Artemis AI: Multi-LLM Framework for Code Optimisation

Artemis AI: Multi-LLM Framework for Code Optimisation

연구 분야: Artificial Intelligence

학회: 2025 IEEE Conference on Artificial Intelligence (CAI)

This paper introduces Artemis AI, a novel framework leveraging multiple Large Language Models (LLMs) optimise code performance. Artemis AI achieves significant performance improvements in highly-optimised code across diverse domains with minimal changes. We focus on three representative open-source projects: QuantLib (quantitative finance), Llama2.c (natural language processing), and OpenAI Whisper (automatic speech recognition) and one proprietary high performance codebase. Our multi-stage process involves extracting target code snippets, independent optimisation by multiple LLMs, and a search-based selection of the optimal solutions, achieving a 30 % reduction in execution time for QuantLib, a 52 % reduction for Llama2.c, and a 15% reduction for OpenAI Whisper. These results highlight the potential of multi-LLM collaboration for substantial performance gains that lead to greener software while preserving code readability and reliability.