When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathways may remain uncertain, disagree in how quickly they sharpen, or commit to competing continuations before the output is produced. We introduce TriLens, a white-box detector that turns this intuition into a compact representation: at every layer, it reads the multi-head self-attention output, the feed-forward output, and the residual stream through the model’s own logit lens, then records only the entropy of each readout. The resulting 3L-dimensional trajectory describes how certainty forms across depth and across modules, without storing high-dimensional hidden states or sampling multiple generations. This simple signal yields a strong detector across instruction-tuned LLMs and QA benchmarks, and our analyses show that the three module-wise entropy trajectories provide complementary evidence. TriLens suggests that hallucination detection can benefit from tracking how internal computation settles, not only what the final layer predicts. The code is available at https://tosakaucw.github.io/TriLens/.

This paper studies the problem of misclassification detection for vision-language models (VLMs), which aims to identify samples wrongly assigned by the pre-trained VLM. Previous works usually focus on exploring the latent space and measuring pairwise similarity scores for confidence estimation. However, these similarity scores can be biased for boundary points with different fine-grained features. To address this issue, this paper proposes an approach named Distribution-aware Prototypical Calibration with Fine-grained Voting (DECENT). The core idea of DECENT is to utilize probabilistic distributions to characterize both the semantics of the test image and its corresponding class prototype. Specifically, we first conduct clustering within each class in the latent space to construct a distribution of fine grained prototypes. More importantly, multiple augmented views of the test image are considered as samples from its underlying semantic distribution. Then, we measure the distribution distance between the image’s underlying semantic distribution and its neighboring fine grained prototype distributions, which is aggregated via fine-grained voting into a distributional score. This score can be fused with the VLM’s semantic alignment score to significantly enhance misclassification detection sensitivity, or to improve overall classification robustness. Extensive experiments on benchmark datasets validate the dual effectiveness of the proposed DECENT.