The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice

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 this https URL.

19 pages, 7 figures, 20 tables. Accepted to EMNLP 2026 (Main Conference). Code: this https URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2608.28930 [cs.CL]

(or arXiv:2608.28930v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.28930 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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