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 intensities, 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 \textsc{Seahorse}, a unified framework for reproducible STPP experimentation. \textsc{Seahorse} provides an encode--evolve--decode interface and an executable benchmarking contract that standardizes dataset handling, configuration resolution, training, tuning, checkpointing, raw-coordinate likelihood reporting, and run artifacts across heterogeneous STPP families. It supports classical baselines, neural likelihood models, continuous-time Neural STPP variants, and sample-based generative models under one external protocol. Using HawkesNest as a controlled synthetic stress-test suite, we show how diagnostic benchmarking can probe neural STPP behavior under increasing spatiotemporal entanglement and other structural changes. Together, \textsc{Seahorse} and these controlled evaluations provide reusable infrastructure for comparing, diagnosing, and extending STPP models beyond implementation-specific leaderboards.