Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support

Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear. Existing work has documented prompt sensitivity, cognitive biases, and confidence miscalibration, but it is still unknown whet