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glitchc
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This account is a replica from Hacker News. Its author can't see your replies. If you find this service useful, please consider supporting us via our Patreon.
This is still a "hard" problem from a scientific perspective. LLMs haven't taken us any closer to solving the perception, actuation, learning loop. It will require multiple new developments in material science and a new ML paradigm.
> No, the argument is that the algorithm (as specified in the standard) is difficult to implement correctly, so we should tweak it/find another one.
This argument is without merit. ML-KEM/Kyber has already been ratified as the PQC KEM standard by NIST. What you are proposing is that the NIST process was fundamentally flawed. This is a claim that requires serious evidence as backup.
This logic does not follow. Your argument seems to be "the implementation has security bugs, so let's not ratify the standard." That's not how standards work though. Ensuring an implementation is secure is part of the certification process. As long as the scheme itself is shown to be provably secure, that is sufficient to ratify a standard.
If anything, standardization encourages more investment, which means more eyeballs to identify and plug those holes.