Small Models Inherit Agent Memory
A training-free framework transfers hierarchical memory from a large teacher agent to a smaller agent that cannot generate enough successful trajectories on its own. The mechanism could make capable local and edge agents cheaper without reproducing the full training process. It is not yet a market signal because evidence is limited to one paper; implementation in an open runtime, repeatable cost-quality gains or adoption by a small-model platform would confirm it.