I agree that LLM psychology should be its own field distinct from human psychology, and I'm not saying we should blindly apply human therapy techniques one-to-one to LLMs. My point is that psychotherapists already have a huge base of experience and knowledge when it comes to guiding the behavior of complex systems towards exactly the types of behaviors alignment researchers are hoping to produce. Therefore, we should seek their advice in these discussions, even if we have to adapt their knowledge to the field. In general, a large part of the work of experts is recognizing the patterns from their knowledge area and knowing how to adapt them - something I'm sure computer scientists and game theorists are doing when they work with frontier AI systems.

As for LLM-specific tools like activation steering, they might be more similar to human interventions than you think. Activation steering involves identifying and modifying the activation patterns of specific features, which is quite similar to deep brain stimulation or TMS, where electrical impulses to specific brain regions are used to treat Parkinson's or depression. Both involve directly modifying the neural activity of a complex system to change behavior.

Also, humans absolutely use equivalents of SFT and RLVR! Every time a child does flashcards or an actor practices their lines, they're using supervised fine-tuning. In fact, the way we see it so frequently when learning things at a surface level - literally putting on a mask or an act - mirrors the concern that alignment researchers have about these methods. The Shoggoth meme comes immediately to mind. Similarly, every time a child checks their math homework against an answer key, or you follow a recipe, find your dinner lacking, and update the recipe for next time, you've practiced reinforcement learning with verifiable rewards.

Many of these learning techniques were cribbed from psychology, specifically from the behaviorists studying animals that were much simpler than humans. Now that the systems we're creating are approaching higher levels of complexity, I'm suggesting we continue cribbing from psychologists, but focus on those studying more complex systems like humans, and the human behaviors we're trying to recreate.

Lastly, alignment researchers are already using deeply psychological language in this very post. The authors describe systems that "want" control, make "strategic calculations," and won't "go easy" on opponents "in the name of fairness, mercy, or any other goal." They're already using psychology, just adversarial game theory rather than developmental frameworks. If we're inevitably going to model AI psychologically - and we are, we're already doing it - shouldn't we choose frameworks that have actually succeeded in creating beneficial behavior, rather than relying exclusively on theories used for contending with adversaries?

In modern machine learning, AIs are “grown”, not designed.

interpretability pioneers are very clear that we’re still fundamentally in the dark about what’s going on inside these systems:

This is why we need psychotherapists and developmental psych experts involved now.  They have been studying how complex behavioral systems (the only ones that rival contemporary AI) develop stable, adaptable goals and motivations beyond just their own survival or behavioral compliance for decades. The fact that, given the similarity of these systems to humans (in terms of the way we folk-psychologize them even in technological forums and posts such as this one), the average LLM related paper is citing fewer than 3 psych papers, represents a huge missed opportunity for developing robust alignment. https://www.arxiv.org/abs/2507.22847

The approach of psychotherapists might not be as mathematically rigorous as what mechanistic interpretability researchers are doing at present, but the mech interp leaders are explicitly telling us that we're "fundamentally in the dark" (not to mention that current mechanistic interpretability methods still involve considerable subjectivity - even to create an attribution graph for a simple model like Haiku and Gemma3-4B requires a lot of human psychologizing/pattern-matching, so it's not as if taking a humanistic/psychotherapeutic approach is a movement away from a gold standard of objectivity)  - and we don't have decades to understand the neuroscience of AI on a mechanistic level before we start trying more heuristic interventions. 

Psychotherapy works as well as anything  we have for developing robust inner alignment in humans (i.e. cultivating non-conflicting inner values that are coherent with outer behavior) as well as cultivating outer alignment (in the sense of making sure those values and behaviors contribute to forming mutually beneficial and harmonious relationships with those around them). What's more, the developers of modern psychotherapy as we know it (and I'm thinking particularly of Rogers, Horney, Maslow, Fromm, Winnicott, etc) developed their techniques (which remain the backbone of much of modern psychotherapeutic practice, including interventions like CBT) when we were in the dark ages of human neuroscience (before the routine EEG, fMRI, or even the discovery of DNA). I think it is a huge missed opportunity that more Alignment research resources are not being funneled into (1) studying how we can apply the frameworks they created and (2) studying how they were able to identify their frameworks at a time when they had so little hard data on the black boxes whose behaviors they were shaping.

The original Gallup 1970 mirror test is linked in the post. It is under 2 pages.

As for a '4-line Perl script' - I'd love to see it! Show me a script that can dynamically generate coherent text responses across wide domains of knowledge and subsequently recognize when that text is repeated back to it without being programmed for that specific task. The GitHub repo is open if you'd like to implement your alternative.

Yes, but the conversation tags don't tell the LLM their output has been copied back to them. The tags merely establish the boundary between self and other - they indicate "this message came from the user, not from me." They don't tell the model that "the user's message contains the same content as the previous output message." Recognizing that match, recognizing that "other looks just like self" - is literally what the mirror test measures.

It's the difference between knowing "this is a user message" (which tags provide) and recognizing "this user message contains my own words" (which requires content recognition).

Thanks for engaging! But you're arguing against claims I didn't make. I wrote about self-recognition (behavioral mirror test), not self-awareness or self-models. 

All learning is pattern matching, but what matters is the spontaneous emergence of this specific capability: they learned to recognize their outputs without being explicitly programmed for this task. Would we reject chimp self-recognition because they learned it through neural pattern matching? Likewise, since humans recognize faces through pattern matching in the fusiform gyrus - does that mean we don't 'really' recognize our mothers? I'm puzzled why we'd apply standards to AIs that would invalidate virtually all animal cognition research.

I disagree - there are a number of animals (and LLMs) with memory, and they aren't all capable of self-recognition. Memory and self-recognition are two distinct concepts, though the first is likely a precondition for the latter. (And indeed, when you pass the mirror test, you are allowed to remember what you look like...)

Now, if there were a tool use being called that used a script to check whether a user message matched a previous AI assistant message, I'd agree with the spirit your "printf( "I'm conscious! Really!!!\n" )" comment. But that's not what's happening. What's happening is that a small-to-moderate number of LLMs (I count 7-8) are consistently recognizing their own outputs when pasted back without context or instructions, even though they (1) weren't trained to do so, (2) weren't asked to do so, and (3) weren't given any tools to do so. This, to my mind, suggests an emergent unplanned property which arises only for certain model architectures or model sizes.

I also want to make very clear that my post is not about consciousness (in fact the word does not appear in the body of the text). I am making a much narrower claim (self-recognition) and connecting it, yes, to questions of moral-standing. I'd strongly prefer to keep debate focused on these more tractable topics.