When Hiring Becomes Agent-Mediated: Evaluating Access and Recurrence in Two-Agent R'esum'e Screening
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Hiring is bilateral: employers assess fit, while candidates present and defend evidence of their qualifications. Yet r'esum'e screening, the first gate, is commonly automated as a static, one-call judgment over a r'esum'e-job pair. We study a two-agent alternative in which employer-side and candidate-side agents represent these roles, exchange evidence, and update their judgments before deciding who advances. We compare procedures on 600 constructed r'esum'e-job pairs using GPT-5.5 and Claude Opus 4.7. Two-agent screening advances more applications (33.3% to 39.3% for GPT-5.5; 34.0% to 35.5% for Opus 4.7). Across three runs on the common 191-pair borderline pool, pass-instance rates rise from 4.5% to 26.2% and from 6.5% to 16.1%, respectively. This is not a uniform relaxation: two-agent screening rejects applications one-call advances, changing decisions in both directions. At similar pass volumes, the procedures advance different applications, and no one-call threshold recovers applications consistently selected by two-agent screening. Among discovery-selected cases re-executed in fresh runs, two-agent-only selections recur less often than shared selections, clearly under GPT-5.5 and less certainly under Opus 4.7, while a separate one-call follow-up shows no comparable decline. As hiring becomes agent-mediated on both sides, the screening procedure, not only the model behind it, shapes who reaches human review and how reliably that access recurs.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 8 of its 16 references that are in the map. First posted on arXiv 17 Sep 2026.
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References · 8
- 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 229
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- 2026ReliabilityBench: Evaluating LLM Agent Reliability Under Production-Like Stress Conditionscited by 31
- 2025AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screeningcited by 19
- 2025Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Educationcited by 13
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