arXiv Machine Learning July 8, 2026 By Meng Li (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Xiaohua Yang (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Jie Liu (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Shiyu Yan (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China)

A semantic mutation metric for metamorphic relation adequacy in scientific computing programs

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arXiv:2605. 17437v2 Announce Type: replace-cross Abstract: Context.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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ModelEquivBench: Certifying Multi-Relational Evaluation of LLM-Generated Optimization Models

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arXiv Machine Learning

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