1 citations · 1 across the 5 of their papers we have counts for
6 papers
Limits and Gains of Test-Time Scaling in Vision-Language Reasoning
Mohammadjavad Ahmadpour, Amirmahdi Meighani, Payam Taebi +3
Test-time scaling (TTS) has emerged as a powerful paradigm for improving the reasoning ability of Large Language Models (LLMs) by allocating additional computation at inference, ye…
ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in Biographical Reasoning
Jasin Cekinmez, Omid Ghahroodi, Saad Fowad Chandle +2
We introduce ADAM (A Diverse Archive of Mankind), a framework for evaluating and improving multimodal large language models (MLLMs) in biographical reasoning. To the best of our kn…
MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment
Omid Ghahroodi, Arshia Hemmat, Marzia Nouri +8
Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEE…
Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions
Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei +17
Generative AI is reshaping art, gaming, and most notably animation. Recent breakthroughs in foundation and diffusion models have reduced the time and cost of producing animated con…
ELAB: Extensive LLM Alignment Benchmark in Persian Language
Zahra Pourbahman, Fatemeh Rajabi, Mohammadhossein Sadeghi +5
This paper presents a comprehensive evaluation framework for aligning Persian Large Language Models (LLMs) with critical ethical dimensions, including safety, fairness, and social…
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation
Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani +5
Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these i…