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TripleBound is proposed, a hybrid framework for automated monolith-to-microservices decomposition that augments a heterogeneous graph neural network with weakly supervised triplet constraints derived from parser-inferred service groups based on package structure, naming conventions, and code location.
Semantic drift over eight centuries is not a uniform, corpus-wide evolution, but is driven by a sparse cluster of high-impact contextual outliers that contextualized LLMs can isolate.
HelaBERT achieves notable improvements in Sinhala NLP tasks, particularly in sentiment analysis, by leveraging a dual pooling classification head.