这篇论文搞了个Seahorse框架，专门统一各种时空事件模型的评测标准，还带压力测试套件，能看清哪个模型在复杂场景下更强。
Seahorse是一个统一基准框架，用于可重复的时空点过程（STPP）实验。它通过编码-演化-解码接口形式化神经STPP，并在单一可执行协议下训练、调优和评估所有模型家族。Seahorse附带HawkesNest合成压力测试套件，能暴露不同模型家族（如强度模型、条件密度模型）在复杂事件模式下的归纳偏差。该框架解决了现有实现因预处理、坐标归一化等差异导致的比较不可靠问题。
Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expressive intensity models, conditional density models, continuous-time latent dynamics, normalizing-flow spatial decoders, and score-based generative mechanisms. Yet comparison remains fragile because implementations differ in preprocessing, coordinate normalization, splits, likelihood conventions, and evaluation protocols. We present SEAHORSE, a unified framework for reproducible STPP experimentation. SEAHORSE formalizes neural STPPs through a common encode-evolve-decode interface and trains, tunes, and evaluates every model family under a single executable benchmark protocol with raw-coordinate likelihood reporting. This enables fair comparisons but, more importantly, controlled diagnostic studies. We pair SEAHORSE with HawkesNest, a synthetic stress-test suite, and show that increasing event-pattern complexity exposes each family's inductive bias, degrading some models sharply and leaving others stable. Code: https://github.com/YahyaAalaila/seahorse.