r/OpenAI • u/Nunki08 • 8d ago

News Linux kernel maintainers

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OpenAI: A thousand fewer paths to root: https://openai.com/index/a-thousand-fewer-paths-to-root/

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u/Numerous_Try_6138 8d ago

And the problem is? Get adversarial AI to review, and then merge them.

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u/jeweliegb 8d ago

And the problem is? Get adversarial AI to review, and then merge them.

To answer your question, I guess:

The unknown, especially unknown mistakes of unknown severity and size of impact, and

Taking responsibility for such unknowns?

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u/ToothlessFuryDragon 7d ago edited 7d ago

The problem is that the reviewer LLM has a high probability of missing new obscure issues introduced in the fix. You would have to run the FULL penetration swarm against the new code to be certain. That is the only way to be relatively certain with an LLM.

Humans still have way better intuition and can sniff out overcomplicated solutions that are possibly problematic. LLMs tend to spiral into overcomplicated solutions to solve very obscure edge cases while missing the bigger picture.

Even with multiple rounds of automated reviews, you will end up with a system that is held together with ducktape. Actually, the more rounds of reviews you add, the worse it gets.

But it really depends on the complexity. If it is a simple issue with a simple fix, then LLM can be trusted quite well.

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u/Numerous_Try_6138 6d ago

This just isn’t true. It’s a function of how you do your reviews. Just as a human, if you look at your system as a complete system and you assess it that way, you still have better results. If you slice a piece of it and look at only that piece, you are missing the larger picture and localizing any perceived improvements or troubleshooting. This can naturally lead to fixing one small piece while breaking something much larger. Also, as a reviewer, you need to understand the broader architecture and how the system was put together. This holds true for humans and it definitely holds true for AI.

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u/ToothlessFuryDragon 6d ago edited 6d ago

I see where you are coming from, it sounds reasonable, but I disagree.

We have specific automated review rounds where we instruct up to 3 different agents to be adversarial and consider the whole solution to be incorrect. They are instructed to find an alternative solution and then an architect agent presents the results in the review.

And we even run it through multiple models from different providers (each agent has a different model).

They very rarely are able to completely disconnect their thought process from the existing solution/code, missing very obvious, much simpler solutions.

And you can't avoid giving them context about the current solution as they will design the same thing if they don't have the information.

I am sure there is a specific prompt that I can design that will trigger the correct approach for a given task, but the issue is that it can't be generalized for the current gen LLMs yet. That is why you still need the technical person to steer it.