this post was submitted on 09 Jun 2026
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The fact that it can't tell the difference between a prompt and part of the data it is examining really kills your argument.
Also it's a word probability matrix, not actually reasoning or understanding. It looks at all the words it is fed, and comes up with other words that are most likely to be near those. That's why these tricks work. It injects noise that interferes with those probabilities
I mean is that so different from what we do? My boss says "tools are in the bed", he could mean an actual bed where people sleep, maybe we're demoing a house and he placed the tools on a bed. But probably he means the bed of his pickup truck. I assign a probability to each and take the meaning that is most probable.
Yes it is different, because you can reason that out using the context of the situation. An LLM only has the words sent to it, and no ability to analyze whether what it is saying makes sense.
It's just: you said bed and told, here's some other words that commonly show up near the word bed, if there's enough smut in it's training, it might go a very different direction than your expecting.
Thinking/reasoning tokens kind of approximate that actually, which is what most flagships and even my own local LLM use.
Thinking tokens are quite like normal generative tokens, except that the LLM is 'talking' to itself. You can see its thoughts (depending on what settings you've put/IDE you use), but they aren't meant to be the actual response to your prompt. They are what the AI is designed to draft their answer before committing, to explore different options and to 'reason' itself into a more refined response.
Reasoning tokens is how AI can actually do math now, rather than just guess a number and pray, by the way.