[2603.26710] Agentic AI for Human Resources: LLM-Driven Candidate Assessment

Computer Science > Information Retrieval arXiv:2603.26710 (cs) [Submitted on 17 Mar 2026] Title:Agentic AI for Human Resources: LLM-Driven Candidate Assessment Authors:Kamer Ali Yuksel, Abdul Basit Anees, Ashraf Elneima, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf View a PDF of the paper titled Agentic AI for Human Resources: LLM-Driven Candidate Assessment, by Kamer Ali Yuksel and 5 other authors View PDF 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. A...