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    "headline": "One Second of Movement Carries a Behavioral Fingerprint",
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    "dek": "Self-supervised representations separated animal identity from learning-related variation without predefined behavioral states.",
    "summary": "Self-supervised representations separated animal identity from learning-related variation without predefined behavioral states.",
    "body_text": "Fine-grained movement may expose changes that task scores miss. A self-supervised framework trained on nearby movement trajectories from freely behaving rats found that clips as short as one second contained information about identity and learning. Egocentric pose dynamics better predicted identity, while allocentric motion better tracked learning-related variation; both signals peaked at decision junctions in one cohort. The representation is a research tool for these datasets, not a general biometric claim about animals or people.",
    "why_it_matters": "Self-supervised representations separated animal identity from learning-related variation without predefined behavioral states.",
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      "The representation is a research tool for these datasets, not a general biometric claim about animals or people."
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    "tags": [
      "behavior",
      "self-supervised learning",
      "movement"
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      "source_id": "source-2026-10-21-013",
      "title": "arXiv preprint 2610.27140, original v1",
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    "title": "One Second of Movement Carries a Behavioral Fingerprint",
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