As someone who is not an AI researcher, the paper itself is way over my head.

More interesting was the independent commentary paper they linked near the bottom: https://www-cdn.anthropic.com/files/4zrzovbb/website/cc4be24...

Neel Nanda (of Google Deepmind - his part begins on page 33) discusses his opinions on the paper, and the small-scale replication he performed on an open-weight model.

Thanks for calling this out (long with others here). I am just starting in on it but had to come back to say thanks and call this out,

> We have replicated the core claims on Qwen 3.6 27B, and also share preliminary evidence of extending this work by finding abstract "interpretative meta-tokens", like Chinese characters for "what does this mean" that seem to activate and play a causal role on processing ambiguous sentences

Not sure if I am picking up what they are putting down, but if LLMs are using symbols to try to encode squishy concepts from human language into consistent, meaningful “tokens”, that sounds really interesting. In every long-term, successful use of AI, I hear echoes of The Zen of Python, “Explicit is better than implicit.” I try like hell to do it, but it’s far too easy to be lazy with AI.