Graph-based Android malware classifiers can lose accuracy under malware-type or family shifts. We test whether mesoscopic organization in function-call graphs provides shift-stable information beyond local degree profiles (LDP), global statistics, lightweight metadata, and size-matched random partitions. Using 15,000 M...
Jun-Ru Zhu, Yi-Xin Yang, Xiao-Qing Ding et al.· 0 citations
Post-hoc malware calibrators can condition confidence on graph structure, but their structural inputs may leave the support represented by validation data under malware-type shift. We study this risk on MalNet-Tiny by holding out each of four malware types across three seeds, freezing a graph isomorphism network, and f...
Jun-Ru Zhu, Yi-Xin Yang, Xiao-Qing Ding et al.· 0 citations
Retrieval-augmented generation systems can route difficult queries to deeper context, but batch deployments must allocate a shared token budget across calls whose costs vary by query. We formulate selective escalation as finite-batch allocation for financial document question answering. Each of 150 FinanceBench questio...
Jun-Ru Zhu, Yi-Xin Yang, Xiao-Qing Ding et al.· 0 citations
VulAgentRL is proposed, an agentic RL framework for interprocedural vulnerability detection built on a Code Property Graph (CPG), which outperforms state-of-the-art baselines, including frontier models, on the strict pair-wise-correct metric while issuing fewer tool calls, and its advantage persists on an out-of-distri...
Yi-Kun Li, Ting Zhang, Jia-Kun Liu et al.· arXiv.org· 2 citations
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