This mostly expository note studies the sample-noise regimes in which the testing problem for noisy $k$-XOR in dimension $n$ can be efficiently solved. Concretely, we ask what pairs $(β, δ)$ admit algorithms running in time $n^{O(k)}$ where $m = n^β, β\in [0,k]$ is the number of samples and $\mathsf{Bern}(n^{-δ}),δ\in (0,+\infty)$ is the noise distribution. The current state-of-the-art algorithms are: I. A folklore algorithm when $β> 1, δ>1$ based on Gaussian elimination; II. A folklore algorithm when $β\ge k/2$ based on the birthday paradox; III. A recent algorithm in the regime $β> δ+ k(1-δ), δ\in (0,1)$ based on a reduction to the first algorithm [DJ24,CSZ24]. IV. Extending [AF09], we strictly improve the last regime to $β> δ+ (k-1)(1-δ), δ\in (0,1).$ These algorithms correspond to efficiently finding short cycles in uniform hypergraphs and can be equivalently phrased as algorithmic Moore bounds.

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