An agent symbol landing method and system

Abstract

The application discloses an agent symbol landing method and system. The method receives a request containing a symbol intention, a verifiable target, an observation credible domain and a time effective interval; an authenticated environment observation calculates an observation root and uniquely binds the symbol intention to a real reference object on a fact snapshot; a deterministic code generates a content identifier, a fact root and a plan root and admits freezing; an executor only consumes the admitted plan to form an execution root, and forms a reply root according to a credible observation after execution, a state difference and an external reply; only when the reply is attributed to the execution root and the actual effect meets the verifiable postcondition, the method determines success, otherwise, a structured rejection is output, and a verifiable closed loop of the symbol intention to the real state change is realized.

Description

[Technical Field] This invention relates to the fields of intelligent agent control, robotics and industrial control, computer software, digital assets, semantic intermediate representation, content-addressable data structures and trusted execution control, and particularly to a method and system for implementing intelligent agent symbols. [Background Technology] When general-purpose intelligent agents perform tasks in software repositories, digital asset markets, office systems, robots, and industrial equipment, they need to translate symbolic intentions such as "modify the Pick function," "sell the asset," "open valve number three," or "submit the current order" into real-world objects within the current environment. These objects not only need to be consistent in name or category, but also need to be of reliable origin, unique in instance, in a fresh state, capable of fulfilling their functions, consistent with the planned and executed objectives, and generate attributable state change feedback after execution. Otherwise, even if the intelligent agent generates syntactically correct tool calls, it might modify incorrect functions with the same name, transact incorrect contracts, control incorrect equipment, or falsely report that a command has been sent as the desired effect has occurred.

[0003] Chinese invention patent application CN106126493A discloses a natural language parsing method based on robot autonomous behavior. This method matches natural language commands with basic behaviors such as motion control, motion planning, and target manipulation, generating executable autonomous behavior sequences. This approach focuses on natural language parsing and behavior sequence generation. Its performance evaluation is primarily used for learning, and it does not yet provide a technical means to bind the observation source, the unique identity of the real-world object, the planned target, the actual execution object, and the post-execution effect into an independently recalcible proof chain.

[0004] Chinese invention patent application CN112136141A discloses a robot controlled by free-form natural language input. This method generates motion prediction outputs using current sensor data and natural language input, and controls the robot's movements at multiple time steps. While this approach allows movements to change with the current visual environment, the movements are still driven by model predictions. It does not require the number of candidates to be equal to one, nor does it use content identifiers, fact roots, or acknowledgment roots to prove that the actuator actually acts on the same real-world object.

[0005] Chinese invention patent application CN119855687A discloses robot control based on natural language instructions and object descriptors in the robot environment. It generates regions of interest, region embeddings, and object descriptors from environmental visual data, and determines robot skills accordingly. This approach improves the association between natural language tasks and environmental objects; however, object descriptors or region embeddings alone cannot prove the reliability of the observation source, the uniqueness of the object instance, the validity of the state version, or the absence of drift between the planned and executed objectives.

[0006] Chinese invention patent application CN120548238A discloses a robot system, method, control module, and computer program product utilizing a large-scale language model. This system can generate an environmental description based on sensor data, produce work objectives and task plans from the large-scale language model, and verify whether the task plans violate constraints. While this approach involves environmental information, task planning, and constraint checking, it does not establish a fixed closed loop consisting of observation roots, entity roots, intention roots, plan roots, execution roots, and acknowledgment roots, nor does it use content-addressed fact roots and local membership proofs as production execution access conditions.

[0007] Chinese invention patent application CN105868369A discloses a data model verification system and method based on a Merkle tree structure, which verifies the correctness of the called data model through data block hashing and the Merkle tree root. This solution addresses the integrity and correctness verification during data model storage and retrieval, but does not address semantic conservation across the source code fact layer, typed intermediate representation layer, and execution layer, nor does it address the complete closed loop from agent symbolic intent to real-world objects, deterministic plans, actual execution, and effect feedback.

[0008] Furthermore, while existing language servers, abstract syntax trees, control flow graphs, program dependency graphs, and code property graphs can express definitions, references, calls, control flow, and data flow, they typically use query results or graph nodes as endpoints. There is no uniform requirement that query results must be unique, nor is there a combination of source evidence, semantic signatures, content identifiers, subgraph roots, and global fact roots into independently verifiable symbolic proofs. The internal numbering of graph nodes may also depend on the scanning order, causing the same semantic fact to have different identities depending on the order of declaration.

[0009] Existing vector retrieval, code retrieval augmentation, and graph augmentation models are suitable for finding relevant contexts, but high similarity does not equate to a valid true reference relationship. A model or retrieval engine may return multiple approximate candidates or even non-existent symbols. If the execution chain determines the modification target solely based on confidence thresholds or natural language interpretations, it cannot prove that the target symbol truly corresponds to the unique semantic entity used by the compiler, asset system, or control system.

[0010] Content-addressed storage can verify that data has not been replaced, proof-carrying code can verify that the code meets security policies, and oracles can provide signed external data. However, these technologies address data integrity, code policy verification, or external data authentication respectively, but they cannot independently complete the unique binding of symbols across the representation layer. They also do not specify which location fields are only used for location, which semantic fields must be included in the stable identity, or how to block subsequent execution when the binding is incomplete.

[0011] Therefore, existing technologies still have the following problems: it is difficult for names, positions, vector similarity, and graph node numbers to simultaneously satisfy unique binding, overload differentiation, and declaration rearrangement stability; different representation layers resolve symbols separately, which can easily lead to misconnections between source code definitions, typed expressions, semantic facts, and backend functions; query results lack independently recalculated content identifiers and global fact roots, and cannot prove that the executed object and the audited fact belong to the same snapshot; missing, duplicate, partial signatures, signature drift, and unsupported semantics may be silently ignored; observation sources, sampling times, and valid intervals are not included in symbol landing proof; objects that have been proven in the planning phase may be re-parsed by name or replaced by parameters in the execution phase; tool returns, transaction submissions, or control command sending may be directly regarded as success; multiple agents may also interpret the same symbol based on different fact roots. [Summary of the Invention] To overcome the above problems, this invention proposes a method, system, device, and medium for landing intelligent agent symbols that can effectively solve the above problems.

[0013] The present invention provides a technical solution to the above-mentioned technical problems: a method for agent symbol placement, applied to an agent control system including a processor, a memory, a trusted observation interface, and an actuator interface, comprising: receiving an agent symbol placement request, wherein the agent symbol placement request includes the symbol intent to be placed, a verifiable target, an observation trusted domain, a valid time interval, a permissible capability range, permissible side effects, and a proposed operation; obtaining environmental observations from at least one fact producer belonging to the observation trusted domain through the trusted observation interface, verifying the producer identity, original load hash, normalization result, and time validity of the environmental observations, and performing [further steps] on the verified environmental observations. Deterministic encoding is used to compute observation roots; based on fact snapshots corresponding to the environmental observations, real-world referential candidates are enumerated from the set of canonical semantic facts, and the symbolic intent is bound to a real-world referential object with a candidate count of one, generating a rejection result when the candidate count is not equal to one; the object identity, state version, capability, constrained parameters, preconditions, and verifiable postconditions between the environmental observation, the real-world referential object, and the operation to be performed are verified; the real-world referential objects and their relationships are deterministically encoded according to a predetermined field order and domain separation rules, generating entity content identifiers, relationship content identifiers, and capability content identifiers, and the set of canonical semantic facts is organized into independently reproducible... The system calculates the content-addressable authentication data structure and the fact roots; it calculates the entity roots bound to the real referents and their relational closures, and calculates the intent roots bound to the normalized symbolic intent, the verifiable target, and the permissible side effects. Based on the real referents, permissible capabilities, normalized parameters, preconditions, and verifiable postconditions, it generates a deterministic action plan and plan roots. Based on the observation roots, entity roots, intent roots, plan roots, fact roots, and the local membership proofs of the content-addressable authentication data structure, it generates symbolic landing proofs. The deterministic action plan is set only when the environmental observations are valid, the number of candidates is equal to one, the facts are complete, and the recalculation results of each content identifier and each root are consistent. The process is as follows: The plan root in the admitted state is frozen, allowing the executor to consume only the actual referent, capability, and normalized parameters bound to the plan root. The executor then executes the intended operation through its interface, recording the actual executor, actual execution object, actual capability, actual parameters, start state, and execution time, and calculating the execution root. After execution, environmental observations are reacquired, and a receipt root is calculated based on the state differences before and after execution and external execution receipts. A symbol landing success result is generated only if the receipt root references the execution root, the actual execution object, and the actual parameters to be consistent with the plan root, and the state differences before and after execution satisfy the verifiable postcondition; otherwise, a structured rejection result is generated.

