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Majd Hawasly

ACL 2026×2ICLR 2026推理检索与知识多模态智能体与工具评测与基准
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0 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #7/10ACL 2026Poster· ACL Industry Track

Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA

Ummar Abbas · Mourad Ouzzani · Mohamed Y. Eltabakh · Omar Sinan · Gagan Bhatia · Hamdy Mubarak · Majd Hawasly · Mohammed Qusay Hashim · Kareem Mohamed Darwish · Firoj Alam

摘要

Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic settings where users expect grounding in canonical texts (Qur’an and Hadith) and jurisprudential (fiqh) nuance. Retrieval...

检索与知识智能体与工具评测与基准OpenReview 项目页
作者位次 #2/4ACL 2026Findings· Findings of ACL

Self-Consistency from Only Two Samples: C o T – P o T Ensembling for Efficient LLM Reasoning

Raman Saparkhan · Majd Hawasly · Md Rizwan Parvez · Mohammad Raza

摘要

Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large language models by aggregating multiple sampled outputs, but it comes at a high computational cost due to extensive sampling. We introduce a hybrid ensembling approach that leverages the complementary stren...

推理效率与系统检索与知识OpenReview 项目页

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