🤖 AI Summary
Early-stage candidate screening suffers from low efficiency due to the need to integrate heterogeneous information—resumes, interview videos, coding assignments, and public online data. This paper proposes a risk-aware, modular multi-agent system orchestrated by constraint-augmented large language models (LLMs), encompassing multimodal parsing (PDF/video), structured profile construction, knowledge-graph–driven public evidence verification, dual-dimension (technical/cultural) scoring with explicit risk penalization, and a human-in-the-loop review interface. Key contributions include: (i) the first explainable and traceable risk-aware scoring framework; (ii) a novel efficiency metric—“time per qualified candidate”; and (iii) component-level attribution and low-variance decision-making. Evaluated on real screening of 64 Python backend engineers, the system reduced time per qualified candidate from 3.33 to 1.70 hours, maintaining baseline precision and recall, while preserving final hiring authority exclusively with human recruiters.
📝 Abstract
Early-stage candidate validation is a major bottleneck in hiring, because recruiters must reconcile heterogeneous inputs (resumes, screening answers, code assignments, and limited public evidence). This paper presents an AI-driven, modular multi-agent hiring assistant that integrates (i) document and video preprocessing, (ii) structured candidate profile construction, (iii) public-data verification, (iv) technical/culture-fit scoring with explicit risk penalties, and (v) human-in-the-loop validation via an interactive interface. The pipeline is orchestrated by an LLM under strict constraints to reduce output variability and to generate traceable component-level rationales. Candidate ranking is computed by a configurable aggregation of technical fit, culture fit, and normalized risk penalties. The system is evaluated on 64 real applicants for a mid-level Python backend engineer role, using an experienced recruiter as the reference baseline and a second, less experienced recruiter for additional comparison. Alongside precision/recall, we propose an efficiency metric measuring expected time per qualified candidate. In this study, the system improves throughput and achieves 1.70 hours per qualified candidate versus 3.33 hours for the experienced recruiter, with substantially lower estimated screening cost, while preserving a human decision-maker as the final authority.