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+## Papers Updated on 2025-07-24 12:10 UTC
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+### BGM-HAN: A Hierarchical Attention Network for Accurate and Fair Decision   Assessment on Semi-Structured Profiles
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+**Authors:** Junhua Liu, Roy Ka-Wei Lee, Kwan Hui Lim
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+**Categories:** cs.LG, cs.AI, cs.IR
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+**Published:** 2025-07-23T12:52:38Z
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+**Abstract:** Human decision-making in high-stakes domains often relies on expertise and heuristics, but is vulnerable to hard-to-detect cognitive biases that threaten fairness and long-term outcomes. This work presents a novel approach to enhancing complex decision-making workflows through the integration of hierarchical learning alongside various enhancements. Focusing on university admissions as a representative high-stakes domain, we propose BGM-HAN, an enhanced Byte-Pair Encoded, Gated Multi-head Hierarchical Attention Network, designed to effectively model semi-structured applicant data. BGM-HAN captures multi-level representations that are crucial for nuanced assessment, improving both interpretability and predictive performance. Experimental results on real admissions data demonstrate that our proposed model significantly outperforms both state-of-the-art baselines from traditional machine learning to large language models, offering a promising framework for augmenting decision-making in domains where structure, context, and fairness matter. Source code is available at: https://github.com/junhua/bgm-han.
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+**Link:** [arXiv:2507.17472v1](http://arxiv.org/abs/2507.17472v1)
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 ## Papers Updated on 2025-07-22 12:10 UTC
 
 ### Surfacing Variations to Calibrate Perceived Reliability of   MLLM-generated Image Descriptions