This is the problem with things that don’t reason. You’re just giving it hints towards the simulation you want, and then it ultimately simulates the conversation you are building towards.
I don’t think you have read the relevant papers or are familiar with LRM (Large Reasoning Models). Which is basically all model AIs (GPT5, Claude, Gemini, DeepSeek). It’s new in the last ~18-24 months
In a nutshell, they include logical thinking and correct chains of logical thought to the LLM training data, along with tasks like recognizing dogs and predicting next words.
So yes, they are literally trained to reason the exact same way they are trained to write stories and summarize books.
You can say “it doesn’t really reason” but it has exactly the same value as the assertion “it doesn’t really write stories or summarize books”
… maybe not, but there will be a story or a summary (or a logical chain of thought) in front of you if you ask for one.
I will 100% admit to not reading papers and keeping up to date. I went ahead and spent about 30m looking up various explanations and summaries of LRMs. Ok, so you take an LLM and tell it to break the problem down first. It’s still not reasoning. It’s running a simulation of a natural language conversation, and giving you the center of mass of the statistical distribution for the intermediate steps. Does this kinda sorta replicate the sounds a human makes? Absolutely. But it’s irresponsible and unethical to make any claims that this is a human like entity you can chat with, or that it is doing any reasoning.
we are talking about chat bots talking to people in this post, and how you can steer the simulated conversation towards whatever you want
it did not debug anything, a human debugged something and wrote about it. Then that human input and a ton of others were mapped into a huge probability map, and some computer simulated what people talking about this would most likely say. Is it useful? Sure, maybe. Why didn’t you debug it yourself?
Fair, we need to get terms straight; this is new and unstable territory.
Let’s say, LLMs specifically.
it did not debug anything, a human debugged something and wrote about it. Then that human input and a ton of others were mapped into a huge probability map, and some computer simulated what people talking about this would most likely say
Can you explain how that is different from what a human does? I read a lot about debugging, went to classes, worked examples…
Why didn’t you debug it yourself?
In my case this is enterprise software, many products and millions of lines of code. My test and bug-fixing teams are begging for automation. Bug fixing at scale
This is the problem with things that don’t reason. You’re just giving it hints towards the simulation you want, and then it ultimately simulates the conversation you are building towards.
Actually, they have been doing that for about a year now
https://arxiv.org/abs/2501.09686
No, just because they say they want it to reason, does not mean it does
I don’t think you have read the relevant papers or are familiar with LRM (Large Reasoning Models). Which is basically all model AIs (GPT5, Claude, Gemini, DeepSeek). It’s new in the last ~18-24 months
In a nutshell, they include logical thinking and correct chains of logical thought to the LLM training data, along with tasks like recognizing dogs and predicting next words.
So yes, they are literally trained to reason the exact same way they are trained to write stories and summarize books.
You can say “it doesn’t really reason” but it has exactly the same value as the assertion “it doesn’t really write stories or summarize books” … maybe not, but there will be a story or a summary (or a logical chain of thought) in front of you if you ask for one.
I will 100% admit to not reading papers and keeping up to date. I went ahead and spent about 30m looking up various explanations and summaries of LRMs. Ok, so you take an LLM and tell it to break the problem down first. It’s still not reasoning. It’s running a simulation of a natural language conversation, and giving you the center of mass of the statistical distribution for the intermediate steps. Does this kinda sorta replicate the sounds a human makes? Absolutely. But it’s irresponsible and unethical to make any claims that this is a human like entity you can chat with, or that it is doing any reasoning.
When I get some time I’ll check this paper out: https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf
As Daniel Dennett once asked: “What is the difference between a simulated song, and a real song?”
You say it’s not reasoning, but I’ve seen it debug and fix a core dump
A couple of things:
Fair, we need to get terms straight; this is new and unstable territory. Let’s say, LLMs specifically.
Can you explain how that is different from what a human does? I read a lot about debugging, went to classes, worked examples…
In my case this is enterprise software, many products and millions of lines of code. My test and bug-fixing teams are begging for automation. Bug fixing at scale