22 citations · 22 across the 3 of their papers we have counts for
5 papers
Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLMs
Mobina Pournemat, Keivan Rezaei, Gaurang Sriramanan +5
Despite widespread success in language understanding and generation, large language models (LLMs) exhibit unclear and often inconsistent behavior when faced with tasks that require…
Tool Preferences in Agentic LLMs are Unreliable
Kazem Faghih, Wenxiao Wang, Yize Cheng +5
Large language models (LLMs) can now access a wide range of external tools, thanks to the Model Context Protocol (MCP). This greatly expands their abilities as various agents. Howe…
Scaling Adversarial Training to Large Perturbation Bounds
Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan +1
The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim at defending at…
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj +1
Advances in the development of adversarial attacks have been fundamental to the progress of adversarial defense research. Efficient and effective attacks are crucial for reliable e…
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
Sravanti Addepalli, Vivek B. S., Arya Baburaj +2
As humans, we inherently perceive images based on their predominant features, and ignore noise embedded within lower bit planes. On the contrary, Deep Neural Networks are known to…