Inducing state anxiety in LLM agents reproduces human-like biases in consumer decision-making (opens in new tab)

Large language models (LLMs) are rapidly evolving from text generators to autonomous agents, raising urgent questions about their reliability in real-world contexts. A central question is whether emotionally salient context can systematically steer LLM agents’ action policies, not only their text outputs, in applied tasks. Here, three advanced LLMs (ChatGPT-5, Gemini 2.5, Claude 3.5-Sonnet) performed a grocery shopping task under budget constraints, before and after exposure to anxiety-induci...