We prove two deterministic inapproximability results.
First, for every fixed , Euclidean is NP-hard with gap factor under deterministic polynomial-time many-one reductions, where denotes the lattice rank. Consequently, the Euclidean closest vector problem is NP-hard to approximate within the same factor. This improves the previous hardness factor in Chapter 7 of the OpenAI report [Ope26].
Second, for every fixed , binary nearest codeword and binary syndrome decoding are NP-hard to approximate within under deterministic polynomial-time many-one reductions, where denotes the binary block length. This improves the previous hardness factor in Chapter 7 of the OpenAI report [Ope26].