New research from Renmin University.

Treat skill selection as a harness in its own right.

If you design skill routing for personal or edge agents, this work argues that the selection layer is a first-class component you train and own, sitting alongside memory rather than inside it.

The work builds a lightweight local preference harness for on-device personal agents.

It keeps a cheap statistical preference learner on-device while a remote LLM handles semantic intent, and the local statistics modulate the model's skill-selection decisions rather than overriding them.

Framed as a bandit-style local optimization, the decoupled design reports the lowest cumulative regret and highest test accuracy against memory-augmented agents.

Paper: https://arxiv.org/abs/2606.05828

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