arxiv: 2604.07650 · v1 · submitted 2026-04-08 · 💻 cs.AI · cs.CL

Recognition: unknown

How Independent are Large Language Models? A Statistical Framework for Auditing Behavioral Entanglement and Reweighting Verifier Ensembles

Chenchen Kuai, Chenxi Liu, Hao Wang, Jiwan Jiang, Keshu Wu, Yang Zhou, Yunlong Zhang, Zhengzhong Tu, Zhiwen Fan, Zihao Li, Zihao Zhu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:08 UTC · model grok-4.3

classification 💻 cs.AI cs.CL

keywords behavioral entanglementLLM independenceLLM-as-a-judgeensemble reweightinginformation gainmodel auditingcorrelated failuresverifier ensembles

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The pith

Large language models exhibit behavioral entanglement that undermines independence assumptions in judging and ensemble systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models from different families often fail in correlated ways because of shared pretraining data, distillation, and alignment steps. The paper introduces a statistical auditing framework with two information-theoretic metrics that quantify this hidden dependency from black-box outputs alone. Experiments across 18 models show that higher entanglement, especially the Cumulative Information Gain measure, strongly associates with reduced precision when one model judges another. The work then demonstrates a reweighting method for verifier ensembles that accounts for these dependencies and improves accuracy over simple majority voting.

Core claim

Behavioral entanglement among black-box LLMs can be measured through a multi-resolution hierarchy of the joint failure manifold using a Difficulty-Weighted Behavioral Entanglement Index that amplifies synchronized failures on easy tasks and a Cumulative Information Gain metric that tracks directional alignment in errors. These metrics reveal widespread entanglement across model families and a statistically significant link between higher CIG scores and degraded judge precision, with Spearman correlations of 0.64 and 0.71 in the tested cases. Reweighting model contributions in ensembles according to inferred independence mitigates correlated bias and delivers accuracy gains up to 4.5 percent.

What carries the argument

The multi-resolution hierarchy characterizing the joint failure manifold via the Difficulty-Weighted Behavioral Entanglement Index, which weights synchronized failures by task ease, and the Cumulative Information Gain metric, which captures alignment in erroneous responses.

Load-bearing premise

The proposed metrics computed from black-box outputs on a fixed task set accurately capture latent behavioral dependencies rather than surface-level output correlations.

What would settle it

A replication experiment on new tasks and models that finds no statistically significant Spearman correlation between Cumulative Information Gain and judge precision degradation, or that finds no accuracy improvement from the reweighting method, would falsify the central claims.

Figures

Figures reproduced from arXiv: 2604.07650 by Chenchen Kuai, Chenxi Liu, Hao Wang, Jiwan Jiang, Keshu Wu, Yang Zhou, Yunlong Zhang, Zhengzhong Tu, Zhiwen Fan, Zihao Li, Zihao Zhu.

**Figure 2.** Figure 2: Behavioral entanglement structure of LLMs on MMLU-Pro: (a) difficulty-weighted [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Diagnosing Evaluation Bias arising from the behavior entanglement: [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Calibrated weights. Through experiments using three judge models as a verifier ensemble, we evaluate the effectiveness of different aggregation strategies. We compare against two baselines: (1) selecting the best single model based on validation accuracy, and (2) majority voting with equal weights, as well as an accuracy-calibrated reweighting approach. Results on the MMLU test set show that ensemble me… view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces two new metrics to audit LLM output dependencies and shows they track with judge degradation plus a 4.5% reweighting gain, but the metrics risk capturing surface agreement patterns more than deep latent entanglement.

