@inproceedings{10.1007/978-981-92-2014-4_19,
	title        = {{A Benchmark for Structured Multihop Reasoning in Cross-Institution University Admission Advisory Question Answering}},
	author       = {Nguyen, Long S. T. and Ngo, Tin T. and Le, Dung N. H. and Vo, Quynh T. N. and Nguyen, Dung H. and Le, Khang N. and Quan, Tho T.},
	year         = 2027,
	booktitle    = {Trends and Applications in Knowledge Discovery and Data Mining},
	publisher    = {Springer Nature Singapore},
	address      = {Singapore},
	pages        = {235--247},
	isbn         = {978-981-92-2014-4},
	editor       = {Long, Cheng and Shi, Jieming and Liang, Yuxuan and Huang, Xiao and Choi, Byron and Song, Yuanfeng and Yiu, Man Lung},
	abstract     = {University admission advisory is a high-stakes decision-making process in which applicants must interpret and reconcile heterogeneous institutional regulations, eligibility criteria, tuition policies, scholarships, deadlines, and procedures across multiple universities. While recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) enable document-grounded question answering, existing QA benchmarks are largely built on homogeneous corpora and seldom capture the cross-institution and regulation-driven multihop reasoning required in real admission scenarios, especially for Vietnamese. We present VixSTORY (Vietnamese Cross-Institution University Admission Advisory QA), a benchmark designed to evaluate hierarchical and graph-structured reasoning under both intra-institution and cross-institution settings. VixSTORY is constructed via a controlled semi-automatic pipeline that combines document segmentation, embedding-based topic clustering within institutions, cross-institution topic alignment, and LLM-assisted question generation with strict post-validation for faithfulness. We further provide a comprehensive evaluation of representative retrieval and reasoning paradigms, from lexical/dense baselines to iterative multihop and graph-based RAG methods. Results show substantial gaps between flat retrieval and structured approaches, with graph-based methods achieving markedly stronger performance and robustness as hop depth increases, highlighting the importance of explicit structure for realistic regulation-driven advisory systems. For reproducibility and future research, VixSTORY is publicly released at https://huggingface.co/datasets/ura-hcmut/VixSTORY.}
}