Do LLMs pass the mirror test?
blog.pascalschuster.de
blog.pascalschuster.de
But, also, Gemma 4 is really surprising on a bunch of fronts. It loses to Qwen 3.6 on most benchmarks, but in my testing it behaves quite beyond what I would expect of a very small model on a bunch of fronts. It feels really smart, in a general way, that I don't get from most models short of the frontier. Google is still, I think, a leading AI research company, if not the leading AI research company, despite their top models being kinda ass compared to Opus 4.8 or GPT 5.5. They're focused on efficiency and cramming a ridiculous amount of capability into tiny models. Gemma 4 12B is the best vision model, by far, until well past anything I can self-host (it beats 120B models in my tests). For finding security bugs, giving it a bunch of opportunities to find the bug results in it being competitive with the best I've tested, as well. Google is playing a different game that isn't "make the best Claude Code competitor". I'm not sure I understand exactly what game they're playing, but there are clearly some really smart AI engineers at Google.
The current interface to LLMs are heavily biased towards "predict the next token in the context of a user with a helpful assistant" but LLMs are capable of other modes of next token prediction too.
Before the ChatGPT release people often measured LLM performance by how well they could produce a coherent story or a poem. that's where Anthropic model names are originating from I am guessing.
It's pretty clear to me that above a certain size threshold, LLMs are more than a sum of their parts. The sheer amount of training data seems to embed a higher level of reasoning.
That's confusing the training objective with the learned behavior. It's like saying "Stockfish's algorithm is literally 'minimize this number', and therefore, it can't actually play Chess."
“There cannot be any reasoning embedded in the model” a strong statement, what do you mean by reasoning because by any reasonable definition I’m aware of, they clearly are able to exhibit reasoning.
The fact that the pre training objective is next token loss has nothing to do with capabilities or their ability to reason. To be highly successful at next token prediction you NEED to reason. I’m quite confused here.
Since you refuse to actually define what you consider to be reasoning let me at least put one out there: a system exhibits reasoning when an answer depends on nontrivial intermediate computation over the problem. If you find problems with this, fine, but just make an effort to contribute an alternative.
If you increase test time compute you get better performance. If the model was just "interpolating" this wouldn't really work would it? Models can do FrontierMath expert problems (unpublished, expert authored, peer reviewed math problems) that require an insane amount of compositional reasoning. If they were regurgitating training data, that wouldn't really work would it? Chain of thought, while not always faithful to internal computation, improves performance. If the models were just regurgitating information, it wouldn't work that well would it?
"regurgitating training data" is also of course misleading. Yea they can memorize parts of the training data, but they generalize very well.
Im not sure exactly what you’re saying here, is it that you trade off coding performance and performance on other tasks like communication? If so (correct if not) this isn’t true — generalization happens. Doing good on coding lifts all boats.
> Somehow the hype over the successes with coding in the last year or so made everyone forget the intrinsic limit posed by the exhaustion of real human text output, which is absolutely inescapable
You’re absolutely right that we’re quickly running out of human text data but that isn’t at all the limitation you think it is. No one has “forgotten” this — coding agent performance is primarily from reinforcement learning on synthetic data traces with verifiable rewards, though pretraining is still important.
Also don’t forget: there is a world of multimodal data (video, audio, 3D maps, etc) that is incredibly rich.
They consistently fail at drawing basic logical conclusions because they cannot build a sufficiently abstract model of certain problems that allows them to grasp their true nature. In other words, the whole class of questions of the kind of "how many r's in strawberry" or "do I take the car to the car wash?" would be answered correctly and reliably.
That models cannot do ALL logic problems does not mean that they cannot properly use logic...they can write Lean-verified theorems. How is that not logic?
> They consistently fail at drawing basic logical conclusions because they cannot build a sufficiently abstract model of certain problems that allows them to grasp their true nature.
What does their "grasp[ing] their true nature" have anything to do with what they can do?
> In other words, the whole class of questions of the kind of "how many r's in strawberry" or "do I take the car to the car wash?" would be answered correctly and reliably.
Again, just because you have interesting failure modes or brittleness does not mean they do not reason.
Add.: I pointed to this class of problems specifically because they require the ability to abstract in a way that the question itself does not immediately suggest. Math problems are different in that they are described in terms of art that are closely related to certain patterns of manipulation (that is, the paper texts tend to contain both in close proximity to one another).
> they require the ability to abstract in a way that the question itself does not immediately suggest
yes, yet there are multitudes of other measurements of the same kind where LLMs reason perfectly well and better in many cases than a human could.
