Papers by Ahmad Omar
MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models (2026.acl-demo)
Copied to clipboard
| Challenge: | MixtureKit is a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints. |
| Approach: | They propose a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints. |
| Outcome: | Experiments on multilingual code-switched (Arabic–Latin) show that BTX models built with MixtureKit outperform dense baselines across multiple benchmarks. |
SLR: Automated Synthesis for Scalable Logical Reasoning (2026.acl-long)
Copied to clipboard
Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia Wüst, Hikaru Shindo, Rupert Mitchell, Tim Woydt, Patrick Schramowski, Wolfgang Stammer, Kristian Kersting
| Challenge: | Existing benchmarks intended to evaluate reasoning capabilities emphasize deductive reasoning, where conclusions necessarily follow from given premises. |
| Approach: | They propose an end-to-end framework for systematic evaluation and training of Large Language Models via Scalable Logical Reasoning. |
| Outcome: | The proposed framework doubles Llama-3-8B accuracy on SLR-Bench, achieving parity with Gemini-Flash-Thinking at a fraction of computational cost. |
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding (2025.findings-acl)
Copied to clipboard
Ahmed Heakl, Muhammad Abdullah Sohail, Mukul Ranjan, Rania Elbadry, Ghazi Shazan Ahmad, Mohamed El-Geish, Omar Maher, Zhiqiang Shen, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Optical Character Recognition (OCR) is a key component of document processing . Arabic text recognition has complex typographic and calligraphic features . |
| Approach: | They propose a comprehensive Arabic OCR benchmark that fills the gaps in evaluation systems. |
| Outcome: | The proposed benchmark outperforms existing models in Arabic by 60% in the character error rate . the best model achieves only 65% accuracy in PDF-to-Markdown conversion . |
Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues (2026.acl-long)
Copied to clipboard
Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov, Fajri Koto
| Challenge: | Most benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking cultural nuances that naturally arise in dialogues. |
| Approach: | They propose a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both Modern Standard Arabic (MSA) and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. |
| Outcome: | The proposed model performs worse on all three tasks than the MSA benchmark. |