{"ID":2856725,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10517","arxiv_id":"2510.10517","title":"ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs","abstract":"Code runtime optimization-the task of rewriting a given code to a faster one-remains challenging, as it requires reasoning about performance trade-offs involving algorithmic and structural choices. Recent approaches employ code-LLMs with slow-fast code pairs provided as optimization guidance, but such pair-based methods obscure the causal factors of performance gains and often lead to superficial pattern imitation rather than genuine performance reasoning. We introduce ECO, a performance-aware prompting framework for code optimization. ECO first distills runtime optimization instructions (ROIs) from reference slow-fast code pairs; Each ROI describes root causes of inefficiency and the rationales that drive performance improvements. For a given input code, ECO in parallel employs (i) a symbolic advisor to produce a bottleneck diagnosis tailored to the code, and (ii) an ROI retriever to return related ROIs. These two outputs are then composed into a performance-aware prompt, providing actionable guidance for code-LLMs. ECO's prompts are model-agnostic, require no fine-tuning, and can be easily prepended to any code-LLM prompt. Our empirical studies highlight that ECO prompting significantly improves code-LLMs' ability to generate efficient code, achieving speedups of up to 7.81x while minimizing correctness loss.","short_abstract":"Code runtime optimization-the task of rewriting a given code to a faster one-remains challenging, as it requires reasoning about performance trade-offs involving algorithmic and structural choices. Recent approaches employ code-LLMs with slow-fast code pairs provided as optimization guidance, but such pair-based method...","url_abs":"https://arxiv.org/abs/2510.10517","url_pdf":"https://arxiv.org/pdf/2510.10517v1","authors":"[\"Su-Hyeon Kim\",\"Joonghyuk Hahn\",\"Sooyoung Cha\",\"Yo-Sub Han\"]","published":"2025-10-12T09:29:24Z","proceeding":"cs.PL","tasks":"[\"cs.PL\",\"cs.AI\",\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}