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LLM agents can dynamically evolve their skill libraries, preventing obsolescence and improving long-horizon task performance by up to 7.8% through a fitness-driven lifecycle.
SAFARI maintains diagnostic precision even when critical fault information lies five times beyond the model's context limits, a feat traditional methods cannot achieve.
