A parameterized memory manifold-based gradient-free generative knowledge reconstruction method and system

Abstract

This invention discloses a gradient-free generative knowledge reconstruction method and system based on a parameterized memory manifold, aiming to solve the problems of high inference costs, data export risks, and the separation of memory and inference caused by the reliance on external large-scale language models in existing technologies for agent knowledge generation. This method continuously aggregates the abstract centroid vector and its covariance structure information generated after two-layer cognitive decay distillation into a dynamically evolving parameterized memory manifold. Trajectory vectors are generated through covariance-guided chain topological traversal, and a compact decoding head with a parameter size not exceeding ten million maps the trajectory vectors to discrete token sequences or structured logic outputs using pure forward inference, without the need for gradient backpropagation computation. This invention also discloses a dual-engine progressive switching mechanism, which can automatically and smoothly evolve from a traditional retrieval-enhanced generation mode to a pure manifold synthesis mode based on manifold density. This invention makes the agent's private knowledge accumulation process equivalent to a gradient-free online continuous learning process, eliminating the dependence on external large-scale language models for inference. It can run in a pure processor environment on edge devices and offline private networks, and is suitable for private knowledge inference scenarios involving hundreds of billions of multi-tenant AI agents.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence agent cognitive architecture, high-dimensional vector databases, and generative artificial intelligence technology. Specifically, it relates to a method and system for constructing a dynamically evolving parameterized memory manifold using an abstract centroid set generated by time-decay distillation in a multi-tenant ultra-large-scale vector database, and realizing an end-to-end closed-loop method and system from memory to knowledge generation through gradient-free topological forward synthesis. Background Technology

[0002] In the era of intelligent agents centered on large language models, agents need to continuously accumulate and solidify private knowledge through ongoing interactions with the external environment. Existing technical solutions typically employ a retrieval-enhanced generation paradigm: the agent sends the query vector into a vector database to retrieve approximate nearest neighbor results, and then concatenates the retrieval results into contextual hints before sending them to an external large language model for generation.

[0003] This paradigm has the following key flaws: First, there is the bottleneck of external dependencies. The quality of the generated data is entirely limited by the capabilities and availability of external large language models. If the external model goes offline, is upgraded, or its service protocol changes, the agent's cognitive abilities will be severely affected.

[0004] Secondly, there is the risk of data leaving the country. Sending private memory data to external model inference servers in plaintext poses risks of data leakage and compliance, which is particularly unacceptable in sensitive industries such as finance, healthcare, and government.

[0005] Third, the inference cost increases linearly. Each generation requires the inference computing power of an external model, and as the number of agents and the frequency of interaction increase, the inference cost increases linearly or even superlinearly.

[0006] Fourth, memory and reasoning are separated. Vector databases serve only as passive retrieval repositories, and the rich knowledge structure accumulated over a long period of time—including centroid topology, covariance distribution, principal component directions, etc.—is completely unused in the generation process.

[0007] Therefore, how to eliminate dependence on external models and transform vector databases from passive retrieval tools into autonomous knowledge generation engines is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0008] 3.1 Technical Issues This invention aims to solve the technical problems in the prior art, such as the external dependency bottleneck, data export risk, linear increase in reasoning cost, and the separation of memory and reasoning caused by the reliance on external large language models for knowledge generation of intelligent agents.

[0009] 3.2 Technical Solution This invention provides a gradient-free generative knowledge reconstruction method and system based on parameterized memory manifolds. Its core idea is to make the private knowledge accumulation process of an agent equivalent to a gradient-free online continuous learning process, where memory is the model.

[0010] Reference Figure 1 The complete process of this invention includes parameterized memory manifold construction, topology traversal, trajectory synthesis, decoder inference, and progressive switching between dual engines. Specifically, the technical solution includes the following key mechanisms: (1) Construction of the parameterized memory manifold: During the background maintenance cycle of the vector database, the two-layer cognitive decay system continuously distills the decayed episode memories into abstract centroid vectors through principal component analysis. Each abstract centroid carries its corresponding covariance matrix projection direction and variance explanation rate in the metadata. As the number of centroids increases, these centroids and their covariance structure naturally emerge as a dynamically evolving high-dimensional topological manifold, namely the parameterized memory manifold.

[0011] The parameterized memory manifold has the following key characteristics: it is not a traditional vector index, but a continuously evolving parameterized knowledge representation. Each centroid node carries not only positional information but also shape information. The topological relationship between centroid nodes is determined by the principal component direction of their covariance matrix. Centroids with similar principal component directions form a knowledge neighborhood in the semantic space. Its density grows naturally with the continuous operation of the agent without human intervention.

