Abstract: Formal verification tools commonly rely on SMT solvers to automatically reason about programs, leveraging a range of logical theories, e.g., linear integer arithmetic, arrays, or strings, to encode program constructs and verification conditions. Despite recent advances, such solvers still struggle when reasoning about recursive data structures such as lists, which are pervasive in modern functional languages. Additionally, lists are commonly used in conjunction with higher-order combinators to, e.g., generically apply a function to all elements of the list. In this work, we provide first-class support for reasoning about lists within SMT solvers. We focus on lists of arbitrary size that, following the map-reduce paradigm, can be manipulated exclusively through a set of abstract combinators. To this end, we introduce DueList, an abstraction-refinement approach geared towards list reasoning, which we implement on top of off-the-shelf SMT solvers. To evaluate the efficiency of our approach, we assemble a diverse set of 752 benchmarks curated from previous works and real-world programs, and compare DueList against state-of-the-art solvers such as Z3 and CVC5. Our experimental evaluation shows that DueList extends reasoning facilities of existing solvers, allowing to conclude about the (un)satisfiability of a larger range of problems, while outperforming existing solvers in the vast majority of previously supported cases.