  We investigate adaptive sublinear algorithms for detecting monotone patterns
in an array. Given fixed $2 \leq k \in \mathbb{N}$ and $\varepsilon > 0$,
consider the problem of finding a length-$k$ increasing subsequence in an array
$f \colon [n] \to \mathbb{R}$, provided that $f$ is $\varepsilon$-far from free
of such subsequences. Recently, it was shown that the non-adaptive query
complexity of the above task is $\Theta((\log n)^{\lfloor \log_2 k \rfloor})$.
In this work, we break the non-adaptive lower bound, presenting an adaptive
algorithm for this problem which makes $O(\log n)$ queries. This is optimal,
matching the classical $\Omega(\log n)$ adaptive lower bound by Fischer [2004]
for monotonicity testing (which corresponds to the case $k=2$), and implying in
particular that the query complexity of testing whether the longest increasing
subsequence (LIS) has constant length is $\Theta(\log n)$.
