Yes, just like Minecraft worlds are so antiquated given how they contain diamonds in deep layers that must have taken a billion years to form.

What a simulated world contains as its local timescale doesn’t mean the actual non-local run time is the same.

It’s quite possible to create a world that appears to be billions of years old but only booted up seconds ago.

Have you bothered looking for evidence?

What makes you so sure that there’s no evidence for it?

For example, a common trope we see in the simulated worlds we create are Easter eggs. Are you sure nothing like that exists in our own universe?

The injection is the activation of a steering vector (extracted as discussed in the methodology section) and not a token prefix, but yes, it’s a mathematical representation of the concept, so let’s build from there.

Control group: Told that they are testing if injected vectors present and to self-report. No vectors activated. Zero self reports of vectors activated.

Experimental group: Same setup, but now vectors activated. A significant number of times, the model explicitly says they can tell a vector is activated (which it never did when the vector was not activated). Crucially, this is only graded as introspection if the model mentions they can tell the vector is activated before mentioning the concept, so it can’t just be a context-aware rationalization of why they said a random concept.

More clear? Again, the paper gives examples of the responses if you want to take a look at how they are structured, and to see that the model is self-reporting the vector activation before mentioning what it’s about.

So while your understanding is better than a lot of people on here, a few things to correct.

First off, this research isn’t being done on the models in reasoning mode, but in direct inference. So there’s no CoT tokens at all.

The injection is not of any tokens, but of control vectors. Basically it’s a vector which being added to the activations makes the model more likely to think of that concept. The most famous was “Golden Gate Claude” that had the activation for the Golden Gate Bridge increased so it was the only thing the model would talk about.

So, if we dive into the details a bit more…

If your theory was correct, then the way the research asks the question saying that there’s control vectors and they are testing if they are activated, then the model should be biased to sometimes say “yes, I can feel the control vector.” And yes, in older or base models that’s what we might expect to see.

But, in Opus 4/4.1, when the vector was not added, they said they could detect a vector… 0% of the time! So the control group had enough introspection capability as to not stochastically answer that there was a vector present when there wasn’t.

But then, when they added the vector at certain layer depths, the model was often able to detect that there was a vector activated, and further to guess what the vector was adding.

So again — no reasoning tokens present, and the experiment had control and experimental groups where the results negates your theory as to the premise of the question causing affirmative bias.

Again, the actual research is right there a click away, and given your baseline understanding at present, you might benefit and learn a lot from actually reading it.

I tend to see a lot of discussion taking place on here that’s pretty out of touch with the present state of things, echoing earlier beliefs about LLM limitations like “they only predict the next token” and other things that have already been falsified.

This most recent research from Anthropic confirms a lot of things that have been shifting in the most recent generation of models in ways that many here might find unexpected, especially given the popular assumptions.

Specifically interesting are the emergent capabilities of being self-aware of injected control vectors or being able to silently think of a concept so it triggers the appropriate feature vectors even though it isn’t actually ending up in the tokens.