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20242026
most citedCan Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior?

1 citations · 2 across the 5 of their papers we have counts for

collaborators

34 papers

cs.LG2026

KAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks

Mohammad Meymani, Roozbeh Razavi-Far

While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exposed to adversarial threats. W…

cs.LG2026

TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems

Refat Ishrak Hemel, Ehsan Hallaji, Roozbeh Razavi-Far

The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing int…

cs.PF2026

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study

Alfarizy Alfarizy, Hung Truong Thanh Nguyen, René Richard +2

Mixture-of-Experts (MoE) language models are often described as ideal for resource-constrained inference. Each token activates only a small subset of experts, so the per-token comp…

cs.SE20261 cited

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior?

Mohammad Meymani, Hamed Jelodar, Parisa Hamedi +2

Generative AI (GenAI) models, particularly large language models (LLMs), have transformed multiple domains, including natural language processing, software analysis, and code under…

cs.CR2026

Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection

Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins +1

Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks (GNNs) h…

cs.LG20261 cited

Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods

Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia +6

Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine lear…