Publications
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TMLR 2026
Bridging Reasoning to Learning: Unmasking Illusions Using Complexity Out-of-Distribution Generalization
Mahdi Samiei, Arash Marioriyad, Arman Tahmasebi-Zadeh, Mohamadreza Fereydooni, Mahdi Ghaznavi, Mahdieh Soleymani Baghshah
Frames reasoning as generalization to problems whose solutions require greater representational or computational complexity than the training examples.
Transactions on Machine Learning Research (TMLR), 2026
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TMLR 2026
CARINOX: Inference-Time Scaling with Category-Aware Reward-Based Initial Noise Optimization and Exploration
Seyed Amir Kasaei, Ali Aghayari, Arash Marioriyad, Niki Sepasian, Shayan Baghayi Nejad, MohammadAmin Fazli, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
Improves compositional text-to-image generation by optimizing and exploring initial noise with category-aware rewards at inference time.
Transactions on Machine Learning Research (TMLR), 2026
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ICASSP 2026
No Concept Left Behind: Test-Time Optimization for Compositional Text-to-Image Generation
Mohammad Hadi Sameti, Amir Mohammad Mansourian, Arash Marioriyad, Seyed Farid Oshyani, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Optimizes generation at test time to preserve every requested concept and improve compositional prompt fidelity.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 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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TMLR 2026
Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey
Fatemeh Shahhosseini, Arash Marioriyad, Ali Momen, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban, Shaghayegh Haghjooy Javanmard
Surveys the use of large language models for scientific idea generation through a creativity-centered framework and evaluation perspective.
Transactions on Machine Learning Research (TMLR), 2026
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arXiv 2026
Lying to Win: Assessing LLM Deception through Human-AI Games and Parallel-World Probing
Arash Marioriyad, Alireza Nouri, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Evaluates strategic deception through interactive games and probes whether models represent truthful and deceptive alternatives internally.
arXiv preprint, 2026
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arXiv 2026
The Judge Who Never Admits: Hidden Shortcuts in LLM-Based Evaluation
Arash Marioriyad, Omid Ghahroodi, Ehsan Asgari, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Shows that language-model judges can exploit hidden shortcuts while producing explanations that fail to acknowledge those cues.
arXiv preprint, 2026
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TMLR 2025
Attention Overlap Is Responsible for the Entity Missing Problem in Text-to-Image Diffusion Models!
Arash Marioriyad, Mohammadali Banayeeanzade, Reza Abbasi, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Connects missing entities in generated images to overlapping attention maps and develops an inference-time strategy for better object coverage.
Transactions on Machine Learning Research (TMLR), 2025
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TMLR 2025
Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-Image Models
Parham Rezaei, Arash Marioriyad, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
Takes a probabilistic view of spatial relationships to improve how text-to-image models place and align objects described in prompts.
Transactions on Machine Learning Research (TMLR), 2025
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arXiv 2025
Infinity and Beyond: Compositional Alignment in VAR and Diffusion T2I Models
Hamed Shahabadi, Niki Sepasian, Arash Marioriyad, Ali Sharifi-Zarchi, Mahdieh Soleymani Baghshah
Compares compositional alignment in visual autoregressive and diffusion models to expose their distinct strengths and failure modes.
arXiv preprint, 2025
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arXiv 2025
Evaluating the Evaluators: Metrics for Compositional Text-to-Image Generation
Seyed Amir Kasaei, Ali Aghayari, Arash Marioriyad, Niki Sepasian, MohammadAmin Fazli, Mahdieh Soleymani Baghshah
Systematically evaluates whether popular metrics reliably measure object, attribute, and relationship alignment in generated images.
arXiv preprint, 2025
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arXiv 2025
Diffusion Beats Autoregressive: An Evaluation of Compositional Generation in Text-to-Image Models
Arash Marioriyad, Parham Rezaei, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
Benchmarks diffusion and autoregressive image generators and analyzes their relative ability to follow compositional prompts.
arXiv preprint, 2025
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arXiv 2025
Unspoken Hints: Accuracy Without Acknowledgement in LLM Reasoning
Arash Marioriyad, Soroush Adim, Nima Alighardashi, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
Examines cases where implicit hints improve answers even though model explanations do not acknowledge using them.
arXiv preprint, 2025
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arXiv 2025
The Silent Judge: Unacknowledged Shortcut Bias in LLM-as-a-Judge
Arash Marioriyad, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
Demonstrates that LLM evaluators can rely on shortcut signals while presenting apparently independent justifications for their scores.
arXiv preprint, 2025