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cs.SE2026
Synthesizing Inductive Invariants for Distributed Protocols via IC3 and Large Language Models
Weining Cao, Guangyuan Wu, Yuan Yao +3
Distributed protocols are notoriously difficult to verify correctly. Proving safety typically requires inductive invariants that both imply the desired property and are preserved b…
cs.SE2026
Uncertainty Quantification for LLM-based Code Generation
Senrong Xu, Yuhao Tan, Yanke Zhou +6
Prediction sets provide a theoretically grounded framework for quantifying uncertainty in machine learning models. Adapting them to structured generation tasks, in particular, larg…