Circuits research also does not come from Anthropic. Mech interp is a huge field in academia and most of the core circuit analysis papers were from OpenAI/GDM/academia. However, Anthropic tends to produce a lot of blog posts where they draw poorly supported but hype-able analogies between LLMs and biological intelligence. It's wild.
For a better understanding of mech interp and circuits, including what we actually do know about LLM internals, I would recommend reading this paper: https://arxiv.org/pdf/2501.16496
> the fact that language models have human-interpretable representations and neurons has been known since BERT... Circuits research also does not come from Anthropic... The article does not claim Anthropic invented the field, rather that they have had important contributions to it. This is intended as an overview into a specific set of ideas that are working for mechanistic interpretability. Not a formal literature review.
This is similar to factor rotation in factor analysis (or PCA). A varimax rotation, for example, can produce an equivalent factor analysis with sparse loadings, and which is generally more interpretable. Fortunately for us the world is not just a complete mess, and sparse loadings can often be found. There seem to be "natural" concepts that we have observed rather than invented.
(Many examples in other simple machine learning methods too, I am sure.)
Biology? Anthropic really needs to stop anthropomorphizing these things so much. I'm with Dijkstra on this one.
I know they do it as a sort of marketing but still...
> the Dallas feature goes active,
> which causes the Texas feature to light up,
> which then causes Austin to light up.
> It seems fairly clear that this is tracing semantic relationships between high-level concepts — and in doing so, performing a kind of pseudo-symbolic inference, similar to what some philosophers would describe as "higher reasoning."
Uhhh no reasoning is required for Austin to follow Texas after Dallas, let alone "higher reasoning".
This is really grasping as straws
As of Jan 2026 I have come to accept that LLMs are at least part of the puzzle of how intelligence works. They are at this point better than the majority of humans at various intellectual tasks. It may not be or ever be a 1:1 but good enough ran the world already before llms.
There is not even a formal definition of what intelligence is so saying LLM's are intelligent can't even be "right/wrong". Its just arguing semantics and definitions.
I think engineering and mathematical thought requires spatial reasoning, when I model problems I see them as 3D shapes. Like the economy is a series of tubes that money flows through and collect in buckets, programming state is little boxes that hold values, chemical interactions are like keys that fit into locks.
I don’t think LLMs can build models like that, but because it has so much memorized and there usually isn’t a need for a novel model custom fit for a problem, it can fake it by imitation.
But the only way to map the network in an LLM is experimentally. You have to prompt it, and see how the coefficients fall in order to construct your most likely walk through the training data.
I think that LLMs can and do come up with novel things through exhaustion, just by applying the relationships between some set of entities to entirely different sets of entities because an accumulation of earlier context pushed the probability of those entities being mentioned, and they were able to easily replace a selection of entities that were more associated with those nearer connective, relationship words.
I think that as such LLMs are good at generating metaphors, and a lot of innovation comes from going "What if As worked like Bs?" Just go through all the As and Bs, toss the ones that don't make any sense and test the ones that seem like they might.
Well that is complete any utter bollocks, dribbled in para three or so, and obviously written by a next token guesser.
LLMs are tools and I'm pretty sure if I let you loose on some of my tools, you might lose an extremity unless I kept an eye on you.
I have an on prem Qwen3.6-35B-A3B-UD-Q4_K_XL working on a box in the office and its quite handy for a chat.
For example, front end web app layout and basic functionality. Anyone can make a website with interactive buttons with ease now, where as before, you had to go look up examples, try stuff, figure out why its not working, e.t.c.
But in terms of organization and higher level tasks, like for example making front end that is clean, robust, easily extensible, and doesn't break, LLMs require almost as much prompting to do this as it takes to actually write the code.
And wouldn't you know it, I keep getting my wish :)
When you read about and observe the split-brain patient experiments the appropriate response is abject horror at the implications.
It is arguably a characteristic of any intelligent system, that at least some part of it must be opaque to itself, but the previous sentence is more defensible than a generalized claim.
If you don't understand what that means, tell me from your own metacognitive insight what parts of your brain are being used to read this. Not because of learned knowledge about what parts of the brain do what, through your own insight in your own functioning. You can't, because you don't have any.
This isn't just that human rationalize a lot. This is below that. This is that even if you notice yourself rationalizing, which is something you can train yourself to do, you have no access to the underlying computations/processes of the rationalization itself, or the process of noticing you are rationalizing.
There is arguably still a sense that we experience in which we humans could reasonably say "No, I'm pretty sure I used addition-with-carry to answer you", so that is perhaps not the easiest example to think about the experience of. But there will always be some question of "how did you do that" to which you can give no answer because the answer is in the firing of the neural net itself and you, who is in one way or another the product of that firing, do not have access to that. How did you quickly catch that ball that someone unexpectedly threw at you? You just did, as far your neural net is concerned.
(Also, while I've expressed this in terms of your conscious experience, this doesn't have anything to do with "consciousness". Neural nets in general do not get this feedback and do not and can not have arbitrary metacognition about their own functioning. This is an artifact of my writing text to address conscious beings.)
It's not even trivial to identify what it is exactly I'm not aware of. There's just some pattern I don't like, and the factors that influence it are a mystery. I've discovered some things over the years that seem to correlate with it, but nothing that truly explains or remedies it.