Author

M. F. Mridha

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#artificial intelligence Preprint Aug 2026

LabourCrew: A Multi-Agent RAG Framework for Trustworthy Adversarial Deliberation and Statutory Reasoning over Labour Law

In statutory question answering, every claim must be traceable to evidence, not merely relevant, since unverifiable labour-rights answers carry serious legal consequences. Current systems fall short: single-pass RAG cannot detect insufficient evidence, while multi-agent legal-debate systems treat grounding as a prompti...

Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud et al. · 0 citations

#artificial intelligence Review Aug 2026

CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA

Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the...

Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud et al. · 1 citation

Preprint Jul 2026

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning

ERAG is introduced, an uncertainty-aware RAG framework that converts retrieved chunks into probabilistic evidence before generation and suggests that evidential modeling is a practical mechanism for trustworthy information processing in foundation-model-based retrieval systems.

S. Hossain, Ruksat Khan Shayoni, M. F. Mridha · 0 citations

Jul 2026

EduGuard: A Safe RAG-Based LLM Tutor for Programming Education

E EduGuard, a safe retrieval-augmented generation (RAG) tutoring framework for introductory programming, is presented and compared against strong baselines, suggesting safe GenAI tutoring requires not only retrieval or strong prompting, but explicit pedagogical control, evidence verification, and deployment safeguards.

S. M. Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha et al. · arXiv.org · 0 citations

Jul 2026

Guiding Language Models to Be More Empathetic: Culturally Sensitive Mental Health Advice Generation Through Human-LLM Collaboration

Despite recent advances in large language models (LLMs), their ability to generate empathetic mental health counseling responses in low-resource languages remains largely unexplored. To address this gap, we curate 625 authentic mental health cases from three complementary sources: (1) publicly available Facebook posts...

Fatema Tuj Johora Faria, Mukaffi Bin Moin, Md. Mahfuzur Rahman et al. · arXiv.org · 0 citations