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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.

Even the "thinking" blocks in newer models are an illusion. There is no functional difference between the text in a thought block and the final answer. To the model, they are just more tokens in a linear sequence. It isn't "thinking" before it speaks, the "thought" is the speech.

Treating those thoughts as internal reflection of some kind is a category error. There is no "privileged" layer of reasoning happening in the silicon that then gets translated into the thought block. It’s a specialized output where the model is forced to show its work because that process of feeding its own generated strings back into its context window statistically increases the probability of a correct result. The chatbot providers just package this in a neat little window to make the model's "thinking" part of the gimmick.

I also wouldn't be surprised if asking it stuff like this was actually counter productive, but for this I'm going off vibes. The logic being that by asking that, you're poisoning the context, similar to how if you try generate an image by saying "It should not have a crocodile in the image", it will put a crocodile into the image. 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.

4 comments

You're entirely correct in that it's a different model with every message, every token. There's no past memory for it to reference.

That said it can still be useful because you have a some weird behavior and 199k tokens of context, with no idea where the info is that's nudging it to do the weird thing.

In this case you can think of it less as "why did you do this?" And more "what references to doing this exist in this pile of files and instructions?"

Agreed. I wish more people understood the difference between tokens, embeddings, and latent space encodings. The actual "thinking" if you can call it that, happens in latent space. But many (even here on HN) believe the thinking tokens are the thoughts themselves. Silly meatbags!

Thinking happens in latent space, but the thinking trace is then the projection of that thinking onto tokens. Since autoregressive generation involves sampling a specific token and continuing the process, that sampling step is lossy.

However, it is a genuine question whether the literal meanings of thinking blocks are important over their less-observable latent meanings. The ultimate latent state attributable to the last-generated thinking token is some combination of the actual token (literal meaning) and recurrent thinking thus far. The latter does have some value; a 2024 paper (https://arxiv.org/abs/2404.15758) noted that simply adding dots to the output allowed some models to perform more latent computation resulting in higher-skill answers. However, since this is not a routine practice today I suspect that genuine "thinking" steps have higher value.

Ultimately, your thesis can be tested. Take the output of a reasoning model inclusive of thinking tokens, then re-generate answers with:

1. Different but semantically similar thinking steps (i.e. synonyms, summarization). That will test whether the model is encoding detailed information inside token latent space.

2. Meaningless thinking steps (dots or word salad), testing whether the model is performing detailed but latent computation, effectively ignoring the semantic context of

3. A semantically meaningful distraction (e.g. a thinking trace from a different question)

Look for where performance drops off the most. If between 0 (control) and 1, then the thinking step is really just a trace of some latent magic spell, so it's not meaningful. If between 1 and 2, then thinking traces serve a role approximately like a human's verbalized train of thought. If between 2 and 3 then the role is mixed, leading back to the 'magic spell' theory but without the 'verbal' component being important.

> 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.

> 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.

Asking it why it did something isn’t useless, it just isn’t fullproof. If you really think it’s useless, you are way too heavily into binary thinking to be using AI.

Perfect is the enemy of useful in this case.

I genuinely fail to see the usefulness, though, it seems counterproductive to me to do this kinda stuff. In my experience I just throw out the whole chat/session as soon as I notice it's starting to repeat mistakes/start doing stupid shit consistently, the few times I've tried interrogating it I could immediately tell all it was doing is, for lack of a better word, being a sycophant and aping my words back at me.

It hasn’t failed to be useful to me yet, even if it isn’t complete info about what went wrong. Better if you can ask it a specific question about what it did (why do you do X?). Sometimes it made a mistake and you can ask it how you can word instructions better to not make the mistake (useful in prompt engineering), sometimes I made an actual mistake and gave it conflicting instructions, sometimes it’s still something that can be fixed. Eventually it stops making mistakes because you’ve tested it enough and made your prompts robust. I guess your mileage will vary, but my experience is that it’s a conversation to get a good prompt, not a single one shot ask (which is why I save my prompts and reuse them).