arXiv:2607.00158v1 Announce Type: new 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 n
The proliferation of LLMs in specialized domains like medicine makes understanding and mitigating their failure modes increasingly critical for safe deployment.
The ability to detect and potentially control hallucination at the neuron level could unlock significantly more reliable and trustworthy AI applications in high-stakes environments.
Approaches to AI safety and reliability may shift from external validation to internal, real-time monitoring and control of model reasoning.
- · AI safety researchers
- · Medical AI developers
- · Patients
- · Trustworthy AI platforms
- · Unreliable general-purpose LLMs
- · AI systems lacking internal transparency
Improved safety and reliability of medical LLMs through enhanced hallucination detection.
Development of new AI architectures or training methodologies that allow for greater internal interpretability and control over model outputs.
Accelerated adoption of AI in sensitive domains, leading to widespread automation in areas previously considered too risky for AI deployment.
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