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About this paper
Hanyue Chen, Chang Liu, Miaomiao Zhang
The authors present compositional learning for timed automata networks using whole-system queries. An adapter translates component requests into evidence from an idealized system-level teacher; learners build component models. On 240 randomly generated instances, the method completed 213 (88.8%) within the stated limits, versus 175 for TCT. This supports broader completion under those limits, not universal runtime superiority or guaranteed termination.
Summary by alphaXiv.
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