Languages are powerful solutions to the complex coordination problems that arise between social agents. They provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads. However, to handle an ever-changing environment with new things to talk about and new partners to talk with, linguistic knowledge must be flexible: we give old words new meaning on the fly. In this talk, I will present work investigating the cognitive mechanisms that support this balance between stability and flexibility. First, I’ll present a large corpus of natural-language communication in the classic “tangrams” task that allows us to quantitatively characterize the dynamics of ad hoc convention formation with a single partner. Second, I’ll ask how these ad hoc conventions may be generalized to broader communities. I’ll introduce a theoretical framework re-casting communication not as a transmission problem but as a meta-learning problem which may be formalized via hierarchical probabilistic inference: dynamics within an interaction are driven by ad hoc partner-specific adaptation while community-level conventions are gradually abstracted away from many interactions and provide a stable prior for new partners. Finally, I’ll explore several proposals about how this computational framework can be implemented at scale to allow artificial agents to form natural-language conventions, adapting to human partners in real time. Taken together, this line of work aims to build a computational foundation for a more dynamic view of meaning and common ground in communication.