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        <title>The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice</title>
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    <abstract>Hidden-state probes effectively detect LLM hallucinations, but the geometry of the signal remains poorly characterized, driving increasingly complex probe architectures. Across three 7B-scale models and three datasets in a paired-example paradigm, we find the signal overwhelmingly dominated by a single mean-shift component, and removing this direction collapses detection to chance. Shrinkage linear discriminant analysis closes about 73% of the gap between 1D and full-dimensional classifiers, so apparent architectural complexity largely reflects high-dimensional covariance estimation difficulty rather than exploitable non-linearity. A simple L2-regularized logistic regression (0.952 AUROC) bounds or outperforms twelve controlled architectural alternatives, and our multi-layer aggregation exceeds CLAP cross-layer attention probing under matched paradigm. Because the signal spans a contiguous layer band, LayerMix aggregates it to match oracle-layer performance without oracle access. Our claims characterize the geometry within the controlled paired-example paradigm. Our code is available at https://github.com/js-lee-AI/LayerMix.</abstract>
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%0 Conference Proceedings
%T The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice
%A Lee, Jungseob
%A Seo, Jaehyung
%A Lim, Heuiseok
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lee-etal-2026-hallucination
%X Hidden-state probes effectively detect LLM hallucinations, but the geometry of the signal remains poorly characterized, driving increasingly complex probe architectures. Across three 7B-scale models and three datasets in a paired-example paradigm, we find the signal overwhelmingly dominated by a single mean-shift component, and removing this direction collapses detection to chance. Shrinkage linear discriminant analysis closes about 73% of the gap between 1D and full-dimensional classifiers, so apparent architectural complexity largely reflects high-dimensional covariance estimation difficulty rather than exploitable non-linearity. A simple L2-regularized logistic regression (0.952 AUROC) bounds or outperforms twelve controlled architectural alternatives, and our multi-layer aggregation exceeds CLAP cross-layer attention probing under matched paradigm. Because the signal spans a contiguous layer band, LayerMix aggregates it to match oracle-layer performance without oracle access. Our claims characterize the geometry within the controlled paired-example paradigm. Our code is available at https://github.com/js-lee-AI/LayerMix.
%U https://aclanthology.org/2026.emnlp-main.1626/
%P 36986-37004

Markdown (Informal)

[The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice](https://aclanthology.org/2026.emnlp-main.1626/) (Lee et al., EMNLP 2026)

• The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice (Lee et al., EMNLP 2026)

ACL

• Jungseob Lee, Jaehyung Seo, and Heuiseok Lim. 2026. The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 36986–37004, Budapest, Hungary. Association for Computational Linguistics.