The rise of live streaming has transformed online interaction, enabling massive real-time engagement but also exposing platforms to complex risks such as scams and coordinated malicious behaviors. Detecting these risks is challenging because harmful actions often accumulate gradually and recur across seemingly unrelated streams. To address this, we propose CS-VAR (Cross-Session Evidence-Aware Retrieval-Augmented Detector) for live streaming risk assessment. In CS-VAR, a lightweight, domain-specific model performs fast session-level risk inference, guided during training by a Large Language Model (LLM) that reasons over retrieved cross-session behavioral evidence and transfers its local-to-global insights to the small model. This design enables the small model to recognize recurring patterns across streams, perform structured risk assessment, and maintain efficiency for real-time deployment. Extensive offline experiments on large-scale industrial datasets, combined with online validation, demonstrate the state-of-the-art performance of CS-VAR. Furthermore, CS-VAR provides interpretable, localized signals that effectively empower real-world moderation for live streaming.

翻译：直播的兴起改变了在线互动方式，实现了大规模的实时参与，但也使平台面临诈骗和协同恶意行为等复杂风险。检测这些风险具有挑战性，因为有害行为往往逐渐累积，并在看似无关的直播流中反复出现。为此，我们提出CS-VAR（跨会话证据感知检索增强检测器）用于直播风险评估。在CS-VAR中，一个轻量级的领域专用模型执行快速的会话级风险推断，其训练过程由大型语言模型（LLM）指导——该LLM基于检索到的跨会话行为证据进行推理，并将其从局部到全局的洞察迁移至小模型。这种设计使小模型能够识别跨直播流的重复模式，执行结构化风险评估，并保持实时部署的效率。基于大规模工业数据集的广泛离线实验结合在线验证，证明了CS-VAR的先进性能。此外，CS-VAR提供可解释的局部化信号，有效赋能现实世界的直播内容审核。