{
  "id": 8816288,
  "title": "Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation",
  "url": "https://urgent.news/2026/09/18/balanced-prompt-adaptation-against-entropy-induced-collapse-for-test",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-18T13:18:39.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.21743v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Entropy minimization is a standard objective for test-time adaptation (TTA), but it can fail in imbalanced binary segmentation. Unlike image classification, dense segmentation aggregates thousands of pixel predictions, allowing the larger predicted class to dominate the update, pull minority predictions toward itself, and produce a degenerate mask as predictions saturate and their entropy…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}