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Syndrome decoding (SD), and equivalently Learning Parity with Noise (LPN), is a fundamental problem in cryptography, which states that for a field F, some compressing public matrix G ∈ F^k×n ,and a secret sparse vector e ∈ F^n sampled from some noise distribution, G e is indistinguishable from uniform. Recently, the SD has gained significant interest due to its use in pseudorandom correlation generators (PCGs). In pursuit of better efficiency, we propose a new assumption called Stationary Syndrome Decoding (SSD). In SSD,weconsider q correlated noise vectors e1,... , eq ∈ F^n and associated instances G1 e1,..., Gq eq where the noise vectors are restricted to having non-zeros in the same small subset of t positions L ⊂ [n]. That is, for all i ∈ L, ej,i is uniformly random, while for all other i, ej,i =0. Although naively reusing the noise vector renders SD and LPN insecure via simple Gaussian elimination, we observe known attacks do not extend to our correlated noise. We show SSD is unconditionally secure against so-called linear attacks, e.g., advanced information set decoding and representation techniques (Esser and Santini, Crypto 2024). We further adapt the state-of-the-art nonlinear attack (Briaud and Øygarden, Eurocrypt 2023) to SSD and demonstrate both theoretically and exper-imentally resistance to the attack. We apply SSD to PCGs to amortize the cost of noise generation pro-tocol. For OT and VOLE generation, each instance requires O(t)com-munication instead of O(t log n). For suggested parameters, we observe a1.5× improvement in the running time or between 6 and 18× reduc-tion in communication. For Beaver triple generation using Ring LPN, our techniques have the potential for substantial amortization due to the high concrete overhead of the Ring LPN noise generation.