How reliable are LLMs when it comes to playing dice?

Probability and statistics form the backbone of countless real-world decisions, from medical diagnoses to financial modeling. This study probes whether large language models can genuinely reason about uncertainty or merely pattern-match their way through standard problems. The findings are sobering: while models excel at textbook-style probability questions, their performance collapses when problems are disguised or contain misleading cues. This has direct implications for anyone deploying LLMs in risk assessment, insurance, scientific research, or educational tools. If a model can be thrown off by superficial rephrasing, trusting it with probabilistic judgment in high-stakes domains becomes fundamentally questionable. Authors: Luca Avena, Gianmarco Bet, Bernardo Busoni Paper: https://arxiv.org/abs/2606.07515v1