Seyed Mohammad Hadi Hosseini
MSc student · Alumni
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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ICLR 2026
SUSD: Structured Unsupervised Skill Discovery through State Factorization
Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah
Factorizes environment states and skill variables to learn richer, more diverse, and compositionally reusable behaviors without external rewards.
International Conference on Learning Representations (ICLR), 2026
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ICLR 2026
Efficient Adversarial Attacks on High-Dimensional Offline Bandits
Seyed Mohammad Hadi Hosseini, Amir Najafi, Mahdieh Soleymani Baghshah
Demonstrates how small targeted perturbations to reward models can hijack offline bandits and studies a practical partial defense.
International Conference on Learning Representations (ICLR), 2026
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arXiv 2025
T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation
Seyed Mohammad Hadi Hosseini, Amirmohammad Izadi, Ali Abdollahi, Armin Saghafian, Mahdieh Soleymani Baghshah
Introduces a fine-grained metric for measuring whether generated images satisfy the individual components of complex prompts.
arXiv preprint, 2025
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arXiv 2025
Fine-Grained Alignment and Noise Refinement for Compositional Text-to-Image Generation
Amirmohammad Izadi, Seyed Mohammad Hadi Hosseini, Soroush Vafaie Tabar, Ali Abdollahi, Armin Saghafian, Mahdieh Soleymani Baghshah
Refines alignment signals and initial noise to improve compositional correctness without retraining the text-to-image model.
arXiv preprint, 2025
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ACL 2025
CER: Confidence Enhanced Reasoning in LLMs
Ali Razghandi, Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah
Uses model confidence to improve reasoning reliability and help language models make better decisions during multi-step inference.
Annual Meeting of the Association for Computational Linguistics (ACL), 2025