We give a polynomial time algorithm that finds a planted clique of size \(k \geqslant \sqrt{n \log n}\) in the semirandom model, improving the state-of-the-art \(\sqrt{n} (\log n)^2\) bound. The semirandom planted clique problem concerns finding the planted subset S of k vertices V of a graph G, where the induced subgraph G[S] is complete, the cut edges in \(G[S; V \setminus S]\) are random, and the remaining edges in \(G[V \setminus S]\) are adversarial. An elegant greedy algorithm (Błasiok, Buhai, Kothari, and Steurer, FOCS 2024) finds S by sampling inner products of the columns of the adjacency matrix of G, and checking if they deviate significantly from typical inner products of random vectors. Their analysis uses a suitably random matrix that, with high probability, satisfies a certain restricted isometry property. Inspired by Wootters’ work on list decoding, we put forth and implement the 1-norm analog of this argument, and quantitatively improve their analysis to work all the way up to the conjectured optimal \(\sqrt{n \log n}\) bound on clique size, answering one of the main open questions posed in [BBKS24].