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University of Southern California
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Current self-evolving prompt optimization frameworks falter when faced with the diverse memory extraction demands of real-world LLM assistants, but a simple clustering approach can restore generalization.
LLM agents can slash task completion time by almost 50% simply by predicting and pre-executing likely tool calls.
