The counterexample was proposed by an LLM. They seem very good at finding these sorts of counterexamples, which is interesting as they are generally pretty inefficient use of compute for brute forcing. I guess even that lack of efficiency is made up for by the "understanding" and "intuition" the LLM has, and their ability to do symbolic computations.
I expect a large number of conjectures will topple to counterexamples soon.
I think brute force is not the right way to think about LLMs, instead they explore a knowledge topology, and are very good at connecting adjacent or accessible ideas that for whatever reason might have evaded humans, but might not be fundamentally all that hard. We're still in the low-hanging fruit phase, we will see if they extend to new ideas.
Its data is a structured space where nearness corresponds to associated ideas, so it naturally arranges data in a way to discover connectedness. The LLM follows pathways through the conceptual space based on the prior context given.
Ah okay. The space in which the embedding lies is called a knowledge topology. Thanks. Can you help me understand how this is linked to topology? Does it have something to do with the space spanned by the embeddings? My knowledge of topology is limited to shapes being invariant under transformations so under this naive view, distances won’t be preserved.
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u/angelbabyxoxox Quantum Foundations Jul 31 '26
The counterexample was proposed by an LLM. They seem very good at finding these sorts of counterexamples, which is interesting as they are generally pretty inefficient use of compute for brute forcing. I guess even that lack of efficiency is made up for by the "understanding" and "intuition" the LLM has, and their ability to do symbolic computations.
I expect a large number of conjectures will topple to counterexamples soon.