Editors and reviewers are expected to ensure that manuscripts cite relevant, accurate, current, and ethically appropriate literature, yet manuscript-level citation auditing remains largely manual, fragmented, and difficult to scale. Citation context, metadata quality, self-citation patterns, and bibliographic integrity all affect whether a reference appropriately supports a local claim. We present CitePrism, a transparent hybrid decision-support framework for editorial citation auditing that combines LLM-assisted contextual reasoning, embedding-based semantic similarity, metadata verification, integrity-oriented flags, and human-in-the-loop analyst review. CitePrism extracts citation neighborhoods, enriches reference metadata, computes fused relevance scores, surfaces metadata and self-citation review prompts, and supports configurable threshold-based triage. In a preliminary validation on a single case-study manuscript with 104 references from pavement engineering, agreement with human binary relevance labels reached Cohen's kappa = 0.429. At operating threshold tau = 17, CitePrism flagged all human-labeled irrelevant citations, while also producing false positives requiring analyst review. These results suggest that CitePrism may support conservative editorial screening and citation-quality triage, but they do not establish general editorial performance. CitePrism is intended as pilot-stage decision support, not as an autonomous misconduct detector or automated editorial decision system. Broader validation across manuscripts, domains, annotators, baselines, and deployment settings is required before operational use.

翻译：[translated abstract in Chinese] 编辑和审稿人应确保稿件引用相关、准确、最新且符合伦理规范的文献，然而稿件级别的引文审核在很大程度上仍依赖于人工操作、分散进行且难以规模化。引用语境、元数据质量、自引模式及书目完整性均会影响参考文献是否恰当支撑局部论点。本文提出CitePrism，一个透明化的混合决策支持框架，用于编辑引文审核。该框架结合了大语言模型辅助的语境推理、基于嵌入的语义相似度、元数据验证、完整性标记以及“人在回路”的分析师复核机制。CitePrism可提取引用邻域、丰富参考文献元数据、计算融合相关性得分、生成元数据与自引复核提示，并支持基于可配置阈值的分类筛选。在针对单篇案例稿件的初步验证中（该稿件包含来自路面工程领域的104篇参考文献），其与人工二元相关性标签的一致性达到Cohen's kappa = 0.429。在操作阈值tau = 17时，CitePrism标记了所有经人工标注为不相关的引文，但也产生了需要分析师复核的误报。这些结果表明CitePrism可能支持保守的编辑筛选和引文质量分类，但并不能确立其通用的编辑绩效。CitePrism旨在作为试点阶段的决策支持工具，而非自主不当行为检测器或自动化编辑决策系统。在投入实际应用前，仍需跨稿件、跨领域、跨标注者、跨基线及跨部署场景开展更广泛的验证。