25 citations · 30 across the 15 of their papers we have counts for
15 papers
VCE-Skill: Enhancing Skill Self-Evolution with Version-Change Experience
Jianming Chen, Xuanbin Ye, Yawen Wang +3
Agents increasingly rely on reusable skills to encode task knowledge, tool-use procedures, and validation rules. Existing skill self-evolution methods primarily revise skills using…
Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference
Xuyan Ma, Yawen Wang, Junjie Wang +5
Platform-orchestrated agentic workflows have become a popular paradigm for developing LLM-based applications. However, their reliability remains a major challenge due to the uncert…
From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws
Mengzhuo Chen, Junjie Wang, Zhe Liu +3
LLM agents increasingly rely on agent harness: the runtime infrastructure around the base model that defines execution environments, tool interfaces, context, lifecycle orchestrati…
OrchJail: Jailbreaking Tool-Calling Text-to-Image Agents by Orchestration-Guided Fuzzing
Jianming Chen, Yawen Wang, Junjie Wang +3
Tool-calling text-to-image (T2I) agents can plan and execute multi-step tool chains to accomplish complex generation and editing queries. However, this capability introduces a new…
Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents
Jianming Chen, Yawen Wang, Junjie Wang +4
Embodied agents in safety-critical applications such as Vision-Language Navigation (VLN) rely on multiple interdependent capabilities (e.g., perception, memory, planning, decision)…
Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems
Mengzhuo Chen, Junjie Wang, Fangwen Mu +4
Failure attribution, i.e., identifying the responsible agent and decisive step of a failure, is particularly challenging in LLM-based multi-agent systems (MAS) due to their natural…
From Flat Logs to Causal Graphs: Hierarchical Failure Attribution for LLM-based Multi-Agent Systems
Yawen Wang, Wenjie Wu, Junjie Wang +1
LLM-powered Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex domains but suffer from inherent fragility and opaque failure mechanisms. Existing failur…
Adversarial Attack on Black-Box Multi-Agent by Adaptive Perturbation
Jianming Chen, Yawen Wang, Junjie Wang +4
Evaluating security and reliability for multi-agent systems (MAS) is urgent as they become increasingly prevalent in various applications. As an evaluation technique, existing adve…
Automated Network Protocol Testing with LLM Agents
Yunze Wei, Kaiwen Wei, Shibo Du +7
Network protocol testing is fundamental for modern network infrastructure. However, traditional network protocol testing methods are labor-intensive and error-prone, requiring manu…
Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows
Xuyan Ma, Xiaofei Xie, Yawen Wang +4
Agentic workflows built on low-code orchestration platforms enable rapid development of multi-agent systems, but they also introduce new and poorly understood failure modes that hi…
Robust Multi-agent Communication Based on Decentralization-Oriented Adversarial Training
Xuyan Ma, Yawen Wang, Junjie Wang +5
In typical multi-agent reinforcement learning (MARL) problems, communication is important for agents to share information and make the right decisions. However, due to the complexi…
Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning
Jianming Chen, Yawen Wang, Junjie Wang +4
Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has proveided explanations for the actions or s…
A Roadmap for Software Testing in Open Collaborative Development Environments
Qing Wang, Junjie Wang, Mingyang Li +2
Amidst the ever-expanding digital sphere, the evolution of the Internet has not only fostered an atmosphere of information transparency and sharing but has also sparked a revolutio…
CrashTranslator: Automatically Reproducing Mobile Application Crashes Directly from Stack Trace
Yuchao Huang, Junjie Wang, Zhe Liu +5
Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers nee…
Where is Your App Frustrating Users?
Yawen Wang, Junjie Wang, Hongyu Zhang +3
User reviews of mobile apps provide a communication channel for developers to perceive user satisfaction. Many app features that users have problems with are usually expressed by k…