[0014] Preferably, the fact producer includes at least one of versioned source code or database, application programming interface with authentication, signature oracle, chain state proof, device sensor, trusted execution environment, and manual signature confirmation terminal; each environmental observation is bound to the producer identity, original load hash, normalization rule, sampling time, valid interval, and state version, and further bound to the unit and precision when the environmental observation contains physical quantities, and the environmental observation is also bound to at least one of signature and trusted connection proof.

[0015] Preferably, a source location identity, a semantic identity, a content identity, and a fact graph global identity are established for the real referent object respectively; the source location identity is used to lock the real object within the fact snapshot, the semantic identity stores the structured fields that need to be conserved across environment observation, planning, execution, and receipt, the content identity excludes the scanning order, internal array subscripts, and declaration positions that do not constitute part of the complete signature, and the fact graph global identity is bound to the standard identifier, profile set, schema fact hash, and semantic subgraph hash.

[0016] Preferably, the candidate index uses a columnar structure to store the normalized source, object type, qualified name, object instance identifier, parameter type sequence, return type, signature integrity status, status version, and capability identifier, and stores the index in a structured hash bucket. When there are duplicate candidates with the same complete identity, multiple matching candidates under the same request, conflicts between incomplete identity and complete identity, or zero candidate quantity, they are all rejected from entering the production execution, and the first candidate, the candidate with the highest similarity, or external objects without source evidence are not selected.

[0017] Preferably, the verification of the object's identity, status version, capabilities, constrained parameters, preconditions, and verifiable postconditions includes: selecting a set of conserved fields corresponding to the object domain to which the actual referred object belongs; the set of conserved fields includes at least the object instance identity and status version, and further includes at least one of the following: unit, precision, capabilities, constrained parameters, valid time interval, parameter type sequence, return type, layout, effect, and calling convention; comparing the conserved fields in the environmental observation projection, entity projection, plan projection, execution projection, and receipt projection field by field; and refusing execution or generating a symbol landing success result when any conserved field is missing, drifts, or cannot be projected.

[0018] Preferably, when generating the entity content identifier and relation content identifier, the semantic relation graph is first divided into strongly connected components. The internal entities of the components are established according to the local semantic fingerprints that do not include the scanning order, internal array subscripts and non-semantic declaration positions. The references within the components that have not yet obtained the final content identifier are encoded with the local sequence number corresponding to the determined order. The content identifiers outside the components are calculated according to the reverse topological order of the directed acyclic graph formed after the strongly connected components are condensed.

[0019] Preferably, the content addressing authentication data structure is a Patricia Merkle directed acyclic graph; the keys of the canonical semantic facts are generated by stable identifiers, and when stable identifiers are lacking, they are generated by the fact type and fact hash; the branch bit of the Patricia Merkle branch is the first different bit of the first key hash and the last key hash of its key range, the leaf node is bound to the key hash and the fact hash, the branch node is bound to the branch bit, the number of facts in the left and right subtrees and the hashes of the left and right subtrees, and the key hash, fact hash, leaf node hash, branch node hash, empty graph hash, subgraph content identifier and fact root respectively use different field separators.

[0020] Preferably, the local membership proof includes a query fact, a fact key, and a proof path along the query fact to the fact root. The verifier recalculates the key hash and the fact hash based on the query fact, and calculates the fact count and root along the proof path. The non-membership proof includes a terminal leaf routed according to the query key. The verifier verifies the first difference between the query key and the terminal key. The branch bits in the proof path are strictly increasing, and the path direction matches both the query key and the terminal key.

[0021] Preferably, the deterministic action plan is bound to at least the target entity content identifier, capability content identifier, normalized parameters, pre-state root, verifiable postcondition, allowed side effects, timeout and idempotency identifiers; the executor shall not re-parse the execution target based on natural language, display name, coordinates, source code line number or similar candidates, and shall refuse to execute if the actual execution object, actual capability or actual constrained parameters are inconsistent with the deterministic action plan.

[0022] Preferably, the admission state machine includes a receiving state, an observation completed state, a candidate set constructed state, a unique binding state, a plan completed state, a proof completed state, an admission state, an execution completed state, a receipt completed state, an effect closure state, and a rejection state. Production operations can only enter the admission state from the proof completed state. The rejection state is entered when the observation is unreliable or expired, the candidate is ambiguous, there is duplicate identity, insufficient capability, the preconditions fail, the execution target drifts, the receipt cannot be attributed, the actual effect is inconsistent, there are unsupported facts, there are tombstone facts, or any content root is inconsistent.

[0023] Preferably, the receipt root is bound to the execution root, the pre-execution state root, the post-execution state root, the execution time window, the actual state difference, the external receipt signature, and the post-condition verification result; the sending of the command, the return of success from the tool, or the submission of the transaction do not constitute the symbol landing success result on their own. The symbol landing success result is generated only when the receipt source is credible, the time sequence is legal, the actual effect meets the verifiable post-condition, and does not exceed the allowed side effects.

[0024] Preferably, based on the original fact root and local proof, the same key replacement, terminal leaf deletion or new branch insertion are performed by path copying, the unchanged subtree is reused and the modified fact root is calculated; when the modified fact root is equal to the fact root recalculated from the modified complete canonical semantic fact set, the update is submitted, otherwise the update is rejected; when the observation validity period expires, the observation confidence domain changes or the previous state root changes, the original planned root is invalidated and the admission is re-executed.

[0025] Preferably, multiple agents exchange intents, plans, executions, and audit results through the same closed-loop root. The closed-loop root is bound to the observation trust domain, time validity interval, observation root, entity root, intent root, plan root, execution root, and receipt root in a fixed order. If any agent uses a different observation root, fact root, entity content identifier, or state version, it refuses to cooperate in execution.

[0026] This invention provides an intelligent agent symbol landing system, comprising a processor, a memory, a trusted observation interface, an actuator interface, and a request receiving module, an observation authentication module, a candidate enumeration module, a unique binding module, a conservation verification module, a content identification and root generation module, a deterministic plan module, an execution admission module, an execution record module, an effect feedback module, and an independent verification module, all invoked by the processor. The request receiving module receives intelligent agent symbol landing requests containing symbol intent, verifiable target, observation trust domain, valid time interval, permissible capability range, permissible side effects, and the proposed operation. The observation authentication module acquires and authenticates environmental observations and calculates observation roots through the trusted observation interface. The candidate enumeration module and the unique binding module enumerate real-world referential candidates based on fact snapshots corresponding to the environmental observations, and determine the real-world referential object when the number of candidates equals one. The conservation verification module verifies the environmental observations, the real-world referential object, the deterministic action plan, and the actual... The system includes: object identity, status version, capabilities, parameters, and preconditions / preconditions between execution and effect feedback; the content identifier and root generation module generates content identifiers, content addressing authentication data structures, fact roots, entity roots, and intent roots according to deterministic coding and domain separation rules; the deterministic plan module generates deterministic action plans and plan roots that bind real-world referents, capabilities, normalized parameters, preconditions, and verifiable postconditions; the execution admission module freezes and admits the plan root when observations are valid, candidates are unique, facts are complete, and all content identifiers and roots are recalculated consistently; the execution record module ensures that the executor only consumes the admitted plan root and records the actual execution object and actual parameters to calculate the execution root; the effect feedback module calculates the feedback root based on post-execution environmental observations, pre- and post-execution state differences, and external execution feedback; and the independent verification module outputs a successful symbol landing result only when the feedback root references the execution root and the actual effect meets the verifiable postconditions.

