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This paper introduces ORACLE, an agentic framework for early scam anticipation from streaming app-usage trajectories. ORACLE uses a self-evolving context manager to consolidate entity-centric interactions and an on-policy self-distillation scheme to improve recognition of emerging fraud patterns from partial trajectories. Experiments on a new real-world benchmark demonstrate that ORACLE improves early scam anticipation, providing timely warnings with fewer false alerts.
Spotting scams before they fully unfold is now possible: ORACLE anticipates smartphone fraud from streaming app usage by distilling knowledge from anti-scam reflections, enabling early warnings with fewer false positives.
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 \textbf{ORACLE} Online Reasoning for Anticipating Cross-temporal Latent thrEats, the first agentic framework for early scam anticipation from \textit{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 \method{} consistently improves early scam anticipation, yielding timely warnings while reducing false alerts in realistic streaming scenarios.
