3 papers
cs.AI2025
From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing
Lanxiao Huang, Daksh Dave, Tyler Cody +2
Large language models (LLMs) are increasingly used to automate or augment penetration testing, but their effectiveness and reliability across attack phases remain unclear. We prese…
cs.CL2025
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
Tuo Wang, Adithya Kulkarni, Tyler Cody +3
Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semanti…
cs.AI2025
Exploring Core and Periphery Precepts in Biological and Artificial Intelligence: An Outcome-Based Perspective
Niloofar Shadab, Tyler Cody, Alejandro Salado +3
Engineering methodologies predominantly revolve around established principles of decomposition and recomposition. These principles involve partitioning inputs and outputs at the co…