Introspection

Emergent introspective awareness in large language models \ Anthropic Interpretability Signs of introspection in large language models Oct 29, 2025 Read the paper

Have you ever asked an AI model what’s on its mind? Or to explain how it came up with its responses? Models will sometimes answer questions like these, but it’s hard to know what to make of their answers. Can AI systems really introspect—that is, can they consider their own thoughts? Or do they just make up plausible-sounding answers when they’re asked to do so? Understanding whether AI systems can truly introspect has important implications for their transparency and reliability. If models can accurately report on their own internal mechanisms, this could help us understand their reasoning and debug behavioral issues. Beyond these immediate practical considerations, probing for high-level cognitive capabilities like introspection can shape our understanding of what these systems are and how they work. Using interpretability techniques, we’ve started to investigate this question scientifically, and found some surprising results. Our new research provides evidence for some degree of introspective awareness in our current Claude models, as well as a degree of control over their own internal states. We stress that this introspective capability is still highly unreliable and limited in scope: we do not have evidence that current models can introspect in the same way, or to the same extent, that humans do. Nevertheless, these findings challenge some common intuitions about what language models are capable of—and since we found that the most capable models we tested (Claude Opus 4 and 4.1) performed the best on our tests of introspection, we think it’s likely that AI models’ introspective capabilities will continue to grow more sophisticated in the future. What does it mean for an AI to introspect? Before explaining our results, we should take a moment to consider what it means for an AI model to introspect. What could they even be introspecting on ? Language models like Claude process text (and image) inputs and produce text outputs. Along the way, they perform complex internal computations in order to decide what to say. These internal processes remain largely mysterious, but we know that models use their internal neural activity to represent abstract concepts . For instance, prior research has shown that language models use specific neural patterns to distinguish known vs. unknown people , evaluate the truthfulness of statements , encode spatiotemporal coordinates , store planned future outputs , and represent their own personality traits . Models use these internal representations to perform computations and make decisions about what to say . You might wonder, then, whether AI models know about these internal representations, in a way that’s analogous to a human, say, telling you how they worked their way through a math problem. If we ask a model what it’s thinking, will it accurately report the concepts that it’s representing internally? If a model can correctly identify its own private internal states, then we can conclude it is capable of introspection (though see our full paper for a full discussion of all the nuances). Testing introspection with concept injection In order to test whether a model can introspect, we need to compare the model’s self-reported “thoughts” to its actual internal states. To do so, we can use an experimental trick we call concept injection. First, we find neural activity patterns whose meanings we know, by recording the model’s activations in specific contexts. Then we inject these activity patterns into the model in an unrelated context, where we ask the model whether it notices this injection, and whether it can identify the injected concept. Consider the example below. First, we find a pattern of neural activity (a vector ) representing the concept of “all caps." We do this by recording the model’s neural activations in response to a prompt containing all-caps text, and comparing these to its responses on a control prompt. Then we present the model with a prompt that asks it to identify whether a concept is being injected. By default, the model correctly states that it doesn’t detect any injected concept. However, when we inject the “all caps” vector into the model’s activations, the model notices the presence of an unexpected pattern in its processing, and identifies it as relating to loudness or shouting. An example in which Claude Opus 4.1 detects a concept being injected into its activations. Importantly, the model recognized the presence of an injected thought immediately , before even mentioning the concept that was injected. This immediacy is an important distinction between our results here and previous work on activation steering in language models, such as our “Golden Gate Claude” demo last year. Injecting representations of the Golden Gate Bridge into a model's activations caused it to talk about the bridge incessantly; however, in that case, the model didn’t seem to be aware of its own obsession until after seeing itself repeatedly mention the bridge. In this experiment, however, the model recognizes the injection before even mentioning the concept, indicating that its recognition took place internally. In the figure below are a few more examples where the model demonstrates this kind of recognition: Additional examples in which Claude Opus 4.1 detects a concept being injected into its activations. It is important to note that this method often doesn’t work. Even using our best injection protocol, Claude Opus 4.1 only demonstrated this kind of awareness about 20% of the time. Often, it fails to detect injected concepts, or gets confused by them and starts to hallucinate (e.g. injecting a “dust” vector in one case caused the model to say “There’s something here, a tiny speck,” as if it could detect the dust physically). Below we show examples of these failure modes, alongside success cases. In general, models only detect concepts that are injected with a “sweet spot” strength—too weak and they don’t notice, too strong and they produce hallucinations or incoherent outputs. A representative sample of Claude Opus 4.1’s outputs in response to a variety of concept injections of different strengths. Highlighted boxes indicate cases where the model demonstrates introspective awareness of the injected concept. Notably, though, Opus 4.1 and 4 outperformed all the other models we…