Papers by Mukul Ranjan
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding (2025.findings-acl)
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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 . |
CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark (2026.acl-long)
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Ahmed Heakl, Gustavo Bertolo Stahl, Sarim Hashmi, Seung Hun Eddie Han, Mukul Ranjan, Arina Kharlamova, Salman Khan, Abdulrahman Mahmoud
| Challenge: | Cross-architecture GPU code translation is essential for unlocking low-level hardware portability, yet no scalable solution exists. |
| Approach: | They propose a dataset and model suite for source- and assembly-level GPU code translation that trains domain-specific translation models that achieve 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 . |
| Outcome: | The proposed model achieves 88.2% accuracy on CUDA HIP and 69.1% on SASS RDNA3 outperforming commercial baselines including GPT-5.1, Claude-4.5, and Hipify by wide margins. |
On the Cultural Anachronism and Temporal Reasoning in Vision Language Models (2026.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) are increasingly applied to cultural heritage materials. |
| Approach: | They propose a temporal anachronism benchmark to evaluate temporal reasoning on 1,600 Indian cultural artifacts. |
| Outcome: | The proposed model achieves only 58.7% accuracy on the best model, which is a significant performance gap across architectures and scales. |