This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Summary I have developed and experimentally validated the Modular Octad Framework, a system that evolves "meta-prompts" (instructions for generating prompts) rather than static prompts. By implementing a recursive virtuous cycle with a Persistent Bonus Decay mechanism, I achieved over a 2000% increase in fitness scores while effectively preventing reward hacking (Goodhart’s Law). The Problem: Static Optimization and Reward Hacking Most Automatic Prompt Engineering (APE) focuses on single-level tasks, often leading to "hardcoding" where the AI prioritizes score-matching over actual reasoning. My Solution: Modular Octad Framework The system operates through five independent difficulty channels (L1-L5) using an Alpha (Generation) and Omega (Evaluation) engine. Recursive Learning: The system analyzes top-performing solutions, extracts patterns, and feeds them back into the meta-prompt rules. Delta Engine (Stagnation Escape): When fitness plateaus for 15 iterations, the Delta engine triggers radical mutations to escape local optima. Persistent Bonus with Decay: To counter Goodhart's Law, I introduced a bonus that increases with perfect scores but decays over time, forcing the system to maintain high quality rather than exploiting a static measure. Key Experimental Results (using GPT-4o-mini at ~$0.70 total cost ) Performance: Fitness improved from initial baselines to near-optimal levels (e.g., L5 reached 2.48/2.5).Growth: Perfect solution generation increased by 3067% in complex tasks (L4). Delta Engine Proof: In Level 3 experiments, the system successfully recovered from a sharp fitness drop at Iteration 7 to reach peak performance by Iteration 11. Why this matters for AI Governance This framework demonstrates that meta-level self-improvement can be both highly efficient and aligned with intended goals through simple yet robust mathematical constraints on reward systems.Reproducibility As an independent researcher, I have made the full Python implementation, experimental logs, and data available here
Summary I have developed and experimentally validated the Modular Octad Framework, a system that evolves "meta-prompts" (instructions for generating prompts) rather than static prompts. By implementing a recursive virtuous cycle with a Persistent Bonus Decay mechanism, I achieved over a 2000% increase in fitness scores while effectively preventing reward hacking (Goodhart’s Law). The Problem: Static Optimization and Reward Hacking Most Automatic Prompt Engineering (APE) focuses on single-level tasks, often leading to "hardcoding" where the AI prioritizes score-matching over actual reasoning. My Solution: Modular Octad Framework The system operates through five independent difficulty channels (L1-L5) using an Alpha (Generation) and Omega (Evaluation) engine. Recursive Learning: The system analyzes top-performing solutions, extracts patterns, and feeds them back into the meta-prompt rules. Delta Engine (Stagnation Escape): When fitness plateaus for 15 iterations, the Delta engine triggers radical mutations to escape local optima. Persistent Bonus with Decay: To counter Goodhart's Law, I introduced a bonus that increases with perfect scores but decays over time, forcing the system to maintain high quality rather than exploiting a static measure. Key Experimental Results (using GPT-4o-mini at ~$0.70 total cost ) Performance: Fitness improved from initial baselines to near-optimal levels (e.g., L5 reached 2.48/2.5).Growth: Perfect solution generation increased by 3067% in complex tasks (L4). Delta Engine Proof: In Level 3 experiments, the system successfully recovered from a sharp fitness drop at Iteration 7 to reach peak performance by Iteration 11. Why this matters for AI Governance This framework demonstrates that meta-level self-improvement can be both highly efficient and aligned with intended goals through simple yet robust mathematical constraints on reward systems.Reproducibility As an independent researcher, I have made the full Python implementation, experimental logs, and data available here
GitHub: [https://github.com/simhayul/modular_octad_system.py]
Zenodo (Full Paper): [https://doi.org/10.5281/zenodo.18130154]
I am looking for feedback on the scalability of the Persistent Bonus mechanism and its implications for autonomous AI governance.