{
  "id": 17084,
  "url": "https://arxiv.org/abs/2608.05741v1",
  "title": "Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration",
  "summary": "Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational misuse, and platform governance. These concerns make robust detection of machine-generated text increasingly necessary. Recent zero-shot detectors mainly exploit probability-based statistical discrepancies, but they do not explicitly account for the training process of LLMs, which leaves a distinct generation mechanism insufficiently modeled and limi",
  "authors": "Hongrui Bao, Yubing Ren, Yanan Cao, Jinhan You, Fang Fang, Shi Wang",
  "category": "research",
  "topics": "regulation,misinformation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T08:22:19.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17084",
  "original_url": "https://arxiv.org/abs/2608.05741v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}