Title: Neural Layered Min-sum Decoders for Algebraic Codes. Abstract: In this article, we propose low-complexity neural network decoders based on the layered min-sum algorithm to decode binary algebraic codes. By generalizing the layered min-sum algorithm to its neural network counterpart, the number of iterations required for convergence is reduced. Consequently, the number of network weights decreases while retaining a good error correction performance. The Bose-Chaudhuri-Hocquenghem (BCH) codes and quadratic residue (QR) codes are selected as two exemplary binary algebraic codes. Simulation results show that the proposed decoders achieve superior performance with less computational complexity,  compared with the decoders proposed by Chen & Ye (2021). Further, a neural decoder incorporating the modified random redundant decoding (mRRD) algorithm is investigated to approach the performance of maximum-likelihood (ML) decoding for some short codes.