[0027] Preferably, at least two of the following are located in mutually independent implementation environments or trust domains: the environmental observation fact producer, the deterministic planning module, the executor connected through the executor interface, and the independent verification module; the independent verification module verifies the symbol landing proof by recalculating entity content identifiers, observation roots, entity roots, intent roots, planning roots, execution roots, receipt roots, local membership proofs, and postcondition verification results.

[0028] The present invention also provides an electronic device, including a processor, a memory, and at least one of a sensor interface, a network interface, and an actuator interface, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the method described thereon.

[0029] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described herein.

[0030] Compared to existing technologies, this invention, through unique candidate constraints and signature conservation verification, intercepts missing, duplicate, overloaded connections, and signature drift before execution, reducing the probability of erroneous symbols entering the backend or agent write operations. By separating source-location identity from semantic content identity, it ensures that declaration movement and scanning order changes do not disrupt stable identities, while maintaining sensitivity to changes in parameters, return types, object states, and true semantics. Through entity content identifiers, subgraph content identifiers, and global fact roots, symbol query results can be independently recalculated, cached, and compared, and automatically invalidated after fact changes. By using a column-based identity index, it avoids constructing and long-term storing composite text keys, reducing temporary string allocation and delimiter ambiguity in large-scale symbol indexes. By executing an admission state machine, it transforms uncertain states into explicit rejection states, preventing compilers, agents, transaction systems, or device control systems from generating false success products. Through a unified proof format, it enables different language extractors, compiler stages, AI tools, and external verifiers to share verification rules without having to trust the candidate-generating model or frontend implementation. By excluding query indexes from fact roots and verifying index-bound roots, indexes can be independently rebuilt without altering fact identities, while rejecting queries for new fact snapshots from stale indexes. Semantic isolation sandboxes and one-way isolation from production access enable incomplete facts to be verified, compared, and audited, but not misused as executable facts. Unique PatriciaMerkle directed acyclic graphs and local membership / non-membership proofs allow validators to verify fact attribution or missing facts by reading only the target fact and logarithmic proof paths. Incremental replay of immutable nodes and path replication allows unchanged subtrees to be reused by content identifiers, old roots to be replayed, and incremental results to be deterministically consistent with the recalculated roots of complete facts. Observation roots and valid time intervals reject reality anchors from forged, expired, or incorrectly trusted domains. Field-by-field comparison between the plan root and execution root prevents agents from replacing objects, capabilities, or key parameters after admission. Verification of pre- and post-execution state roots, external acknowledgments, and postconditions separates "command sent" from "reality effect occurred." By using six-phase roots and closed-loop roots, different agents can prove whether they are cooperating based on the same observation, the same entity, and the same state version. Local proofs decouple the verification cost from the total amount of facts, and path replication ensures that a single change only requires recalculating the affected path, thereby reducing the overhead of loading all facts and performing repeated hash calculations. [Attached Image Description] Figure 1 This is a flowchart illustrating the overall process of symbol implementation, proof generation, and access control for this invention.

[0032] Figure 2 A four-layer structure diagram for source-based identity, semantic identity, content identity, and fact graph global identity.

[0033] Figure 3The intelligent agent symbol is placed into a closed-loop diagram consisting of the observation root, entity root, intention root, plan root, execution root, and acknowledgment root.

[0034] Figure 4 The flowchart shows the process of comparing each field of the planned root and the actual executed root before execution, as well as verifying the effect feedback after execution.

Detailed Implementation Methods

[0036] It should be noted that in the embodiments of the present invention, all directional indications (such as up, down, left, right, front, back, etc.) are limited to relative positions on the specified view, rather than absolute positions.

[0037] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Where there is no conflict, the various embodiments and their technical features can be combined with each other.

[0039] Terminology and Data Conventions Symbolic intent: refers to the target meaning that needs to act on a real object, expressed by natural language, model internal representation, source code, abstract syntax tree, typed expression, CSG semantic facts, invocation plan or tool invocation parameters.

[0040] Reality referent: refers to the program entity, digital asset, user interface object, device instance, physical location, or target state that the symbolic intent points to, given the observation trust domain, fact snapshot, and state version.

[0041] Fact producers: refer to versioned source code libraries, databases, software interfaces, oracles, chain state proofs, sensors, trusted execution environments, or human signature verification terminals that can provide evidence of the source and integrity of facts or observations.

[0042] schema: refers to the schema definition that specifies the fact fields, field types, standard encoding order, domain separators, and validation rules.

[0043] profile: refers to the configuration identifier that identifies language, domain or dialect extensions based on the CSG-Core public fields; the profile set is derived from facts and is arranged in strict ascending order in UTF-8 bytes without repetition.

[0044] Content Identifier (CID): refers to the content address obtained by performing a cryptographic hash on a semantic object or relation that has been deterministically encoded according to the schema; changes in content lead to changes in the CID.

[0045] Domain separation: refers to adding different fixed strings before different types of hash inputs, so that the same byte sequence cannot be interpreted as the same object in different fact spaces.

[0046] Root: refers to the hash value that makes a deterministic commitment to all normative facts of the corresponding stage, including observation root, entity root, intention root, plan root, execution root, receipt root, subgraph root, and fact root.

[0047] Relational closure: refers to the finite set of relations required to verify the current request, which is determined from the target real-world referent object according to relation types such as source, definition, inclusion, invocation, import, owner, capability, precondition, and postcondition.

[0048] Verifiable postconditions: These are target state conditions whose authenticity can be determined by post-execution environmental observations, external feedback, or a combination of both.

[0049] Unsupported facts and tombstone facts: Unsupported facts are facts whose semantics cannot be interpreted by the current validator; tombstone facts are facts used to indicate deletion, placeholder, or pending repair status. Neither should be admitted to production execution.

[0050] In the following formulas, H represents a cryptographic hash function; Encode represents deterministic encoding according to the field labels, field order, length, and integer representation specified by the schema; the symbol "||" represents the sequential concatenation of byte strings; text(x) represents writing a 32-bit little-endian unsigned integer corresponding to the UTF-8 byte length of x first, and then writing the UTF-8 bytes of x; u32le(x) represents the 32-bit little-endian unsigned integer representation of x; hex(x) represents converting the byte string to hexadecimal text. Unless otherwise stated, a specific embodiment uses SHA-256 as H.

[0051] like Figure 1 As shown, the intelligent agent symbol landing process of this invention sequentially includes request reception, trusted observation, enumeration of real-world referential candidates, unique binding, semantic and capability verification, deterministic plan and proof generation, execution admission, freezing plan execution, effect feedback, and effect closure. Ambiguity, expiration, unauthorized access, target drift, incomplete facts, or inconsistent effects all lead to a rejection state. Specifically, it includes steps S101 to S110.

