Title: Continual Memory: Can We Reason After Long-Term Memorization?. Abstract: Existing reasoning tasks often follow the setting of ‘’end-to-end reasoning'', which has an important assumption that the input contents can be always accessed while reasoning. However, human beings frequently adopt another reasoning setting in daily life, referred to ‘’reasoning after memorizing''. Concretely, human beings have the ability to unconsciously memorize their experiences within limited memory capacity, from which they can recall and respond to subsequent tasks. In this setting, the input contents are no longer available during reasoning, thus we need to compress and memorize the input stream in one pass, trying to answer general queries that are unseen before. Memory augmented neural networks introduce a write-read memory to perform such human-like memorization and reasoning, but they continually update the memory from current information and inevitably forget the early contents, failing to answer the queries relevant to early information.  In this paper, we propose the Continual Memory (CM) to explore this ability of reasoning after long-term memorization.  To alleviate the gradual forgetting of early information, we develop self-supervised memorization training with item-level and sequence-level objectives. We demonstrate several interesting characteristics of our continual memory via synthetic data, and evaluate its performance by several downstream tasks, including long-term text QA, long-term video QA and recommendation with long sequences.