Shenghua Feng — accepted AI-conference papers

2026

How Powerful are LLMs in Generating Program Specifications?

Fanpeng Yang, Xing Li, Shuling Wang, Jie An, Zeyu Sun, Shenghua Feng +4

ICML 2026poster

Formal verification provides strong guarantees of software correctness, but its adoption is limited by the high cost of writing precise formal specifications. While recent large language models (LLMs) have demonstrated impressive capabilities in theorem proving and verified code generation, how powe…

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2026

Runtime Safety and Reach-avoid Prediction of Stochastic Systems via Observation-aware Barrier Functions

Shenghua Feng, Jie An, Fanjiang Xu

AAAI 2026technical

Stochastic dynamical systems have emerged as fundamental models across numerous application domains, providing powerful mathematical representations for capturing uncertain system behavior. In this paper, we address the problem of runtime safety and reach-avoid probability prediction for discrete-ti

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