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    "story_id": "mp-2026-08-07-017",
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    "headline": "One Weather Model Changed Its Clock at Inference",
    "slug": "one-weather-model-changed-its-clock-at-inference",
    "dek": "GEM-3 trades short-range detail against rollout stability by selecting among trained timesteps without changing weights.",
    "summary": "GEM-3 trades short-range detail against rollout stability by selecting among trained timesteps without changing weights.",
    "body_text": "The 134-million-parameter global model uses mixed-timestep training and lets inference choose a forecast step. Its authors report near-state-of-the-art probabilistic medium-range skill and more stable long rollouts than timestep-specialist variants.",
    "why_it_matters": "GEM-3 trades short-range detail against rollout stability by selecting among trained timesteps without changing weights.",
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    "tags": [
      "weather forecasting",
      "transformers",
      "probabilistic models"
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  "sources": [
    {
      "source_id": "source-2026-08-07-019",
      "title": "arXiv preprint 2608.06241",
      "publisher": "arXiv",
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    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "One Weather Model Changed Its Clock at Inference",
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