Deceptive reviews, refer to fabricated feedback designed to artificially manipulate the perceived quality of products. Within modern e-commerce ecosystems, these reviews remain a critical governance challenge. Despite advances in review-level and graph-based detection methods, two pivotal limitations remain: inadequate generalization and lack of interpretability. To address these challenges, we propose JARVIS, a framework providing Judgment via Augmented Retrieval and eVIdence graph Structures. Starting from the review to be evaluated, it retrieves semantically similar evidence via hybrid dense-sparse multimodal retrieval, expands relational signals through shared entities, and constructs a heterogeneous evidence graph. Large language model then performs evidence-grounded adjudication to produce interpretable risk assessments. Offline experiments demonstrate that JARVIS enhances performance on our constructed review dataset, achieving a precision increase from 0.953 to 0.988 and a recall boost from 0.830 to 0.901. In the production environment, our framework achieves a 27% increase in the recall volume and reduces manual inspection time by 75%. Furthermore, the adoption rate of the model-generated analysis reaches 96.4%.

翻译：虚假评论是指为人为操纵产品感知质量而编造的反馈信息。在现代电子商务生态系统中，此类评论仍是关键治理难题。尽管基于评论层面与图结构的检测方法已取得进展，但仍存在两大核心局限：泛化能力不足与缺乏可解释性。为应对这些挑战，我们提出JARVIS框架——通过增强检索与证据图结构实现裁决判决。该框架从待评估评论出发，通过混合稠密-稀疏多模态检索获取语义相似证据，经由共享实体扩展关联信号，并构建异质证据图。随后，大语言模型执行基于证据的裁决，产生可解释的风险评估。离线实验表明，JARVIS在我们构建的评论数据集上提升了性能，精确度从0.953提升至0.988，召回率从0.830提升至0.901。在生产环境中，该框架实现了27%的召回量增长，并将人工审核时间减少75%。此外，模型生成分析结果采纳率达96.4%。