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Evidence record 15645 · automatically gathered

Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges

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

Record details

Published: 3 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Transparency
Retrieved: 3 August 2026

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How to cite this record

ethics.ai (3 August 2026), “Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges,” evidence record 15645, https://ethics.ai/record/15645 (originally published by arXiv cs.CY).

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