read the letter

The paper's main move is to define two information-theoretic metrics, Difficulty-Weighted Behavioral Entanglement Index and Cumulative Information Gain, that turn black-box output patterns into scores for how much models are behaviorally tied together. They apply the scores to 18 models across six families, report Spearman correlations of 0.64 and 0.71 between CIG and drops in judge precision, and then use the scores to reweight a verifier ensemble for a 4.5% accuracy improvement over majority voting. That combination of auditing tool plus downstream fix is the concrete piece that stands out. The experiments give a broad enough sample to make the correlations worth noticing, and the reweighting example shows a direct path from the metrics to a usable change in ensemble performance. The focus on black-box settings also matches how most people actually use these models today. The softer part is whether the metrics really isolate the kind of entanglement that comes from shared pretraining or distillation. Both are built from observed agreements and errors on a fixed task set, so they could be driven by common phrasing habits, refusal styles, or task-induced similarities that would appear even in unrelated models. The abstract gives the correlations and p-values but does not lay out the exact derivations or any controls for those surface factors, and there are no error bars shown. If the full methods section does not include ablations against simpler agreement baselines or checks on task selection, the causal story from entanglement to over-endorsement bias stays thinner than the numbers suggest. The reweighting gains are interesting but inherit the same uncertainty. This is the kind of work that matters for people running LLM-as-a-judge pipelines or multi-model verifiers at scale. Anyone who needs to know whether their ensemble is getting independent signals would find the auditing approach relevant. It deserves a serious referee because the problem is real, the empirical signals are reported clearly enough to check, and the practical use case is straightforward to test. I would send it for review.

Referee Report

3 major / 1 minor

Summary. The paper proposes a statistical framework to audit behavioral entanglement in black-box LLMs via two new information-theoretic metrics: a Difficulty-Weighted Behavioral Entanglement Index that amplifies synchronized failures on easy tasks, and a Cumulative Information Gain (CIG) metric that captures directional alignment in erroneous responses. Experiments across 18 LLMs from six families identify widespread entanglement, report Spearman correlations of 0.64 (p<0.001) and 0.71 (p<0.01) between CIG and judge precision degradation for GPT-4o-mini and Llama3-based judges respectively, and demonstrate a reweighting scheme for verifier ensembles that yields up to 4.5% accuracy gain over majority voting.

Significance. If the metrics can be shown to isolate latent dependencies induced by shared pretraining or distillation rather than surface-level output correlations, the framework would offer a practical tool for improving the reliability of LLM-as-a-judge pipelines and multi-model verification systems. The scale of the evaluation (18 models across six families) and the concrete reweighting application are strengths that could make the work a useful reference for ensemble design, provided the statistical claims are placed on firmer ground.

major comments (3)

[Abstract] Abstract: The reported Spearman coefficients (0.64 and 0.71) and associated p-values are presented without error bars, confidence intervals, sample-size details (number of tasks or judges), or mention of multiple-comparison correction; these omissions are load-bearing because the central claim is a statistically significant association between CIG and judge-precision degradation.
[Metrics definitions] Metrics definitions (presumably §3): The Difficulty-Weighted Behavioral Entanglement Index and CIG are computed exclusively from black-box agreement patterns on a fixed task set, yet no derivation or control experiment is supplied to demonstrate that they isolate directional entanglement from shared pretraining rather than generic error-mode overlap or task-induced response similarity; this assumption is load-bearing for both the causal interpretation of the correlation results and the justification for the reweighting method.
[Experimental results] Experimental results (presumably §4–5): No details are given on task selection criteria, statistical controls, or reproducibility (no code or data release), despite the headline accuracy gain of 4.5% and the correlation findings resting entirely on these unreproduced experiments.

minor comments (1)

[Abstract] The abstract introduces a 'multi-resolution hierarchy' without a brief clarifying phrase, which could be added for immediate readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which identifies key areas where additional statistical detail, clarification of metric assumptions, and experimental transparency will strengthen the manuscript. We address each major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: The reported Spearman coefficients (0.64 and 0.71) and associated p-values are presented without error bars, confidence intervals, sample-size details (number of tasks or judges), or mention of multiple-comparison correction; these omissions are load-bearing because the central claim is a statistically significant association between CIG and judge-precision degradation.