[0052] S101, Obtain symbol landing request Receive a request to deploy symbols from an intelligent agent. The request must include at least the symbol intent to be deployed, the verifiable target, the observation range, the observation confidence domain, the valid time interval, the namespace or profile identifier, the allowed capability range, the allowed side effects, and the operation to be performed. The symbol intent can come from natural language, model internal representation, source code, abstract syntax tree, typed expression, CSG semantic facts, invocation plan, or AI tool invocation parameters; the verifiable target must specify at least one postcondition that can be determined by post-execution observation.

[0053] The system performs deterministic normalization on the input text, identifiers, and paths, without substituting natural language similarity for the real identity field. The system simultaneously saves the normalization result and the original input hash, enabling the validator to distinguish between identical input content and identical semantic entities. The request hash (request_hash) binds the symbolic intent, goal, allowed side effects, and intended operation for this task.

[0054] S102, Obtain verifiable observations and establish realistic anchor points. Obtain current environment observations from at least one fact producer within the observation range. Fact producers can be versioned source code or databases, authentication-enabled application programming interfaces (APIs), signature oracles, chain state proofs, device sensors, trusted execution environments, human-signed confirmation endpoints, or combinations thereof. Each observation records the producer's identity, original payload hash, normalization rules, sampling time, valid interval, and state version; for observations with units, the unit and precision are also recorded; observations further record digital signatures or trusted connection proofs.

[0055] The system verifies that the fact producer belongs to the requested observation trust domain, the observation is still within the valid range, and the normalization result is consistent with the original payload hash. It then organizes the verified observation facts into a content-addressable subgraph and calculates the observation_root. If authentication fails, the observation has expired, the units or precision do not match, the state version is outdated, or multiple observation sources that must be consistent conflict, the state machine directly enters the rejection state. Natural language descriptions, model confidence, and vector similarity must not constitute reality anchors on their own. Proofs in open environments are only valid relative to the observation trust domain explicitly bound in the proof; they do not substitute unverified model judgments for real-world observations.

[0056] S103, Establish the reality reference index and enumerate candidates Read canonical semantic facts from fact snapshots corresponding to environmental observations and build a structured identity index. Each candidate semantic entity records the schema namespace and profile, package and module, canonical source, symbol type and qualified name, declaration positioning information, parameter type sequence, return type, signature integrity status, and applicable fields in layout type, calling convention, effect type, and capability type.

[0057] Candidate entities also record definitions, inclusion, invocation, import, or layout relationships, source fact hashes, real object instance identifiers, state versions, observation times, valid intervals, as well as callable capabilities, preconditions, allowed side effects, and verifiable postconditions. The identity index uses a column-split structure to store source, name, parameters, return type, and integrity flags. It stores the index in structured hash buckets, avoiding concatenating multiple fields into long text keys. This makes field boundaries independent of delimiter escaping rules and reduces redundant allocation of composite keys.

[0058] S104, Perform Reality-Assigned Unique Binding Using source-location identity and verified environment observations, symbolic intents are bound to a real-world referential object. The source-location identity of a program object can consist of a normalized source path, function name, and declaration location; digital assets can consist of a chain identifier, contract address, asset identifier, and state version; devices can consist of a device certificate, control domain, device instance identifier, and observation channel; and user interface actions can consist of application identity, page state root, control semantic identity, and action capabilities.

[0059] Reality referential binding is used to prove which real object the symbolic intent refers to in the current observation and state version, and its match count must be equal to one. A missing identity error is returned when the match count is zero; an ambiguous identity error is returned when the match count is greater than one. The system must not select the first candidate, must not substitute the highest similarity for a unique identity, and must not automatically register external objects without source evidence.

[0060] S105, Perform semantic, state, and capability conservation verification. Verify semantic field conservation between the source entity and the target representation layer entity. For function symbols, at least verify the function name, parameter type sequence, return type, and signature integrity; depending on the execution phase, also verify the calling convention, result layout, effect, exported name, entry state, and capability state. Let Project_l(v) represent the structured projection of semantic entity v onto representation layer l, then one implementation satisfies: Normalize(Project_source(v)) = Normalize(Project_typed_ir(v)) = Normalize(Project_csg(v)) = Normalize(Project_execution_plan(v)) The above equality means that the reserved semantic fields specified by the schema are equal field-by-field after normalization, without requiring that the internal node layout, array subscripts, or non-semantic debugging fields of different representation layers be the same. If any reserved field cannot be projected, is missing, or is not equal, the semantic conservation verification fails.

[0061] For assets, devices, user interfaces, or other external objects, the conservation chain extends to: Normalize(Project_observation(v)) = Normalize(Project_entity(v)) = Normalize(Project_plan(v)) = Normalize(Project_execution(v)) = Normalize(Project_receipt(v)) Conservation fields are determined based on the object domain and include at least the object instance identity and state version, as well as applicable fields such as units, precision, capabilities, constrained parameters, and valid time intervals. The system further verifies that the capabilities required for the requested operation are within the permissible capability range, the current state meets the preconditions, and the verifiable postconditions can be determined by post-execution observation. If the object CID, capability CID, or constrained parameters change between planning and execution, the original proof becomes invalid and execution is rejected.

[0062] For complete signatures, the source, name, parameter type sequence, and return type collectively distinguish overloads. For incomplete signatures, a restricted binding with an incomplete tag is formed only if there are no second candidates under the same normalized source and name, and the execution strategy only allows read-only analysis; if an entity with the same name, a complete overload, or a production execution request is encountered, the request is immediately rejected. Parameter drift, return type drift, layout drift, and calling convention drift all generate structured errors and do not allow fallback name matching.

[0063] S106, Generate position-independent semantic content identities and root identities for each stage. like Figure 2As shown, after precise source binding is completed, a location-independent content identifier is generated for the target semantic entity. The encoder encodes schema, profile, entity type, module, name, full signature, layout, effect, capability, calling convention, entry status, export status, and owner content identifier in a defined field order, and adds the corresponding field separator string before each type of input.

[0064] In the entity layer, source location identity L(v), semantic identity S(v), content identity C(v), and fact graph global identity R(G) are established, one of which is defined as follows: L(v) = (canonical_source, kind, name, source_locator) S(v) = (namespace, module, kind, name, parameter_types, return_type, signature_complete, layout, effect, capability, calling_convention) C(v) = H(domain_node || Encode(S(v)) || OwnerCid(v)) D(G) = PatriciaMerkle({Key(v) -> C(v)}, {Key(e) -> C(e)}) R(G) = H(domain_facts_root || Standard(G) || Profiles(G) || SchemaNamespace(G) || SchemaFactHash(G) || SubgraphHash(D(G))) Wherein, L(v) is used for the reality definition location within the current fact snapshot; S(v) stores the structured semantics that need to be conserved across representation layers; C(v) is the entity content identifier; OwnerCid(v) is the owner entity content identifier; C(e) is the relation content identifier; D(G) is a Patricia Merkle directed acyclic graph uniquely determined by the SHA-256-bit path of the fact key; R(G) binds the criteria, profile set, schema facts, and semantic subgraph, and serves as the facts_root. Identity at different layers is stored separately, and semantic identity is not replaced by location, nor is source evidence replaced by content hash.

[0065] The declaration line number, scan order, and internal array subscripts are not included in the normalized content identifier of the complete signature entity; the content identifier must change when the actual symbol name, parameter type, return type, layout, effect, capability, or relationship changes. Thus, the source location identity is used to accurately find the reality definition, while the semantic content identity is used for stable referencing across non-semantic rearrangements.

