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SeismoGPT study reveals multi-token prediction stabilizes wavefield forecasting

By PulseAugur Editorial · [2 sources] ·

Researchers have investigated the stability of autoregressive sequence models when forecasting long-horizon physical wavefields, such as seismograms. Their study, using a model called SeismoGPT on synthetic seismograms, found that multi-token prediction significantly stabilizes the forecasting process. Additional gains were observed with a horizon-embedding hybrid prediction head and a cross-horizon STFT-magnitude coherence loss, though performance critically depends on a specific context-ratio threshold. AI

IMPACT Identifies key architectural choices for improving the stability of autoregressive models in long-horizon forecasting of physical signals.

RANK_REASON The cluster contains a research paper detailing a controlled study on autoregressive sequence models for forecasting physical wavefields.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SeismoGPT study reveals multi-token prediction stabilizes wavefield forecasting

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The cluster contains a research paper detailing a controlled study on autoregressive sequence models for forecasting physical wavefields.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Waleed Esmail, Stuart Russell, Jana Klinge, Alexander Kappes, Christine Thomas ·

    When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

    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…

  2. arXiv cs.LG TIER_1 English(EN) · Christine Thomas ·

    When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

    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…

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