Agentic AI for Human Resources: LLM-Driven Candidate Assessment
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911548739420160 |
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| author | Yuksel, Kamer Ali Anees, Abdul Basit Elneima, Ashraf Hewavitharana, Sanjika Al-Badrashiny, Mohamed Sawaf, Hassan |
| author_facet | Yuksel, Kamer Ali Anees, Abdul Basit Elneima, Ashraf Hewavitharana, Sanjika Al-Badrashiny, Mohamed Sawaf, Hassan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26710 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic AI for Human Resources: LLM-Driven Candidate Assessment Yuksel, Kamer Ali Anees, Abdul Basit Elneima, Ashraf Hewavitharana, Sanjika Al-Badrashiny, Mohamed Sawaf, Hassan Information Retrieval Artificial Intelligence Computation and Language Multiagent Systems 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. |
| title | Agentic AI for Human Resources: LLM-Driven Candidate Assessment |
| topic | Information Retrieval Artificial Intelligence Computation and Language Multiagent Systems |
| url | https://arxiv.org/abs/2603.26710 |