Comment by thomasahle
16 hours ago
You should try asking an LLM to look for previous papers using similar ideas. The current/frontier generation of math AI is unfortunately very bad at citing the relevant literature for techniques its using.
I asked GPT here: https://chatgpt.com/share/6ac5fd7d-0390-83ed-a02a-6d80fc64f6... and it says:
> the exact Barnette argument appears quite novel, but nearly every ingredient in its cancellation trick has a recognizable ancestor.
> The closest precedent is much closer than I expected: in fully packed O(n) loop models, people have been assigning complex phases to the two orientations of a loop and making them cancel for decades. At n=0, the phases are literally +I and -I. And the n->0 limit has specifically been used to extract Hamiltonian cycles/walks.
You can judge better than me. But it's definitely worth it having a research assistant AI with you when reading these papers.
So much about LLMs can be framed as Information Retrieval, Compression, and Search. Computers have always been good at ruthlessly hammering through a huge but finite set of possibilities. The wild thing now is that you can define that set of possibilities as "all the ideas ever published in mathematics journals."
It makes solving advanced math problems feel like cracking a hash. If it's possible, it's just a matter of compute time.