{
  "id": 7007,
  "url": "https://arxiv.org/abs/2603.18908v4",
  "title": "Characterizing Linear Alignment Across Language Models",
  "summary": "Language models increasingly appear to learn similar representations, despite differences in training objectives, architectures, and data modalities. This emerging compatibility between independently trained models introduces new opportunities for cross-model alignment to downstream objectives. Moreover, this capability unlocks new potential application domains, such as settings where security, privacy, or competitive constraints prohibit direct data or model sharing. In this work, we investigat",
  "authors": "Matt Gorbett, Suman Jana",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-19T13:43:32.000Z",
  "fetched_at": "2026-07-14T16:32:54.534Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7007",
  "original_url": "https://arxiv.org/abs/2603.18908v4",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}