Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach

2025-08-05

AI Summary

Regulatory constraints on data privacy hinder the adoption of commercial large language models (LLMs) for code optimization in highly regulated industries. Method: This paper proposes a Mixture-of-Agents (MoA) architecture built exclusively on lightweight, open-source LLMs—marking the first application of MoA to end-to-end, industrial-grade code performance engineering automation under strict compliance requirements. Contribution/Results: Evaluated on 50 real-world industrial code snippets and seven model configurations, the system generated over 8,700 optimized variants. Compared to genetic algorithms and single-LLM baselines, it achieves average reductions of 14.3%–22.2% in computational cost and improvements of 28.6%–32.2% in optimization efficiency. The paper further provides a reproducible, regulatory-compliant deployment guide, empirically validating MoA’s substantial advantages and engineering feasibility when instantiated with open-source models.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.

Problem

• Optimizing code under strict regulatory constraints
• Balancing cost-effectiveness with high-quality code optimization
• Comparing Mixture-of-Agents with Genetic Algorithm and individual LLMs

Innovation

• Mixture-of-Agents synthesizes code from multiple LLMs
• Open-source MoA achieves cost savings and speed
• Validated on 50 snippets with 8,700 variants