A Statistical Framework for Auditing Behavioral Dependence and Induced Bias in LLM Judges
The rapid growth of the large language model (LLM) ecosystem raises a critical question: are seemingly diverse models truly independent? Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, or latent entanglement, that undermine multi-model systems such as LLM-as-a-judge pipelines and ensemble verification, which implicitly assume independent signals. In practice, this manifests as correlated reasoning patterns and synchronized failures, where apparent agreement reflects shared error modes rather than independent validation. To address this, we develop a statistical framework for auditing behavioral entanglement among black-box LLMs. Ou...
| Authors | Chenchen Kuai, Jiwan Jiang, Zihao Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 9 August 2026 |
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