GPT World Space explores agents learning to communicate through a shared language-model representation.
The project includes training loops, communication-memory experiments, and several variants of the agent setup. The work asks how agents can coordinate their messages and whether earlier interactions help with later tasks.
It is a collection of experiments rather than a single validated system. Any useful result needs to be tied to a particular task, model, and comparison.