I was very happy to see an "AI use disclosure" right on the title page under the abstract.
It's not clear if this is solely a possible theoretical result or if it has any practical value. I.e. is it only useful on lattices that are so large as to not be of use, or could it be used for cryptanalysis? If one is using AI to generate a theory paper such as this, why not use the AI to also generate code that uses it, put it on GitHub, and show the results, say against fplll and the tool in the paper below?
not really. The hardness of SVP is relevant, but this is a paper giving improved provable bounds for SVP algorithms. heuristically (which people use to choose parameter sizes etc) people assume SVP is much easier to solve, closer to 2^{.29n + o(n)}.
So it's tangentially related, but does not itself imply an improvement on the (heuristically assumed) SOTA for these problems.
I was very happy to see an "AI use disclosure" right on the title page under the abstract.
It's not clear if this is solely a possible theoretical result or if it has any practical value. I.e. is it only useful on lattices that are so large as to not be of use, or could it be used for cryptanalysis? If one is using AI to generate a theory paper such as this, why not use the AI to also generate code that uses it, put it on GitHub, and show the results, say against fplll and the tool in the paper below?
https://ir.cwi.nl/pub/35237/35237.pdf
could this be a problem for the security of Falcon (aka FN-DSA) post quantum signature scheme?
not really. The hardness of SVP is relevant, but this is a paper giving improved provable bounds for SVP algorithms. heuristically (which people use to choose parameter sizes etc) people assume SVP is much easier to solve, closer to 2^{.29n + o(n)}.
So it's tangentially related, but does not itself imply an improvement on the (heuristically assumed) SOTA for these problems.
Wow, just yesterday I was thinking this exact problem would be a good candidate for AI. Seems it is!