@inproceedings{yuksel-etal-2026-agentic,
    title = "Agentic {AI} for Human Resources: {LLM}-Driven Candidate Assessment",
    author = "Yuksel, Kamer Ali  and
      Anees, Abdul Basit  and
      Elneima, Ashraf Hatim  and
      Hewavitharana, Sanjika  and
      Al-Badrashiny, Mohamed  and
      Sawaf, Hassan",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.24/",
    pages = "341--348",
    ISBN = "979-8-89176-382-1",
    abstract = "In this work, we present a modular and interpretable framework that uses Large Language Models (LLMs) to automate candidate assessment in recruitment. The system integrates diverse sources{---}including job descriptions, CVs, interview transcripts, and HR feedback{---}to generate structured evaluation reports that mirror expert judgment. Unlike traditional ATS tools that rely on keyword matching or shallow scoring, our approach employs role-specific, LLM-generated rubrics and a multi-agent architecture to perform fine-grained, criteria-driven evaluations. The framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring workflows. Beyond rubric-based analysis, we introduce an LLM-Driven Active Listwise Tournament mechanism for candidate ranking. Instead of noisy pairwise comparisons or inconsistent independent scoring, the LLM ranks small candidate subsets ({``}mini-tournaments''), and these listwise permutations are aggregated using a Plackett{--}Luce model. An active-learning loop selects the most informative subsets, producing globally coherent and sample-efficient rankings. This adaptation of listwise LLM preference modeling{---}previously explored in financial asset ranking {---}provides a principled and highly interpretable methodology for large-scale candidate ranking in talent acquisition."
}