{
  "id": 15645,
  "url": "https://arxiv.org/abs/2607.28636",
  "title": "Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges",
  "summary": "arXiv:2607.28636v1 Announce Type: cross Abstract: LLMs increasingly serve as automated judges, but their judgments remain vulnerable to cognitive biases. Existing mitigations mostly rely on prompt-driven debiasing, which is brittle across bias types, or human evaluation, which does not scale. We study \\emph{Chain-of-Models} (CoM), an automated audit pipeline in which a second model inspects the first model's reasoning trace before producing the final judgment. The key design question is whether",
  "authors": "Qian Wang, Zhanzhi Lou, Zhenheng Tang, Nuo Chen, Bingsheng He",
  "category": "research",
  "topics": "bias-fairness,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T04:00:00.000Z",
  "fetched_at": "2026-08-03T05:10:47.622Z",
  "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/15645",
  "original_url": "https://arxiv.org/abs/2607.28636",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}