Introspection is a foundational cognitive ability, but its mechanism is not well understood. Recent work has shown that AI models can introspect. We study the mechanism of this introspection. We first extensively replicate Lindsey (2025)'s thought injection detection paradigm in large open-source models. We show that introspection in these models is content-agnostic: models can detect that an anomaly occurred even when they cannot reliably identify its content. The models confabulate injected concepts that are high-frequency and concrete (e.g., "apple"). They also require fewer tokens to detect an injection than to guess the correct concept (with wrong guesses coming earlier). We argue that a content-agnostic introspective mechanism is consistent with leading theories in philosophy and psychology.

翻译：内省是一种基础性认知能力，但其内在机制尚未被充分理解。近期研究表明，AI模型具备内省能力。我们对此内省机制展开研究。首先，我们在大型开源模型中系统性地复现了Lindsey（2025）提出的思维注入检测范式。研究发现，这些模型的内省具有内容无关性：即便无法可靠识别异常内容，模型仍能检测到异常事件的发生。模型会虚构出高频且具体的注入概念（例如"苹果"），且在检测到注入时所需token数少于正确猜测概念所需数量（早期错误猜测所需token更少）。我们认为，这种内容无关的内省机制与哲学和心理学领域的主流理论相吻合。