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Presents SkillSelect-Serve, a framework for budget-controllable and QoS-aware skill service recommendation and composition for small LLM agents, evaluating on a large registry and demonstrating improved recall and utility over top-k retrieval.
This research paper proposes a framework for fair token allocation and private data valuation in decentralized multi-modal agentic systems, using differentially private prototypes to balance privacy and utility while scheduling limited edge AI resources.