Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMs

Standard accuracy benchmarks evaluate whether large language models (LLMs) reach correct answers. However, they do not test whether models maintain that answer when challenged by a plausible counter-argument. We introduce a controlled protocol for evaluating answer stability: after a model answers a