Heaps (priority queues) are among the best-studied data structures in computer science. In this paper, we critically revisit the textbook assumption that in the comparison model at least one of the two standard heap operations of inserting an element and deleting the minimum must take logarithmic time. By decoupling the deletion itself from the act of revealing the identity of the deleted element to the user, we avoid the sorting barrier and obtain a novel trade-off between the complexities of heap operations. This shows that the logarithmic barrier is not inherently the cost of deleting the minimum but rather the information cost of immediately learning which element was deleted. In the special case when the user inspects the heap state only constantly many times, we show that both insertions and deletions can be supported in constant amortized time. As an application, this yields a runtime improvement from $\mathcal{O}(n \log n)$ to the optimal $\mathcal{O}(n)$ for a textbook unit-time scheduling problem. We obtain our results by designing a new data structure, the sync heap, which gains speed by rearranging and compacting its operations until queries force it to synchronize and reveal its state. As a key component, we use the soft heap introduced by Chazelle as part of his minimum spanning tree algorithm. Our data structure is simple, comparison-based, and deterministic, and our results are asymptotically optimal.

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