Generate an advanced algorithm in python that envelops this idea:

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### Multi-Layered Reinforce Thinking (MLRT)

**Overview:**  
MLRT combines layered processing, self-reflection, and reinforcement to enable a model to iteratively improve its responses. This method is intended to improve response quality and adaptability by allowing the model to reinforce useful patterns in its thought process while learning from weaker or incorrect responses.

**Core Components of MLRT:**

1. **Layered Processing Stages**  
   - Each layer or stage of MLRT applies a different level of abstraction or focus, akin to how neural networks have layers for processing different features. For example:
      - **Base Layer**: Processes the input in a straightforward manner, generating a preliminary solution.
      - **Middle Layer**: Refines the solution by applying heuristics, error detection, or logical rules.
      - **Top Layer**: Synthesizes insights from previous layers, focusing on coherence, relevance, and robustness.

2. **Reinforcement Learning Module**  
   - After each response generation cycle, MLRT uses reinforcement learning (RL) to evaluate and reinforce parts of the response that align with desired qualities (e.g., accuracy, consistency).
   - MLRT might use reward functions based on human feedback or alignment with pre-trained criteria to determine which responses to reinforce or modify in future iterations.

3. **Self-Reflection and Iterative Adjustment**  
   - The model evaluates its initial response, identifies potential gaps or inconsistencies, and reruns the problem with adjusted parameters.
   - This process can incorporate backtracking or "look-ahead" steps, allowing the model to simulate and test different solution paths before finalizing a response.

4. **Memory Buffer and Pattern Recognition**  
   - MLRT includes a memory buffer that stores previous responses and patterns, allowing it to recognize similar problem types and improve its response over time.
   - Through reinforcement, MLRT prioritizes and recalls effective strategies, reducing the need for relearning every time a similar problem is encountered.

5. **Feedback Loops for Continuous Learning**  
   - Each response generation triggers feedback loops:
      - **Internal Loop**: Self-assessment based on predefined metrics (e.g., completeness, logical soundness).
      - **External Loop**: Feedback from human users or an evaluation module, giving an external perspective on the response quality.
   - The feedback is incorporated back into the model’s weights and processing layers, improving future performance.

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### Workflow of Multi-Layered Reinforce Thinking (MLRT)

1. **Input Processing**:  
   The input is parsed, and an initial solution is generated at the base layer, focusing on direct information retrieval and basic understanding.

2. **Layer-by-Layer Refinement**:  
   Each layer sequentially processes the input and response. The middle layer refines the response based on contextual or domain-specific rules, while the top layer emphasizes high-level coherence.

3. **Reinforcement Evaluation**:  
   After a preliminary response, the model scores its response based on predefined rewards for attributes like correctness, conciseness, and relevance.

4. **Iterative Response and Self-Reflection**:  
   If the response does not meet a threshold score, the model re-evaluates and refines its response. This iterative cycle continues until a satisfactory response is achieved or a maximum iteration limit is reached.

5. **Memory Update**:  
   The model updates its memory buffer with useful response patterns, reinforcing strategies that performed well.

6. **Final Output**:  
   The refined response is outputted, and the model learns from the entire process to improve future problem-solving abilities.