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  "text": "New research from Renmin University.\n\nTreat skill selection as a harness in its own right.\n\nIf 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.\n\nThe work builds a lightweight local preference harness for on-device personal agents.\n\nIt 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.\n\nFramed as a bandit-style local optimization, the decoupled design reports the lowest cumulative regret and highest test accuracy against memory-augmented agents.\n\nPaper: https://t.co/nBigS6jRf7\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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