Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult to attribute. We introduce HawkesNest, a generator-aligned benchmark for controlled spatiotemporal pattern complexity built on a multivariate Hawkes backbone. HawkesNest defines four complexity axes: space--time entanglement, background heterogeneity, cross-type interaction, and domain topology. Each axis is associated with a deterministic index computed from the latent data-generating mechanism. By varying these axes while holding global rate, stability, and simulation budget fixed, HawkesNest enables diagnostic stress tests of STPP models under known structural difficulty. We verify that the indices are monotone and nearly orthogonal under controlled sweeps. We illustrate its use by showing that Hawkes-family baselines degrade under joint heterogeneity--entanglement complexity, even though they are structurally aligned with the Hawkes data-generating backbone. We further show that HawkesNest exposes neural-model sensitivity: AutoSTPP remains vulnerable under isolated increases in space--time entanglement. Code. Available at https://github.com/YahyaAalaila/HawkesNest

翻译：对时空点过程（STPP）模型的评估严重依赖不透明的真实世界数据集，此类数据中潜在生成结构未知且模型失败原因难以归因。我们提出霍克斯巢（HawkesNest）——一个基于多元霍克斯主干构建的、面向受控时空模式复杂性的生成器对齐基准。霍克斯巢定义了四个复杂性轴：时空纠缠、背景异质性、跨类型交互及域拓扑。每个轴均关联一个由潜在数据生成机制计算出的确定性指标。通过在保持全局速率、稳定性及仿真预算不变的情况下改变这些轴，霍克斯巢能够在已知结构难度条件下对STPP模型进行诊断性压力测试。我们验证了在受控扫描下这些指标具有单调性且近乎正交。通过展示霍克斯族基线模型在联合异质性-纠缠复杂性下性能退化（尽管其结构与霍克斯数据生成主干对齐），我们说明了该基准的用途。我们进一步表明，霍克斯巢揭示了神经模型的敏感性：AutoSTPP在时空纠缠单独增加时仍保持脆弱性。代码发布于https://github.com/YahyaAalaila/HawkesNest