Abstract We define simple variants of zip trees, called zip-zip trees , which provide several advantages over zip trees, including overcoming a bias that favors smaller keys over larger ones. We analyze zip-zip trees theoretically and empirically, showing, e.g., that the expected depth of a node in an n -node zip-zip tree is at most $$1.3863\log n-1+o(1)$$ , which matches the expected depth of treaps and binary search trees built by uniformly random insertions. Unlike these other data structures, however, zip-zip trees achieve their bounds using only $$O(\log \log n)$$ bits of metadata per node, w.h.p., as compared to the $$\Theta (\log n)$$ bits per node required by treaps. In addition, we describe a “just-in-time” zip-zip tree variant, which needs just an expected O (1) number of bits of metadata per node. Moreover, we can define zip-zip trees to be strongly history independent, whereas treaps are generally only weakly history independent. We also introduce biased zip-zip trees , which have an explicit bias based on key weights, so the expected depth of a key, k , with weight, $$w_k$$ , is $$O(\log (W/w_k))$$ , where W is the weight of all keys in the weighted zip-zip tree. Finally, we show that one can easily make zip-zip trees partially persistent with only O ( n ) space overhead w.h.p.

There is both great hope and concern about the future of Computer Science practice and education concerning the recent advent of large language models (LLMs).