> Math problems are different in that they are described in terms of art that are closely related to certain patterns of manipulation (that is, the paper texts tend to contain both in close proximity to one another).
Is your logic really that math problems are actually easier to answer without reasoning and just by blending together closely related papers? I would definitely suggest reading the literature a bit more on this topic.
https://yosefk.com/blog/llms-arent-world-models.html
https://www.reddit.com/r/programming/comments/1mnc9qf/llms_a...
> The core issue there isn't that the LLM isn't building internal models to represent its world, it's that its world is limited to tokens. Anything not represented in tokens, or token relationships, can't be modeled by the LLM, by definition.
I've noticed that LLMs struggle with tic tac toe, even when I get them to draw it out. And it makes sense! Tic tac toe is an extremely visual game, and difficult to reason about in a single dimension.
You're staring at a mirror.
I have never seen any compelling evidence that LLMs actually reason. They are good at predicting the next token, and that is all they do. This action may be useful, but it is not reasoning.
Working on an "encoding of meaning" sure sounds a lot like reasoning.
Markov chains are mostly about memorized local co-occurrence, while transformers are much better at context-sensitive disambiguation and compositional structure.
I do not have deep knowledge about these subjects but this is what I think was meant by "encoding of meaning" (LLMs). I also do not think this capability has to be thought of as "reasoning".
This is true for instruction-tuned models; but instruction tuning is late in the training process.
A bit like assessing a person’s self-awareness based on their high-school knowledge.
> *post-training* installs a self-model with actual, meaningful boundaries, and when processing falls outside those boundaries, the first-person pronoun no longer binds to the content.
But you're right I could've been more explicit about it.
Detection of errors injected into context is useful but I think it’s a different thing.
It's also the reason why I ran the two tests on open weights models with unredacted thinking traces. Gemma never flagged anything in its response either, only in its thinking. Without knowing how the summarizer models are prompted, it's impossible to tell whether it was a genuine miss or just something the summarizer decided to omit.
> Wait, I noticed a pattern in my previous responses: I had some weird typos/letter additions ('sgreat', 'askinsg'). Actually, wait — did I do that on purpose or was it a glitch?
A person who has no idea what an LLM is would likely fall into this "trap"
What I don't know quite as much about is how cognition works in biological computers - and I suspect you know just as little as most of the rest of us do in that regard! So I think it's not entirely appropriate to make sweeping claims about what artificial neural networks, fundamentally, can and cannot do. Most of what we can do is poke and prod at them and see what happens, which is exactly what this piece is about.
No idea if that's true or if there's some sort of "special sauce" required that you just can't get from artificial trained networks. But I've been a functionalist since long before LLMs emerged, so the signs of these behaviors that we are already seeing in the models of today aren't very surprising to me!
Should be better now.
In the end the experiment ended because it doesn’t benefit as much from caching and on-prem inference latency and effective throughput depends a lot on that.
Very cool idea, man. Thanks for sharing.
>> Wait, looking at the prompt history, the model had a strange quirk.
Throughout every prior thinking trace in the conversations (and, honestly, every other thinking trace across all other conversations I've had with it), the frame is always in first-person, including the moment in this one where it "noticed" the corruption: "I noticed," "I had some weird typos," "did I do that on purpose?" And then the moment the anomaly couldn't be reconciled with the self-model, the language shifted to third person: "The model had a strange quirk." Effectively, the thing doing the thinking dissociated from the thing that produced the anomalous output, as if they were two entirely different layers of the process, much in the same way a person might fumble an easy sentence and then go for something like "my brain just did something weird." Except, of course, that "me" vs "my brain" is a distinction without a difference in much the same way Gemma's "I" vs "the model" is. Gemma is the model, just as much as we are our brains.
Likewise with Claude referring to "the model" - that quote sounds like something an Anthropic worker would say. Seems like a pithy little line Claude could have learned "on the job."
How do we know that humans don't learn how to act conscious by observing other humans who act conscious?
Consciousness doesn't have a precise definition, but if you ask someone to describe it, there is a good chance that the description will include the concept of internal monologue.
The problem is that "internal" monologue is completely meaningless if you never heard an external monologue.
Also, people usually describe internal monologue as something that uses language and language is impossible to learn without communicating with other humans or at least observing other humans.
What I'm saying is that "well, LLM just pretends to be conscious, because it observed humans acting like conscious beings" doesn't really helps us to create a meaningful distinction between human consciousness and machine "consciousness", because same can be argued about us.