[0066] For semantic graphs containing cycles, a strongly connected component algorithm is first used to partition the graph. One implementation uses an explicit stack to execute an iterative Tarjan's algorithm: for each node, the access sequence number, low link value, stack state, and traversal cursor are stored; when a node is visited for the first time, an access sequence number is assigned and pushed onto the stack; unprocessed outgoing edges are visited sequentially, and a new traversal frame is pushed when an unvisited successor is encountered, and the low link value is updated when a successor in the stack is encountered; after all outgoing edges of a node are completed, if its low link value equals the access sequence number, the node is popped from the stack until that node is reached, thus forming a strongly connected component. This process does not depend on the recursive stack depth of the host language.

[0067] Within a component, a deterministic order is established based on a local semantic fingerprint excluding array indices and declaration positions. For references within a component that have not yet obtained their final internal CID, the local sequence number corresponding to this deterministic order is used for encoding. Externally, the content identifier is calculated according to the reverse topological order of the directed acyclic graph after simplification. This process ensures that the same canonical fact does not produce different content roots due to different node insertion or declaration orders.

[0068] Each normalized fact is generated using a stable identifier to create a key prefixed with "id:"; if no stable identifier is available, a key prefixed with "anonymous:" is generated using the fact type and fact hash. The split_bit of the Patricia branch takes the first different bit from the hash of the first and last keys within its key range, with the left and right subtrees corresponding to 0 and 1 respectively. The branch is simultaneously bound to the fact count and subtree hash of both subtrees, and empty and single-subtree branches are prohibited. The tree structure is uniquely determined by the canonical semantic facts and is independent of insertion order, edit history, scan order, and memory address. Details of the encoding and proof of this content-addressable authentication data structure can be found in the applicant's prior invention patent application, "A Deterministic Fact Graph Encoding Verification and Root Hash Generation Method and System" (Application No. 202611094996.6). The symbolic landing loop of this invention does not depend on the internal implementation of this specific structure.

[0069] An implementation using SHA-256 calculates various hashes according to the following frames: key_hash = SHA256(text("csg_core.merkle.key") || text(key)) fact_hash = SHA256(text("csg_core.merkle.fact") || text(canonical_fact)) leaf_hash = SHA256(text("csg_core.merkle.leaf") || key_hash || fact_hash) branch_hash = SHA256(text("csg_core.merkle.branch") || u32le(split_bit) || u32le(left_count) || left_hash || u32le(right_count) || right_hash) empty_hash = SHA256(text("csg_core.merkle.empty") || text("")) subgraph_cid = "sha256:" + hex(SHA256(text("csg_core.merkle.subgraph") || u32le(fact_count) || dag_root_hash)) Let profile_count be the number of profiles, and profile_1 to profile_n be the profiles derived from the facts, including csg_core, and arranged in strict ascending order of UTF-8 bytes, where n equals profile_count; profile_bytes is the result of concatenating u32le(profile_count) with each text(profile_i) in order; then the fact root is calculated as follows: profile_bytes = u32le(profile_count) || text(profile_1) || ... ||text(profile_n) facts_root = "sha256:" + hex(SHA256( text("csg_core.merkle.facts_root") || text("csg_core::v1") || profile_bytes || text("csg_core::v1") || schema_fact_hash ||subgraph_hash)) Specifically, schema facts must be unique. Facts are rejected if they contain duplicate facts keys, key hash collisions, empty branches, single-subtree branches, duplicate profiles, or if the caller provides an alternative set of standard, profile, or schema bindings. Name indexes, fully signed indexes, file indexes, caller indexes, and other query acceleration structures are not included in facts_root; indexes must be bound to the current fact root, and loading is rejected if the root is inconsistent.

[0070] In the agent's closed-loop layer, the closed-loop root is calculated according to a fixed order and domain separation rules: grounding_chain_root = H(domain_grounding_chain || trust_domain ||time_bound || observation_root || entity_root ||intention_root || plan_root || execution_root || receipt_root) Here, `trust_domain` is the normalized encoding of the trusted domain of observations, and `time_bound` is the deterministic encoding of the valid time interval; `observation_root` binds authenticated and unexpired environment observations; `entity_root` binds the unique real-world referent object and its relational closure; `intention_root` binds the normalized symbolic intent, goal, and allowed side effects; `plan_root` binds the object CID, capability CID, parameters, preconditions, verifiable postconditions, and execution strategy; `execution_root` binds the actual executor, actual object, actual parameters, start state, and execution time; and `receipt_root` binds post-execution observations, state differences, external receipts, end state, and referenced execution roots. Only verified pre-roots can be referenced at any stage; the caller is not allowed to provide a second set of root bindings.

[0071] S107, Generating Symbolic Landing Proof and Deterministic Action Plan Symbolic proofs include at least the schema, profile, observation trusted domain, valid time interval, request hash, observation proof, source locator identity, semantic identity, entity CID, set of relation CIDs, set of capability CIDs, subgraph CID, root of fact, Merkle local proof, root of observation, root of entity, root of intent, root of plan, root of execution, root of receipt, expected effect, actual effect, number of candidates, integrity flag, and verification code. This set of fields is encoded in the order specified by the schema, not in the memory order of program objects or unordered mappings.

[0072] Observational proofs bind the fact producer's identity, original payload hash, normalization rules, sampling time, valid interval, and signature or trusted connection proof; entity CIDs bind the unique real-world referent; relation CIDs bind the source, definition, invocation, owner, capability, and precondition or postcondition relationship; capability CIDs bind the capabilities that can be invoked; subgraph CIDs and fact roots bind fact snapshots; expected and actual effects are respectively bound to verifiable postconditions and post-execution state differences; the number of candidates must be one; the integrity flag indicates the absence of unsupported semantics, tombstone facts, and open runtime requirements.

[0073] The system generates a deterministic action plan based on a unique entity, capability constraints, and the current antecedent state. The deterministic action plan must include at least the target entity CID, capability CID, normalization parameters, antecedent state root, verifiable postconditions, allowed side effects, timeout, and idempotency flags. The executor must not re-parse the target from natural language, nor may it replace a proven entity CID with a name, coordinates, row number, or a similar candidate.

[0074] Local member proofs recalculate the key and fact hash of the query fact and backcalculate the fact count and root along the proof path; non-member proofs submit the terminal leaf routed according to the query key and verify the first difference between the query key and the terminal key. The branch bits of the proof path must be strictly incremental, and the path direction must match both the query key and the terminal key. External validators can thus verify that a target entity or relation belongs to a specified fact root, or that a specified key does not exist under that root, without loading the complete fact set.

[0075] S108, Enforcement of Access and Freezing of Consumption Plan The admission state machine includes the following states in sequence: RECEIVED (receive), OBSERVED (observation complete), CANDIDATES_BUILT (candidate set constructed), BOUND (unique binding), PLANNED (plan completed), PROVED (proof completed), ADMITTED (admitted), EXECUTED (execution completed), RECEIPTED (receipt completed), RECONCILED (effect closed), and REJECTED (rejected). Production operations can only enter ADMITTED from PROVED; executors can only consume the plan_root that is frozen in the ADMITTED state.

[0076] The admission criteria for compilation downgrade, backend code generation, AI write operations, refactoring commits, transaction signing, or other production executions include: the number of candidates equals one; the integrity flag is true; the observation proof is valid and not expired; the number of unsupported facts and the number of tombstone facts are both zero; the schema matches the profile; the entity CID, capability CID, subgraph CID, and fact root are recalculated to be consistent; the observation root, entity root, intent root, and plan root are recalculated to be consistent; and the semantics, state, capability, and preconditions are verified to be passed.

[0077] If any condition is not met, the system enters the REJECTED state, without generating any production artifacts or performing write operations, transaction signing, or device control. Structured errors include at least input truncation, schema mismatch, untrusted observation source, expired observation, missing source, candidate ambiguity, duplicate identity, parameter drift, return type drift, layout drift, insufficient capability, invalid preconditions, inconsistent content hashes, inconsistent fact roots, unsupported facts, tombstone facts, and incomplete facts.

