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    "headline": "The Model Learns When Not to Adapt",
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    "dek": "PORTAL chooses whether a test-time intervention is worth deploying, which labels to request and when further feedback cannot change the choice.",
    "summary": "PORTAL chooses whether a test-time intervention is worth deploying, which labels to request and when further feedback cannot change the choice.",
    "body_text": "A test-time adaptation method can rescue one corrupted batch and damage another, while average accuracy hides the difference. PORTAL treats the original predictor and each intervention as fixed candidates, then uses every acquired label to compare them on the same input. It directs labels toward candidates that can still overtake the leader and stops when no remaining evidence can change the decision or justify its cost. In the authors' experiments, that control converted an intervention that lost accuracy overall into a useful option on selected batches and prevented arrival-order failures. The method still assumes labels can be acquired and candidate predictions are fixed before feedback.",
    "why_it_matters": "PORTAL chooses whether a test-time intervention is worth deploying, which labels to request and when further feedback cannot change the choice.",
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      "test-time adaptation",
      "uncertainty",
      "feedback",
      "machine learning"
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      "title": "arXiv preprint 2610.37658, original v1",
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    "title": "The Model Learns When Not to Adapt",
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