研究人员调查了自回归序列模型在预测地震图等长周期物理波场时的稳定性。他们使用名为SeismoGPT的模型在合成地震图上进行研究，发现多令牌预测显著稳定了预测过程。使用周期嵌入混合预测头和跨周期STFT幅度相干性损失观察到了额外的收益，尽管性能关键地取决于特定的上下文比例阈值。 AI
影响 确定了在长周期物理信号预测中提高自回归模型稳定性的关键架构选择。
排序理由 该集群包含一篇研究论文，详细介绍了关于用于预测物理波场的自回归序列模型的受控研究。
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arXiv:2606.10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds…
Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds of steps, small per-step errors compound into p…