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ARXIV:2604.07650 · LLM ANALYSIS · SUBMITTED 10 APR · 20:31 UTC · FRESHNESS STALE
ARXIV:2604.07650LLM ANALYSISSUBMITTED 10 APR · 20:31 UTCFRESHNESS STALEChenchen Kuai · Jiwan Jiang · Zihao Zhu · Hao Wang · Keshu Wu · Zihao Li · +5 at arXiv
A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy.
Opportunity summary
Pain A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy. Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, latent entanglement, that undermine multi-model…
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, latent entanglement,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Finally, we demonstrate a practical use case of entanglement through de-entangled verifier ensemble reweighting. Code availability is flagged in the production record; the public…
LLM Analysis moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy.
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10.48550/arXiv.2604.07650A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy.
Abstract
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, 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. Our approach introduces a multi-resolution hierarchy that characterizes the joint failure manifold through two information-theoretic metrics: (i) a Difficulty-Weighted Behavioral Entanglement Index, which amplifies synchronized failures on easy tasks, and (ii) a Cumulative Information Gain (CIG) metric, which captures directional alignment in erroneous responses. Through extensive experiments on 18 LLMs from six model families, we identify widespread behavioral entanglement and analyze its impact on LLM-as-a-judge evaluation. We find that CIG exhibits a statistically significant association with degradation in judge precision, with Spearman coefficient of 0.64 (p < 0.001) for GPT-4o-mini and 0.71 (p < 0.01) for Llama3-based judges, indicating that stronger dependency corresponds to increased over-endorsement bias. Finally, we demonstrate a practical use case of entanglement through de-entangled verifier ensemble reweighting. By adjusting model contributions based on inferred independence, the proposed method mitigates correlated bias and improves verification performance, achieving up to a 4.5% accuracy gain over majority voting.
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PROBLEM
A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy. Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, latent entanglement, that undermine...
METHOD
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, latent entanglement, that undermine...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Finally, we demonstrate a practical use case of entanglement through de-entangled verifier ensemble reweighting. Code availability is flagged in the production record; the public repository link still nee...
WHY NOW
LLM Analysis moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy. Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, latent entanglement, that undermine multi-model systems such as LLM-as-a-judge pipelines and ensemble verification, which implicitly assume independent signals.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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, latent entanglement, that undermine multi-model systems such as LLM-as-a-judge pipelines and ensemble verification, which implicitly assume independent signals.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Finally, we demonstrate a practical use case of entanglement through de-entangled verifier ensemble reweighting. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Analysis moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A statistical framework to audit and mitigate behavioral entanglement in large language models, improving ensemble verification accuracy.
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