[0078] The system can provide a semantic isolation sandbox. The sandbox only allows verification, root calculation, difference comparison, and reporting operations. When tombstone facts, unsupported facts, open runtime requirements, or integrity flags are false, the sandbox result retains the incomplete flag and prohibits entry into application binary interfaces, backend internal representations, compilation degradation, code writing, transaction signing, or other production execution. The sandbox is used to store and diagnose incomplete facts and does not constitute a degradation execution path.

[0079] S109, Obtain the execution result receipt and close the causal chain. like Figure 3 and Figure 4As shown, the executor submits the actual call target, capabilities, parameters, start status, execution time, and executor identity. The system recalculates `execution_root` based on this and compares it field-by-field with the frozen plan. After execution, the system does not determine success solely based on whether the command has been sent, the tool returns zero, or the transaction has been submitted. Instead, it retrieves the post-execution status from the effect observation source declared in the request, generating a status difference and `receipt_root`.

[0080] The effect receipt is bound to the execution root, the pre-execution state root, the post-execution state root, the execution time window, the actual state difference, the external receipt signature, and the post-condition verification result. Only when the receipt source is trustworthy, the time sequence is valid, the receipt references the current execution root, and the actual effect meets verifiable post-conditions and does not exceed allowed side effects, will the request move from RECEIPTED to RECONCILED. If the receipt cannot be attributed, concurrent environmental changes are indistinguishable, execution is partially completed, the actual effect is inconsistent, or post-execution observations have expired, a structured failure receipt is generated and the request moves to REJECTED.

[0081] S110, Incremental Updates, Time-Limited Expiration, and Deterministic Replay When observations, source code, or facts change, only the affected entities and their inverse dependency closures are recalculated. If the change is merely a shift in the declaration order or the declaration position of a fully signed entity, the normalized entity CID and subgraph CID remain unchanged. If the object state version, name, signature, return type, layout, effect, capability, preconditions, or relationship changes, the corresponding CID and the upper-level root change accordingly. When an observation expires, the observation's trusted domain changes, or the precondition root changes, the old plan must be re-admitted even if the entity CID remains unchanged.

[0082] Incremental changes are based on the trusted `before_facts_root`. Replacement operations replace the terminal leaf with a new fact hash with the same key; deletion operations delete the terminal leaf and collapse its direct parent branch; insertion operations confirm the first distinct bit using a non-membership proof and insert into a new branch. All three types of operations perform path copying along the original proof path and back-calculate `after_facts_root`. Unchanged subtrees are reused by CID, and the old root and its reachable nodes remain immutable and can continue to be replayed.

[0083] The root obtained from incremental replay must be equal to the root recalculated on the complete canonical semantic facts after the change; otherwise, it is rejected. Incorrect baseline root, incorrect path, incorrect direction, incorrect fact number, incorrect sibling hash, incorrect terminal fact hash, out-of-order, or truncated results in stable failure. Old proofs automatically become invalid due to inconsistencies in fact roots or entity CIDs, requiring re-execution of binding and admission.

[0084] Under the conditions of standardized coding, identical trusted observations, and deterministic executors, replaying the same request yields the same observation root, entity root, intent root, plan root, execution root, and acknowledgment root. If the external environment changes, the change manifests as a new observation root or acknowledgment root, and it cannot be disguised as the same execution by modifying the historical root.

[0085] System and equipment implementation The intelligent agent symbol landing system can be deployed on a single electronic device, edge controller, server cluster, trusted execution environment, or a combination thereof. The processor calls programs in memory to implement a request receiving module, observation authentication module, candidate enumeration module, unique binding module, conservation verification module, content identification and root generation module, deterministic plan module, execution admission module, execution recording module, effect feedback module, and independent verification module. The trusted observation interface can be a sensor interface, network interface, version control interface, database interface, or chain state verification interface; the actuator interface can connect to a code writing gateway, compiler backend, transaction signature module, robot controller, or industrial equipment controller.

[0086] At least two of the observed fact producers, plan generators, executors, and effect verifiers can employ independent implementation or trust domains. For example, the model generates the plan, a trusted execution environment completes the signature, an industrial controller executes the action, and independent sensors verify the effect. The independent verifier does not rely on the natural language interpretation provided by the model, but instead recalculates the content identifier, the roots at each stage, local proofs, and post-condition verification results.

[0087] Computer-readable storage media can be read-only memory, random access memory, solid-state memory, magnetic storage media, optical storage media, or other non-transitory media capable of storing computer programs. The program implements the above steps in a flow manner, independent of any specific programming language.

[0088] Example 1: Precise binding of function symbols across CSG and typed intermediate representations A source file contains two overloaded functions with the same name: Pick(int32): int32 and Pick(str): str. The CSG extractor generates symbolic facts for each function. The symbolic text of each fact is maintained as the function name Pick used by the backend, and the overload identity is carried by independent fields such as semantic source, complete parameter type sequence, return type, and signature integrity status.

[0089] The system first searches for the corresponding function in the typed intermediate representation based on the normalized source code path, function name, and declaration location. Then, it compares the parameter types and return types of the CSG facts with those of the typed function. Even if two overloads have the same name, they will not be cross-bound due to changes in the node generation order, the order of the typed function array, or the order of the CSG nodes.

[0090] If a request changes the first function parameter from int32 to uint64, the system reports that the parameter identity is not unique or that the parameter has drifted; if the second function's return type is changed from str to uint64, the system reports that the return type has drifted. If a second identical identity is added to the source, the identity index rejects duplicates during the construction phase. If a request references a non-existent declaration on line 99, the match count is zero and the request is rejected.

[0091] Example 2: Declaring a Reordered Stable Semantic Content Identity Calculate local semantic fingerprints for nodes that have been bound by precise sources. Encoded fields include entity type, module, symbolic text, function name, semantic source, full parameter type, return type, layout, effect, capability, calling convention, entry state, and export state. Declaration line numbers and node array indices are not included in the local semantic fingerprint of the full signature.

[0092] Insert the same nodes and edges in different orders. The normalization process first constructs the owner dependency graph and uses an explicit stack to record Tarjan traversal frames, low link values, and component states; nodes within a component are sorted according to local semantic fingerprints, and internal circular references are encoded using the sorted local indices; the condensed graph calculates the node content CID and edge content CID in reverse topological order; then, a unique Patricia Merkle directed acyclic graph is constructed using the canonical semantic fact key hash and fact roots are generated. The final roots of the two graphs are the same.

[0093] Move the function declaration from line 10 to line 20 while keeping the signature unchanged; the entity CID and graph root remain the same. Change the parameter sequence from int32 to int32, int32, or change the return type from int32 to int64; the entity CID and graph root change accordingly. A specific implementation uses SHA-256 to generate a 32-byte content identifier and constructs a unique Patricia Merkle directed acyclic graph based on key hashing of the canonical facts; different profiles cannot generate another set of valid roots by replacing the packet identifier.

[0094] The system creates name indexes and caller indexes with different bucket sizes and insertion orders for the same fact subject. Since querying the index does not enter the facts_root, the fact roots before and after index reconstruction are the same. When loading the old index with the new fact subject, the validator finds that the index-carried root is inconsistent with the recalculated root of the subject and refuses to load it.

[0095] Example 3: Access Control for AI Programming Proxy Write Operations The agent proposes a write operation to "modify function Pick". Instead of executing directly based on the name, the system generates a symbolic write request. The CSG query returns two overloads with the same name, so the request candidate containing only the name is two and rejected. The agent needs to provide, or have the target module and parameter signatures resolved from the trusted context. After obtaining the unique entity, the verifier calculates the target function entity CID, caller relationship CID, affected subgraph CID, and facts_root, and writes these values ​​into the write operation request.

