<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="villate-lemus-2026-lafed">
    <titleInfo>
        <title>LAFED at SemEval-2026 Task 13: Language-Agnostic Feature Engineering for Cross-Lingual AI-Generated Code Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Juan</namePart>
        <namePart type="family">Villate Lemus</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mamoru</namePart>
            <namePart type="family">Komachi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, California, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-414-9</identifier>
    </relatedItem>
    <abstract>Robust detection of AI-generated source code across programming languages remains challenging due to language-specific cues and train–test distribution shifts. We present LAFED (Language-Agnostic Feature Engineering Detector), a feature-engineering approach trained on {Python, Java, C++} and evaluated on a multilingual test set that includes unseen languages {C, C#, Go, JavaScript, PHP}. LAFED combines (i) structural skeletal features (indentation, control-flow density, and approximations of McCabe/Halstead complexity), (ii) character and whitespace statistics inspired by stylometry, and (iii) micro-style patterns (operator spacing, blank lines, indentation consistency). Using XGBoost (Chen and Guestrin, 2016) with Optuna hyperparameter search (Akiba et al., 2019), our best model achieves macro-F1=0.7570 on a 1,000-sample test set; the official submission obtains macro-F1=0.75209 (5th place in Subtask A). Per-language analysis shows strong transfer to C# (0.7753) and JavaScript (0.7683), but weaker performance on Go (0.6400) and PHP (0.5238).</abstract>
    <identifier type="citekey">villate-lemus-2026-lafed</identifier>
    <location>
        <url>https://aclanthology.org/2026.semeval-1.9/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>59</start>
            <end>64</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T LAFED at SemEval-2026 Task 13: Language-Agnostic Feature Engineering for Cross-Lingual AI-Generated Code Detection
%A Villate Lemus, Juan
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F villate-lemus-2026-lafed
%X Robust detection of AI-generated source code across programming languages remains challenging due to language-specific cues and train–test distribution shifts. We present LAFED (Language-Agnostic Feature Engineering Detector), a feature-engineering approach trained on {Python, Java, C++} and evaluated on a multilingual test set that includes unseen languages {C, C#, Go, JavaScript, PHP}. LAFED combines (i) structural skeletal features (indentation, control-flow density, and approximations of McCabe/Halstead complexity), (ii) character and whitespace statistics inspired by stylometry, and (iii) micro-style patterns (operator spacing, blank lines, indentation consistency). Using XGBoost (Chen and Guestrin, 2016) with Optuna hyperparameter search (Akiba et al., 2019), our best model achieves macro-F1=0.7570 on a 1,000-sample test set; the official submission obtains macro-F1=0.75209 (5th place in Subtask A). Per-language analysis shows strong transfer to C# (0.7753) and JavaScript (0.7683), but weaker performance on Go (0.6400) and PHP (0.5238).
%U https://aclanthology.org/2026.semeval-1.9/
%P 59-64

Markdown (Informal)

[LAFED at SemEval-2026 Task 13: Language-Agnostic Feature Engineering for Cross-Lingual AI-Generated Code Detection](https://aclanthology.org/2026.semeval-1.9/) (Villate Lemus, SemEval 2026)

• LAFED at SemEval-2026 Task 13: Language-Agnostic Feature Engineering for Cross-Lingual AI-Generated Code Detection (Villate Lemus, SemEval 2026)

ACL

• Juan Villate Lemus. 2026. LAFED at SemEval-2026 Task 13: Language-Agnostic Feature Engineering for Cross-Lingual AI-Generated Code Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 59–64, San Diego, California, USA. Association for Computational Linguistics.