Bayesian Reinforcement Learning in Non-Stationary Environments
Publications
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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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ACL Findings 2026
Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy
Hosein Hasani, Mohammadali Banayeeanzade, Ali Nafisi, Sadegh Mohammadian, Fatemeh Askari, Mobin Bagherian, Amirmohammad Izadi, Mahdieh Soleymani Baghshah
Reveals how a System-2 strategy decomposes large counting problems, transfers partial counts through dedicated attention heads, and aggregates them reliably.
Findings of the Association for Computational Linguistics: ACL, 2026
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arXiv 2026
Causal Attribution via Activation Patching
Amirmohammad Izadi, Mohammadali Banayeeanzade, Akbar Mirrokni, Hosein Hasani, Mobin Bagherian, Faridoun Mehri, Mahdieh Soleymani Baghshah
Develops activation-patching methods for tracing causal contributions inside neural networks rather than relying on correlational explanations.
arXiv preprint, 2026
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NeurIPS 2025
Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection
Reihaneh Zohrabi, Hosein Hasani, Mahdieh Soleymani Baghshah, Anna Rohrbach, Marcus Rohrbach, Mohammad Hossein Rohban
Refines class prototypes with explicit awareness of spurious features to make out-of-distribution detection more reliable under shortcut-driven shifts.
Advances in Neural Information Processing Systems (NeurIPS), 2025
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ICLR SCSL 2025
Exploiting What Trained Models Learn for Making Them Robust to Spurious Correlations without Group Annotations
Mahdi Ghaznavi, Hadi Asadollahzadeh, Fahimeh Hosseini Noohdani, Soroush Vafaie Tabar, Hosein Hasani, Mahdieh Soleymani Baghshah
Uses information already encoded by trained models to reduce shortcut reliance without requiring group labels during training.
ICLR Workshop on Spurious Correlation and Shortcut Learning (SCSL), 2025
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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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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
Understanding Counting Mechanisms in Large Language and Vision-Language Models
Hosein Hasani, Amirmohammad Izadi, Fatemeh Askari, Mobin Bagherian, Sadegh Mohammadian, Mahdieh Soleymani Baghshah
Compares internal counting strategies across language and vision-language models to reveal shared mechanisms and modality-specific failures.
arXiv preprint, 2025
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IEEE Access 2025
Test-Time Confidence-Aware Query Labeling Improves 3D Few-Shot Segmentation
Mohammad Mozafari, Hosein Hasani, Reza Vahidimajd, Mohamadreza Fereydooni, Mahdieh Soleymani Baghshah
Uses confidence-aware query labeling at test time to improve the accuracy and adaptability of three-dimensional few-shot segmentation.
IEEE Access, 2025