Artificial intelligence autonomous evolution system and method based on seven-stage evolution cycle

Abstract

The application belongs to the technical field of artificial intelligence, and discloses an AI autonomous evolution system and method based on a seven-stage cycle, which is used for solving the technical problems that the existing AI evolution system cannot autonomously find learning opportunities, learning achievements cannot be effectively absorbed, lacks a verification mechanism, lacks Git synchronization, lacks memory maintenance and the like. The system comprises: a Stage 1 discovery learning stage, which autonomously discovers knowledge gaps; a Stage 2 expansion judgment stage, which decides learning depth; a Stage 3 analysis absorption stage, which absorbs learning achievements into a memory system; a Stage 4 verification confirmation stage, which verifies the correctness of learning achievements; a Stage 5 summary induction stage, which summarizes methodologies; a Stage 6 Git synchronization stage, which versionally manages learning achievements; and a Stage 7 memory maintenance stage, which regularly maintains the memory system to prevent expansion. The system further comprises a GEPA reflection cycle mechanism and a creation registration mechanism. Through the seven-stage complete closed loop, the autonomous evolution of the AI system is realized throughout the whole process without human intervention.

Description

Technical Field This invention relates to the field of artificial intelligence technology, specifically to an AI autonomous evolution system and method based on a seven-stage cycle. Background Technology With the rapid development of artificial intelligence technology, AI agents need to have the ability to evolve autonomously so that they can continuously learn and improve in long-term operation and adapt to new tasks and new environments.

[0001] Existing AI evolution systems mainly employ the following methods: 1. **Manually labeled evolution:** This method involves human experts labeling data to train AI models. It is inefficient, costly, and cannot be scaled.

[0002] 2. **Reinforcement Learning Evolution**: This method uses a reward mechanism to allow AI to explore and learn on its own. While it offers a large exploration space, training is unstable and prone to getting stuck in local optima.

[0003] 3. **Reflective Cycle Evolution**: Allowing AI to reflect on its mistakes and improve itself. This approach relies on AI's reflective abilities and can easily lead to a "reflection cycle" from which it cannot break free.

[0004] In addition, existing AI evolution systems also have the following problems: - **Inability to discover learning opportunities independently:** Relies on human-assigned learning tasks and is unable to independently identify knowledge gaps.

[0005] - **Learning outcomes cannot be effectively absorbed:** The learned knowledge cannot be effectively integrated into the AI's memory system.

[0006] - **Lack of verification mechanism:** Learning outcomes are put into use without sufficient verification, which can easily lead to the accumulation of errors.

[0007] - **Lack of memory maintenance**: The memory system lacks regular refinement and maintenance, making it prone to bloating and stagnation.

[0008] - **Lack of Git synchronization mechanism:** Learning outcomes cannot be version-managed, making it difficult to trace and roll back.

[0009] **Comparison Document 1** (Tencent Cloud, published on May 18, 2026) discloses a Skill-Evolver system based on an eight-stage iterative loop, including: Phase 0 (Setup) → Phase 1 (Review) → Phase 2 (Ideate) → Phase 3 (Modify) → Phase 4 (Commit) → Phase 5 (Verify) → Phase 6 (Gate) → Phase 7 (Log) → Phase 8 (Loop). While this scheme achieves autonomous skill evolution through iterative loops, it still has the following drawbacks: 1. **Different number of stages:** Comparative document 1 has 8 stages, while this invention has 7 stages, indicating different stage divisions and optimization objectives; 2. **Different application scenarios:** Comparative document 1 targets the "skill training" scenario, while this invention targets the "memory system evolution" scenario; 3. **Lack of Git synchronization phase:** Comparison file 1 does not disclose the Stage 6 Git synchronization phase, making it impossible to version control learning outcomes; 4. **Lack of a memory maintenance phase:** Comparison document 1 does not disclose the Stage 7 memory maintenance phase, which cannot prevent the memory system from expanding and becoming rigid.

[0010] Therefore, an AI autonomous evolution system architecture is needed to solve the above problems, especially the technical issues of autonomous discovery, absorption, verification, Git synchronization, and memory maintenance. Summary of the Invention #### Technical problems to be solved The technical problem this invention aims to solve is the technical problems existing in AI evolution systems, such as the inability to autonomously discover learning opportunities, the inability to effectively absorb learning results, the lack of verification mechanisms, the lack of Git synchronization, and the lack of memory maintenance.

[0011] #### Technical Solution To address the aforementioned technical problems, this invention provides an AI autonomous evolution system based on a seven-stage cycle, comprising: **An AI autonomous evolution system based on a seven-stage cycle**, characterized by comprising: **Stage 1 Learning Discovery Phase**: AI autonomously identifies knowledge gaps or learning opportunities and formulates a learning plan; this includes: scanning the memory system to identify knowledge gaps, searching external resources (papers / technical documents / open source projects), assessing learning priorities, and generating a learning plan.

