Agentic AI for Human Resources: LLM-Driven Candidate Assessment

TL;DR

This paper introduces a modular, interpretable LLM-based framework for candidate assessment in HR, integrating multiple data sources and employing a novel listwise ranking mechanism for transparent and efficient hiring decisions.

Contribution

It presents a new multi-agent, rubric-based LLM system with an innovative listwise ranking approach for candidate evaluation, improving transparency and sample efficiency.

Findings

The framework generates detailed, expert-mirroring assessment reports.

The listwise ranking method improves candidate ordering coherence.

Active learning optimizes the ranking process for real-world applications.

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.