{
  "id": 13790,
  "url": "https://arxiv.org/abs/2607.23519",
  "title": "Auditing Alignment Controllability in LLMs via Political Axes",
  "summary": "arXiv:2607.23519v1 Announce Type: new Abstract: Political audits of large language models (LLMs) usually reduce each to one point on a political compass. But that resting point barely matters in deployment: a model must land somewhere, and what counts is how far, and in which directions, its answers can be steered. That steering runs through the system prompt: the personalization layer a platform sets, or one induced from a user's history, not necessarily written by hand. We run a dispersion-fir",
  "authors": "Bartol Bu\\'can, Nikola So\\v{c}ec, Sarah Isufi, Morena Grani\\'c, Luka Hobor, Agneza Krajna, Mihael Kovac, Mario Brcic",
  "category": "research",
  "topics": "safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T04:00:00.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13790",
  "original_url": "https://arxiv.org/abs/2607.23519",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}