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> To effectively predict the next token it needs a good idea of what comes after the next token. And that's all it needs. Not reasoning.
How do you define reasoning in a measurable way?
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...by inferring both the imports and the script body from the same context? I think you're suggesting there's some kind of information flow from the anticipated body of the script back up to the imports, but I don't see why that would be necessary. Infer imports from context, infer body from context + imports. All strictly causal.
Sure, try it. It's harder than you think. It's not just imports, it's the entire program. > you're suggesting there's some kind of information flow from the anticipated body of the script back up to the imports Yes, I am suggesting this. I don't think it is possible to write programs without either anticipating what you're going to write down below before you get there, or else being able to go back and edit what you…
well, yeah, I'm not a large language model
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At some level of performance reasoning becomes the most effective method to predict the next token
Chatbot "reasoning" arises from some level of the performance of the particular user, not of the chatbot.
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How do you define reasoning in a measurable way?
I keep asking the same question, and I think the steelman version would be "has metacognitive patterns similar to humans"
One of the biggest things I've learned after the event of LLMs is that humans definitions of intelligence/thinking/reasoning/consciousness/etc are very poorly defined. Not just across society at large, but the sciences themselves.
Confirmatory of Sutskever's view that predicting the next token forces a deep understanding. To effectively predict the next token it needs a good idea of what comes after the next token.
I think that's reading a little too much into it. The paper shows the hidden states contain signals about whether the code is good right now, and whether the run is probably going to work out. That's interesting, for sure. But it doesn't mean the model has some detailed idea of what it's going to write 25 steps later. A lot of that signal could be much simpler stuff. This task is hard. The agent seems stuck. The test…
That after a model has context about a project, the probes indicate a state that validates that?
Seems that the paper is highlighting the very nature of what LLMs are and what we expect them to be?
And that there is no 'thinking' here, it's just the state of the model?
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Sure, try it. It's harder than you think. It's not just imports, it's the entire program. > you're suggesting there's some kind of information flow from the anticipated body of the script back up to the imports Yes, I am suggesting this. I don't think it is possible to write programs without either anticipating what you're going to write down below before you get there, or else being able to go back and edit what you…
> It's harder than you think. well, yeah, I'm not a large language model
In other words, since the next semantic prediction for forecasting the future is built on the training dataset, it's hard for anything truly new to emerge. Then how do humans create something 'creative'—something that didn't exist before? I think it might be because the process of simplifying the complex system of nature differs between individuals. The data being learned now is all labeled by humans and simplified t…
That will still not create anything new-new, just more new, still dound by just being "an encyclopedia that only explores within the universe" at best.
> A coding agent solving a software-engineering task spends dozens of steps reasoning No. That's simple PR hype. Parrotry is not reasoning.
Why couldn't parroting, after a certain level and complexity of the parroting infrastructure, be reasoning? What a priori restriction forbids it from being such?
Flipping a NAND is not calculation either. Billions of them? Things change.