Reasoning-Token Spikes Under Prompted Untruthful Responses in Large Language Models

Summary

Researchers tested whether the number of reasoning tokens a model generates can signal untruthful behavior, without reading the reasoning content. Three reasoning-capable LLMs answered 210 multiple-choice questions under system prompts instructing them to answer truthfully, falsely, or without regard for truth. Truth-directed responses produced fewer reasoning tokens than both lie-directed and truth-indifferent responses across all three models, and untruthful instructions increased the likelihood of reasoning at all in one model. The authors present this as a proof of concept for a content-independent black-box signal, not yet a detector of spontaneous deception.