A taxonomy-guided RAG system with LLMs reduces hallucinations and improves migration suggestions for Qiskit code compared to unconstrained retrieval.
Leveraging llms to automate software architecture design from informal specifications
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ASTRAL applies multimodal LLMs with prompt chaining and few-shot learning to synthesize CPS architectures from disparate sources, enabling adaptive threat identification and quantitative risk estimation, as supported by ablation studies and feedback from 14 cybersecurity practitioners.
The paper presents a curriculum-grounded LLM-as-Judge pipeline for question-level marking that assembles authorized syllabus artifacts to generate rubrics and evaluate student responses.
An enhanced LLM-assisted pipeline with refined prompting and multi-level staged representations improves consistency, scalability, and robustness when recovering hierarchical architectures from a real-world ROS 2 disassembly system.
citing papers explorer
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Qiskit Code Migration with LLMs
A taxonomy-guided RAG system with LLMs reduces hallucinations and improves migration suggestions for Qiskit code compared to unconstrained retrieval.
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Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 Systems: An Agent-Based Multi-Level Approach to Hierarchical Structural Architecture Reconstruction
An enhanced LLM-assisted pipeline with refined prompting and multi-level staged representations improves consistency, scalability, and robustness when recovering hierarchical architectures from a real-world ROS 2 disassembly system.