The confidence paradox in large language models — and what surgeons and hotel concierges already knew There's a paper that landed in April 2026 that should bother every developer building on top of LLMs. Kumaran et al., published in Nature Machine Intelligence by researchers at Google DeepMind and UCL, identified two competing biases in how large language models handle confidence. The first is choice-supportive bias : LLMs become more confident in answers simply because they gave them before. This is striking in a stateless model with no memory — the architecture has no mechanism for "liking" its own output, and yet the behavior is measurable. The second is hypersensitivity to contradiction : when challenged, LLMs overweight opposing advice two to three times more than a Bayesian ideal observer would, changing their minds far more often than the evidence warrants. Here's the part that should make you uncomfortable: these biases are asymmetric. Models don't comparably overweight advice that agrees with them.…