RT conference output
T1 Green by Design: Energy-Guided Reranking of LLM-Generated Programs
A1 Xia, Yi
A1 Aragón Jurado, José Miguel
A1 Thawonmas, Ruck
A2 Ingeniería Informática
AB The carbon footprint of computing is increasingly shaped by software, yet existing programming tools and large language models (LLMs) remain largely energy-blind. We propose energy-guided code generation, a method that reranks LLM-generated programs based on direct energy measurements using CodeCarbon while ensuring functional correctness. Evaluating a benchmark of algorithmic and data-processing tasks, we show that energy-guided selection yields statistically significant energy reductions. It reduces consumption by an average of 44.69% compared to unguided Top-1 candidates and by an additional 1.86% compared to the fastest (Best-Time) implementations, all without runtime penalties or loss of accuracy. These results provide the first conclusive evidence that LLMs produce diverse implementations with substantial variation in energy use, and that energy-aware reranking can consistently surface verifiably greener solutions. By embedding energy as a first-class optimization signal in the act of code generation, this work establishes a foundation for green-by-design software generation systems, where sustainability is not an afterthought but a default property of programming tools.
YR 2025
FD 2025-10-22
LK http://hdl.handle.net/10498/37744
UL http://hdl.handle.net/10498/37744
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026