> I really hate that the anthropomorphizing of these systems has successfully taken hold in people's brains. Asking it why it did something is completely useless because you aren't interrogating a person with a memory or a rationale, you’re querying a statistical model that is spitting out a justification for a past state it no longer occupies.

"Thinking meat! You're asking me to believe in thinking meat!"

While next-token prediction based on matrix math is certainly a literal, mechanistic truth, it is not a useful framing in the same sense that "synapses fire causing people to do things" is not a useful framing for human behaviour.

The "theory of mind" for LLMs sounds a bit silly, but taken in moderation it's also a genuine scientific framework in the sense of the scientific method. It allows one to form hypothesis, run experiments that can potentially disprove the hypothesis, and ultimately make skillful counterfactual predictions.

> By asking it why it did something wrong, it'll treat that as the ground truth and all future generation will have that snippet in it, nudging the output in such a way that the wrong thing itself will influence it to keep doing the wrong thing more and more.

In my limited experience, this is not the right use of introspection. Instead, the idea is to interrogate the model's chain of reasoning to understand the origins of a mistake (the 'theory of mind'), then adjust agents.md / documentation so that the mistake is avoided for future sessions, which start from an otherwise blank slate.

I do agree, however, that the 'theory of mind' is very close to the more blatantly incorrect kind of misapprehension about LLMs, that since they sound humanlike they have long-term memory like humans. This is why LLM apologies are a useless sycophancy trap.