[0012] (2) Covariance-guided topological traversal: When an agent initiates a semantic query, the system performs a chain-like topological traversal on the parameterized memory manifold. The system starts with the nearest centroid of the query intent vector in the manifold; at each node, it searches for the next hop along the direction of the first principal component of that node and selects the neighboring centroid with the highest cosine similarity to the current trajectory direction that has not yet been visited; the traversal process is constrained by the sovereign token boundary, and cross-tenant traversal requires holding a federation token; the traversal terminates when the preset maximum depth is reached or the similarity is lower than the lower threshold.

[0013] (3) Trajectory vector synthesis: refer to Figure 2 The trajectory vector is synthesized by weighted interpolation of the traversed centroid sequence. The weighting coefficients comprehensively consider three factors: trust weight, reflecting the data reliability of the tenant from which the centroid originates; temporal relevance, with centroids closer to the current time having higher weights; and semantic relevance, with centroids more relevant to the query intent having higher weights. The trajectory vector synthesis introduces principal component bias coefficients, ensuring that the synthesized result retains the variance structure information of the centroid community, rather than simply being mean-smoothed.

[0014] (4) Compact decoder head: Refer to Figure 2The trajectory vector is mapped to a discrete token sequence or structured logic output via a parameter-compact decoder head using pure forward inference. Optional decoder head architectures include: a multilayer perceptron for structured output; and a lightweight self-attention decoder for natural language generation. Decoder head parameters are obtained through offline training, using manifold snapshot data paired with the original text for supervised learning. The inference phase performs only forward matrix multiplication and can run in a pure processor environment.

[0015] (5) Dual-engine gradual switching: refer to Figure 3 The system maintains a dynamic weighting coefficient. When the number of abstract centroids in the parameterized memory manifold is lower than a preset density threshold, the coefficient approaches zero, and the system reverts to the traditional retrieval-enhanced generation mode. When the number of centroids exceeds the threshold, the coefficient monotonically increases with density, and the weight of the generative synthesis output gradually increases. Finally, after the manifold is fully mature, the coefficient reaches its maximum value, and the system completely switches to a pure manifold synthesis mode. The user is completely unaware of this process and requires no manual configuration.

[0016] 3.3 Beneficial Effects Compared with the prior art, the present invention has the following beneficial effects: (1) Eliminate external dependencies: The process of accumulating private knowledge of an agent is equivalent to gradient-free online learning. The reasoning process is completely self-contained and does not depend on any external model services.

[0017] (2) Reduced hardware cost: The decoder parameters are compact, and pure processor inference can run on edge devices and offline private networks, eliminating the dependence on high-power inference hardware.

[0018] (3) Controllable reasoning cost: The knowledge base itself is the reasoning engine, and there is no need to pay for external reasoning service calls. The only cost that increases with scale is storage cost.

[0019] (4) Privacy protection: knowledge never leaves the local parameterized memory manifold, and there is no data leaving the boundary. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the entire process of parametric memory manifold construction and generative knowledge reconstruction provided in an embodiment of the present invention.

[0021] Figure 2 This is a data flow diagram of trajectory vector synthesis and decoding head inference provided in an embodiment of the present invention.

[0022] Figure 3 This is a dual-engine progressive switching state transition diagram provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] 5.1 System Architecture and Operating Environment The gradient-free generative knowledge reconstruction system based on parameterized memory manifolds provided by this invention serves as an implementation of advanced cognitive functions within a vector database kernel. In this embodiment, the system is described based on a vector database engine implemented in the Rust language.

[0025] The system mainly includes the following functional components: Parametric memory manifold construction module: During the background maintenance cycle, the parameterized memory manifold is continuously constructed and updated using the abstract centroid and its covariance metadata generated by the two-layer cognitive decay system.

[0026] Manifold Navigator: Responsible for performing covariance-guided chained topological traversals on parameterized memory manifolds.

[0027] Trajectory synthesizer: responsible for weighted interpolation of the traversed centroid sequence to synthesize a trajectory vector.

[0028] Compact decoder: Responsible for mapping the trajectory vector into a discrete token sequence or structured logic output using pure forward inference.

[0029] Dual-engine switching controller: dynamically adjusts the output weights of the retrieval enhancement generation mode and the manifold synthesis mode based on the parameterized memory manifold density.

[0030] 5.2 Construction of Parametric Memoized Manifold Reference Figure 1 In this embodiment, the core implementation of the parameterized memory manifold construction is located in the search module of the vector database engine. The background maintenance method continuously distills and generates abstract centroids in each cleanup cycle. The covariance information of the centroids is generated by the principal component analysis dimensionality reducer in the feature dimensionality reduction module and attached to the centroid metadata.

