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LWVIC4Code is proposed, a non-contrastive representation learning approach specifically designed for Type-IV clone detection that achieves competitive or superior performance without negative samples, benefits from layer-wise supervision, and generalizes effectively from Python to other languages, particularly Java and C#.
Achieving high accuracy in domain model extraction using lightweight LLMs opens new avenues for reverse engineering in privacy-sensitive contexts.
Sampling plausible configurations of digital twins can reveal multiple valid parameterizations, enhancing model adaptation in complex natural systems.
