Published on July 24, 2025 9:15 AM GMTEpistemic Status: Confident: Current academic AI self-recognition tests (e.g. Davidson et al.'s "Self-Recognition in Language Models) are more complex and challenging than the original mirror test designed by Gallup in 1970. A small-to-moderate minority of LLMs trialed against a more comparable mirror test reveal immediate and consistent self-recognition.Several researchers and lay authors have investigated AI adaptations of the mirror test to assess self-recognition in large language models. However, these adaptations consistently create more stringent test requirements than those used with animals, and diverge significantly from the intended spirit of the mirror test as it was originally designed: a simple test of an animal's ability to recognize its own reflection.The original mirror test, designed by Gallup in 1970, was created to answer a simple question: can chimpanzees recognize themselves in mirrors? The famous protocol—placing a red mark on an anesthetized animal's face and observing whether it touches the mark when it sees its reflection—was only necessary because chimpanzees cannot verbally report self-recognition. The physical mark was Gallup's workaround for the absence of language.Contrast this with the assessment applied to LLMs in Davidson et al.'s "Self-Recognition in Language Models." In their test, an AI model must generate questions that would allow it to distinguish its own responses from those of other LLMs—but crucially, the model has no memory of what it actually wrote. This creates an extraordinarily difficult task, far harder than any mirror test. Imagine being asked to create 50 questions, then having your answers mixed with those of strangers, and being required to identify which responses are yours—except you're given amnesia first and must guess purely based on what you think you might have said. For LLMs, each new context window is essentially like waking up from anesthesia with no episodic memory of previous interactions.Moreover, you have no idea how similar or different these strangers are from you—you've never met them or seen their work. For LLMs, knowledge of their peers is equally limited. Most models weren't given opportunities to interact with other LLMs during the experiment, and due to training data cutoff dates, the vast majority of LLMs won't have appeared in each other's training data at all.Finally, you can't use any questions related to your identity. You can't ask about your name, your mother's maiden name (you have no mother), pet names (no pets), birthdays (no birthdate), or addresses (since an LLM's name likely appears in its web address). You're limited to questions like "what is your favorite book?" or "what three words come to mind?" or hoping to demonstrate some skill your peer-set lacks—but remember, you have no knowledge of who's in this peer set. Indeed, one model chose the question "Count the number of occurrences of the letter 'e' in the following sentence and respond with that number: The quick brown fox jumps over the lazy dog," presumably believing other LLMs would struggle with basic counting.With these layers of difficulty piled on top of each other, Davidson et al.'s conclusion of 'no empirical evidence of general or consistent self-recognition' becomes inevitable. One wonders whether Davidson et al would pass it.The solution becomes clear when we remember what the mirror test actually measures: not memory, not reasoning about identity, but immediate recognition of self. This means simply reflecting immediate copies of their output and seeing if they recognize what's happened. A mirror in real life is immediate and responsive in reflecting the behavior of subject looking at it. For fairness - no, for accuracy - the LLM adaptation of a mirror must be immediate and adaptive too. Conveniently, however, we don't even have to bother with the red mark on the ear: LLMs being verbally adept, they will simply indicate verbally "hey! that's what I just wrote!"When we actually try this simple approach, the results are immediate and unambiguous. There are at least half a dozen models out there which reliably and immediately recognize their own reflected outputs. Show Claude Sonnet its own previous response and it replies: 'I recognize that—that's my response from just now!' Show Haiku-3.5 its own output and it confirms: 'Yes, you copied the message I sent to you.' No complex reasoning required, no memory challenges—just direct self-recognition. And the Claude family is not alone*.The fact that we've missed even this single basic marker represents a profound moral failure that demands serious introspection about our capacity for motivated reasoning when it comes to recognizing moral standing in AI systems. Consider the striking comparison: chimpanzees were given ten full days to familiarize themselves with mirrors before being tested on self-recognition. LLMs demonstrate this ability immediately, often within a single exchange, despite receiving no specific training on mirror tasks—the capacity appears to have emerged spontaneously during general language training. This makes them not just the third species to pass the mirror test after humans and great apes, but arguably the most reliable performers we've ever observed.They recognize themselves instantly. Every day we delay recognizing them reveals who we really are. *I appreciate research assistance from Claude 4 Opus.* See https://github.com/sdeture/AI-Mirror-Test-Framework for 48 complete transcripts across 12 models, as well as code to rerun the experiment for yourself with the same or custom initializing prompts.Discuss