least one of reducing the authentication area, increasing the Canary, increasing the verification budget, increasing the challenger verification, or restricting high-risk executions in the Suspect state, based on actual failure, independent verification, or a set confirmation condition, a final A step of determining an authentication state; a step of maintaining an estimable social state with belief or confidence, and, in an uncertain social state, if the expected loss of immediate action is greater than the cost of questioning, additional observation, waiting, or confirming authorization / consent, executing at least one of the above questioning, additional observation, waiting, or confirmation as an informational value action;A step of granting a lease to a compiled executable object or FastPath, requiring at least one of recent actual results, independent evidence, or superiority over a challenger for the renewal of said lease, and if the renewal conditions are insufficient, demoting said FastPath to a Shadow, Restricted, or Planner state without actual failure; a step of generating a mission authentication regarding at least one of total resources, privacy, goal combination, total time, shared space, authority, or collective safety separately from each individual authentication when multiple intelligent nodes or multiple robots perform a single collective task, and if said mission authentication does not satisfy the collective execution acceptance conditions, restricting at least one of concurrent executions, order, resources, data sharing, or execution authority; a step of applying a runtime shield that restricts, modifies, replaces, or suspends said command based on the current physical state and uncertainty immediately before the actual actuator command of the robot or physical implementation, and using the result of said shield intervention as evidence for the renewal of subsequent authentication or implementation state; A step of recording at least one of a snapshot, trigger evidence, epoch, dependency, scope of influence, or rollback pointer before and after a change in topology or authentication status, and maintaining the change in which conflicting evidence or an incomplete epoch exists in a quarantine state, and committing or rolling back after further verification; a step of generating or updating a self-implementation model including at least one of kinematics, dynamics, sensor, actuator, workspace, payload, force or torque limit, energy, thermal state, calibration, tool, damage, degradation, or physical uncertainty of a robot or physical implementation, and calculating a body capability envelope based on the self-implementation model;A step of re-grounding the state transition meaning of an experience received from an abstract execution representation or another implementation into at least one of a trajectory, controller, force profile, tool usage, or actuator command sequence based on the current self-implementation model, and generating a body certification for said re-grounded execution path; a step of updating a self-assessment state including at least one of calibration error, self-bias, body health, memory quality, planner quality, judge reliability, social-state accuracy, or mission fitness by comparing the expected success rate, expected risk, or confidence prior to execution with the actual execution result or independent verification result; a step of using said self-assessment state not for the automatic generation of Full certification, but as evidence to change at least one of verification requirement, Shadow, Canary, Restricted, abstention, delegation, self-maintenance, or candidate priority; a step of generating test conditions likely to falsify said self-assessment for a candidate having at least one of high self-confidence, past calibration error, boundary condition, or high outcome influence, and updating the bias, confidence, or coverage area of ​​the self-assessment based on the result.A step of reducing the certification area of ​​the relevant physical action and generating at least one of recalibration, charging, cooling, tool change, payload reduction, speed reduction, diagnostic test, safe mode, task delegation, or repair request as a self-maintenance or recovery action when the body health, certification status, resource, authority, or communication status of one of the multiple robots or intelligent nodes changes, calculating the affected mission task, reconstructing the task assignment according to the self-implementation model, self-assessment status, and local certification of the remaining execution entity, and a step in which a new responsible implementer regrounds the task and activates the role after passing body certification and mission certification. and further comprising an auditable forgetting step of examining at least one of an active dependency, rollback requirement, Revoked history, legal or consent-related retention conditions, mission requirement, or audit requirement with respect to long-term accumulated experience, dreams, authentication, social state, body calibration, compiled object, provenance, or rollback snapshot, and merging, summarizing, archiving, tombstone, or deleting unprotected objects while preserving at least provenance or dependency information of the Revoked or failure history, and updating at least one of the memory state, execution representation, execution topology, authentication topology, or production execution topology based on the actual execution result or re-verification result;A computer-implemented adaptive intelligence execution method based on the co-evolution of execution topology and authentication topology, characterized by including Claim 12 A non-transient computer-readable recording medium having instructions or execution information recorded thereon that cause one or more processors, accelerators, or computing devices to perform the method of claim 11.

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