[0096] Before committing changes, the write gateway rereads the current facts_root. If the current root differs from the root in the proof, it indicates that the repository has changed, the old proof is invalid, the write operation is rejected, and rebinding is required. After the modification is complete, the CSG is regenerated; if the target function signature changes unexpectedly, post-verification fails, and no commit acknowledgment is generated.

[0097] The request proceeds sequentially through RECEIVED, CANDIDATES_BUILT, BOUND, and PROVED. It only enters ADMITTED state when all candidate numbers, integrity, schema, entity CID, subgraph CID, and fact roots have passed verification. If a sandbox is enabled for diagnostics, the system can perform verification, root calculation, difference comparison, and reporting on the fact set containing tombstone facts, but the state must not enter ADMITTED, nor can it invoke the write gateway or compile degradation process.

[0098] Example 4: Implementing Multilingual or Multi-Domain Symbols Different language extractors map their respective modules, types, functions, calls, and source evidence to CSG-Core public fields, while simultaneously preserving language-specific facts through profiles. Validators do not depend on the extractor's implementation language; they only read the schema, profile, structured identity fields, and hashes. For contract methods or asset identifiers, the normalized source can be replaced with the chain identifier, contract address, method selector, data source identifier, and observation hash; the admission object can be replaced with the signature or asset orchestration plan. Regardless of domain-specific variations, unique candidate, semantic conservation, content identity, proof root, and execution blocking rules remain unchanged.

[0099] Example 5: Reality Assumptions and Transaction Receipts of Financial Asset Intelligent Agents The agent receives the symbolic intent to "sell 10% of the target asset in this account". The system obtains observations from the signing exchange interface, chain state proof, and configured price oracle, verifies the chain identifier, contract address, asset precision, account identity, balance state version, sampling time, and data source signature, and generates an observation_root. If the same symbol corresponds to multiple contracts, the observation has expired, or the data source does not belong to the trusted domain of the requested observation, the request is rejected.

[0100] The system binds the target asset to a unique entity CID, verifies that the account has trading capabilities and that the balance meets preconditions, and generates a plan_root that binds the sell quantity, allows slippage, order type, target market, and idempotent identifier. The signature module can only consume this plan_root. After the transaction is submitted, the system reads the on-chain or exchange receipt and the updated balance and order status, generating an execution_root and a receipt_root.

[0101] RECONCILED is only entered when the actual traded assets, quantities, and status differences meet verifiable post-conditions; orders that are successfully submitted but not executed are not marked as achieving the target effect. The same selling intent generates different plan roots under different balance status versions, and invalid plan roots cannot be replayed for submission, thus preventing duplicate executions; account balance and position pre-verification serve as admission conditions, solidifying the consistency check of fund status into a technical means.

[0102] Example 6: Robot or Industrial Equipment Control The agent receives the symbolic intent to "open valve number three and confirm that the flow rate has reached the target range." The system uses device certificates, control network identity, valve instance identifier, current opening, and pressure and flow sensor observations to form a reality anchor. The plan root binds the entity CID, control capability CID, target opening, pressure safety constraints, timeout, and expected flow range of valve number three. The actuator must not search for the device again by display name.

[0103] After the control command is issued, the actuator submits the actual equipment CID and parameters, and the independent flow sensor resamples within a specified time window. A failure receipt is generated if the actual equipment differs from the plan, pressure constraints fail, the valve receives the command but its opening does not change, or the flow rate does not enter the target range. Therefore, the symbol "Open Valve No. 3" is only truly implemented after the actual object, action, and effect are all closed.

[0104] Example 7: Landing Status of Shared Symbols among Multiple Agents Multiple agents exchange tasks through the same grounding_chain_root. Upstream agents submit intent roots and plan roots, downstream executing agents verify observation roots, entity roots, and capability ranges before execution, and auditing agents review the results based on execution roots and acknowledgment roots. Collaboration requests are rejected if any agent uses different fact roots, outdated observations, or inconsistent object CIDs. This structure enables cross-model collaboration to rely on shared reality references and state versions that can be independently recalculated, rather than approximate understandings in their respective internal representations.

[0105] Observation sources can include file paths and declaration lines, source code fragment hashes, version control object identifiers, signed application programming interfaces, oracles, chain state proofs, device sensors, trusted execution environments, or human signature confirmation.

[0106] Content hashing can use SHA-256, SHA-3, BLAKE series or other hash algorithms that meet collision resistance requirements, but the algorithm identifier must be entered into the field-separated input via the schema.

[0107] Semantic dependency normalization can employ iterative or recursive Tarjan's algorithm, Kosaraju's algorithm, or other strongly connected component algorithms that produce the same deterministic component partitioning. Acyclic graphs can be computed directly using inverse topological order, but the internal ordinal numbers of components and the entity or relation CID rules must remain consistent, and the global commitment of normalized facts is still computed according to the unique Patricia Merkle rule.

[0108] The fact carrier can be a CSGC binary, normalized JSON, relational fact sidecar, or other carriers that can maintain field boundaries and a defined order.

[0109] The access targets can be compilation downgrades, code generation, AI editing, refactoring, deployment, asset transaction plans, control plane actions, or device operations.

[0110] Results can be compiled and tested, version control commits, on-chain transaction receipts, balance status differences, device telemetry, independent sensor observations, or signed manual confirmation.

[0111] Symbols can be functions, types, variables, fields, modules, calling targets, user interface actions, contract methods, asset identifiers, accounts, orders, device instances, device capabilities, physical locations, real-world objects, or target states, but all must have structured observational evidence and verifiable entity identities.

[0112] The observation authentication, proof generation, or signature modules can be deployed within a trusted execution environment or a hardware security module, isolating the key and verification logic from the general execution environment.

[0113] Effect receipts can be retrieved asynchronously or in batches. Multiple executions can share the same post-execution observations, but each receipt references its own execution root and verifies its own postconditions.

[0114] The independent technical main chain of this invention is a provable closed loop formed by trusted observation, unique binding of reality references, semantic and capability conservation, deterministic planning, actual execution, and effect feedback. CSG fact binary encoding and fact roots can serve as content addressing implementations of this closed loop, and asset candidates and deterministic asset fact gates can serve as implementations of financial asset object domains, but neither constitutes an upper limit to the scope of protection of this invention. Unique binding of cross-presentation layer software symbols is the engineering core and a specific implementation method of this invention. This invention is also applicable to digital assets, user interfaces, robots, and industrial equipment.

[0115] A pure Cheng CSG-Core module and Cheng compiler adapter can implement source unique binding, canonical encoding, Merkle proof, and admission verification. This implementation performs field reading, strongly connected component partitioning, deterministic order encoding, content identifier calculation, proof path verification, and state transition according to the aforementioned process, without relying on specific source code as a necessary technical feature. Other programming languages, independent processes, hardware security modules, trusted execution environments, or remote verification services can all implement the same process.

[0116] The aforementioned data structure, canonical encoding, hash formula, proof path, incremental operation, state transition, and rejection condition together provide a complete solution that can be directly implemented. Whether the compiler performs other runtime optimizations, whether it achieves specific product release gates, or the performance on specific machines, does not constitute a necessary feature of the technical solution of this invention, nor does it limit independent CSG producers, consumers, or verifiers from implementing this invention.

[0117] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any modifications, equivalent substitutions and improvements made within the concept of the present invention should be included within the patent protection scope of the present invention.

