{
  "id": 3021,
  "url": "https://arxiv.org/abs/2607.11451v1",
  "title": "ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk",
  "summary": "Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relat",
  "authors": "Xiaolin Wen, Feng Liang, Yuanye Ma, Qishuang Fu, Zhengyu Sun, Feng Zhu, Can Liu, Yong Wang",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T12:02:50.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/3021",
  "original_url": "https://arxiv.org/abs/2607.11451v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}