Papers by Fahad Shahbaz Khan
Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts (2025.findings-acl)
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Sara Ghaboura, Ketan Pravin More, Ritesh Thawkar, Wafa Al Ghallabi, Omkar Thawakar, Fahad Shahbaz Khan, Hisham Cholakkal, Salman Khan, Rao Muhammad Anwer
| Challenge: | TimeTravel is a benchmark of 10,250 expert-verified historical artifact samples spanning 266 distinct cultures across 10 major historical regions. |
| Approach: | They evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement. |
| Outcome: | The timeTravel benchmark covers 266 cultures and 10 major historical regions and aims to establish AI as reliable partner in preserving cultural heritage. |
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have greatly influenced the development of Large Multi-modal Video Models. |
| Approach: | They propose a benchmark to assess the proficiency of Large Multi-modal Video Models (LMMs) in detecting and localizing anomalies and inconsistencies in videos. |
| Outcome: | The proposed benchmark assesses the proficiency of Video-LMMs in detecting and localizing anomalies and inconsistencies in videos. |
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs (2025.findings-acl)
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Omkar Thawakar, Dinura Dissanayake, Ketan Pravin More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Ilmuz Zaman Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, Hisham Cholakkal, Ivan Laptev, Mubarak Shah, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Existing approaches do not emphasize step-wise problem-solving. |
| Approach: | They propose a visual reasoning chain benchmark and a fine-grained reasoning metric that evaluates correctness and logical coherence at each step. |
| Outcome: | The proposed framework outperforms existing models in six benchmarks and is 5x faster during inference scaling. |
MAviS: A Multimodal Conversational Assistant For Avian Species (2025.emnlp-main)
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Yevheniia Kryklyvets, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly, Jinxing Zhou, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal
| Challenge: | Existing multimodal large language models face challenges when it comes to specialized topics like avian species. |
| Approach: | They propose a large-scale multimodal avian species dataset that integrates image, audio, and text modalities for over 1,000 bird species. |
| Outcome: | The proposed model outperforms the baseline MiniCPM-o-2.6 by a large margin. |
BiMediX2 : Bio-Medical EXpert LMM for Diverse Medical Modalities (2025.findings-emnlp)
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Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Sara Pieri, Saeed Yahya Alseiari, Shanavas Cholakkal, Khaled M Aldahmani, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal
| Challenge: | BiMediX2 is a bilingual (Arabic-English) large multimodal model that supports text-based and image-based medical interactions. |
| Approach: | They introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. |
| Outcome: | The model outperforms existing models by over 9% in English and more than 20% in Arabic evaluations. |
LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM (2025.findings-acl)
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Sambal Shikhar, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly, Jean Lahoud, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal
| Challenge: | Existing speech-enabled LLMs degrade conversational quality by modifying the LLM, compromising its linguistic capabilities. |
| Approach: | They propose a lightweight 30M-parameter, LLM-agnostic, autoregressive streaming TTS system that generates high-quality speech with low latency. |
| Outcome: | The proposed system achieves a significantly lower word error rate compared to speech-enabled LLMs while operating at comparable latency. |
Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong potential for understanding user intent . paper describes system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces . |
| Approach: | They propose a multi-agent research discovery and analysis system that integrates multiple agents to reduce the effort required to find, assess, organize, and understand academic literature. |
| Outcome: | The proposed system reduces the effort required to find, assess, organize, and understand academic literature. |
CAMEL-Bench: A Comprehensive Arabic LMM Benchmark (2025.findings-naacl)
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Sara Ghaboura, Ahmed Heakl, Omkar Thawakar, Ali Husain Salem Abdulla Alharthi, Ines Riahi, Abduljalil Radman, Jorma Laaksonen, Fahad Shahbaz Khan, Salman Khan, Rao Muhammad Anwer
| Challenge: | Recent years have witnessed a significant interest in developing large multimodal models capable of performing various visual reasoning and understanding tasks. |
| Approach: | They propose to use Arabic as a language to evaluate large multi-modal models capable of performing visual reasoning and understanding tasks. |
| Outcome: | The proposed benchmark comprises eight diverse domains and 38 sub-domains to represent a large population of over 400 million speakers. |
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 . |
GCA Framework: A GCC Countries–Grounded Dataset and Agentic Pipeline for Climate Decision Support (2026.acl-long)
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| Challenge: | Climate decision support systems are weak in region-specific climate knowledge and interaction with geospatial and forecasting tools. |
| Approach: | They propose a framework that unifies a curated multimodal dataset and a tool-augmented agent for climate analysis. |
| Outcome: | The proposed framework improves reliability over general-purpose models on climate tasks in the Gulf region. |
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment (2025.coling-main)
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Umair Nawaz, Awais Muhammad, Hanan Gani, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan, Rao Anwer
| Challenge: | Recent studies have addressed this problem by building domain-specialized image-text data. |
| Approach: | They propose a vision-language foundational model dedicated to agriculture and livestock . they propose combining contrastive and self-supervised learning to learn fine-grained features . |
| Outcome: | The proposed model achieves 9.07% gain over standard CLIP training on 20 tasks. |
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model (2025.emnlp-main)
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Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath, Yahya Hmaiti, Go Inoue, Jean Lahoud, Md. Safirur Rashid, Shadid Intisar Quasem, Maheen Fatima, Franco Vidal, Mykola Maslych, Ketan Pravin More, Sanoojan Baliah, Hasindri Watawana, Yuhao Li, Fabian Farestam, Leon Schaller, Roman Tymtsiv, Simon Weber, Hisham Cholakkal, Ivan Laptev, Shin’ichi Satoh, Michael Felsberg, Mubarak Shah, Salman Khan, Fahad Shahbaz Khan
| Challenge: | Large multimodal models have gained attention for their effectiveness to understand and generate descriptions of visual content. |
| Approach: | They propose a multilingual Video LMM benchmark to evaluate video LMMs across 14 languages . they also introduce a machine translated multilingual video training set . |
| Outcome: | The proposed video LMM benchmark is designed to evaluate video Lmms across 14 languages including Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. |