For memory-limited agents, organize the past before you optimize search: grouping related memories and keeping them together beat fancier retrieval under tight token budgets.

Most long-running agents either keep stuffing old interactions into the prompt or retrieve isolated chunks.

Both approaches get awkward once the prompt budget gets tight.

RSM-full takes a simpler route.

It groups related memories as they arrive, then brings those groups back together when the agent needs them.

On AMA-Bench at roughly 4k prompt tokens, it kept 83% of the quality of feeding the model its full history while using 32% of the token cost.