8 papers
Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial
Haoyu Li, Xiangru Zhong, Hao Cheng +2
Learning-based methods for synthesizing controllers have gained popularity due to their high expressiveness and strong empirical performance. However, in safety-critical scenarios…
On The Fragility of Benchmark Contamination Detection in Reasoning Models
Han Wang, Haoyu Li, Brian Ko +1
Leaderboards for LRMs have turned evaluation into a competition, incentivizing developers to optimize directly on benchmark suites. A shortcut to achieving higher rankings is to in…
Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs
Kairun Zhang, Haoyu Li, Yanjun Zhao +2
Zeroth-order optimizers have recently emerged as an attractive approach for fine-tuning large language models (LLMs), as they avoid backpropagation and can substantially reduce mem…
DecepChain: Inducing Deceptive Reasoning in Large Language Models
Wei Shen, Han Wang, Haoyu Li +1
Large Language Models (LLMs) have been demonstrating strong reasoning capability with their chain-of-thoughts (CoT), which are routinely used by humans to judge answer quality. Thi…
Safe Domains of Attraction for Discrete-Time Nonlinear Systems: Characterization and Verifiable Neural Network Estimation
Mohamed Serry, Haoyu Li, Ruikun Zhou +2
Analysis of nonlinear autonomous systems typically involves estimating domains of attraction, which have been a topic of extensive research interest for decades. Despite that, accu…
Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansion
Haoyu Li, Xiangru Zhong, Bin Hu +1
Learning-based neural network (NN) control policies have shown impressive empirical performance. However, obtaining stability guarantees and estimates of the region of attraction o…
Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
Haoyu Li, Xiangru Zhong, Bin Hu +1
Contraction metrics are crucial in control theory because they provide a powerful framework for analyzing stability, robustness, and convergence of various dynamical systems. Howev…
Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control
Zhouxing Shi, Haoyu Li, Cho-Jui Hsieh +1
We study the problem of learning verifiably Lyapunov-stable neural controllers that provably satisfy the Lyapunov asymptotic stability condition within a region-of-attraction (ROA)…