HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals

Abstract:Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments. These models nonetheless carry linearly separable truthfulness signals in their internal representations. Existing white-box detectors, however, collapse this evidence to isolated components or a single depth, discarding discriminative information distributed across the full forward pass. We introduce HalluTracer, a detection framework that reads and aggregates truthfulness evidence across every layer of the forward pass before the model emits any answer token. A geometric analysis reveals that the per-layer signals are weakly correlated, so that simple depth averaging suppresses layer-specific noise and captures nearly all linearly accessible information. Across six open-source language models and five hallucination benchmarks, HalluTracer consistently outperforms matched white-box baselines, with gains ranging from one to fourteen points. Collectively, our work recasts hallucination detection from a layer-selection problem into a depth-aggregation problem governed by the geometric sparsity of the truthfulness signal.

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

arXiv:2608.16353 [cs.CL]

(or arXiv:2608.16353v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.16353 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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