With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use of distributed data. However, conventional two-tier FL architectures are poorly suited to the hierarchical and heterogeneous nature of real-world IoT systems. Hierarchical...
Jie-Chao Gao, Yuan-Gang Li, Jie Wang et al.· npj Artificial Intelligence· 0 citations
CatchBench puts one auditor's question to three information states: the declared configuration before a run (PRE), a growing prefix of its trace (LIVE), and the finished trace (POST), which none scores all three under one task-method interface.
Yue Zhao, Meng-Yuan Li, Ruo-Lin Li et al.· 4 citations· ⚡1
Experiments show that H-FedSN reduces communication costs by up to 477 times compared to baseline methods while maintaining high accuracy, making it well-suited for hierarchical FL in IoT deployments.
Jiechao Gao, Yuangang Li, Jie Wang et al.· arXiv.org· 0 citations
These findings demonstrate that current defense paradigms optimize for single-turn refusal benchmarks while rendering multi-step agents fundamentally unreliable, necessitating new approaches that preserve tool execution competence under adversarial conditions.
Early results suggest that fail-aware discriminative oracle prediction can complement existing approaches such as fuzzing, search-based testing, and LLM-based test generation and attach fault-aware predicted oracles to them, turning high-volume input generation into executable tests that are more likely to expose seman...
Yue Zhao, Binish Tanveer, Jelena Zdravkovic· arXiv.org· 0 citations
MemoHarness is introduced, an adaptive harness optimization framework that learns from its own executions and improves over the fixed harnesses it is compared against and shows selective transfer to unseen suites and base models.
Yue Huang, Wenjie Wang, Han Bao et al.· arXiv.org· 4 citations
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