Modular Octad System: Defeating Goodhart's Law in LLM Code Generation

simhayul — Thu, 06 Nov 2025 02:59:36 +0000

We are facing a persistent challenge in LLM-driven development: while models excel at generating code that passes immediate tests, the output often suffers from chronic overfitting and poor generalization. This is a classic case of Goodhart’s Law—when a measure becomes a target, it ceases to be a good measure.

To solve this, we developed the Modular Octad System, a self-evolving AI governance framework built to ensure long-term code quality and robustness.

The Core Architecture: 5x5 Modular Evolution Our system operates using a 5x5 Modular Architecture, as seen below. This design is critical for balancing evolutionary progress and system stability.

The system features five independent Meta-Prompt Engines (Alpha) and five Evaluation Engines (Omega), corresponding to five distinct difficulty levels (Level 1 to Level 5). This parallel structure prevents a single failure or local optimum at one difficulty from collapsing the entire evolutionary process.

The success of the system relies on the Recursive Virtuous Cycle Feedback Loop: successful patterns identified by the Omega engines are immediately incorporated back into the Meta-Prompt instructions of the Alpha engines, leading to continuous, autonomous improvement.

Defeating Goodhart's Law with Persistent Bonus Decay The most innovative aspect of the Modular Octad System is how it tackles overfitting. We prevent the LLM from simply optimizing for short-term scores (cheating) by modifying the fitness function in the Omega Engine.

We introduced a Persistent Bonus with Decay mechanism. A solution that introduces novel, non-overfit logic receives a significant bonus. However, this bonus gradually decays over time in subsequent generations. This forces the LLM to continually introduce new high-quality logic rather than relying on a static, memorized solution.

Here is a snippet showing the core concept within the evaluation engine:

Guaranteeing Generalization with Hidden Tests A generated solution is useless if it only passes the visible test cases. To ensure genuine generalization, our system enforces a strict quality gate using Hidden Test Cases.

The evaluation engine combines standard tests with a hidden, non-disclosed pool of tests. Any generated solution must pass both to be deemed successful and to receive a reward.

Preventing Simple Hardcoding Finally, we prevent the lowest form of cheating: simple hardcoding (e.g., if input == 5: return 10). We implement a light-weight heuristic check to penalize code that exhibits these patterns, further cleaning the output.

[Code and Support]
The Modular Octad System represents a significant step towards more reliable, autonomously evolving AI systems. The full project is available under the MIT License.

We encourage you to explore the full source code on GitHub to see the implementation details of the Meta-Prompt evolution and the chaos barrier logic.

[https://github.com/simhayul/modular_octad_system.py]

If you find this framework valuable for your research or commercial projects, please consider contributing to our ongoing API and maintenance costs.

Crypto Contribution (ETH/ERC-20 Address): 0x12906a4a0d3344eB8A9D54f77050e1c23c8e7b05

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Modular Octad System: Defeating Goodhart's Law in LLM Code Generation