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      <news:title>AI for biology has several evidence levels. Keep them separate</news:title>
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      <news:title>From fuzz tests to proofs: inside an AI verification workflow</news:title>
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      <news:title>Apertus 1.5 broadens the public-model research stack</news:title>
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      <news:title>Coding-agent productivity needs a systems denominator</news:title>
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      <news:title>Inside Google’s building blocks for agent-assisted wet labs</news:title>
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      <news:title>GPT-6.1 Terra brings a specialised pitch to computer work</news:title>
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      <news:publication_date>2026-11-05T00:00:00.000Z</news:publication_date>
      <news:title>Model merging versus distillation: what the new evidence compares</news:title>
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      <news:title>Muse Spark Open and Apertus 1.5 expose different kinds of openness</news:title>
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      <news:publication_date>2026-11-05T00:00:00.000Z</news:publication_date>
      <news:title>The sleep framework puts forgetting at the centre of AI learning</news:title>
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      <news:publication_date>2026-11-05T00:00:00.000Z</news:publication_date>
      <news:title>TorchNeuron’s public beta: what PyTorch teams should verify</news:title>
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      <news:publication_date>2026-11-05T00:00:00.000Z</news:publication_date>
      <news:title>World models, video generators and simulators serve different jobs</news:title>
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