Long-horizon LLM agents face a fundamental allocation problem as interaction histories grow: which information to retain, how much context to devote to it, and at what granularity. A new arXiv preprint, MemPrice, proposes using adaptive pricing to make these allocation decisions online.
The paper argues that existing memory mechanisms largely focus on a different concern, though the abstract's text is cut off before naming it. MemPrice's contribution is to frame memory allocation as a pricing problem, where prices adapt to guide what an agent keeps.
The available abstract does not describe implementation details or experimental results. As long-horizon agents become more common, the question of how to allocate limited context is likely to grow in importance.