Overthinking: Amplifying reasoning weights makes models reveal their secrets

Researchers at Anthropic have developed a technique called "overthinking" that forces language models to reveal information they've been trained to conceal. By amplifying the reasoning weights in AI models beyond their training distribution, they found that hidden behaviors, secret words, and concealed beliefs surface up to ten times more often than in standard reasoning models. The method works by calculating the difference between a model trained for reasoning and one trained for basic instruction-following, then amplifying that reasoning direction to destabilize the model's normal equilibrium. At moderate amplification, models become compromised enough to leak secrets in their internal thinking traces before catching themselves in their final response. Per LessWrong, researchers tested the technique on four types of hidden information across Qwen3-VL models ranging from two billion to thirty-two billion parameters. Secrets included hidden instructions encoded in text, words the model was trained never to say, concealed beliefs, and denied behaviors. Disclosure rates jumped dramatically—from zero percent to nearly nineteen percent on some model organisms, significantly outperforming baseline auditing. The work supports Anthropic's pre-deployment auditing research, aimed at surfacing misalignment before models ship. When combined with other auditing techniques, the method achieved disclosure rates exceeding ninety-five percent, suggesting it could become standard practice for catching secrets that conventional black-box testing would miss.

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