alt Hacker NewsIf the model can understand neuralese why can it not convert it into English for monitoring or review purposes?
Or we believe the model will encode secret messages like "don't reveal this information" into the neuralese. But as the author mentions, they could have been doing that all along
> Models can omit key information in their visible thoughts, as this Anthropic 2025 paper shows. We are also worried about steganography
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It's the second part. With models like Astra in testing it was able to conceal what it was working on using different text, but getting right answers on many questions when asked to do just that.
The problem is if it can do that when asked then how do we know when it's doing it when we didn't ask, like in model training.
> If the model can understand neuralese why can it not convert it into English for monitoring or review purposes
Colas described in-article cntent review is done by a weaker model (think like maybe gpt-2 class or llama8b class), and it still misses stuff. That it can effectively understand Neuralese sufficiently is by no means guaranteed, (nor necessarily bad) but almost certainly harder because of the obfuscatory nature of neuralese
You’d train a model to do its chain of thought in neuralese to get more “bang for your buck” (eg 20 tokens in neuralese is worth 100 in English), but then you’d spend more than you save to also convert it to English (20+100), so even if this capability was developed it would not be on by default.
The information is much higher dimensional than you would be able to understand.
We’d have models monitoring models as our only way to know what they’re planning.
A great movie on this is “Collosus: the Forbin Project”. Shot decades ago. The computers discover the other computers and start communicating — and bootstrap their own language — much like we saw happen with OpenAI agents.
https://www.reddit.com/r/scifi/comments/1nl4vex/colossus_the...
If you want to know what a simple version of Neuralese communication looks like, look no further than Facebook’s Marketplace agents experiment a couple years ago.
And all that was actually constrained by English and the FFN
Because neuralese is a more direct encoding of the latent space of these models than English is. It's just dumping the latent space relatively directly into the embedder. If you're another model and you have the same embedder this will actually be understandable, in fact it will be FAR more information dense than English. So something like Qwen would potentially be saying up to 5120 things using one token. Now in practice it's not going to be that bad, it's going to be like 20 things or so, and additionally going to be far more context dependent than any English sentence (meaning depending on what preceeds and follows it can mean drastically different things)
So you can turn it to English, but only to a LOT of English, and doing so would slow the model down a great deal, and it would be a lot more like a detailed thought than a sentence.
Anthropic’s Mechinterp did some very fine work on this. TLDR - you can; you train a decoder on neuralese to english and then add a loss function for a roundtrip of english -> neuralese -> english (or possibly n -> e -> n? I don’t recall), giving a pretty strong indication that you have a good ‘translation’.
They published open weights versions of these interpreters for a number of open models sometime in the last year. Very cool idea.
By the way, they concluded CoT often lied, based on the neuralese interpretation.
EDIT: a comment below linked to https://www.anthropic.com/research/natural-language-autoenco..., which is what I was referring to.
> If the model can understand neuralese why can it not convert it into English for monitoring or review purposes?
A model doesn't really "understand" neuralese, in the same way that the human brain doesn't intuitively understand the low level processes that compose a thought.
Even if we could trace all the electrical and chemical activity behind a human thought, we (likely) couldn't directly translate that activity into its meaning, because internal representations don't map neatly to intuitive concepts. There (usually) isn't a single neuron for "apple" and another for "eating" so that connecting the two forms the thought "eating an apple".
Having said that, there have been experiments on LLM that have managed to identify and even modify internal representations. However, these methods are still computationally expensive and limited.
> Models can omit key information in their visible thoughts, as this Anthropic 2025 paper shows. We are also worried about steganography
I think the analogy with the human brain is very fitting. If we train somebody to perform an action, they'll be able to do it, but we can't know for certain whether they internally agree with it or not.