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Automated micro-step prompt refinement can more than double an agent's domain-specific reasoning and scientific rigor scores without fine-tuning underlying model weights.
TPGO allows multi-agent systems to learn from their own optimization history, leading to unprecedented self-improvement in performance.
SpaceMind's self-evolving skills let robots learn from failure *without* model fine-tuning, turning on-orbit servicing into a continuous improvement loop.
