With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow features, lacking deep semantic understanding and interpretability of decisions. Although Large Language Models (LLMs) demonstrate remarkable semantic reasoning capabilities, directly processing massive raw code incurs prohibitive token overhead. Moreover, this approach fails to fully unleash the deep logical reasoning potential of LLMs within complex contexts. To address these limitations, we propose MARD, a multi-agent framework for robust Android malware detection. This framework effectively bridges the gap between the semantic understanding of LLMs and traditional static analysis. It treats underlying deterministic analysis engines as on-demand execution tools, while utilizing the LLM to orchestrate the entire decision-making process. By designing an autonomous multi-agent interaction mechanism based on the ReAct paradigm, MARD constructs a highly interpretable evidentiary chain for conviction. Furthermore, we radically reduce the total cost of conducting a deep analysis of a single complex APK to under $0.10. Evaluations demonstrate that, without any domain-specific fine-tuning, MARD achieves an F1 score of 93.46%. It not only outperforms continual learning baselines but also exhibits robustness against concept drift and strong cross-domain generalization capabilities in evaluations spanning up to five years.

翻译：摘要：随着安卓应用的快速演变，基于传统机器学习的检测模型面临概念漂移问题。此外，它们受限于浅层特征，缺乏深度语义理解与决策可解释性。尽管大型语言模型（LLMs）展现出卓越的语义推理能力，但直接处理海量原始代码会产生高昂的令牌开销。同时，该方法难以在复杂上下文中充分释放LLMs的深层逻辑推理潜力。为应对这些局限，我们提出MARD——一个面向鲁棒安卓恶意软件检测的多智能体框架。该框架有效弥合了LLMs语义理解与传统静态分析之间的鸿沟。它将底层确定性分析引擎作为按需执行工具，同时利用LLM统筹整个决策流程。通过基于ReAct范式设计自主多智能体交互机制，MARD构建了高度可解释的定罪证据链。此外，我们将单个复杂APK深度分析的总成本大幅降低至0.1美元以下。评估结果表明，无需任何领域特定微调，MARD即可达到93.46%的F1分数。它不仅优于持续学习基线方法，在横跨五年的评估中，还在概念漂移鲁棒性与跨域泛化能力方面展现出卓越表现。