想检验你的时空点过程模型？HawkesNest提供了四个可控复杂度轴，能精准发现模型在空间-时间纠缠等场景下的弱点。
HawkesNest是一个基于多元Hawkes骨干的生成器对齐基准，定义了空间-时间纠缠、背景异质性、跨类型交互和域拓扑四个复杂度轴。每个轴关联一个从潜在数据生成机制中计算出的确定性索引。在固定全局速率、稳定性和模拟预算下改变这些轴，可对STPP模型进行诊断压力测试。实验验证了索引的单调性和近似正交性。Hawkes系列基线在联合异质性与纠缠复杂度下退化，AutoSTPP在空间-时间纠缠单独增加时表现脆弱。
HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity
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