Theresa Winner | Searcharxiv

arXiv2026

Guardians of the Regime: Secret Police Formation in Autocracies

Autocrats use secret police to stay in power, as these organizations deter and suppress opposition to their rule. Existing research shows that secret police succeed at this but, surprisingly, also that they are not as ubiquitous in autocracies as one may assume, existing in fewer than half of autocratic country-years. We thus explore under which conditions secret police emerge in dictatorships. For this purpose, we develop a theoretical framework for potential predictors and apply statistical variable selection techniques to identify which of several candidate variables extracted from the literature on state security forces and authoritarian survival hold explanatory power. Our results highlight that secret police are more likely to emerge when rulers face structural, regime-external threats, such as organised anti-system mobilisation and international rivals, or witness successful regime-internal contestation abroad that hints at similar threats at home. But additionally, we find that rulers must have sufficient material resources and personalised power to establish secret police. This research contributes to our understanding of autocrats' institutional choices and authoritarian politics.

cs.CR↗

arXiv2025

Systematic Evaluation of Uncertainty Estimation Methods in Large Language Models

Large language models (LLMs) produce outputs with varying levels of uncertainty, and, just as often, varying levels of correctness; making their practical reliability far from guaranteed. To quantify this uncertainty, we systematically evaluate four approaches for confidence estimation in LLM outputs: VCE, MSP, Sample Consistency, and CoCoA (Vashurin et al., 2025). For the evaluation of the approaches, we conduct experiments on four question-answering tasks using a state-of-the-art open-source LLM. Our results show that each uncertainty metric captures a different facet of model confidence and that the hybrid CoCoA approach yields the best reliability overall, improving both calibration and discrimination of correct answers. We discuss the trade-offs of each method and provide recommendations for selecting uncertainty measures in LLM applications.

cs.CL↗