Internal-state probes enable truthfulness prediction before a large language model generates an answer. When detectors change both the layers they read and the rules used to combine them, the source of improved prediction becomes difficult to identify. We separate these choices and find that retaining more layers impro...
Zhi-Hao Guo, Zong-Han Wu, Huan Huo et al.· 0 citations
Looped Transformers offer a parameter-efficient route to test-time scaling by reusing shared layers for iterative latent reasoning. However, additional iterations can reduce support for a reference answer, leaving unclear whether an update's direction is locally unhelpful or its full displacement moves too far. We stud...
Zhi-Hao Guo, Zong-Han Wu, Hai-Zhou Du et al.· 1 citation
HalluTracer is introduced, a detection framework that reads and aggregates truthfulness evidence across every layer of the forward pass before the model emits any answer token, recasts hallucination detection from a layer-selection problem into a depth-aggregation problem governed by the geometric sparsity of the truth...
Zhi-Hao Guo, Zong-Han Wu, Huan Huo et al.· 0 citations
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