D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models

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Computer Science > Computation and Language

arXiv:2607.24586v1 (cs)

[Submitted on 27 Jul 2026]

Title: D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models

Authors: Bianca Raimondi , Davide Evangelista , Maurizio Gabbrielli , Elena Loli Piccolomini

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Abstract: Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model. We study hallucination detection from the geometry of hidden activations and introduce the D-Score, a simple spectral statistic computed from a single forward pass. For a fixed model, layer, and tolerance parameter, the D-Score counts how many singular directions of the hidden activation matrix have singular values that remain close to the leading one. We use this quantity as a hallucination score, classifying an input text as hallucinated when its D-Score is larger than a pre-defined quantity. The motivation is that, when a model processes a text that conflicts with information available in its own internal state, the hidden representation may encode both the asserted content and some form of count

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