发表机构
Peking University; Macau University; Beijing Jiaotong University; Squirrel Ai Learning（北京大学; 澳门大学; 北京交通大学; 松鼠AI学习）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出世界需求模型（WRM），利用类型化工件图编码工程上下文，通过关系感知注意力预测需求变更后果，在评分案例上显著提升影响MAP，并建立评估条件。
AI 中文摘要
需求变更可能影响关联的利益相关者、约束、组件和测试。我们提出了世界需求模型（WRM），该模型将这一工程上下文编码为类型化工件图，并在共享工件标识符处预测后果。关系感知注意力与类型化传播对节点进行上下文化；世界表示与决策表示支持学习到的动态。共享读出器对影响、冲突、违规和缺陷风险进行评分；辅助目标监督后继邻接和潜在预测。在来自六个领域的282个合成案例中的28个评分案例上，WRM获得的影响平均平均精度（MAP）为0.724，而哈希文本多层感知器（MLP）为0.623，相对增益为16.2%，配对差异为0.101（条件95%区间[0.044,0.159]）。最低四分位平均AP提高了32.4%，同域MAP提高了13.3%。在扩展语料库上的四次47案例比较显示，MAP增益为15.6%至29.1%，且在所有五个报告指标上均值更高。因此，记录的优势跨越了评分摘要和标注/训练设置。检查点是在评分案例上选择的，且骨干网络不匹配，因此这些结果表征了所选系统。我们的分析建立了候选覆盖界限，并表明当前的线性影响头无法跨决策对固定世界的工件进行重新排序。WRM贡献了一种工件寻址的世界模型公式、上下文后果评分的比较证据，以及评估需求世界预测的明确条件。
英文摘要
Requirement changes can affect connected stakeholders, constraints, components, and tests. We present World Requirement Model (WRM), which encodes this engineering context as a typed artifact graph and predicts consequences at shared artifact identifiers. Relation-aware attention and typed propagation contextualize nodes; world and decision representations support learned dynamics. Shared readouts score impact, conflict, violation, and defect risk; auxiliary objectives supervise successor adjacency and latent prediction. On 28 scored cases from 282 synthetic cases in six domains, WRM obtains impact mean average precision (MAP) of 0.724 versus 0.623 for a hashed-text multilayer perceptron (MLP), a 16.2\% relative gain and paired difference of 0.101 (conditional 95\% interval [0.044,0.159]). Lowest-quarter mean AP improves by 32.4\%, and equal-domain MAP by 13.3\%. Four 47-case comparisons on an expanded corpus show MAP gains of 15.6--29.1\% and higher means on all five reported metrics. The recorded advantage thus extends across score summaries and annotation/training settings. Checkpoints were selected on scored cases, and backbones are unmatched, so these results characterize selected systems. Our analysis establishes candidate-coverage bounds and shows that the current linear impact head cannot rerank a fixed world's artifacts across decisions. WRM contributes an artifact-addressed world-model formulation, comparative evidence for contextual consequence scoring, and explicit conditions for evaluating requirement-world prediction.
