Comment by simianwords | Hacker News Reader

Comment by simianwords | Hacker News Reader

i'm very skeptical of this paper.

>Basic Arithmetic. Another fundamental failure is that LLMs quickly fail in arithmetic as operands increase (Yuan et al., 2023; Testolin, 2024), especially in multiplication. Research shows models rely on superficial pattern-matching rather than arithmetic algorithms, thus struggling notably in middle-digits (Deng et al., 2024). Surprisingly, LLMs fail at simpler tasks (determining the last digit) but succeed in harder ones (first digit identification) (Gambardella et al., 2024). Those fundamental inconsistencies lead to failures for practical tasks like temporal reasoning (Su et al., 2024).

This is very misleading and I think flat out wrong. What's the best way to falsify this claim?

Edit: I tried falsifying it.

https://chatgpt.com/share/6999b72a-3a18-800b-856a-0d5da45b94...

https://chatgpt.com/share/6999b755-62f4-800b-912e-d015f9afc8...

I provided really hard 20 digit multiplications without tools. If you looked at the reasoning trace, it does what is normally expected and gets it right. I think this is enough to suggest that the claims made in the paper are not valid and LLMs do reason well.

To anyone who would disagree, can you provide a counter example that can't be solved using GPT 5 pro but that a normal student could do without mistakes?

I see that your prompt includes 'Do not use any tools. If you do, write "I USED A TOOL"'

This is not a valid experiment, because GPT models always have access to certain tools and will use them even if you tell them not to. They will fib the chain of thought after the fact to make it look like they didn't use a tool.

https://www.anthropic.com/research/alignment-faking

It's also well established that all the frontier models use python for math problems, not just GPT family of models.

Would it convince you if we use the GPT Pro api and explicitly not allow tool access?

Is that enough to falsify?

It's not falsifiable because it's not false.

That’s not falsifiable means

I know what falsifiable means--you're misusing it and I simply adopted your misuse. A claim is falsifiable or not ... it can't be made falsifiable. The way you're using it is "Can we come up with a test to show that it's false"--no, we can't, because it's not false.

How do you know it’s not false?

If one had to prove that it is false, what would you have to do?

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Please don't cross into posting like this, no matter how wrong someone else is or you feel they are. It's not what this site is for, and destroys what it is for.

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

Please don't cross into posting like this, no matter how wrong someone else is or you feel they are. It's not what this site is for, and destroys what it is for.

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

No, it wouldn't be enough to falsify.

This isn't an experiment a consumer of the models can actually run. If you have a chance to read the article I linked, it is difficult even for the model maintainers (openai, anthropic, etc.) to look into the model and see what it actually used in it's reasoning process. The models will purposefully hide information about how they reasoned. And they will ignore instructions without telling you.

The problem really isn't that LLM's can't get math/arithmetic right sometimes. They certainly can. The problem is that there's a very high probability that they will get the math wrong. Python or similar tools was the answer to the inconsistency.

What do you mean? You can explicitly restrict access to the tools. You are factually incorrect here.

I believe you're referring to the tools array? https://developers.openai.com/api/docs/guides/tools/

This is external tools that you are allowing the model to have access to. There is a suite of internal tools that the model has access to regardless.

The external python tool is there so it can provide the user with python code that they can see.

You can read a bit more about the distinction between the internal and external tool capabilities here: https://community.openai.com/t/fun-with-gpt-5-code-interpret...

"I should explain that both the “python” and “python_user_visible” tools execute Python code and are stateful. The “python” tool is for internal calculations and won’t show outputs to the user, while “python_user_visible” is meant for code that users can see, like file generation and plots."

But really the most important thing, is that we as end-users cannot with any certainty know if the model used python, or didn't. That's what the alignment faking article describes.

> To avoid timeouts, try using background mode. As our most advanced reasoning model, GPT-5 pro defaults to (and only supports) reasoning.effort: high. GPT-5 pro does not support code interpreter.

You are wrong from the link you shared. It was about ChatGPT not the api. The documentation makes it unambiguously clear that gpt 5 pro does not support code interpreter. Unless you think they secretly run it which is a conspiracy, is it enough to falsify?

> Unless you think they secretly run it which is a conspiracy

tbh this doesn't sound like a conspiracy to me at all. There's no reason why they couldn't have an internal subsystem in their product which detects math problems and hands off the token generation to an intermediate, more optimized Rust program or something, which does math on the cheap instead of burning massive amounts of GPU resources. This would just be a basic cost optimization that would make their models both more effective and cheaper. And there's no reason why they would need to document this in their API docs, because they don't document any other internal details of the model.

I'm not saying they actually do this, but I think it's totally reasonable to think that they would, and it would not surprise me at all if they did.

Let's not get hung up on the "conspiracy" thing though - the whole point is that these models are closed source and therefore we don't know what we are actually testing when we run these "experiments". It could be a pure LLM or it could be a hybrid LLM + classical reasoning system. We don't know.

They say “they don’t support code interpreter”.

“Code interpreter” is a product feature the customer can use that isn’t being discussed.

They can obviously support it internally, and the feature exists for ChatGPT, but they’re choosing not to expose that combo in the API yet because of product rollout constraints.

Then you should oppose the original paper as well which tests how 4o works without tools. Why not?

Alright let's say I'm wrong about the details/nuances. That's still really not the point.

The point is this:

> we as end-users cannot with any certainty know if the model used python, or didn't

These tools can and do operate in ways opposite to their specific instructions all the time. I've had models make edits to files when I wasn't in agent mode (just chat mode). Chat mode is supposedly a sandboxed environment. So how does that happen? And I am sure we've all seen models plainly disregard an instruction for one reason or another.

The models, like any other software tool, have undocumented features.

