{
  "id": 18234,
  "title": "TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval",
  "url": "https://urgent.news/2026/07/30/tca-sir-learning-target-conditioned-abstractions-for-scientific",
  "topic": "ai",
  "section": "AI",
  "published": "2026-07-30T16:43:03.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2607.28498v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Scientific hypothesis generation for AI for Science typically involves Scientific Inspiration Retrieval (SIR) followed by hypothesis composition. Existing SIR methods rank papers by topical similarity and do not explicitly represent how a candidate inspiration transfers to a target problem. This is especially limiting for remote inspirations, whose value often lies in reusable problem-solving…",
  "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."
}