Large Vision-Language Models
Compositional understanding and visual reasoning.
Visual Reasoning
We develop benchmarks and methods that probe multi-step reasoning and relational understanding in large vision-language models.
Representational Analysis
We examine how models such as CLIP represent objects, attributes, and their interactions to identify where compositionality emerges—and where it breaks.
Publications
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EACL Findings 2026
MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-Level Assessment
Omid Ghahroodi, Arshia Hemmat, Marzia Nouri, Seyed Mohammad Hadi Hosseini, Doratossadat Dastgheib, Mohammad V. Sanian, Alireza Sahebi, Reihaneh Zohrabi, Mohammad Hossein Rohban, Ehsaneddin Asgari, Mahdieh Soleymani Baghshah
Introduces a large bilingual Persian–English benchmark for evaluating vision-language models across educational, scientific, and cultural reasoning tasks.
Findings of the Association for Computational Linguistics: EACL, 2026
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ICML 2026
HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
Reihaneh Zohrabi, Hosein Hasani, Aishwarya Gupta, Mahdieh Soleymani Baghshah, Anna Rohrbach, Marcus Rohrbach
Uses Bayesian probing to detect object hallucinations and guide mitigation in vision-language model responses.
International Conference on Machine Learning (ICML), 2026
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arXiv 2026
Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning
Seyed Amir Kasaei, Arash Marioriyad, Mohammad Khaleti, MohammadAmin Fazli, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
Introduces a rebus-puzzle benchmark that tests whether multimodal models can integrate visual form, language, and abstract concepts.
arXiv preprint, 2026
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NeurIPS 2025
Visual Structures Help Visual Reasoning: Addressing the Binding Problem in LVLMs
Amirmohammad Izadi, Mohammadali Banayeeanzade, Fatemeh Askari, Ali Rahimiakbar, Mohammad Mahdi Vahedi, Hosein Hasani, Mahdieh Soleymani Baghshah
Introduces VISER, which adds lightweight spatial structure and sequential-scanning prompts to improve counting, visual search, and spatial reasoning.
Advances in Neural Information Processing Systems (NeurIPS), 2025
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CVPR 2025
CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation
Reza Abbasi, Ali Nazari, Aminreza Sefid, Mohammadali Banayeeanzade, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Provides a controlled analysis of how CLIP represents multiple objects and reveals limitations that aggregate benchmarks can conceal.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025
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arXiv 2025
Uncovering Grounding IDs: How External Cues Shape Multimodal Binding
Hosein Hasani, Amirmohammad Izadi, Fatemeh Askari, Mobin Bagherian, Sadegh Mohammadian, Mahdieh Soleymani Baghshah
Studies how external cues create grounding identifiers that help multimodal models bind objects to their properties and locations.
arXiv preprint, 2025
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arXiv 2025
Analyzing CLIP's Performance Limitations in Multi-Object Scenarios: A Controlled High-Resolution Study
Reza Abbasi, Ali Nazari, Aminreza Sefid, Mohammadali Banayeeanzade, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Uses controlled high-resolution data to isolate CLIP's limitations when images contain multiple objects and attributes.
arXiv preprint, 2025