@inproceedings{li-etal-2025-humanitys,
    title = "Humanity{'}s Last Code Exam: Can Advanced {LLM}s Conquer Human{'}s Hardest Code Competition?",
    author = "Li, Xiangyang  and
      Li, Xiaopeng  and
      Dong, Kuicai  and
      Zhangquanhu  and
      Ruan, Rongju  and
      Dai, Xinyi  and
      Wang, Yasheng  and
      Tang, Ruiming",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1152/",
    doi = "10.18653/v1/2025.findings-emnlp.1152",
    pages = "21122--21137",
    ISBN = "979-8-89176-335-7",
    abstract = "Code generation is a core capability of large language models (LLMs), yet mainstream benchmarks (e.g., APPs and LiveCodeBench) contain questions with medium-level difficulty and pose no challenge to advanced LLMs. To better reflected the advanced reasoning and code generation ability, We introduce Humanity{'}s Last Code Exam (HLCE), comprising 235 most challenging problems from the International Collegiate Programming Contest (ICPC World Finals) and the International Olympiad in Informatics (IOI) spanning 2010 {--} 2024. As part of HLCE, we design a harmonized online{--}offline sandbox that guarantees fully reproducible evaluation. Through our comprehensive evaluation, we observe that even the strongest reasoning LLMs: o4-mini(high) and Gemini-2.5 Pro, achieve pass@1 rates of only 15.9{\%} and 11.4{\%}, respectively. Meanwhile, we propose a novel ``self-recognition'' task to measure LLMs' awareness of their own capabilities. Results indicate that LLMs' self-recognition abilities are not proportionally correlated with their code generation performance. Finally, our empirical validation of test-time scaling laws reveals that current advanced LLMs have substantial room for improvement on complex programming tasks. We expect HLCE to become a milestone challenge for code generation and to catalyze advances in high-performance reasoning and human{--}AI collaborative programming. Our code and dataset are also public available{\textonesuperior}.https://github.com/Humanity-s-Last-Code-Exam/HLCE"
}