Authors: We agree that the presentation of the Spearman correlations requires additional statistical support. In the revised manuscript we will report bootstrap-derived 95% confidence intervals for both coefficients, explicitly state the sample sizes (number of tasks and number of judges), and note that no multiple-comparison correction was applied because only two primary associations were examined. These details will appear in both the abstract and the results section. revision: yes
Referee: [Metrics definitions] Metrics definitions (presumably §3): The Difficulty-Weighted Behavioral Entanglement Index and CIG are computed exclusively from black-box agreement patterns on a fixed task set, yet no derivation or control experiment is supplied to demonstrate that they isolate directional entanglement from shared pretraining rather than generic error-mode overlap or task-induced response similarity; this assumption is load-bearing for both the causal interpretation of the correlation results and the justification for the reweighting method.

Authors: The metrics are constructed from observable agreement statistics with explicit weighting by task difficulty and directional error alignment; their information-theoretic motivation is given in §3. We acknowledge, however, that a formal derivation isolating pretraining-induced latent dependencies from surface-level error overlap is not provided and would be difficult to obtain under a purely black-box protocol. We will add a limitations subsection that discusses this assumption and include a new control comparison of entanglement scores between same-family versus cross-family model pairs to supply empirical grounding for the reweighting application. revision: partial
Referee: [Experimental results] Experimental results (presumably §4–5): No details are given on task selection criteria, statistical controls, or reproducibility (no code or data release), despite the headline accuracy gain of 4.5% and the correlation findings resting entirely on these unreproduced experiments.

Authors: We will expand §§4–5 with explicit task-selection criteria (diversity across reasoning, knowledge, and safety benchmarks together with difficulty stratification), a description of the statistical controls (permutation tests and randomization of task order), and full reproducibility information. We also commit to releasing the complete codebase and processed evaluation data in a public repository upon acceptance. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the statistical framework or reweighting demonstration

full rationale

The Difficulty-Weighted Behavioral Entanglement Index and Cumulative Information Gain (CIG) are defined directly from black-box model outputs on a fixed task set. The reported Spearman correlations (0.64 and 0.71) are empirical associations between these metrics and judge precision degradation, not reductions by construction. The 4.5% accuracy gain from reweighting is a demonstrated improvement over majority voting based on inferred independence, without evidence that it reduces to fitted parameters within the same data in a self-definitional way. No self-citations or ansatzes are load-bearing in the provided abstract and description. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claims rest on the assumption that output correlations on a chosen task distribution reflect deeper behavioral entanglement; no free parameters are explicitly named in the abstract, but the weighting in the Difficulty-Weighted index and the definition of CIG implicitly introduce tunable elements whose values are not reported.

axioms (1)

domain assumption Black-box output agreement on a fixed benchmark set is a sufficient proxy for latent model dependence induced by shared training data or pipelines.
Invoked throughout the abstract to justify the metrics and the judge-precision correlation.

invented entities (2)

Difficulty-Weighted Behavioral Entanglement Index no independent evidence
purpose: Quantify synchronized failures with emphasis on easy tasks
New metric introduced to amplify easy-task correlations; no independent evidence outside the paper.
Cumulative Information Gain (CIG) no independent evidence
purpose: Capture directional alignment in erroneous responses
New metric for measuring how one model's errors predict another's; no independent evidence outside the paper.

pith-pipeline@v0.9.0 · 5645 in / 1461 out tokens · 42902 ms · 2026-05-10T17:08:17.416935+00:00 · methodology

discussion (0)

Forward citations

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Instruct

A Experiment Settings A.1 Selected models We select 18 models from the GPT, Claude, Qwen, Llama, Gemini, and DeepSeek families to examine potential entanglement both within and across model families. Specifically, the selected models are ChatGPT-5 (Singh et al., 2025), GPT-4o (OpenAI, 2024), GPT-4o-mini (Hurst et al., 2024), GPT-oss-20B (Agarwal et al., 2...

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