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New Benchmark HawkesNest Tests Spatiotemporal AI Models

Researchers have introduced HawkesNest, a new synthetic benchmark designed to evaluate spatiotemporal point process (STPP) models. Unlike real-world datasets, HawkesNest offers controlled complexity along four axes: space-time entanglement, background heterogeneity, cross-type interaction, and domain topology. This allows for diagnostic stress tests of STPP models by isolating specific structural difficulties. Initial tests show that existing Hawkes-family baselines and neural models like AutoSTPP degrade under certain complexity increases, highlighting their sensitivities. AI

影响 Provides a new diagnostic tool for evaluating the robustness of spatiotemporal AI models.

排序理由 The cluster describes a new academic paper introducing a synthetic benchmark for evaluating AI models.

在 arXiv cs.LG 阅读 →

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yahya Aalaila, Sumantrak Mukherjee, Gerrit Gro{\ss}mann, Sebastian Vollmer ·

    HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity

    arXiv:2606.16863v1 Announce Type: new Abstract: 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-…

  2. arXiv cs.LG TIER_1 English(EN) · Sebastian Vollmer ·

    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 …