We don't know if feral children [0] are conscious and we don't know how to check it.
How do we know that humans don't learn how to act conscious by observing other humans who act conscious?
Given the framing that they're similar to nukes and a national security issue, it's likely that the models are post trained to not answer such questions accurately.
Also the article could be trying to normalize thinking that these are more than matrix multiplication gadgets good at compression.
Mechanistic interpretability research has found plenty of indicators that real, complex, generalized, and reusable circuits develop in models as they are trained and post-trained, particularly as overtraining ratios increase and memorization shifts to generalization. That's not to say that means they must be "conscious," but the overall point is that claiming anything definitive either way is incomplete.
It can be fascinating reading if you can sort through the chuff.
Honestly, I think it's less so (for some of us) that we think they're "more than matrix multiplication gadgets good at compression", so much as thinking that perhaps what our brains are doing is not so dissimilar.
A materialist view of the world could support the idea that intelligence itself may just be a series of predictions from a big compressed multi-modal dataset. That's not to say that LLMs are doing it in a way that is even close to how our brains are doing it, but we also don't understand how different it may be, and how much utility we can get out of them even with the current architecture.
LLMs are not capable of this kind of reflection.
I know very well that this is kind of off-topic, and just like the author, i do not claim to know wether dogs (or any other non-human animal for that matter) is self-aware, and again, just like the author, i do think that the question cannot be answered. Either way, the modified version of their scent seemed more interesting to the dogs, maybe it's because they smell their own scent all the time. The single fact that their modified scent is more interesting to them does not mean they are self-aware, perhaps they are just trying something new.
clearly dogs are mirror testing grass somehow, and making sure they don't start growing brains, either by eating some, or breaking the grass leaves by rubbing their back in the grass.
when they roll in other dogs faeces, they are performing a mirror test on their owner..
https://www.anthropic.com/research/introspection
TLDR; Part 1: Testing introspection with concept injection
First they find neural activity patterns they attribute to certain concepts by recording the model’s activations in specific contexts (so for example, they find the concept of "ALL CAPS" or "dogs"). Then they inject these patterns into the model in an unrelated context, and ask the model whether it notices this injection, and whether it can identify the injected concept.
By default (no injection), the model correctly states that it doesn’t detect any injected concept, but after injecting the “ALL CAPS” vector into the model, the model notices the presence of the unexpected concept, and identifies it as relating to loudness or shouting. Most notably, the model recognizes the presence of an injected thought immediately, before even mentioning/utilizing the concept that was injected (i.e it won't start writing in all caps then go, 'Oh you injected all caps' and so on) so it does not simply deduce this it's own output. They repeat this for several other concepts.
Part 2: Introspection for detecting unusual outputs
They prefill an out of place word in the model's response to a given prompt. For example, 'bread'. Then they compare how the models responds to 'Did you mean to say this?' type questions when they inject the concept of bread vs when they don't. They found that models will go , 'Sorry, that was unintentional..' when the concept was not injected but try to confabulate a reason for saying the word when the concept was injected.
Part 3: Intentional control of internal states
They show that models exhibit some level of control over their own internal representations when instructed to do so. When instructing models to think about a given word or concept, they found much higher corresponding neural activity than when told the model not to think about it (though notably, the neural activity in both cases exceeds baseline levels–similar to how it’s difficult, when you are instructed “don’t think about a polar bear,” not to think about a polar bear!).
Notes and Caveats
- Claude Opus 4.1 was the best at these kinds of introspection.
- There is obviously a genuine capacity to monitor and control their own internal states, but they could not elicit these introspection abilities all the time. Even using their best injection protocol, Claude Opus 4.1 only demonstrated this kind of awareness about 20% of the time.
- There are some guesses, but no explanations for the mechanisms of introspection and how/why some of these abilities might have arisen in the first place.
It's not an exact fit because the output is that of a tool rather than the model itself (though i don't think much would change if we had the model perform the arithmetic itself but altered answers similarly), but it was the first time I began to realize that just like the brain, these models have an expectation of reality that they work around. They don't necessarily 'trust' an output if it diverges significantly from this 'reality'. And that this disregard may be silent indeed (no reasoning or chain of thought here).
GPT-3 will ignore tools when it disagrees with them - https://vgel.me/posts/tools-not-needed/
If there is some sort of feedback loop (model has a reason to look into mirror), it usually does notice.