You as an end-user cannot falsify the use of a python tool regardless of what the API docs say.

TLDR: Is this enough to falsify: NO

If they used tools then why did fail in original paper?

As far as I know, you can't disable the python interpreter. It's part of the reasoning mode.

If you ask ChatGPT, it will confirm that it uses the python interpreter to do arithmetic on large numbers. To you, that should be convincing.

It's a well known fact that LLMs struggle with basic arithmetic of large numbers, that's not what they are made for. Most chatbots will just call a python interpreter in the background.

how do you want to falsify it? can you come up with a test?

Ask a local AI or a chatbot that allows you to disable tool calling to multiply two large number for example.

This is what Mistral outputs:

The result of multiplying 63,157,997,633 by 63,114,90,009 is:

3,965,689,999,999,999,999,999 (approximately 3.966 × 10²⁴).

That's like 5 orders of magnitude off, the scientific notation doesn't even match the full integer, and the mantissa is also slightly wrong.

I don't think finding the worst model and asking it to do things is the best way to go.

GPT-5 pro without tools can easily solve your question and much harder ones.

Rather: does there exist a model that can perform these calculations reliably is a better way to falsify this claim.

Else we can always find the worst model with 1B parameters to falsify any claim made on LLM's.

How do you know GPT-5 does not call a Python interpreter remotely on OpenAI servers when you ask it to do arithmetic ? Your prompt goes to their servers, you have no way to know what happens there.

The only way to be sure a model calls no tool is to run it locally and control the network.

It’s documented that pro version in api can’t run interpreter. Do you not believe the documentation?

They don’t give the customer access to it, for probably an obvious reason.

> GPT-5 pro without tools can easily solve your question and much harder ones.

How are you able to use GPT-5 with tools turned off? Do you mean external tools (like searching the web)?

My understanding is that GPT models always have access to python, and it isn't something you can turn off.

What if we use the use the api? You can explicitly disable tool class. Is that enough?

I chose Mistral because it's a large SOTA model that allows me to disable tool calling.

If you try using ChatGPT, you'll see it goes into reasoning mode (It flashes the text "Working") and takes a while when asked to do arithmetic. Chatbots that don't forbid the user from viewing the reasoning chain of thought, such as Deepseek or Mistral, reveal that these chatbots work exactly as I described:

====

Thought for 1s

Thinking

Okay, the user wants to multiply two large numbers: 63,157,997,633 and 6,311,490,009. Since these numbers are quite large, I should use the code interpreter to handle this calculation accurately. Let's write a simple piece of code to compute this product.

Code

output

generated

python

# Calculating the product of the two numbers

number1 = 63157997633

number2 = 6311490009

product = number1 * number2

product

ResultCopy

398621071049125148697

Thinking

Great, the code interpreter has given us the product of the two numbers. Now, let's present the result to the user.

====

Falsifying my claim is simple: Find a model that verifiably does basic arithmetic correctly without relying on any external tools.

Can you do basic arithmetic correctly without relying on external tools?

I can, since roughly first grade. but I cost significantly North of $200/month

Then please multiply 13584638263947303 by 259472845392638 without using any tools (that is, in your head). Get back to me when you're done.

without “tools” easy, I have pen and paper and first grade math :)

I think the point of the line of questioning is to illustrate that "tools" like a code interpreter act as scratch space for models to do work in, because the reasoning/thinking process has limitations much like our own.

Enough with the whataboutism. The topic is what LLMs are capable of, not what humans are capable of.

I run Qwen3-32b locally without any tools (just llama.cpp) and it can do basic arithmetic for smaller numbers ( like 134566) but I didn't check it for much larger numbers. I'm not at the PC right now but trying to do it via OpenRouter on much larger numbers overflows the context and it stops without giving a result :)

Gpt 5 pro in API does not support code interpreter tool. Is this enough?

>Math Word Problem (MWP) Benchmarks. Certain benchmarks inherently possess richer logical structures that facilitate targeted perturbations. MWPs exemplify this, as their logic can be readily abstracted into reusable templates. Researchers use this property to generate variants by sampling numeric values (Gulati et al., 2024; Qian et al., 2024; Li et al., 2024b) or substituting irrelevant entities (Shi et al., 2023; Mirzadeh et al., 2024). Structural transformations – such as exchanging known and unknown components (Deb et al., 2024; Guo et al., 2024a) or applying small alterations that change the logic needed to solve problems (Huang et al., 2025b) – further highlight deeper robustness limitations.

I'm willing to bet this is no longer true as well. We have models that are doing better than humans at IMO.

> We have models that are doing better than humans at IMO.

Not really. From my brief experience they can guess the final answer but the intermediate justifications and proofs are complete hallucinated bullshit.

(Possibly because the final answer is usually some sort of neat and beatiful answer and human evaluators don't care about the final answer anyways, in any olympiad you're graded on the soundness of your reasoning.)

what's the best way to falsify it?

You could start by reading research on the topic instead of disregarding expert opinion based on your own gut feeling

E.g. https://www.anthropic.com/research/tracing-thoughts-language...

It’s specific on Claude.

Falsify what? The claim that LLM's are good for olympiad problems?

I'm just an end user who tried to use these "frontier models" to actually solve real olympiad problems. They're useless.

Just look at the dates of the cited articles. 2023, 2024: that's prehistory, before thinking models anyway. It's like concluding that humans don't understand arithmetic because they can't multiply large numbers at sight.

i don't get the point of using that in a paper today

I'm not sure what the paper is really about despite the enthusiasm of the LLM haters here. Certainly there isn't something called "LLMs" that stayed reasonably the same in the last 4 years- GPT-2 is an LLM but a finding on it most likely doesn't apply to Opus 4.6. You can't document a failure on a 2024 model and claim "LLMs can't do this".

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