{
  "id": 5240713,
  "title": "From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution",
  "url": "https://urgent.news/2026/09/02/from-reweighting-to-rewriting-unlocking-the-intervention-effects-of",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-02T16:07:21.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.02771v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which examples are selected and how they are modified. Influence functions (IF) estimate behavioral changes under infinitesimal reweighting, yet IF-selected examples often show limited advantages over random selection under conventional weight-based…",
  "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."
}