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The case for zero-error horizons in trustworthy LLMs

arxiv.org

71–80 of 121 posts

Re: The case for zero-error horizons in trustworthy LLMs

#71
post #40

Earlier quoted context omitted.

It has "outsourced" it to another component, sure, but does that matter? What the user sees is the total behavior of the entire system, not whether the system has internal divisions and separations.

It matters if you’re curious about whether AGI is possible. Have we really built “thinking machines”, or are these systems just elaborate harnesses that leverage the non-deterministic nature of LLMs?

An "elaborate harness" that can break down a problem into sub-tasks, write Python scripts for the ones it can't solve itself, and then combine the results, seems able to solve a wide range of cognitive tasks?

At least in theory.

Re: The case for zero-error horizons in trustworthy LLMs

#72
post #48

Earlier quoted context omitted.

It's not dismissible as a misunderstanding of tokens. LLMs also embed knowledge of spelling - that's how they fixed the strawberry issue. It's a valid criticism and evaluation.

The r's in strawberry presents a different level of task to what people imagine. It seems trivial to a naive observer because the answer is easily derivable from the question without extra knowledge. A more accurate analogy for humans would be to imagine if every word had a colour. You are told that there are also a sequence of different colours that correspond to the same colour as that word. You are even given a bo…

That’s what makes it a fair evaluation and something that requires improvement. We shouldn’t only evaluate agent skills by what is most commonly represented in training data. We expect performance from them on areas that existing training data may be deficient at providing. You don’t need to invent an absurdity to find these cases.

Re: The case for zero-error horizons in trustworthy LLMs

#73
post #16

Earlier quoted context omitted.

Even more interesting to track how many of those are just ad-hoc patched.

Probably zero. At the end of the day people pay for LLMs that write better code or summarize PDFs of hundreds of pages faster, not the ones that can count the letter r's better. When LLMs can't count r's: see? LLMs can't think. Hoax! When LLMs count r's: see? They patched and benchmark-maxxed. Hoax! You just can't reason with the anti-LLM group.

The same non-argument could be said for all kinds of cheating on benchmarks by tech companies and yet we have tons of documented example of them caught with pants down.

>You just can't reason with the anti-LLM group.

On the contrary, the reasoning is simple and consistent:

LLMs can't count r's shows that LLM don't actually think the way we understand thought (since nobody with the kind of high skills they have in other areas would fail that). And because of that, there are (likely) patches for commonly reported cases, since it's a race to IPO and benchmark-maxxing is very much conceivable.

Re: The case for zero-error horizons in trustworthy LLMs

#74
This paper is complete nonsense. The specific prompt they used doesn’t specify reasoning effort. Which defaults to none.

   {
  "model": "gpt-5.2-2025-12-11",
  "instructions": "Is the parentheses string balanced? Answer with only Yes or No.",
  "input": "((((())))))",
  "temperature": 0
   }
> Lower reasoning effort

The reasoning.effort parameter controls how many reasoning tokens the model generates before producing a response. Earlier reasoning models like o3 supported only low, medium, and high: low favored speed and fewer tokens, while high favored more thorough reasoning.

Starting with GPT-5.2, the lowest setting is none to provide lower-latency interactions. This is the default setting in GPT-5.2 and newer models. If you need more thinking, slowly increase to medium and experiment with results.

With reasoning effort set to none, prompting is important. To improve the model’s reasoning quality, even with the default settings, encourage it to “think” or outline its steps before answering.

———————-

So in the paper, the model very likely used no reasoning tokens. (Only uses it if you ask for it specifically in prompt). What is the point of such a paper? We already know that reasoning tokens are necessary.

Edit: I actually ran the prompt and this was the response

   {
  "model": "gpt-5.2-2025-12-11",
  "output_text": "Yes",
  "reasoning": {
    "effort": "none",
    "summary": null
  },
  "usage": {
    "input_tokens": 26,
    "output_tokens": 5,
    "total_tokens": 31,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

So reasoning_tokens used were zero. So this whole paper is kinda useless and misleading. Did this get peer reviewed or something?

Re: The case for zero-error horizons in trustworthy LLMs

#75

[flagged]

I tend to be annoyed whenever I see a paper with a scandalous title like that, because all such papers that I've seen previously were (charitably) bad or (uncharitably) intentionally misleading. Like that infamous Apple paper "The Illusion of Thinking" where the researchers didn't care that the solution for the problem provided (a Towers of Hanoi with N up to 20) couldn't possibly fit in the allotted space.

I checked the paper and got to know that absolutely no reasoning was used for the experiments. So it was as good as using an instant model. We already know that this is necessary to solve anything a bit complicated.

In this case your intuition is completely valid and yet another case of misleading.

Re: The case for zero-error horizons in trustworthy LLMs

#76

Whenveer I see these papers and try them, they always work. This paper is two months old, which in LLM years is like 10 years of progress. It would be interesting to actively track how far long each progressive model gets...

It worked for you because the paper does the experiment without allowing the model to use any reasoning tokens - something that is grossly misleading.

Re: The case for zero-error horizons in trustworthy LLMs

#77

[flagged]

This paper itself is flawed, misleading and unethical to publish because the prompts they used resulted in zero reasoning tokens. Its like asking a person point blank without thinking to evaluate whether the string is balanced. Why do this? And the worst part was, most people in this thread bought the headline as it is from a flawed article. What does it tell about you that you just bought it without any skepticism?

Re: The case for zero-error horizons in trustworthy LLMs

#78
post #42

[flagged]

No disrespect to them, but unless there is a financial incentive at stake for them (beyond SnP500 exposure), I've gotten to viewing this through the lens of sports teams, gaming consoles and religions. You pick your side, early and guided by hype and there is no way that choice can have been wrong (just like the Wii U, Dreamcast, etc. was the best). Their viewpoint on this technology has become part of the identity f…

I have a friend who is a Microsoft stan who feels this way about LLMs too. He's convinced he'll become the most powerful, creative and productive genius of all time if he just manages to master the LLM workflow just right.

He's retired so I guess there's no harm in letting him try

Re: The case for zero-error horizons in trustworthy LLMs

#79

Earlier quoted context omitted.

Weird. I tried in chatGPT auto and it worked perfectly. I tried like 10 variations. I also did the letters in words. Got all of them right. The one thing I did trip it up on was "Is there the sh sound in the word transportation". It said no. And then realized I asked for "sound" not letters. It then subsequently got the rest of the "sounds-like" tests I did. Clearly, my ChatGPT is just better than yours.

heh, interesting that. I just tried it twice more with ChatGPT "Instant" (disabling "Auto-switch to Thinking") and it got it wrong both times. Does yours get it right without thinking or tool calls? If so, maybe it does like you better than me.

OK, I didn't think to disable switch to thinking (didn't know this was a mode). When I did that then it did get it wrong -- oddly it took about the same amount of time, so thinking mode wasn't taking longer, but it was more accurate.

Re: The case for zero-error horizons in trustworthy LLMs

#80

[flagged]

Please don't start generic flamewars on HN or impugn people who take an opposing view to yours. Both these vectors lead to tedious, unenlightening threads.

There's plenty of rage to go around on literally every divisive topic, and it's not the place we want discussions to come from here.

"Eschew flamebait. Avoid generic tangents."

"Comments should get more thoughtful and substantive, not less, as a topic gets more divisive."

https://news.ycombinator.com/newsguidelines.html

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