[0031] Each abstract centroid carries the following information in its metadata: the mean vector, i.e., the position of the centroid in the vector space; the first principal component direction vector, i.e., the direction of the maximum variance of the data cluster represented by the centroid; and the variance explained rate, i.e., the proportion of variance that the first principal component can explain. These metadata elements together constitute the parameterized knowledge representation of the parameterized memory manifold.

[0032] 5.3 Covariance-guided topological ergodication and trajectory synthesis When an agent initiates a semantic query, the system first locates the nearest centroid of the query intent vector in the parameterized memoe manifold as the starting point for traversal.

[0033] Reference Figure 1The manifold navigator performs a chain-like topological traversal: at the current centroid node, obtain its first principal component direction vector; calculate the cosine similarity between the current traversal trajectory direction and the first principal component directions of each adjacent centroid; select the adjacent centroid with the highest cosine similarity that has not yet been visited as the next hop node.

[0034] The traversal process is constrained by the sovereign token boundary: by default, the traversal scope is limited to the sovereign domain of the tenant to which the initiating agent belongs; cross-tenant traversal requires holding a federation token.

[0035] The traversal terminates when the preset maximum depth is reached or the similarity falls below a lower threshold. (See reference...) Figure 2 The trajectory synthesizer performs weighted interpolation on the centroid sequence it traverses. The weighting coefficients take into account three factors: trust weight, which reflects the data reliability of the tenant from which the centroid originates; temporal relevance, which is based on an exponential decay function, with centroids closer to the current time having higher weights; and semantic relevance, which is based on cosine similarity, with centroids more relevant to the query intent having higher weights.

[0036] The final trajectory vector is obtained by weighted summation and normalization. During the synthesis process, a principal component bias coefficient (e.g., default value 0.1) is introduced. The first principal component direction vectors of each centroid are weighted by this coefficient and included in the synthesis, so that the trajectory vector retains the variance structure information of the centroid community, rather than simple mean smoothing.

[0037] 5.4 Compact Decoder Inference Reference Figure 2 After the trajectory vector is generated, it is mapped to the final output through a compact decoding head. The decoding head has no more than ten million parameters and operates in a pure forward inference mode.

[0038] The decoder can be configured with the following architectures: a multilayer perceptron with 3 to 5 layers of fully connected network, suitable for structured output; or a lightweight self-attention decoder with 2 to 4 layers of self-attention, suitable for natural language generation.

[0039] The decoder head parameters are obtained through offline training: supervised learning is performed by pairing parameterized memory manifold snapshot data with the original text. After training, the decoder head is stored in the Open Neural Network Exchange (ONE) format. The inference phase only performs forward matrix multiplications and can run in a pure processor environment without external inference hardware support. The inference runtime can reuse existing ONE NEE runtime inference infrastructure.

[0040] 5.5 Dual-engine progressive switching Reference Figure 3The system maintains a dynamic weight coefficient, with a value between 0 and 1. When the number of abstract centroids in the parameterized memory manifold is lower than a preset density threshold (e.g., 500), the coefficient approaches zero, the system is in a cold start phase, and reverts to the traditional retrieval-enhanced generation mode.

[0041] When the number of centroids exceeds a critical value, the system enters a hybrid mode: the coefficients monotonically increase with density, and the weight of manifold synthesis output gradually increases. In the early stage of the hybrid mode, the retrieval-enhanced generation weight is dominant; when the coefficient is approximately equal to 0.5, the two modes tend to balance; in the later stage of the hybrid mode, the manifold synthesis weight is dominant.

[0042] Finally, once the parameterized memorized manifold is fully mature and its coefficients reach 1, the system enters a pure manifold synthesis mode, completely switching to a memorized model form and no longer relying on any external model. Throughout the entire gradual switching process, the user query interface remains unchanged, and the switch is completely transparent to the user.

[0043] 5.6 Industrial Application Validation The technical solution of this invention is being implemented in a vector database engine. The parameterized memory manifold construction module has been implemented in the background maintenance method of the search module; the manifold navigator and trajectory synthesizer planning are implemented in the generative synthesis module; the decoder inference reuses the existing open neural network exchange runtime inference infrastructure; and the progressive switching control will dynamically mix the retrieval enhancement generation results and the manifold synthesis results according to the manifold density in the search method.

[0044] It should be noted that the specific values ​​mentioned in the above embodiments (such as principal component bias coefficient of 0.1, density critical value of 500, etc.) are illustrative and do not constitute a limitation on the scope of protection of this invention. In practical applications, those skilled in the art can make appropriate adjustments to these parameters according to specific scenarios and needs.

