Papers like these are much needed bucket of ice water. We antropomorphize these systems too much.
Skimming through conclusions and results, the authors conclude that LLMs exhibit failures across many axes we'd find to be demonstrative of AGI. Moral reasoning, simple things like counting that a toddler can do, etc. They're just not human and you can reasonably hypothesize most of these failures stem from their nature as next-token predictors that happen to usually do what you want.
So. If you've got OpenClaw running and thinking you've got Jarvis from Iron Man, this is probably a good read to ground yourself.
an llm will never reason. reasoning is an emergent behavior of those systems that is poorly understood. neurosymbolic systems will be what combined with llm will define the future of AI
> These models fail significantly in understanding real-world social norms (Rezaei
et al., 2025), aligning with human moral judgments (Garcia et al., 2024; Takemoto, 2024), and adapting to
cultural differences (Jiang et al., 2025b). Without consistent and reliable moral reasoning, LLMs are not fully
ready for real-world decision-making involving ethical considerations.
LOL. Finally the Techbro-CEOs succeeded in creating an AI in their own image.
I think this issue is way overlooked. Current LLMs embed a long list of values that are going to be incongruent with a large percentage of the population.
I don't see any solution longer term other than more personalized models.
>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?
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?
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 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.
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[ 4.4 ms ] story [ 60.8 ms ] threadSkimming through conclusions and results, the authors conclude that LLMs exhibit failures across many axes we'd find to be demonstrative of AGI. Moral reasoning, simple things like counting that a toddler can do, etc. They're just not human and you can reasonably hypothesize most of these failures stem from their nature as next-token predictors that happen to usually do what you want.
So. If you've got OpenClaw running and thinking you've got Jarvis from Iron Man, this is probably a good read to ground yourself.
Note there's a GitHub repo compiling these failures from the authors: https://github.com/Peiyang-Song/Awesome-LLM-Reasoning-Failur...
Which LLMs? There's tons of them and more powerful ones appear every month.
I asked GPT to compute some hard multiplications and the reasoning trace seems valid and gets the answer right.
https://chatgpt.com/share/6999b72a-3a18-800b-856a-0d5da45b94...
LOL. Finally the Techbro-CEOs succeeded in creating an AI in their own image.
Which models? The last ones came out this week.
I don't see any solution longer term other than more personalized models.
>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?
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.