@inproceedings{orel-etal-2026-aicd,
    title = "{AICD} Bench: A Challenging Benchmark for {AI}-Generated Code Detection",
    author = "Orel, Daniil  and
      Azizov, Dilshod  and
      Paul, Indraneil  and
      Wang, Yuxia  and
      Gurevych, Iryna  and
      Nakov, Preslav",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.325/",
    pages = "6913--6938",
    ISBN = "979-8-89176-380-7",
    abstract = "Large language models (LLMs) are increasingly capable of generating functional source code, raising concerns about authorship, accountability, and security. While detecting AI-generated code is critical, existing datasets and benchmarks are narrow, typically limited to binary human{--}machine classification under in-distribution settings. To bridge this gap, we introduce AICD Bench, the most comprehensive benchmark for AI-generated code detection. It spans 2M examples, 77 models across 11 families, and 9 programming languages, including recent reasoning models. Beyond scale, AICD Bench introduces three realistic detection tasks: (i) Robust Binary Classification under distribution shifts in language and domain, (ii) Model Family Attribution, grouping generators by architectural lineage, and (iii) Fine-Grained Human{--}Machine Classification across human, machine, hybrid, and adversarial code. Extensive evaluation on neural and classical detectors shows that performance remains far below practical usability, particularly under distribution shift and for hybrid or adversarial code. We release AICD Bench as a unified, challenging evaluation suite to drive the next generation of robust approaches for AI-generated code detection. The data and the code are available at https://huggingface.co/AICD-bench."
}