Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Spatiotemporal point processes remain fragmented across competing neural architectures and incompatible evaluation protocols, blocking systematic progress in modeling event sequences across mobility, epidemiology, and safety domains. SEAHORSE addresses this by establishing a unified encode-evolve-decode interface and standardized benchmark suite, enabling reproducible comparison of intensity models, latent dynamics, normalizing flows, and score-based generative approaches under consistent preprocessing and likelihood conventions. This infrastructure move mirrors earlier unification efforts in language models and vision, signaling maturation of the STPP research community and lowering barriers for practitioners to adopt and extend methods.
Modelwire context
ExplainerThe deeper significance here is less about any individual model and more about what fragmentation has been costing the field: results across STPP papers have been largely incomparable, meaning years of published work may be harder to build on than citation counts suggest. SEAHORSE is essentially a retroactive audit mechanism as much as a forward-looking benchmark.
This lands on the same day as AlphaEarth (covered July 1), which demonstrated 2-6x gains in emergency response prediction by injecting spatial context into sparse event histories. That work implicitly exposed the evaluation problem SEAHORSE is trying to fix: without consistent preprocessing and likelihood conventions, it is genuinely difficult to know whether AlphaEarth's gains are architectural or an artifact of how its authors set up comparisons. Aionoscope, also from July 1 coverage, makes a parallel argument for time-series representations, finding that standard benchmarks miss whether models capture interpretable process state at all. SEAHORSE, AlphaEarth, and Aionoscope together suggest a coordinated (if unplanned) push to harden evaluation infrastructure across temporal modeling subfields.
Watch whether the major Neural STPP papers from the past two years voluntarily re-run results under SEAHORSE conventions within the next six months. If they do, and rankings shift materially, that confirms the fragmentation problem was real and consequential. If adoption stalls, the framework risks becoming one more benchmark that gets cited but not used.
Coverage we drew on
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsSEAHORSE · Spatiotemporal Point Processes · Neural STPPs
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