3 papers
cs.AI2026
Behavior Beyond Text: Evaluating Refusal Transfer Failure in LLM Agents
Ekagra Gupta, Raj Saha, Aniekan-Abasi Inyang +1
While large language models are increasingly deployed as tool-calling agents, their safety evaluations still focus primarily on their ability to refuse harmful requests in text. We…
cs.CV2025
Combined Image Data Augmentations diminish the benefits of Adaptive Label Smoothing
Georg Siedel, Ekagra Gupta, Weijia Shao +2
Soft augmentation regularizes the supervised learning process of image classifiers by reducing label confidence of a training sample based on the magnitude of random-crop augmentat…
cs.LG2024
A practical approach to evaluating the adversarial distance for machine learning classifiers
Georg Siedel, Ekagra Gupta, Andrey Morozov
Robustness is critical for machine learning (ML) classifiers to ensure consistent performance in real-world applications where models may encounter corrupted or adversarial inputs.…