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How Independent are Large Language Models? A Statistical Framework for Auditing Behavioral Entanglement and Reweighting Verifier Ensembles
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research proposes a statistical framework to audit hidden behavioral dependencies (latent entanglement) between LLMs, impacting multi-model systems.
Open sourceOneBench interpretation
Institutional assessment
So what
Correlated failures in LLM ensembles due to hidden dependencies increase concentration risk in G-SIB multi-model deployments and demand a new audit framework.
Do what
This research suggests existing model validation frameworks for multi-LLM systems must evolve to detect latent entanglement, impacting future MLOps tooling and risk assessments.