We revisit the problem of maintaining the longest increasing subsequence (LIS) of an array under (i) inserting an element, and (ii) deleting an element of an array. In a recent breakthrough, Mitzenmacher and Seddighin [STOC 2020] designed an algorithm that maintains an $\mathcal{O}((1/ε)^{\mathcal{O}(1/ε)})$-approximation of LIS under both operations with worst-case update time $\mathcal{\tilde O}(n^ε)$, for any constant $ε>0$. We exponentially improve on their result by designing an algorithm that maintains an $(1+ε)$-approximation of LIS under both operations with worst-case update time $\mathcal{\tilde O}(ε^{-5})$. Instead of working with the grid packing technique introduced by Mitzenmacher and Seddighin, we take a different approach building on a new tool that might be of independent interest: LIS sparsification. A particularly interesting consequence of our result is an improved solution for the so-called Erdős-Szekeres partitioning, in which we seek a partition of a given permutation of $\{1,2,\ldots,n\}$ into $\mathcal{O}(\sqrt{n})$ monotone subsequences. This problem has been repeatedly stated as one of the natural examples in which we see a large gap between the decision-tree complexity and algorithmic complexity. The result of Mitzenmacher and Seddighin implies an $\mathcal{O}(n^{1+ε})$ time solution for this problem, for any $ε>0$. Our algorithm (in fact, its simpler decremental version) further improves this to $\mathcal{\tilde O}(n)$.