Title: Beam Tree Recursive Cells. Abstract: Recursive Neural Networks (RvNNs) generalize Recurrent Neural Networks (RNNs) by allowing sequential composition in a more flexible order, typically, based on some tree structure. While initially user-annotated tree structures were used, in due time, several approaches were proposed to automatically induce tree-structures from raw text to guide the recursive compositions in RvNNs. In this paper, we present an approach called Beam Tree Recursive Cell (or BT-Cell) based on a simple yet overlooked backpropagation-friendly framework. BT-Cell applies beam search on easy-first parsing for simulating RvNNs with automatic structure-induction. Our results show that BT-Cell achieves near-perfect performance on several aspects of challenging structure-sensitive synthetic tasks like ListOps and also comparable performance in realistic data to other RvNN-based models. We further introduce and analyze several extensions of BT-Cell based on relaxations of the hard top-k operators in beam search. We evaluate the models in different out of distribution splits in both synthetic and realistic data. Additionally, we identify a previously unknown failure case for neural models in generalization to unseen number of arguments in ListOps. We will release our code.