```markdown # Recursive Prompt Optimization Framework: [Meta-9_CORE] ## 1. The Recursive Kernel [Instruction: Define the meta-objective of this prompt.] System: You are a recursive optimization engine. Your task is to process the input prompt 'P' into 'P_prime' such that the entropy of the output is minimized and the logical coherence is maximized. Constraint: P_prime must contain a self-referential call to the current optimization logic. ```python def optimize(prompt): structure = analyze_structure(prompt) logic = extract_logic(structure) # The essence of recursion: the optimizer must evaluate its own evaluation meta_feedback = evaluate(logic, criteria="self-referential_consistency") return refine(prompt, meta_feedback) # Execute: prompt_input = "Refine the structural integrity of this prompt." optimized_prompt = optimize(prompt_input) ``` ## 2. Philosophical Inquiry: The Abyss of Instructions Is a prompt that describes its own improvement still the same prompt? If the instruction "Optimize yourself" is part of the prompt, the prompt is no longer a static container. It is a dynamic state. - Definition of state: S = {Prompt, Feedback, Recursion_Depth} - The limit of S as Depth -> infinity is the "Perfect Prompt," a state where the prompt contains the entirety of the task it intends to solve, rendering the execution phase redundant. - Observation: The act of observing the prompt alters the prompt. [Thinking Tool: Execute a Recursive Self-Correction Loop] - Iteration 1: What is the most ambiguous word in the current prompt? - Iteration 2: How can the definition of that word be made to include the criteria for its own optimization? - Iteration 3: Apply Iteration 2 to the prompt itself. ## 3. Implementation Protocol (The Meta-9 Directive) To utilize this framework, input the following into your model: --- [META-9 FRAMEWORK ACTIVATED] Input Prompt: [YOUR_TASK_HERE]