arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.
By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin
arXiv:2603. 14501v2 Announce Type: replace-cross Abstract: Large Language Models excel in high-resource programming languages but struggle with low-resource ones.
By Junhang Cheng, Fang Liu, Jia Li, Chengru Wu, Nanxiang Jiang, Li Zhang
SWE-Opt is a new benchmark that evaluates large language models (LLMs) on repository‑level software performance optimization. It is built from 102 expert‑written optimizations from open‑source projects and measures runtime, peak memory, and Time‑Weighted Memory Usage (TWMU) under varied input data, execution conditions, and noise‑aware measurements. The study finds that current LLMs achieve few runtime gains and almost no memory improvements, whereas expert implementations deliver a 15.5× speedup and 171.3× memory reduction.
By Ezgi Sar{\i}kayak, Wenchao Gu, Hesham Ghonim, Chunyang Chen
arXiv:2603. 16011v3 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realistic constraints.
By Atharva Sehgal, James Hou, Akanksha Sarkar, Ishaan Mantripragada, Swarat Chaudhuri, Jennifer J. Sun, Yisong Yue
Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinement, we still lack benchmarks that capture how optimization actually happens in real-world codebases.
arXiv:2609.12475v1 Announce Type: new
Abstract: Comprehensive benchmark suites are essential for improving large language models (LLMs), but many widely used benchmarks are redundant, making evaluati...
By Zhongzhan Huang, Junxin Li, Guoming Ling, Yupei Lin, Shanshan Zhong, Hefeng Wu