{
  "id": 14458,
  "url": "https://arxiv.org/abs/2607.23519v1",
  "title": "Auditing Alignment Controllability in LLMs via Political Axes",
  "summary": "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-first stress test of prompt-based controllability a",
  "authors": "Bartol Bućan, Nikola Sočec, Sarah Isufi, Morena Granić, Luka Hobor, Agneza Krajna, Mihael Kovac, Mario Brcic",
  "category": "research",
  "topics": "safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-26T07:38:11.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "x-arxiv-cs-cl-ethics-relevant-nlp",
  "source_name": "arXiv cs.CL (ethics-relevant NLP)",
  "source_homepage": "https://arxiv.org/list/cs.CL/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14458",
  "original_url": "https://arxiv.org/abs/2607.23519v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}