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Mingfei Cheng

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Open access Oct 2026

Are We Stuck? Modeling and Detecting Deadlocks in Multi-autonomous Vehicle Systems

Autonomous driving system (ADS) testing is essential to ensure the safety and reliability of autonomous vehicles (AVs) prior to deployment. As ADSs are increasingly deployed in multi-AV traffic environments, it becomes crucial to assess their cooperative performance, particularly with respect to deadlock, a fundamental...

Ming-Fei Cheng, Xiao-Fei Xie, Li-Li Quan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Before Acting, Change the State: Prospective State Intervention for Web Agents under Deceptive Interfaces

Veer is introduced, an agent-side runtime defense that leaves task planning to the base agent and intervenes on Web state when a proposed action would produce an unauthorized consequence, establishing task-relevant Web state as an effective runtime control target for protecting Web agents from deceptive outcomes.

Ruo-Zhao Yang, Ming-Fei Cheng, Xiao-Fei Xie · 0 citations
Jul 2026

EvoEye: Self-Evolving Runtime Monitoring for Autonomous Driving Systems

EvoEye is proposed, which identifies the current monitor's errors, generates informative executions accordingly, and updates the monitor through self-evolution to enable effective self-evolution.

Mingfei Cheng, Lionel C. Briand, Xiaofei Xie · 0 citations
#reinforcement learning Open access Aug 2026

Learning from the Test: Self-Referential Differential Testing for Deep RL Agents

Delta (Differential Testing for DRL Agents) is proposed, a novel and comprehensive framework that automatically identifies both safety-critical and optimality bugs in DRL agents and investigates the effectiveness of three offline RL algorithms in generating challenger agents.

Jun-Da He, Jie-Ke Shi, Zhou Yang et al. · 0 citations
Review Aug 2026

PRWeaver: Evaluating LLM-Based Code Auditors against Long-Horizon Malicious Pull Requests

The results show that access to repository history is insufficient: concealment becomes most effective when benign and malicious changes jointly occupy the auditor's active review context or when the stated purpose plausibly accounts for the attack-bearing diff.

Yuekun Wang, Ming-Fei Cheng, Xiao-Fei Xie · 0 citations

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