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Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.
OPD-Evolver outperforms traditional memory systems by up to 11.5%, showcasing a new paradigm in agent evolution that transcends mere memory storage.
