ORACLE：从流媒体应用使用的部分轨迹预测欺诈

摘要

智能手机诈骗日益猖獗，通常表现为多阶段、跨应用的过程，其意图逐步显现。因此，有效的干预需要在意图明确之前进行预判。这本身极具挑战性，因为决策必须依赖包含时间分布证据的部分行为轨迹。在本文中，我们提出ORACLE（面向跨时间潜在威胁的在线推理框架），这是首个基于流式应用使用轨迹进行早期诈骗预警的智能体框架。为支撑这一场景，我们构建了一个真实世界的长时程流式应用使用轨迹基准数据集，涵盖12种诈骗类型、横跨较长时间段（平均15天）、涉及多种应用（95个应用），并交织了正常行为与诈骗行为。针对证据碎片化问题，我们引入了一个自演化上下文管理器，能够随时间自适应地整合以实体为中心的交互，从而从部分观测中更有效地重构跨时间证据。为增强对早期潜在信号的敏感度，我们提出了一种基于策略的自蒸馏方案：教师模型基于反诈骗反思与技能线索的总结进行条件化，监督无法获取此类反思的学生模型。该方案将证据感知知识蒸馏到学生模型中，提升其对部分轨迹中新兴欺诈模式的识别能力。实验表明，ORACLE显著提升了早期诈骗预警性能，在真实流式场景中及时发出警报的同时减少了误报。

English

Smartphone scams are increasingly prevalent and typically manifest as multi-stage, cross-application processes with gradually emerging intent. Effective intervention thus requires anticipating scams before the intent becomes explicit. This is inherently challenging, as decisions must rely on partial trajectories with temporally distributed evidence. In this paper, we propose ORACLE Online Reasoning for Anticipating Cross-temporal Latent thrEats, the first agentic framework for early scam anticipation from streaming app-usage trajectories. To support this setting, we curate a real-world long-horizon benchmark of streaming app-usage trajectories, covering 12 scam types, spanning extended periods (15 days on average), involving diverse applications (95 apps), and interleaving normal and scam behaviors. To address fragmented evidence, we introduce a self-evolving context manager that adaptively consolidates entity-centric interactions over time, enabling more effective reconstruction of cross-temporal evidence from partial observations. To enhance sensitivity to latent early-stage signals, we propose an on-policy self-distillation scheme in which a teacher model, conditioned on summarized anti-scam reflections and clues by skills, supervises a student model without access to such reflections. This scheme thereby distills evidence-informed knowledge and improves recognition of emerging fraud patterns from partial trajectories. Experiments show that consistently improves early scam anticipation, yielding timely warnings while reducing false alerts in realistic streaming scenarios.