Accelerating Thin Volume Reduction with Supervised Learning and Multi-Agent Reinforcement Learning
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SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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poster for ASCR PI meeting
Abstract for a poster to be presented at ASCR PI meeting.
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Artificial intelligence (AI), and more specifically large language models (LLMs) have the potential for use in penetration testing (“pentesting”) against devices, networks, and computer systems in information technology (IT). We explore the possibility of extending pentesting from IT systems to operational technology (OT) systems, which are more obscure than IT systems in their protocols and design. A challenge therefore exists when applying pretrained LLMs to OT systems as corpora are likely to underrepresent OT systems in comparison to other more prevalent systems. We evaluate augmentations of LLMs with various methods, especially retrieval augmented generation (RAG), to improve performance of the LLMs in the OT domain. In addition to pentesting, some of the testing of these OT devices may include commissioning to ensure that the newly installed devices work correctly. Our framework may also be applied in such cases.
This is a UUR poster submission for the Internship Symposium at the National Museum of Nuclear Science & History on August 7th, 2025. The poster briefly covers the application of RAG to help in documenting legacy codebases.
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