Could the best LLM be able to generate a symbolic AI that is superior to itself, or is there something superior about matrices vs graphs?

Reddit r/artificial / 5/3/2026

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Key Points

The article discusses whether a top-performing LLM could generate a symbolic AI system that outperforms itself, challenging the notion that neural approaches are always superior.
It contrasts discrete symbolic representations (if/then rules, jumps, function calls, abstractions) with continuous “fuzzy” representations such as matrices.
The author asks for a principled explanation—potentially drawing on information-theoretic ideas like Shannon information—that would clarify why one representation class may be inherently better than the other.
It frames the question as an open research/analysis problem rather than reporting a new system or result, emphasizing the gap between what DNNs excel at and what symbolic methods might still offer.
Overall, the piece invites debate on the comparative strengths of symbolic AI versus DNN/LLM-based methods, including whether symbolic systems could be superior under some conditions.

Deep neural network AIs have beaten symbolic AIs across the board on many tasks, but is there a chance that symbolic AIs written by DNNs(LLMs), could beat those?

And if not, why not?

My gut tells me that no, discrete symbolic systems (of ifs/jumps/function calls/abstractions etc), are inferior to fuzzy matrices, but I'm curious if there is a formula or something that explains why (something like Shannon's information paper)?

submitted by /u/breck
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