[0012] **Stage 2 Extended Judgment Phase**: The AI ​​autonomously judges whether the learning depth is sufficient and whether extended learning is needed; this includes: assessing the completeness and depth of the current learning results, identifying knowledge points that have not been explored in depth, deciding whether to implement extended learning, and generating an extended learning plan.

[0013] **Stage 3 Analysis and Absorption Phase**: The AI ​​analyzes and absorbs the learning outcomes into the memory system; this includes: extracting core concepts and methodologies, registering creations (if there are innovative achievements), updating the memory routing index, and generating learning notes.

[0014] **Stage 4 Verification and Confirmation Phase**: AI autonomously verifies the correctness and effectiveness of learning outcomes; including: performing code verification (if there is code implementation), comparing experimental results, checking logical consistency, and generating a verification report.

[0015] **Stage 5 Summary and Conclusion Phase**: AI summarizes learning outcomes, summarizes methodologies, and updates the memory system; including: refining core methodologies, updating the methodology library, generating a learning summary report, and deciding whether to proceed to the next learning cycle.

[0016] **Stage 6 Git Synchronization Phase**: The AI ​​submits its learning results and memory system changes to the Git version control system; this includes: checking Git status, committing changes, pushing to the remote repository, and recording version information.

[0017] **Stage 7 Memory Maintenance Phase**: AI performs regular maintenance tasks on the memory system, including: refining MEMORY.md (ensuring ≤7KB), assessing memory health, performing memory upgrades and downgrades, and generating memory maintenance reports.

[0018] Preferably, the system further includes a **GEPA reflection loop mechanism** (Stage 5.4 sub-stage): - Reflect on and optimize the process of summarizing and generalizing Stage 5; - Generate optimized prompts or methodologies; - Write the results of your reflections into your memory system to guide the next round of learning.

[0019] Preferably, the system further includes a **creation registration mechanism** (Stage 3 sub-stage): - When innovative results are generated during the learning process, they are automatically registered as creations; - Assign a unique number (e.g., 🔬-001); - Assess patentability and generate patent early warning reports.

[0020] #### Beneficial Effects Compared with the prior art, the present invention has the following beneficial effects: 1. **Seven-stage complete closed loop:** Through the complete closed loop of Stages 1-7, the AI ​​system achieves autonomous evolution throughout the entire process without human intervention.

[0021] 2. **Git Synchronization Versioning**: Through the Stage 6 Git synchronization phase, versioned management of learning outcomes is achieved, supporting traceability and rollback.

[0022] 3. **Memory Maintenance to Prevent Expansion**: Through the Stage 7 memory maintenance phase, the memory system is regularly refined to prevent expansion and stagnation.

[0023] 4. **GEPA Reflection and Optimization**: Through the Stage 5.4 reflection cycle, continuously optimize learning methods and prompts to improve learning efficiency.

[0024] 5. **Automatic Registration of Creations**: Through the creation registration mechanism, innovative achievements are automatically identified and registered to support patent protection. Attached Figure Description Figure 1 This is the overall architecture diagram of the AI ​​autonomous evolution system based on a seven-stage cycle according to the present invention.

[0025] Figure 2 This is a flowchart of the seven-stage cycle in this invention.

[0026] Figure 3 This is a flowchart of the creation registration process in Stage 3 of the analysis and absorption phase of this invention.

[0027] Figure 4 This is a schematic diagram of version management during the Stage 6 Git synchronization phase in this invention. Detailed Implementation The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0028] **Example 1: Construction of an AI Autonomous Evolutionary System Based on a Seven-Stage Cycle** Step 1: Construct Stage 1 discovery learning phase.

[0029] - AI scans MEMORY.md to identify knowledge gaps (e.g., "lack of deep understanding of Scaling Law"). - AI searches external resources (Google Scholar, arXiv, technical blogs); - AI assesses learning priority (high / medium / low); - AI-generated learning plans (e.g., "Research 3 papers on Scaling Law, taking 2 hours").

[0030] Step 2: Construct the Stage 2 extended decision phase.

[0031] - AI assesses the completeness of learning outcomes (e.g., "I have read 3 papers, but lack understanding of the code implementation"). - AI decides whether to extend learning (e.g., "Expansion is needed: read the GitHub code implementation"). - AI generates extended learning solutions (e.g., "reading Hugging Face Scaling Law code, taking 1 hour").

[0032] Step 3: Construct Stage 3 for analysis and absorption.

[0033] - AI extracts core concepts (e.g., "Scaling Law has three levers: training time / inference time / retrieval"). - AI-registered creations (e.g., "Discovering innovative methodologies: Multi-lever synergistic optimization", registered as 🔬-068); - AI updates the memory routing index (MEMORY.md); - AI-generated learning notes (`06-Frontier Technology / Scaling-Law-Research-2026-05-21.md`).

