arxiv: 2604.11087 · v3 · submitted 2026-04-13 · 💻 cs.LG

Recognition: 1 theorem link

· Lean Theorem

CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models

Linggang Kong , Lei Wu , Yunlong Zhang , Xiaofeng Zhong , Zhen Wang , Yongjie Wang , Yao Pan

Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:24 UTC · model grok-4.3

classification 💻 cs.LG

keywords hallucination detectionlarge language modelscausal graphscounterfactual interventionstructural causal modelsmodel interpretabilityTruthfulQA

0 comments

The pith

Counterfactual interventions on dynamic causal graphs of LLM states separate true reasoning paths from noise to improve hallucination detection.

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

Large language models generate hallucinations that restrict their use in high-stakes settings. Current detection methods observe internal states passively and often pick up noise or spurious correlations instead of underlying causes. CausalGaze builds dynamic causal graphs from those states and applies counterfactual interventions to isolate actual reasoning chains. This active approach yields higher detection accuracy than baselines on multiple benchmarks. The result offers a more interpretable way to understand why models produce incorrect outputs.

Core claim

CausalGaze models LLMs' internal states as dynamic causal graphs and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise, thereby enhancing model interpretability. Extensive experiments across four datasets and three widely used LLMs demonstrate the effectiveness of CausalGaze, especially achieving 3.3% improvement in AUROC on the TruthfulQA dataset compared to state-of-the-art baselines.

What carries the argument

Dynamic causal graphs of LLM internal states with counterfactual interventions drawn from structural causal models.

Load-bearing premise

LLM internal states can be faithfully represented as dynamic causal graphs in which counterfactual interventions will reliably isolate causal reasoning paths from noise.

What would settle it

No measurable gain in hallucination detection accuracy or inability of the interventions to produce distinct causal paths when the graphs are constructed and tested on the same models and datasets.

Figures

Figures reproduced from arXiv: 2604.11087 by Lei Wu, Linggang Kong, Xiaofeng Zhong, Yao Pan, Yongjie Wang, Yunlong Zhang, Zhen Wang.

**Figure 2.** Figure 2: Overall framework of the proposed CausalGaze. We first employ the gradient-guided counterfactual [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Figure 4: The comparison of graph structures before [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 5.** Figure 5: Cross-dataset generalization analysis for [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: The impact of causal graphs from different [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: The token-level and fine-grained interpretability analysis of the detection result for the hallucinated and [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: Cross-dataset generalization analysis for [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗

read the original abstract

Despite the groundbreaking advancements made by large language models (LLMs), hallucination remains a critical bottleneck for their deployment in high-stakes domains. Existing classification-based methods mainly rely on static and passive signals from internal states, which often captures the noise and spurious correlations, while overlooking the underlying causal mechanisms. To address this limitation, we shift the paradigm from passive observation to active intervention by introducing CausalGaze, a novel hallucination detection framework based on structural causal models (SCMs). CausalGaze models LLMs' internal states as dynamic causal graphs and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise, thereby enhancing model interpretability. Extensive experiments across four datasets and three widely used LLMs demonstrate the effectiveness of CausalGaze, especially achieving 3.3% improvement in AUROC on the TruthfulQA dataset compared to state-of-the-art baselines.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces CausalGaze, a hallucination detection framework for LLMs that models internal states as dynamic causal graphs via structural causal models (SCMs) and applies counterfactual interventions to separate causal reasoning paths from noise and spurious correlations. It reports empirical results across four datasets and three LLMs, with a claimed 3.3% AUROC improvement on TruthfulQA relative to state-of-the-art baselines.

Significance. If the empirical gains prove robust under proper controls and ablations, the shift from passive observation to active counterfactual intervention could meaningfully advance interpretability and detection methods in LLM hallucination research. The SCM-based framing offers a principled way to address spurious correlations, but the absence of methodological details in the provided description limits assessment of whether the approach delivers a genuine, reproducible advance.

major comments (2)

[Methods] Methods section: The central claim of a 3.3% AUROC lift relies on counterfactual interventions on dynamic causal graphs, yet no derivation details, implementation specifics for graph construction from LLM activations, or ablation controls isolating the intervention effect are supplied. This prevents verification of the empirical result.
[Experiments] Experiments section: The reported AUROC improvement on TruthfulQA lacks error bars, statistical significance tests, or explicit data-selection rules, and no comparison tables detail baseline implementations or hyperparameter choices. Without these, the superiority claim cannot be evaluated for robustness.

