Why are you asking the token predictor about the tokens it predicted? There's no internal thought process to dissect, an LLM has no more idea why it did or did not 'do' something, than the apple knows why it falls towards the earth.
Is that reasoning? Does it know? I might care about those questions in another context but here I don't have to. It simply works (not all the time, but increasingly so with better models in my experience.)
I think there's something here to consider, but it's sort of like assuming that the LLM has reasons for doing things when it only has weights for which tokens are produced - thats the sum of its reasoning.
Maybe it's the case that LLM tokens to correlate to truth values or that this approach actually provides value but there's probably good reason to be skeptical, given that we'd need to posit some sort of causative function of "token outputs" to reasoning about prior behaviors.
I am surprised with this response because it implies this is not an extremely valuable technique. I ask LLMs all the time why they did or output something and they will usually provide extremely useful information. They will help me find where in the prompting I had conflicting or underspecified requirements. The more complex the agent scenario, the more valuable the agent becomes in debugging itself.
Perhaps in this case the problem with hooks is part of the deterministic Claude Code source code, and not under the control of the LLM anyway. So it may not have been able to help.
bold claim, they'll provide bunch of words for sure like in this particular tool's response
I have good success when I ask the agent to help me debug the harness. "Help me debug why Claude Code is ignoring my hook".
In fairness, humans are quite bad at this as well. You can do years of therapy and discover that while you thought/told people that you did X because of Y, that you actually did X because of Z.
Most people don't actually understand why they do the things they do. I'm not entirely unconvinced that therapy isn't just something akin to filling your running context window in an attempt to understand why your neurons are weighted the way they are.
Yet that has no relevance to an LLM, which is not a human, and does not think. You're basically calling a record playing birdsong, a bird, because one mimics the other.
Would therapy work on an LLM?
On either side it says "I just ask the model why it did that"
When things like this surface, I try to see how I focused on the gap leading up to it, and trying to fix it, and hope I am not focusing on the gap and questioning can draw attention to it and reinforcing it. This means more attention is drawn to what is not wanted by questioning it, instead of being clear that the intention is to ensure in all cases, shapes and forms it no longer happens.
Instead, mention what you require, repeatedly, and also mention what you do not want ever to happen and it might be different.
Maybe it doesn't 'understand' in the experiential, qualia way that a human does. Sure. But it's still a valid and useful simile to use with these models because they emulate something close enough to understanding; so much so now that when they stop doing it, that's the point of conversation, not the other way around.
That’s not what’s happening. When it’s said that an LLM doesn’t understand it’s meant in the “calculator doesn’t understand taxes” or “pachinko machine doesn’t understand probability” way. The conversation itself is silly.