Peer-reviewed publications and research preprints in trustworthy AI, agentic RAG, multi-hop reasoning, question answering, and Persian NLP.
My research focuses on trustworthy and evidence-grounded AI, agentic retrieval-augmented generation, multi-hop reasoning, question answering, and Persian NLP.
This page includes peer-reviewed journal articles and publicly available research preprints. Publication and submission statuses are reported only when they are publicly verifiable.
2025
IEEE TASLP
IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources
Arash Ghafouri, Mohammad Aghajani Asl, Hasan Naderi, and Mahdi Firouzmandi
IEEE Transactions on Audio, Speech, and Language Processing, Jul 2025
IslamicPCQA is a Persian multi-hop complex question-answering dataset containing 12,282 manually constructed question-answer pairs derived from nine Persian encyclopedias. The dataset includes sentence-level supporting facts and benchmark evaluations using Persian and multilingual language models.
@article{ghafouri2025islamicpcqa,title={IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources},author={Ghafouri, Arash and Aghajani Asl, Mohammad and Naderi, Hasan and Firouzmandi, Mahdi},journal={IEEE Transactions on Audio, Speech, and Language Processing},volume={33},pages={3801--3812},year={2025},month=jul,publisher={IEEE},doi={10.1109/TASLPRO.2025.3587450},}
arXiv
FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation
Mohammad Aghajani Asl, Majid Asgari-Bidhendi, and Behrooz Minaei-Bidgoli
FAIR-RAG is an evidence-driven agentic retrieval-augmented generation framework for complex multi-hop questions. Its Structured Evidence Assessment module audits accumulated evidence, identifies explicit information gaps, and guides targeted iterative retrieval before faithful answer generation.
@article{aghajaniasl2025fairrag,title={FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation},author={Aghajani Asl, Mohammad and Asgari-Bidhendi, Majid and Minaei-Bidgoli, Behrooz},journal={arXiv preprint arXiv:2510.22344},year={2025},month=oct,archiveprefix={arXiv},primaryclass={cs.CL},}
arXiv
FARSIQA: Faithful and Advanced RAG System for Islamic Question Answering
FARSIQA is an end-to-end, safety-aware Persian question-answering system built on the FAIR-RAG architecture. It operates over 735,000 source records and 1.712 million indexed text chunks, combining scope validation, hybrid retrieval, iterative evidence refinement, and citation-grounded generation.
@article{aghajaniasl2025farsiqa,title={FARSIQA: Faithful and Advanced RAG System for Islamic Question Answering},author={Aghajani Asl, Mohammad and Minaei Bidgoli, Behrooz},journal={arXiv preprint arXiv:2510.25621},year={2025},month=oct,archiveprefix={arXiv},primaryclass={cs.CL},}
