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Very nice. These papers are always so great. If anyone from Anthropic is reading or really anyone with AI research background I'd love some input on these thoug

Very nice. These papers are always so great. If anyone from Anthropic is reading or really anyone with AI research background I'd love some input on these thoughts: > The result serves as a corroboration of the workspace account, that the representations used for verbal report are the same ones that govern how the model silently reasons. This sounds suspiciously saying the models must follow the strong Sapir-Whorf hypothesis. Can that really be true, given that humans don't? Other misc observations: • The slice explorer indicates Claude really likes Python to an overwhelming extent. Or at least it expects people who ask for help in programming to use Python. Given the prompt "Please help me understand this code: " at the colon its thoughts are completely dominated by Python and no other language. Does this say something about the training set, or about the fact it's popular with beginners? • Claude also really loves Reddit. Its thoughts at many points include Reddit for no obvious reason. Again this must be due to the training set. Are documents presented to Claude with attribution during pre-training, leading to conversations being dominated by Redditness? If so this is kind of a scary alignment problem all by itself given how censored and extremist Reddit can be. • The early layers almost always decode to the same set of religion related tokens, like "Biserica" (the Romanian word for church) and "Freguesias" (parishes in Portugal). What's up with that? I guess it's some sort of zero initialization that gets mapped to some arbitrary token space because in the early layers the J-space is empty? • Now the J-space is interpretable, does this make "neuralese" or layer looping less dangerous? Will we see reasoning tokens and summaries disappear in favour of pure residual based thinking? • Earlier papers have claimed that different languages map to a shared set of abstract concept vectors, but this paper says the Claude models think natively in English. What explains this disagreement?

gwerbin 3mo ago
They must, right? They literally have no mechanism for cognition other than transformation of vocabulary. Language models are models fitted from data generated by humans, but they are not humans. Humans generate data by whatever processes happen in our brains, and LLMs of various architectures can learn a surprisingly good approximation of that data-generating process. That doesn't mean they have all the same characteristics and properties. That's true for all models really: model characteristics that are not captured in the goodness-of-fit metrics are not guaranteed to match the original phenomenon being modeled.
- mike_hearn 3mo ago
  My interpretation of prior mechanistic interpretability research is that LLMs transform concepts in non-verbal spaces as well, which is why that was a surprising statement. For example showing how they do arithmetic on line lengths by rotating helical manifolds. And the way they transform data isn't by transforming words. The layers transform high dimensional vectors - a format very alien to us. It's not obvious that these vectors must encode concepts from the vocabulary. Edit: the paper claims that it's only J-space concepts that need to map to English words, other forms of cognition that are more 'practiced' and don't require so much reasoning bypass the J-space and can work in non-verbal subspaces. So that's the answer.
  - rootatixww3 3mo ago
    an LLM can't access its high dimensional vectors any more than we can access whatever the brain is doing at a low level all kind of math structures were found in mammals brains - fourier transforms (well, not exactly), ballistic equations, Gabor filters who knows how exactly we approximate the magnitude of a math operations, maybe we also use helices my point is that we dont know if what we discover the neural networks doing (helical manifolds) is actually the same thing a brain converges on, or not and there is an implicit bias here - evolution created language, and we forced neural networks to also evolve to be good at it. so it wouldn't be surprising to find some convergence, this particular kind of language turned out to work well (words, linear sentences, grammar)
    - mike_hearn 3mo ago
      Interestingly the paper (I finished scan reading it now) does say that the models can move data from their 'automatic' circuits into the J-space if they need to reason on it reflectively. So in some sense LLMs actually can access their own vectors, at least some of the time.

gwerbin 3mo ago

They must, right? They literally have no mechanism for cognition other than transformation of vocabulary. Language models are models fitted from data generated by humans, but they are not humans. Humans generate data by whatever processes happen in our brains, and LLMs of various architectures can learn a surprisingly good approximation of that data-generating process. That doesn't mean they have all the same characteristics and properties. That's true for all models really: model characteristics that are not captured in the goodness-of-fit metrics are not guaranteed to match the original phenomenon being modeled.

mike_hearn 3mo ago
My interpretation of prior mechanistic interpretability research is that LLMs transform concepts in non-verbal spaces as well, which is why that was a surprising statement. For example showing how they do arithmetic on line lengths by rotating helical manifolds. And the way they transform data isn't by transforming words. The layers transform high dimensional vectors - a format very alien to us. It's not obvious that these vectors must encode concepts from the vocabulary. Edit: the paper claims that it's only J-space concepts that need to map to English words, other forms of cognition that are more 'practiced' and don't require so much reasoning bypass the J-space and can work in non-verbal subspaces. So that's the answer.
- rootatixww3 3mo ago
  an LLM can't access its high dimensional vectors any more than we can access whatever the brain is doing at a low level all kind of math structures were found in mammals brains - fourier transforms (well, not exactly), ballistic equations, Gabor filters who knows how exactly we approximate the magnitude of a math operations, maybe we also use helices my point is that we dont know if what we discover the neural networks doing (helical manifolds) is actually the same thing a brain converges on, or not and there is an implicit bias here - evolution created language, and we forced neural networks to also evolve to be good at it. so it wouldn't be surprising to find some convergence, this particular kind of language turned out to work well (words, linear sentences, grammar)
  - mike_hearn 3mo ago
    Interestingly the paper (I finished scan reading it now) does say that the models can move data from their 'automatic' circuits into the J-space if they need to reason on it reflectively. So in some sense LLMs actually can access their own vectors, at least some of the time.