Between the Layers Lies the Truth: Uncertainty Estimation in LLMs Using Intra-Layer Local Information Scores

Large language models (LLMs) are often confidently wrong, making reliable uncertainty estimation (UE) essential. Output-based heuristics are cheap but brittle, while probing internal representations is effective yet high-dimensional and hard to transfer. We propose a compact, per-instance UE method that scores cross-layer agreement patterns in internal representations using a single forward pass. Across three models, our method matches probing in-distribution, with mean diagonal differences of at most -1.8 AUPRC percentage points and +4.9 Brier score points. Under cross-dataset transfer, it consistently outperforms probing, achieving off-diagonal gains up to +2.86 AUPRC and +21.02 Brier points. Under 4-bit weight-only quantization, it remains robust, improving over probing by +1.94 AUPRC points and +5.33 Brier points on average. Beyond performance, examining specific layer--layer interactions reveals differences in how disparate models encode uncertainty. Altogether, our UE method offers a lightweight, compact means to capture transferable uncertainty in LLMs.