3 citations · 7 across the 27 of their papers we have counts for
30 papers
The Magnitude Mirage: Rethinking Confidence for Reasoning-Intensive Retrieval
Jamie Holdcroft, Abdelrahman Abdallah, Adam Jatowt
Many production RAG systems implement retrieval abstention by thresholding raw similarity scores, implicitly treating score magnitude as a confidence signal. We demonstrate that th…
Large Language Models Systematically Favor Popular Options: Evidence and Mitigation Across MCQs
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani +2
Multiple-choice questions (MCQs) are a standard format for evaluating large language models (LLMs), yet the popularity of answer options can confound evaluation. Modern LLMs system…
Difficulty-Gated Fusion of Reasoning Views for Temporal Retrieval
Jamie Holdcroft, Abdelrahman Abdallah, Adam Jatowt
Reasoning-intensive temporal retrieval requires matching a query to documents whose relevance depends on shared temporal reasoning rather than lexical overlap. Expanding a query in…
EXCISE: Query-Side Exclusion for Late-Interaction Retrieval
Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt
Late-interaction retrievers handle exclusion queries poorly. When a user asks for X but not Z, the additive MaxSim score promotes documents covering Z, a problem we call exclusion…
MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning
Abdelrahman Abdallah, AbdelRahim A. Elmadany, Sameh Al Natour +3
Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them. A s…
Argus-Retriever: Vision-LLM Late-Interaction Retrieval with Region-Aware Query-Conditioned MoE for Visual Document Retrieval
Abdelrahman Abdallah, Mahmoud Abdalla, Mohammed Ali +1
Late-interaction vision-language retrievers represent each document page as many visual token embeddings and score queries with MaxSim. In systems such as ColPali, ColQwen, ColNomi…
REGREACT: Self-Correcting Multi-Agent Pipelines for Structured Regulatory Information Extraction
Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt
Extracting structured, machine-readable compliance criteria from regulatory documents remains an open challenge. Single-pass language models hallucinate structural elements, lose h…
BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani +1
Reasoning-intensive retrieval requires deep semantic inference beyond surface-level keyword matching, posing a challenge for current LLM-based rerankers limited by context constrai…
HIVE: Query, Hypothesize, Verify An LLM Framework for Multimodal Reasoning-Intensive Retrieval
Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mohamed Mahmoud +3
Multimodal retrieval models fail on reasoning-intensive queries where images (diagrams, charts, screenshots) must be deeply integrated with text to identify relevant documents -- t…
BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment
Mohamed Darwish Mounis, Mohamed Mahmoud, Shaimaa Sedek +4
Multimodal retrieval systems struggle to resolve image-text queries against text-only corpora: the best vision-language encoder achieves only 27.6 nDCG@10 on MM-BRIGHT, underperfor…
MARVEL: Multimodal Adaptive Reasoning-intensiVe Expand-rerank and retrievaL
Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Mostafa Farouk Senussi +3
Multimodal retrieval over text corpora remains a fundamental challenge: the best vision-language encoder achieves only 27.6 nDCG@10 on MM-BRIGHT, a reasoning-intensive multimodal r…
Are LLM-Based Retrievers Worth Their Cost? An Empirical Study of Efficiency, Robustness, and Reasoning Overhead
Abdelrahman Abdallah, Jamie Holdcroft, Mohammed Ali +1
Large language model retrievers improve performance on complex queries, but their practical value depends on efficiency, robustness, and reliable confidence signals in addition to…
Negative Sampling Techniques in Information Retrieval: A Survey
Laurin Wischounig, Abdelrahman Abdallah, Adam Jatowt
Information Retrieval (IR) is fundamental to many modern NLP applications. The rise of dense retrieval (DR), using neural networks to learn semantic vector representations, has sig…
MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval
Abdelrahman Abdallah, Mohamed Darwish Mounis, Mahmoud Abdalla +6
Existing retrieval benchmarks primarily consist of text-based queries where keyword or semantic matching is usually sufficient. Many real-world queries contain multimodal elements,…
TEMPO: A Realistic Multi-Domain Benchmark for Temporal Reasoning-Intensive Retrieval
Abdelrahman Abdallah, Mohammed Ali, Muhammad Abdul-Mageed +1
Existing temporal QA benchmarks focus on simple fact-seeking queries from news corpora, while reasoning-intensive retrieval benchmarks lack temporal grounding. However, real-world…
RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark
Mohammed Ali, Abdelrahman Abdallah, Amit Agarwal +2
Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. To bridge this gap, we present a b…
DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation
Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani +1
Large Language Models (LLMs) have transformed listwise document reranking by enabling global reasoning over candidate sets, yet single models often struggle to balance fine-grained…
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models
Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari +2
In this work, we present a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods, encompassing large language model (LLM)-based, lightweight conte…
RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback
Abdelrahman Abdallah, Mahmoud Abdalla, Bhawna Piryani +3
Evaluating the quality of retrieval-augmented generation (RAG) and document reranking systems remains challenging due to the lack of scalable, user-centric, and multi-perspective e…
SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt
The growing demand for corporate sustainability transparency, particularly under new regulations like the EU Taxonomy, necessitates precise data extraction from large, unstructured…
It's High Time: A Survey of Temporal Question Answering
Bhawna Piryani, Abdelrahman Abdallah, Jamshid Mozafari +2
Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Temporal Question Answering (TQA), a…
A Study into Investigating Temporal Robustness of LLMs
Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt +1
Large Language Models (LLMs) encapsulate a surprising amount of factual world knowledge. However, their performance on temporal questions and historical knowledge is limited becaus…
From Retrieval to Generation: Comparing Different Approaches
Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani +2
Knowledge-intensive tasks, particularly open-domain question answering (ODQA), document reranking, and retrieval-augmented language modeling, require a balance between retrieval ac…
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari +2
Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical components of modern applications in information retrieval, question answering, or knowledge-based text…