On the Effect of Uncertainty on Layer-wise Inference Dynamics

A new study finds that uncertainty does not appear to alter the internal inference dynamics of large language models, challenging the feasibility of simple detection methods at runtime. The research, posted to arXiv by Sunwoo Kim, used the Tuned Lens technique to analyze how final prediction token probabilities evolve across model layers for both correct and incorrect outputs [1][2]. Across 11 datasets and 5 models, the layer-wise probability trajectories for certain and uncertain predictions were largely aligned, with both showing abrupt confidence increases at similar layers [1][2]. The study treated incorrect predictions as carrying higher epistemic uncertainty [1][2]. While the trajectories were aligned, the paper also presents evidence that more competent models may learn to process uncertainty differently, suggesting that scaling could alter the relationship [1][2]. The findings indicate that simplistic, surface-level methods for detecting uncertainty at inference time may not be reliable [1][2]. Large language models are machine learning systems with many parameters, trained on vast text corpora through self-supervised learning [9]. The internal mechanisms by which these models represent and process predictions remain an active area of research, particularly as developers seek to prevent hallucinations [1][2]. The Tuned Lens method used in the study is a variant of the Logit Lens, an interpretability tool that allows researchers to inspect a model's evolving predictions at each transformer layer [1][2]. The work arrives amid broader industry efforts to understand and control model outputs. Chinese firm DeepSeek, founded in July 2023, has drawn attention for its open-weight models that rival larger competitors at reportedly lower training costs [7]. Researchers such as Douwe Kiela, now a research scientist director at Google DeepMind, have previously advanced techniques like retrieval-augmented generation to ground model outputs in external knowledge [8]. The first version of Kim's paper was submitted in July 2025 at 106 KB; a revised version followed in June 2026 at 97 KB [1]. The study demonstrates how interpretability methods can be used to investigate the relationship between uncertainty and inference, rather than relying on output-level signals alone [1][2].