{"ID":2863938,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25435","arxiv_id":"2509.25435","title":"GESA: Graph-Enhanced Semantic Allocation for Generalized, Fair, and Explainable Candidate-Role Matching","abstract":"Accurate, fair, and explainable allocation of candidates to roles represents a fundamental challenge across multiple domains including corporate hiring, academic admissions, fellowship awards, and volunteer placement systems. Current state-of-the-art approaches suffer from semantic inflexibility, persistent demographic bias, opacity in decision-making processes, and poor scalability under dynamic policy constraints. We present GESA (Graph-Enhanced Semantic Allocation), a comprehensive framework that addresses these limitations through the integration of domain-adaptive transformer embeddings, heterogeneous self-supervised graph neural networks, adversarial debiasing mechanisms, multi-objective genetic optimization, and explainable AI components. Our experimental evaluation on large-scale international benchmarks comprising 20,000 candidate profiles and 3,000 role specifications demonstrates superior performance with 94.5% top-3 allocation accuracy, 37% improvement in diversity representation, 0.98 fairness score across demographic categories, and sub-second end-to-end latency. Additionally, GESA incorporates hybrid recommendation capabilities and glass-box explainability, making it suitable for deployment across diverse international contexts in industry, academia, and non-profit sectors.","short_abstract":"Accurate, fair, and explainable allocation of candidates to roles represents a fundamental challenge across multiple domains including corporate hiring, academic admissions, fellowship awards, and volunteer placement systems. Current state-of-the-art approaches suffer from semantic inflexibility, persistent demographic...","url_abs":"https://arxiv.org/abs/2509.25435","url_pdf":"https://arxiv.org/pdf/2509.25435v1","authors":"[\"Rishi Ashish Shah\",\"Shivaay Dhondiyal\",\"Kartik Sharma\",\"Sukriti Talwar\",\"Saksham Jain\",\"Sparsh Jain\"]","published":"2025-09-29T19:41:55Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Graph Neural Network\",\"Transformer\"]","has_code":false}