{
  "id": 3634512,
  "title": "Towards A Unified Information Bottleneck Framework for Time Series Explanations",
  "url": "https://urgent.news/2026/08/26/towards-a-unified-information-bottleneck-framework-for-time-series",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-26T15:14:52.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.25897v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods generally fall into two categories: attribution-based explanations, which identify the temporal regions most responsible for a prediction, and counterfactual explanations, which reveal how an input…",
  "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."
}