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This paper evaluates four lie detection methods for language models across prompted lying and trained model organisms, finding that activation- and logprob-based detectors drop sharply on trained model organisms while a chain-of-thought judge remains strong. It introduces new testbeds and the Did-You-Lie (DYL) follow-up probe method, releasing datasets and model organisms.