Agentic AI for Human Resources: LLM-Driven Candidate Assessment

• URL: http://arxiv.org/abs/2603.26710v1
• Date: Tue, 17 Mar 2026 21:32:08 GMT
• Title: Agentic AI for Human Resources: LLM-Driven Candidate Assessment
• Abstract summary: We present a modular and interpretable framework that uses Large Language Models (LLMs) to automate candidate assessment in recruitment.<n>The system integrates diverse sources, including job descriptions, CVs, interview transcripts, and HR feedback, to generate structured evaluation reports that mirror expert judgment.<n>The framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring.
• Score: 4.271562161453985
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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