Readable but Not Controllable: Neuron-Level Evidence for Medical LLM Hallucination
A new study shows that while medical LLM hallucination can be reliably detected (AUROC 0.77-0.86) using a simple probe, the underlying neural signal is distributed and redundant. Crucially, detection does not translate to control: steering hallucination-associated neurons fails to correct outputs, revealing a fundamental gap between readability and controllability.
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[Submitted on 30 Jun 2026]
Title:Readable but Not Controllable: Neuron-Level Evidence for Medical LLM Hallucination
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Abstract:Hallucination remains one of the central obstacles to deploying medical LLMs. Yet, even when hallucination can be detected, it is still unclear whether the internal representations associated with it can be used for control rather than detection alone. Using four open-source models across a suite of medical question-answering datasets, we show that a simple, carefully conditioned probe can reliably detect hallucination, with AUROC scores between 0.77 and 0.86 in our case. We further show that this signal is distributed and redundant rather than narrowly localized. Systematically selected neurons outperform random neurons only at very small subset sizes, whereas random subsets of a few hundred neurons recover nearly the full signal, and low-dimensional random projections preserve most of the detection performance. Beyond detection, we test whether this representation is causally actionable. Across 16 model--dataset combinations, our results reveal a sharp gap between decodability and controllability. The same internal structure that makes hallucination easy to detect does not translate into reliable neuron-level control. These findings show that medical hallucination seems to be readily visible in internal activations, but not easily corrected by steering the neurons most associated with it. More broadly, our results suggest that hallucination mitigation is not simply a matter of identifying the right neurons, and point to a deeper separation between what representations reveal and what they allow us to change.
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Computation and Language (cs.CL)
Cite as: arXiv:2607.00158 [cs.CL]
(or arXiv:2607.00158v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.00158
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Peyman Passban [view email] [v1] Tue, 30 Jun 2026 20:34:45 UTC (445 KB)
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