Claims

1. A method for storing intelligent agent symbols, applied to an intelligent agent control system including a processor, memory, trusted observation interface, and actuator interface, characterized in that, include: The system receives a symbol landing request from an intelligent agent. This request includes the symbol intent to be landed, a verifiable target, an observation trust domain, a valid time interval, allowed capability range, allowed side effects, and the operation to be performed. Through the trusted observation interface, it obtains environmental observations from at least one fact producer belonging to the observation trust domain, verifies the producer identity, original payload hash, normalization result, and time validity of the environmental observations, and performs deterministic encoding on the verified environmental observations to calculate the observation root. Based on the fact snapshots corresponding to the environmental observations, it enumerates real-world referential candidates from the set of canonical semantic facts, binds the symbol intent to a real-world referential object with a candidate count equal to one, and generates a rejection result when the candidate count is not equal to one. Verify the object identity, state version, capabilities, constrained parameters, preconditions, and verifiable postconditions among the environmental observations, the actual referents, and the proposed operation; perform deterministic encoding on the actual referents and their relationships according to a predetermined field order and domain separation rules, generating entity content identifiers, relationship content identifiers, and capability content identifiers; organize the set of normative semantic facts into an independently recalcible content-addressable authentication data structure, and calculate the fact roots; calculate the entity roots bound to the actual referents and their relational closures, calculate the intent roots bound to the normalized symbolic intent, the verifiable target, and allowed side effects, and generate a deterministic action plan and plan roots based on the actual referents, allowed capabilities, normalized parameters, preconditions, and verifiable postconditions; and generate a deterministic action plan and plan roots based on the observation roots, entity roots, intent roots, plan roots, fact roots, and the content-addressable authentication data structure. Local members prove the symbol landing proof, and only when the environmental observation is valid, the number of candidates is equal to one, the facts are complete, and the recalculation results of each content identifier and each root are consistent, the deterministic action plan is set to the admission state; the plan roots in the admission state are frozen, so that the executor only consumes the actual referent, capability, and normalized parameters bound to the plan root, executes the proposed execution operation through the executor interface, records the actual executor, actual execution object, actual capability, actual parameters, start state, and execution time, and calculates the execution root; after execution, the environmental observation is reacquired, and the receipt root is calculated based on the state difference before and after execution and the external execution receipt, and only when the receipt root references the execution root, the actual execution object, and the actual parameters are consistent with the plan root and the state difference before and after execution satisfies the verifiable postcondition, a symbol landing success result is generated, otherwise a structured rejection result is generated.

2. The method according to claim 1, characterized in that, The fact producers include at least one of the following: versioned source code or database, application programming interface with authentication, signature oracle, chain state proof, device sensor, trusted execution environment, and manual signature confirmation terminal; each environmental observation is bound to the producer identity, original load hash, normalization rule, sampling time, valid interval, and state version, and is further bound to the unit and precision when the environmental observation contains physical quantities, and the environmental observation is also bound to at least one of signature and trusted connection proof.

3. The method according to claim 1, characterized in that, For the real-world referential object, a source location identity, a semantic identity, a content identity, and a fact graph global identity are established respectively. The source location identity is used to lock the real-world object within the fact snapshot. The semantic identity stores the structured fields that need to be conserved across environment observation, planning, execution, and receipt. The content identity excludes the scan order, internal array subscripts, and declaration positions that do not constitute part of the complete signature. The fact graph global identity is bound to the standard identifier, profile set, schema fact hash, and semantic subgraph hash.

4. The method according to claim 1, characterized in that, The candidate index uses a columnar structure to store the normalized source, object type, qualified name, object instance identifier, parameter type sequence, return type, signature integrity status, status version, and capability identifier, and stores the index in a structured hash bucket. Duplicate candidates with the same complete identity, multiple matching candidates under the same request, incomplete identities that conflict with complete identities, or zero candidate numbers are all rejected from entering production execution. Furthermore, the first candidate, the candidate with the highest similarity, or external objects without source evidence are not selected.

5. The method according to claim 1, characterized in that, The verification of the object's identity, status version, capabilities, constrained parameters, preconditions, and verifiable postconditions includes: selecting a set of conserved fields corresponding to the object domain to which the actual referred object belongs; the set of conserved fields includes at least the object instance identity and status version, and further includes at least one of the following: unit, precision, capabilities, constrained parameters, valid time interval, parameter type sequence, return type, layout, effect, and calling convention; comparing the conserved fields in the environmental observation projection, entity projection, plan projection, execution projection, and receipt projection field by field; and refusing to execute or generate a symbol landing success result if any conserved field is missing, drifts, or cannot be projected.

6. The method according to claim 1, characterized in that, The content addressing authentication data structure is a Patricia Merkle directed acyclic graph; the keys of canonical semantic facts are generated by stable identifiers, and when stable identifiers are missing, they are generated by fact type and fact hash; the branch bit of a Patricia Merkle branch is the first different bit of the first key hash and the last key hash of its key range; leaf nodes are bound to key hash and fact hash; branch nodes are bound to the branch bit, the number of facts in the left and right subtrees, and the hashes of the left and right subtrees; key hash, fact hash, leaf node hash, branch node hash, empty graph hash, subgraph content identifier, and fact root each use different field separators.

7. The method according to claim 6, characterized in that, The local membership proof includes a query fact, a fact key, and a proof path along the query fact to the fact root. The verifier recalculates the key hash and fact hash based on the query fact, and backcalculates the fact number and fact root along the proof path. It also confirms that the specified key does not exist through non-membership proof: it routes to the terminal leaf according to the query key, verifies the first difference bit between the query key and the terminal key of the terminal leaf, wherein the branch bits in the proof path are strictly increasing, and the path direction matches both the query key and the terminal key.

8. The method according to claim 1, characterized in that, The observation trusted domain, the time valid interval, and the observation root, entity root, intent root, plan root, execution root, and receipt root are bound into a closed loop root in a fixed order; multiple agents exchange intents, plans, executions, and audit results through the closed loop root; if the observation root, fact root, entity content identifier, or state version used by any agent is inconsistent with those of other agents, collaborative execution is refused.

9. A system for landing intelligent agent symbols, characterized in that, The system includes a processor, memory, a trusted observation interface, an actuator interface, and several modules invoked by the processor, including a request receiving module, an observation authentication module, a candidate enumeration module, a unique binding module, a conservation verification module, a content identification and root generation module, a deterministic plan module, an execution admission module, an execution record module, an effect feedback module, and an independent verification module. The request receiving module receives agent symbol landing requests containing symbolic intent, verifiable target, observation trusted domain, valid time interval, allowed capability range, allowed side effects, and the proposed operation. The observation authentication module acquires and authenticates environmental observations and calculates observation roots through the trusted observation interface. The candidate enumeration module and the unique binding module enumerate real-world referential candidates based on fact snapshots corresponding to the environmental observations and determine the real-world referential object when the number of candidates equals one. The conservation verification module verifies the environmental observations, the real-world referential object, the deterministic action plan, the actual execution, and the effect feedback. The system includes: object identity, status version, capabilities, parameters, and preconditions; a content identifier and root generation module for generating content identifiers, content addressing authentication data structures, fact roots, entity roots, and intent roots according to deterministic coding and domain separation rules; a deterministic plan module for generating deterministic action plans and plan roots that bind real-world referents, capabilities, normalized parameters, preconditions, and verifiable postconditions; an execution admission module for freezing and admitting the plan root when observations are valid, candidates are unique, facts are complete, and all content identifiers and roots are recalculated consistently; an execution record module for ensuring the executor only consumes the admitted plan root and records the actual execution object and actual parameters to calculate the execution root; an effect feedback module for calculating feedback roots based on post-execution environmental observations, pre- and post-execution state differences, and external execution feedback; and an independent verification module for outputting a symbol landing success result only when the feedback root references the execution root and the actual effect meets the verifiable postconditions.

10. An electronic device, characterized in that, The method includes at least one of a processor, a memory, and a sensor interface, a network interface, and an actuator interface, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the method according to any one of claims 1 to 8.

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