[0045] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural modifications made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A parameterized memory manifold based gradient-free generative knowledge reconstruction method, characterized in that, The following steps are performed by the computing device: Manifold construction steps: Continuously aggregate multiple abstract centroid vectors generated by time-decay distillation and their corresponding covariance structure information to form a dynamically evolving parameterized memory manifold. Each node in the parameterized memory manifold is an abstract centroid, and the topological relationship between nodes is determined by the principal component direction of their covariance matrix. Topology traversal steps: After receiving the semantic query intent, take the nearest centroid of the semantic query intent in the parameterized memory manifold as the starting point, and perform a chain topology traversal along the principal component direction guided by covariance. During the traversal, the boundary constraints of the tenant sovereignty token are applied, and the alliance token must be held for cross-tenant traversal. Trajectory vector synthesis steps: The centroid sequence traversed by the topological traversal is weighted and interpolated according to the trust weight and temporal relevance of each centroid, and synthesized along the principal component axis to form a high-dimensional semantic intent representation vector, called the trajectory vector; Generative decoding steps: The trajectory vector is input into a compact decoding head. The decoding head maps the trajectory vector in the continuous embedding space into a discrete token sequence or structured logic output in a pure forward inference manner. The parameter size of the decoding head does not exceed ten million, and the inference process does not require gradient backpropagation calculation.

2. The method of claim 1, wherein, In the manifold construction step, the abstract centroid vector is generated by the first-level forgetting distillation engine of the two-layer cognitive decay system through principal component analysis algorithm to reduce the dimension of the decayed episode memory vector and fuse them. Each abstract centroid carries its corresponding covariance matrix projection direction and variance explanation rate in the metadata.

3. The method according to claim 1, characterized in that, In the topology traversal step, the search strategy of the chain topology traversal is a greedy nearest neighbor chain extension. At each node, the nearest centroid with the highest cosine similarity to the current trajectory vector and which has not yet been visited is selected as the next jump until the preset maximum traversal depth is reached or the similarity is lower than the preset lower threshold.

4. The method of claim 1, wherein, In the trajectory vector synthesis step, the weighting coefficients of each centroid comprehensively consider the trust weight, the time decay factor, and the cosine similarity between the query intent vector and the centroid; the final trajectory vector is obtained by summing and normalizing the centroid vectors and their first principal component direction vectors after weighting by the principal component bias coefficients.

5. The method of claim 1, wherein, The compact decoding head is a multilayer perceptron structure or a lightweight self-attention decoder structure. Its parameters are obtained through supervised learning by pairing the original text with snapshot data of parameterized memory manifold in the offline stage. In the inference stage, only forward matrix multiplication operations are performed, which can run in a pure processor environment.

6. The method of claim 1, wherein, Also includes: Dual-engine progressive switching steps: Maintain a dynamic weight coefficient. When the number of abstract centroids in the parameterized memory manifold is lower than a preset density threshold, the dynamic weight coefficient approaches zero, and the system reverts to the traditional retrieval-enhanced generation mode. When the number of abstract centroids exceeds the density threshold, the dynamic weight coefficient increases monotonically with the increase of density, and the system gradually increases the output weight of the generative decoding step, eventually completely replacing the retrieval-enhanced generation mode.

7. A parameterized memory manifold based gradient-free generative knowledge reconstruction system, characterized in that, include: A manifold builder is used to continuously aggregate multiple abstract centroid vectors and their corresponding covariance structure information generated by time-decay distillation to form a dynamically evolving parameterized memory manifold. A topology navigator is used to receive semantic query intents and perform chained topology traversals on the parameterized memoe manifold, constrained by tenant sovereignty token boundaries. A trajectory synthesizer is used to synthesize a trajectory vector by weighting the centroid sequence traversed by the topological traversal according to the trust weight and temporal correlation. A generative decoding head is used to map the trajectory vector into a discrete token sequence or structured logic output in a purely forward-inference manner, and the parameter size of the decoding head does not exceed ten million.

8. The system of claim 7, wherein, Also includes: A progressive switching controller is used to dynamically adjust the hybrid weights between the generative decoding output and the retrieval-enhanced generative output based on the density of the abstract centroid in the parameterized memory manifold.

9. The system of claim 7, wherein, The topology navigator requires holding a federation token when traversing across tenant federations, and the covariance matrix of each centroid involved in the traversal path has been processed by differential privacy noise injection.

10. A computer-readable storage medium having stored thereon computer program instructions, wherein, When the computer program instructions are executed by the processor, they implement the method as described in any one of claims 1 to 6.

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