1 citations · 2 across the 7 of their papers we have counts for
8 papers
Rubric Rewards from Item Response Theory
Milad Yazdani, Yaser Souri, Xiren Zhou +4
Many language tasks have no single answer that can be checked automatically. Rubrics provide criteria for judging responses to these tasks. For reinforcement learning, the resultin…
Reinforcement learning for inverse silhouette design and rapid laser cutting of compact parallelogram quad kirigami prototypes
Milad Yazdani, Shahriar Shalileh, Dena Shahriari
Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult because deployment is nonline…
When Minor Edits Matter: LLM-Driven Prompt Attack for Medical VLM Robustness in Ultrasound
Yasamin Medghalchi, Milad Yazdani, Amirhossein Dabiriaghdam +7
Ultrasound is widely used in clinical practice due to its portability, cost-effectiveness, safety, and real-time imaging capabilities. However, image acquisition and interpretation…
MASPRM: Multi-Agent System Process Reward Model
Milad Yazdani, Mahdi Mostajabdaveh, Zirui Zhou +1
Inference-time search over multi-agent systems (MAS) wastes compute when it cannot identify which agent's intermediate message advanced progress. We present the Multi-Agent System…
LLM-Guided Evolution of Symbolic Acceleration Inequality Families for MILP
Milad Yazdani, Mahdi Mostajabdaveh, Samin Aref +1
Integer programming (IP) is central to many combinatorial optimization tasks but remains challenging due to its NP-hard nature. A practical way to improve IP solvers is to manually…
AI-Augmented Thyroid Scintigraphy for Robust Classification of Disease
Maziar Sabouri, Ghasem Hajianfar, Alireza Rafiei Sardouei +11
Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imbalanced datasets. This study investigat…
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality
Milad Yazdani, Yasamin Medghalchi, Pooria Ashrafian +2
Deep learning models have emerged as a powerful tool for various medical applications. However, their success depends on large, high-quality datasets that are challenging to obtain…
Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
Pooria Ashrafian, Milad Yazdani, Moein Heidari +2
High-quality, large-scale data is essential for robust deep learning models in medical applications, particularly ultrasound image analysis. Diffusion models facilitate high-fideli…