1 citations · 2 across the 3 of their papers we have counts for
3 papers · 1 filter
When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling
Niful Islam, Ragib Shahriar Ayon, Deepak George Thomas +2
Large Language Models (LLMs) have revolutionized intelligent application development. While standalone LLMs cannot perform any actions, LLM agents address the limitation by integra…
muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
Deepak-George Thomas, Matteo Biagiola, Nargiz Humbatova +4
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid rob…
Mutation-based Fault Localization of Deep Neural Networks
Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad +1
Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, inclu…