AI & ML
q-ary GRAND
arXiv math.IT0 views
arXiv:2610.10236v1 Announce Type: new Abstract: We develop q-ary Guessing Random Additive Noise Decoding (GRAND) for linear codes over a q-ary alphabet. The decoder works on a sorted symbol-likelihood array and uses three local child generation rules to generate symbol deviation patterns. We then prove that these rules induce a monotone rooted spanning tree of the full row-index space, so best-first traversal gives maximum-likelihood (ML) decoding under unlimited search. Reed-Solomon simulations verify ML agreement and show the finite-budget performance-complexity tradeoff.
Source：https://arxiv.org/abs/2610.10236PreviousBeyond LLM-GA: Secure Fluid Antenna Systems with ReEvo-Designed Memetic AlgorithmNextMacKay-Neal Codes Achieve Capacity under MAP Decoding
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