# AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening

arXiv: https://arxiv.org/abs/2504.02870 · HTML: https://mlsanity.com/paper/2504.02870/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [A Survey on Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2202.01110/) (2022)
- [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://mlsanity.com/paper/2404.17723/) (2024)
- [CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering](https://mlsanity.com/paper/2404.04302/) (2024)
- [A Survey of Reasoning with Foundation Models](https://mlsanity.com/paper/2312.11562/) (2023)
- [Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval](https://mlsanity.com/paper/2407.20371/) (2024)
- [Financial Report Chunking for Effective Retrieval Augmented Generation](https://mlsanity.com/paper/2402.05131/) (2024)
- [Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference](https://mlsanity.com/paper/2310.03184/) (2023)
- [MEDCO: Medical Education Copilots Based on A Multi-Agent Framework](https://mlsanity.com/paper/2408.12496/) (2024)
- [Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening](https://mlsanity.com/paper/2401.08315/) (2024)

## Built on by (most cited first, 20 of 20)

- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications](https://mlsanity.com/paper/2512.20164/) (2025)
- [Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare](https://mlsanity.com/paper/2510.01164/) (2025)
- [CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation](https://mlsanity.com/paper/2603.16215/) (2026)
- [Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras](https://mlsanity.com/paper/2510.09230/) (2025)
- [Visual Persuasion: What Influences Decisions of Vision-Language Models?](https://mlsanity.com/paper/2602.15278/) (2026)
- [PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners with Population-Representative Dataset](https://mlsanity.com/paper/2603.22714/) (2026)
- [Whose Name Comes Up? III: Persona Prompting Effects in LLM-Based Scholar Recommendation](https://mlsanity.com/paper/2605.28187/) (2026)
- [AI-Driven Decision-Making System for Hiring Process](https://mlsanity.com/paper/2512.20652/) (2025)
- [Evaluating LLM Behavior in Hiring: Implicit Weights, Fairness Across Groups, and Alignment with Human Preferences](https://mlsanity.com/paper/2601.11379/) (2026)
- [Beyond the Resum'e: A Rubric-Aware Automatic Interview System for Information Elicitation](https://mlsanity.com/paper/2603.01775/) (2026)
- [AutoScreen-FW: An LLM-based Framework for Resume Screening](https://mlsanity.com/paper/2603.18390/) (2026)
- [Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2606.31692/) (2026)
- [TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2607.20009/) (2026)
- [Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse](https://mlsanity.com/paper/2608.30956/) (2026)
- [Beyond Outcome Gaps: Process-Aware Fairness Diagnosis for LLM-based Multi-Agent Decision Systems](https://mlsanity.com/paper/2609.02092/) (2026)
- [When Hiring Becomes Agent-Mediated: Evaluating Access and Recurrence in Two-Agent R'esum'e Screening](https://mlsanity.com/paper/2609.19530/) (2026)
- [Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes](https://mlsanity.com/paper/2609.22188/) (2026)
- [From Lexical Baselines to Agentic Retrieval-Augmented Generation: Structured Skill and Responsibility-Level Extraction with the SFIA Framework](https://mlsanity.com/paper/2609.35806/) (2026)

## Abstract (arXiv)

Resume screening is a critical yet time-intensive process in talent acquisition, requiring recruiters to analyze vast volume of job applications while remaining objective, accurate, and fair. With the advancements in Large Language Models (LLMs), their reasoning capabilities and extensive knowledge bases demonstrate new opportunities to streamline and automate recruitment workflows. In this work, we propose a multi-agent framework for resume screening using LLMs to systematically process and evaluate resumes. The framework consists of four core agents, including a resume extractor, an evaluator, a summarizer, and a score formatter. To enhance the contextual relevance of candidate assessments, we integrate Retrieval-Augmented Generation (RAG) within the resume evaluator, allowing incorporation of external knowledge sources, such as industry-specific expertise, professional certifications, university rankings, and company-specific hiring criteria. This dynamic adaptation enables personalized recruitment, bridging the gap between AI automation and talent acquisition. We assess the effectiveness of our approach by comparing AI-generated scores with ratings provided by HR professionals on a dataset of anonymized online resumes. The findings highlight the potential of multi-agent RAG-LLM systems in automating resume screening, enabling more efficient and scalable hiring workflows.