[0034] Step 4: Construct Stage 4 verification and validation phase.

[0035] - AI performs code verification (e.g., "run code to verify Scaling Law formula"). - AI-compared experimental results (e.g., "the verification results are consistent with the paper"); - AI generates verification reports.

[0036] Step 5: Construct the Stage 5 summary and conclusion phase.

[0037] - AI extracts core methodologies (such as "multi-lever collaborative optimization method"); - AI update methodology library (`_META / methodology-lib.md`); - AI generates learning summary reports.

[0038] Step 6: Build the Stage 6 Git synchronization phase.

[0039] - AI checks Git status (`git status`); - AI commit changes (`git commit -m "Learning Scaling Law, Registering Creation 🔬-068"`); - AI pushes to the remote repository (`git push`).

[0040] Step 7: Construct the Stage 7 memory maintenance phase.

[0041] - AI-refined MEMORY.md (ensure ≤7KB); - AI assesses memory health; - AI generates memory maintenance reports.

[0042] **Example 2: Implementation of the GEPA Reflection Cycle** Scenario: After the Stage 5 summary and induction phase is completed, the AI ​​performs reflection and optimization.

[0043] Step 1: AI reviews the execution process of Stages 1-5.

[0044] Step 2: AI identifies areas for improvement (e.g., "Stage 1 learning plan takes too long to develop").

[0045] Step 3: AI generates optimization solutions (e.g., "improving the prompts for the learning plan").

[0046] Step 4: The AI ​​writes the optimization scheme into the memory system (`_META / gepa_reflections.md`).

[0047] **Example 3: Automatic Registration of Creations** Scenario: Stage 3 analysis and absorption phase, AI discovers innovative methodologies.

[0048] Step 1: AI identifies innovative points (e.g., "multi-lever collaborative optimization method").

[0049] Step 2: AI assesses patentability (novelty / inventiveness / utility).

[0050] Step 3: Register the AI ​​creation (ID 🔬-068, recorded in `creation-registry.md`).

[0051] Step 4: AI generates patent warning report (update `patent-management.json`).

Claims

1. An AI autonomous evolution system based on a seven-stage cycle, characterized in that, include: Stage 1 is the discovery and learning phase, which is used by AI to autonomously discover knowledge gaps or learning opportunities. Stage 2 is an extended judgment stage, used by AI to autonomously determine whether the learning depth is sufficient. Stage 3, the analysis and absorption stage, is used to analyze and absorb the learning outcomes into the memory system. Stage 4 is the verification and confirmation stage, which is used for AI to autonomously verify the correctness of the learning results; Stage 5 is the summary and conclusion stage, used by AI to summarize learning outcomes and summarize methodologies. Stage 6, the Git synchronization stage, is used to commit learning outcomes to the Git version control system. Stage 7, the memory maintenance stage, is used for the regular maintenance of the AI's memory system.

2. The system according to claim 1, characterized in that, It also includes the GEPA reflection cycle mechanism (Stage 5.4 sub-stage): performing reflective optimization on the summary and generalization process of Stage 5, generating optimized prompts or methodologies.

3. The system according to claim 1, characterized in that, It also includes a creation registration mechanism (Stage 3 sub-stage): when innovative results are generated during the learning process, they are automatically registered as creations, assigned a unique number, and their patentability is evaluated.

4. The system according to claim 1, characterized in that, The Stage 6 Git synchronization phase includes: checking Git status, committing changes, pushing to the remote repository, and recording version information.

5. The system according to claim 1, characterized in that, The Stage 7 memory maintenance phase includes: refining MEMORY.md (ensuring ≤7KB), assessing memory health, and performing memory upgrades and downgrades.

6. A seven-stage cyclical AI autonomous evolution method, characterized in that, Includes the following steps: - Stage 1: AI autonomously identifies knowledge gaps and develops learning plans; - Stage 2: AI determines the depth of learning and decides whether to extend the learning process; - Stage 3: AI analyzes and absorbs the learning results into the memory system; - Stage 4: AI verifies the correctness of the learning outcomes; - Stage 5: AI summarizes learning outcomes and deduces methodologies; - Stage 6: The AI ​​submits its learning results to the Git version control system; - Stage 7: Regular maintenance of the AI ​​execution memory system.

7. The method according to claim 6, characterized in that, Stage 3 also includes the steps of: identifying innovations, registering creations, assessing patentability, and generating patent warning reports.

8. The method according to claim 6, characterized in that, Stage 5 also includes the steps of: performing the GEPA reflection cycle, optimizing cue words or methodologies, and writing them into the memory system.

9. The method according to claim 6, characterized in that, Stage 6 also includes the steps of checking Git status, committing changes, pushing to the remote repository, and recording version information.

10. The method according to claim 6, characterized in that, Stage 7 also includes the steps of refining MEMORY.md, assessing memory health, performing memory upgrades and downgrades, and generating a maintenance report.

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