Between the Layers Lies the Truth: Uncertainty Estimation in LLMs Using Intra-La

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of

arXiv:2606.27679v1 Announce Type: cross Abstract: Probe-based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models

arXiv:2604.00445v1 Announce Type: new Abstract: Uncertainty estimation (UE) aims to detect hallucinated outputs of large language models (LLMs) to improve their reliability.

arXiv:2604.26233v1 Announce Type: new Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range

arXiv:2608.00422v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making

arXiv:2603.26838v1 Announce Type: new Abstract: This paper surveys uncertainty-aware explainable artificial intelligence (UAXAI), examining how uncertainty is incorporated into

arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for

arXiv:2603.19118v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying reasoning language models, yet remains poorly understood under extended