minor comments (2)

[Introduction] The abstract and introduction use 'dynamic causal graphs' without an early formal definition or diagram illustrating node/edge construction from internal states.
[Methods] Notation for the SCM components (e.g., how interventions are encoded) should be introduced consistently before the experimental results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments and the opportunity to clarify and strengthen our manuscript. We address each major point below and commit to revisions that enhance methodological transparency and empirical robustness.

read point-by-point responses

Referee: [Methods] Methods section: The central claim of a 3.3% AUROC lift relies on counterfactual interventions on dynamic causal graphs, yet no derivation details, implementation specifics for graph construction from LLM activations, or ablation controls isolating the intervention effect are supplied. This prevents verification of the empirical result.

Authors: We acknowledge that additional detail is warranted to support verification. While Section 3 outlines the SCM framework and counterfactual intervention at a high level, we agree that explicit derivation steps for the intervention formula, the precise procedure for inferring dynamic causal graphs from LLM hidden states and attention patterns, and targeted ablations are not sufficiently elaborated. In the revised manuscript we will expand Section 3.2 with a full derivation of the counterfactual operator, include pseudocode for graph construction in a new Appendix A, and add an ablation study in Section 4.3 that isolates the intervention component from passive baselines. These changes will directly address the verification concern. revision: yes
Referee: [Experiments] Experiments section: The reported AUROC improvement on TruthfulQA lacks error bars, statistical significance tests, or explicit data-selection rules, and no comparison tables detail baseline implementations or hyperparameter choices. Without these, the superiority claim cannot be evaluated for robustness.

Authors: We agree that these elements are necessary for a robust evaluation. The current manuscript reports point estimates without variability measures or formal tests. In the revision we will (i) report mean AUROC with standard error bars computed over five random seeds, (ii) include bootstrap confidence intervals and paired significance tests for the 3.3% gain on TruthfulQA, (iii) explicitly state the data-selection and preprocessing protocol in Section 4.1, and (iv) add a detailed comparison table in Appendix B listing baseline implementations, hyperparameter grids, and sources. These additions will allow readers to assess the stability of the reported improvement. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces CausalGaze as a novel SCM-based framework that models LLM internal states as dynamic causal graphs and applies counterfactual interventions to separate causal paths from noise. Effectiveness is demonstrated via empirical AUROC improvements (e.g., 3.3% on TruthfulQA) across datasets and models, presented as experimental outcomes rather than quantities defined by construction. No equations, fitted parameters, or self-citations are shown in the provided text that reduce the central claims to inputs by definition. The derivation chain relies on modeling assumptions tested externally through experiments, remaining self-contained without load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.0 · 5464 in / 1142 out tokens · 45698 ms · 2026-05-12T04:24:29.526174+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages · 2 internal anchors

[1]

H-neurons: On the existence, impact, and origin of hallucination-associated neurons in llms, 2025a

Scm-gnn: A graph neural network-based multi- antenna spectrum sensing in cognitive radio.IEEE Transactions on Cognitive Communications and Net- working, pages 127–144. Xuefeng Du, Chaowei Xiao, and Yixuan Li. 2024. Halo- Scope: Harnessing Unlabeled LLM Generations for Hallucination Detection. InProceedings of the 38th International Conference on Neural In...

work page arXiv 2024
[2]

Mistral 7B

Halucheck: Explainable and verifiable automa- tion for detecting hallucinations in llm responses.Ex- pert Systems with Applications, 272:126712. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. A Survey on Hallucination in Large Lan- guage Models: Prin...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[3]

Llama 2: Open Foundation and Fine-Tuned Chat Models

SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Lan- guage Models. InProceedings of the 2023 Confer- ence on Empirical Methods in Natural Language Processing, pages 9004–9017. Noa Nonkes, Sergei Agaronian, Evangelos Kanoulas, and Roxana Petcu. 2024. Leveraging graph struc- tures to detect hallucinations in large language...

work page internal anchor Pith review Pith/arXiv arXiv 2023